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inst/shiny_app/app.R
In QurvE: Robust and User-Friendly Analysis of Growth and Fluorescence Curves
options(shiny.maxRequestSize=60*1024^2)
#list of packages required
list.of.packages <- c("shiny", "shinythemes", "shinyFiles", "shinyjs", "shinyBS", "shinycssloaders", "QurvE")
#list of packages required
new_packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
#install missing packages
for( i in new_packages ){
# require returns TRUE invisibly if it was able to load package
if( ! require( i , character.only = TRUE ) ){
# If package was not able to be loaded then re-install
install.packages( i , dependencies = TRUE )
# Load package after installing
require( i , character.only = TRUE , quietly = T)
}
}
library(shiny, quietly = T)
library(QurvE, quietly = T)
library(shinyBS, quietly = T)
library(shinycssloaders, quietly = T)
library(shinyFiles, quietly = T)
library(shinyjs, quietly = T)
library(shinythemes, quietly = T)
library(ggplot2, quietly = T)
library(magrittr)
# Define icon set from custom SVG files
# iconset <- icons::icon_set("icons/")
jscode <- "
shinyjs.disableTab = function(name) {
var tab = $('.nav li a[data-value=' + name + ']');
tab.bind('click.tab', function(e) {
e.preventDefault();
return false;
});
tab.addClass('disabled');
}
shinyjs.enableTab = function(name) {
var tab = $('.nav li a[data-value=' + name + ']');
tab.unbind('click.tab');
tab.removeClass('disabled');
}
"
css <- "
.nav li a.disabled {
background-color: #65675F !important;
color: #333 !important;
cursor: not-allowed !important;
border-color: #aaa !important;
}"
load_data <- function() {
Sys.sleep(2)
hide("loading_page")
show("main_content")
}
widePopover <-
'<div class="popover popover-lg" role="tooltip"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'
ui <- fluidPage(theme = shinythemes::shinytheme(theme = "spacelab"),
tags$head(
tags$style(HTML(".popover.popover-lg {width: 600px; max-width: 600px;}"))
),
tags$style(
type = 'text/css',
'.modal-dialog.gcFitLinear { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.rdm.data { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.gcFitSpline { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.gcFitModel { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.flFitLinear { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.flFitSpline { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.flworkflow { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.fldr { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.growthworkflow { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.growthdr { width: fit-content !important; }'
),
tagList(
useShinyjs(),
shinyjs::extendShinyjs(text = jscode, functions = c("disableTab","enableTab")),
shinyjs::inlineCSS(css),
div(
id = "loading_page",
HTML("<br>"),
HTML("<br>"),
HTML('<center><img src="QurvE_logo.png" width="500" vertical-align="middle"></center>'),
HTML("<br>"),
HTML("<br>"),
h1("Initializing...", align = "center")
),
hidden(
div(
id = "main_content",
navbarPage(
'QurvE',
id = "navbar",
# load input file
#____DATA____####
tabPanel(span("Data", title = "Upload custom formatted data or parse results from plate readers and similar devices."),
icon = icon("file-lines"),
value = "tabPanel",
tabsetPanel(type = "tabs", id = "tabs_data",
##____CUSTOM____####
tabPanel(value = "Custom", span("Custom", title="Upload manually formatted data. Data from different experiments can be added. In column format, the first three table rows contain (see figure):\n1. sample description\n2. replicate number (optional: followed by a letter to indicate technical replicates)\n3. concentration value (optional, for dose-response analysis)"),
sidebarPanel(
style='border-color: #ADADAD',
# Growth data
wellPanel(
h4(strong("Growth data"), style = "line-height: 0.4;font-size: 150%; margin-bottom: 15px;"),
style='padding: 0.1; border-color: #ADADAD; padding: 1; padding-bottom: 0',
fileInput(inputId = 'custom_file_growth',
label = 'Choose growth data file',
accept = c('.xlsx', '.xls', '.csv', '.txt', '.tsv')
),
conditionalPanel(
condition = "output.growthfileUploaded && output.custom_growth_format == 'xlsx'",
div(style = "margin-bottom: -20px"),
selectInput(inputId = "custom_growth_sheets",
label = "Select Sheet",
choices = "Sheet1")
), # select sheet: conditional
conditionalPanel(
condition = "output.growthfileUploaded && output.custom_growth_format == 'csv'",
selectInput(inputId = "separator_custom_growth",
label = "Select separator",
choices = c("," = ",",
";" = ";")
),
selectInput(inputId = "decimal_separator_custom_growth",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
),
conditionalPanel(
condition = "output.growthfileUploaded && (output.custom_growth_format == 'tsv' || output.custom_growth_format == 'txt')",
selectInput(inputId = "decimal_separator_custom_growth",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
),
div(style = "margin-bottom: -15px"),
tags$div(title="Provide an equation in the form 'y = function(x)' (for example: 'y = x^2 * 0.3 - 0.5') to convert growth values. This can be used to, e.g., convert plate reader absorbance values into OD600.",
checkboxInput(inputId = 'calibration_growth_custom',
label = 'Apply calibration')
),
conditionalPanel(
condition = 'input.calibration_growth_custom',
div(style = "margin-bottom: -10px"),
textInput(inputId = "calibration_equation_growth_custom",
label = "Type equation in the form 'y = function(x)'",
placeholder = 'y = x * 0.5 - 1'
)
),
),
#____Fluorescence___________
wellPanel(
h4(strong("Fluorescence data"), style = "line-height: 1;font-size: 150%; margin-bottom: 15px;"),
style='padding: 0.1; border-color: #ADADAD; padding: 1; padding-bottom: 0',
fileInput(inputId = 'custom_file_fluorescence',
label = 'Choose fluorescence data file',
accept = c('.xlsx', '.xls', '.csv', '.txt', '.tsv')
),
conditionalPanel(
condition = "output.fluorescencefileUploaded && output.custom_fluorescence_format == 'xlsx'",
div(style = "margin-bottom: -20px"),
selectInput(inputId = "custom_fluorescence_sheets",
label = "Select Sheet",
choices = "Sheet1")
), # select sheet: conditional
conditionalPanel(
condition = "output.fluorescencefileUploaded && output.custom_fluorescence_format == 'csv'",
selectInput(inputId = "separator_custom_fluorescence",
label = "Select separator",
choices = c("," = ",",
";" = ";")
),
selectInput(inputId = "decimal_separator_custom_fluorescence",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
),
conditionalPanel(
condition = "output.fluorescencefileUploaded && (output.custom_fluorescence_format == 'tsv' || output.custom_fluorescence_format == 'txt')",
selectInput(inputId = "decimal_separator_custom_fluorescence",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
),
div(style = "margin-top: -15px"),
tags$div(title="Provide an equation in the form 'y = function(x)' (for example: 'y = x^2 * 0.3 - 0.5') to convert fluorescence values. This can be used to, e.g., convert fluorescence intensities into molecule concentrations.",
checkboxInput(inputId = 'calibration_fluorescence_custom',
label = 'Apply calibration')
),
conditionalPanel(
condition = 'input.calibration_fluorescence_custom',
div(style = "margin-bottom: -10px"),
textInput(inputId = "calibration_equation_fluorescence_custom",
label = "Type equation in the form 'y = function(x)'",
placeholder = 'y = x * 0.5 - 1'
)
),
), # wellpanel
conditionalPanel(
condition = "output.fluorescencefileUploaded",
checkboxInput(inputId = 'load_fl2_data_custom',
label = 'Use second fluorescence to normalize fluorescence.',
value = FALSE),
bsPopover("load_fl2_data_custom", title = "Provide a table file with fluorescence 2 data",
content = "Table layout must mimic that of growth data. Fluorescence 2 data is only used to normalize of fluorescence!")
),
# #_____Fluorescence 2___________
conditionalPanel(
condition = "input.load_fl2_data_custom",
wellPanel(
h4(strong("Fluorescence 2 data"), style = "line-height: 1;font-size: 150%; margin-bottom: 15px;"),
style='padding: 0.1; border-color: #ADADAD; padding: 1; padding-bottom: 0',
fileInput(inputId = 'custom_file_fluorescence2',
label = 'Choose fluorescence2 data file',
accept = c('.xlsx', '.xls', '.csv', '.txt', '.tsv')
),
conditionalPanel(
condition = "output.fluorescence2fileUploaded && output.custom_fluorescence2_format == 'xlsx'",
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-bottom: 0',
div(style = "margin-bottom: -20px"),
selectInput(inputId = "custom_fluorescence2_sheets",
label = "Select Sheet",
choices = "Sheet1")
)
), # select sheet: conditional
conditionalPanel(
condition = "output.fluorescence2fileUploaded && output.custom_fluorescence2_format == 'csv'",
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-bottom: 0',
selectInput(inputId = "separator_custom_fluorescence2",
label = "Select separator",
choices = c("," = ",",
";" = ";")
),
selectInput(inputId = "decimal_separator_custom_fluorescence2",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
)
),
conditionalPanel(
condition = "output.fluorescence2fileUploaded && (output.custom_fluorescence2_format == 'tsv' || output.custom_fluorescence2_format == 'txt')",
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-bottom: 0',
selectInput(inputId = "decimal_separator_custom_fluorescence2",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
)
),
div(style = "margin-top: -15px"),
tags$div(title="Provide an equation in the form 'y = function(x)' (for example: 'y = x^2 * 0.3 - 0.5') to convert fluorescence2 values. This can be used to, e.g., convert fluorescence intensities into molecule concentrations.",
checkboxInput(inputId = 'calibration_fluorescence2_custom',
label = 'Apply calibration')
),
conditionalPanel(
condition = 'input.calibration_fluorescence2_custom',
div(style = "margin-bottom: -10px"),
textInput(inputId = "calibration_equation_fluorescence2_custom",
label = "Type equation in the form 'y = function(x)'",
placeholder = 'y = x * 0.5 - 1'
)
),
) # wellPanel
),
selectInput(inputId = 'norm_type_custom',
label = 'Select data type for fluorescence normalization',
choices = ""),
tags$div(title="Shall blank values (the mean of samples identified by 'Blank' IDs) be subtracted from values within the same experiment?",
checkboxInput(inputId = 'subtract_blank_custom',
label = 'Subtract blank',
value = TRUE)
),
tags$div(title=HTML(paste("Provide an equation in the form 'y = function(x)' to convert time values. For example, type 'y = x / 60' to convert minutes to hours.\n", "Note: the time unit will affect calculated parameters (e.g., the growth rate in 1/h, 1/min, or 1/s) as well as the time displayed in all plots.")),
checkboxInput(inputId = 'convert_time_values_custom',
label = 'Convert time values',
value = FALSE)
),
conditionalPanel(
condition = 'input.convert_time_values_custom',
tags$div(title=HTML(paste("Provide an equation in the form 'y = function(x)' to convert time values. For example, type 'y = x / 60' to convert minutes to hours.\n", "Note: the time unit will affect calculated parameters (e.g., the growth rate in 1/h, 1/min, or 1/s) as well as the time displayed in all plots.")),
div(style = "margin-bottom: -10px"),
textInput(inputId = "convert_time_equation_custom",
label = "Type equation in the form 'y = function(x)'",
placeholder = 'y = x / 24')
)
),
conditionalPanel(
condition = 'output.growthfileUploaded || output.fluorescencefileUploaded',
fluidRow(
column(12,
div(
actionButton(inputId = "read_custom",
label = "Read data",
icon=icon("file-lines"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
),
HTML("<br>"),
HTML("<br>"),
tags$div(title="Simulate growth curves to generate a random demo dataset.",
actionButton(inputId = "random_data_growth",
label = "Create random growth dataset",
icon=icon("shuffle"),
style="padding:4px; font-size:80%; color: white; background-color: #35e51d")
),
),# sidebar panel
), # Custom tabPanel
##____PLATE READER____####
tabPanel(value = "Parse Raw Data", span("Parse Raw Data", title="Upload a results table file generated with the default export function of a plate reader (or similar device) software. Sample information need to be provided in a separate table."),
sidebarPanel(
style='border-color: #ADADAD',
wellPanel(
div(style = "margin-top: -10px"),
h3(strong("1. Load data"), style = "line-height: 0.4;font-size: 150%; margin-bottom: 15px;"),
style='padding: 5px; border-color: #ADADAD;',
# select file type
fileInput(inputId = 'parse_file',
label = 'Choose raw export file',
accept = c('.xlsx', '.xls', '.csv', '.tsv', '.txt')),
div(style = "margin-top: -10px"),
conditionalPanel(
condition = "output.parsefileUploaded && (output.parse_file_format == 'xlsx' | output.parse_file_format == 'xls')",
selectInput(inputId = "parse_data_sheets",
label = "Select sheet with read data",
choices = "Sheet1")
), # select sheet: conditional
conditionalPanel(
condition = "output.parsefileUploaded && output.parse_file_format == 'csv'",
selectInput(inputId = "separator_parse",
label = "Select separator",
choices = c("," = ",",
";" = ";")
),
selectInput(inputId = "decimal_separator_parse",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
),
conditionalPanel(
condition = "output.parsefileUploaded && (output.parse_file_format == 'tsv' || output.parse_file_format == 'txt')",
selectInput(inputId = "decimal_separator_parse",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
)
),
div(style = "margin-top: -15px"),
conditionalPanel(
condition = "output.parsefileUploaded",
wellPanel(
style='padding: 5px; border-color: #ADADAD; padding-bottom: 0',
div(style = "margin-top: -10px"),
h3(strong("2. Format"), style = "line-height: 0.4;font-size: 150%; margin-bottom: 15px;"),
selectizeInput(inputId = "platereader_software",
label = "Platereader software",
choices = c("Biotek - Gen5/Gen6" = "Gen5",
"Biolector" = "Biolector",
"Chi.Bio" = "Chi.Bio",
"Growth Profiler 960" = "GrowthProfiler",
"Tecan i-control" = "Tecan",
"PerkinElmer - Victor Nivo" = "VictorNivo",
"PerkinElmer - Victor X3" = "VictorX3"
),
multiple = TRUE,
options = list(maxItems = 1)
)
)
),
div(style = "margin-top: -15px"),
conditionalPanel(
condition = "output.parsefileUploaded",
wellPanel(
style='padding: 5px; border-color: #ADADAD; padding-bottom: 0',
div(style = "margin-top: -10px"),
h3(strong("3. Assign data type"), style = "line-height: 0.4; font-size: 150%; margin-bottom: 15px;"),
conditionalPanel(
condition = "input.platereader_software != 'GrowthProfiler'",
# Growth Read
selectInput(inputId = "parsed_reads_growth",
label = "Growth data",
choices = ""
),
conditionalPanel(
condition = "input.parsed_reads_growth.length > 0 && input.parsed_reads_growth != 'Ignore'",
div(style = "margin-top: -15px"),
tags$div(title="Provide an equation in the form 'y = function(x)' (for example: 'y = x^2 * 0.3 - 0.5') to convert growth values. This can be used to, e.g., convert plate reader absorbance values into OD600.",
checkboxInput(inputId = 'calibration_growth_plate_reader',
label = 'Apply calibration'),
),
conditionalPanel(
condition = "input.calibration_growth_plate_reader",
div(style = "margin-bottom: -10px"),
textInput(inputId = "calibration_equation_growth_plate_reader",
label = NULL,
placeholder = 'y = x * 0.5 - 1',
width = "85%")
)
),
# Fluorescence Read
selectInput(inputId = "parsed_reads_fluorescence",
label = "Fluorescence data",
choices = ""
),
conditionalPanel(
condition = "input.parsed_reads_fluorescence.length > 0 && input.parsed_reads_fluorescence != 'Ignore'",
div(style = "margin-top: -15px"),
tags$div(title="Provide an equation in the form 'y = function(x)' (for example: 'y = x^2 * 0.3 - 0.5') to convert fluorescence values. This can be used to, e.g., convert fluorescence intensities into molecule concentrations.",
checkboxInput(inputId = 'calibration_fluorescence_plate_reader',
label = 'Apply calibration')
),
conditionalPanel(
condition = "input.calibration_fluorescence_plate_reader",
div(style = "margin-bottom: -10px"),
textInput(inputId = "calibration_equation_fluorescence_plate_reader",
label = NULL,
placeholder = 'y = x * 0.5 - 1',
width = "85%"),
)
),
# Fluorescence2 Read
selectInput(inputId = "parsed_reads_fluorescence2",
label = "Fluorescence data 2 (used only for normalization)",
choices = ""
),
conditionalPanel(
condition = "input.parsed_reads_fluorescence2.length > 0 && input.parsed_reads_fluorescence2 != 'Ignore'",
div(style = "margin-top: -15px"),
tags$div(title="Provide an equation in the form 'y = function(x)' (for example: 'y = x^2 * 0.3 - 0.5') to convert fluorescence values. This can be used to, e.g., convert fluorescence intensities into molecule concentrations.",
checkboxInput(inputId = 'calibration_fluorescence2_plate_reader',
label = 'Apply calibration')
),
conditionalPanel(
condition = "input.calibration_fluorescence2_plate_reader",
div(style = "margin-bottom: -10px"),
textInput(inputId = "calibration_equation_fluorescence2_plate_reader",
label = NULL,
placeholder = 'y = x * 0.5 - 1',
width = "85%")
)
),
), # conditionalPanel
) # wellPanel
),
div(style = "margin-top: -15px"),
conditionalPanel(
condition = "output.parsefileUploaded",
wellPanel(
style='padding: 5px; border-color: #ADADAD; padding-bottom: 0',
div(style = "margin-top: -10px"),
h3(strong("4. Load mapping"), style = "line-height: 0.4; font-size: 150%; margin-bottom: 15px;"),
conditionalPanel(
condition = "output.parse_file_format == 'xlsx' | output.parse_file_format == 'xls'",
tags$div(title="A table with mapping information is stored within the same Excel file that contains experimental data as separate sheet.",
checkboxInput(inputId = 'mapping_included_in_parse',
label = 'Included in data file (xlsx/xls)',
value = FALSE)
)
),
tags$div(title = "Table with four columns: Well | Description | Replicate | Concentration",
fileInput(inputId = 'map_file',
label = 'Choose mapping file',
accept = c('.xlsx', '.xls', '.csv', '.tsv', '.txt'),
placeholder = "map file",
)
),
conditionalPanel(
condition = "(input.mapping_included_in_parse && (output.parse_file_format == 'xlsx' | output.parse_file_format == 'xls')) || (!input.mapping_included_in_parse && output.mapfileUploaded && (output.map_file_format == 'xlsx' | output.map_file_format == 'xls'))",
selectInput(inputId = "map_data_sheets",
label = "Select sheet with mapping information",
choices = "Sheet1")
),
conditionalPanel(
condition = "!input.mapping_included_in_parse && output.mapfileUploaded && output.map_file_format == 'csv'",
selectInput(inputId = "separator_map",
label = "Select separator",
choices = c("," = ",",
";" = ";")
),
selectInput(inputId = "decimal_separator_map",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
),
conditionalPanel(
condition = "!input.mapping_included_in_parse && output.mapfileUploaded && (output.map_file_format == 'tsv' || output.map_file_format == 'txt')",
style='padding: 5px; border-color: #ADADAD; padding-bottom: 0',
selectInput(inputId = "decimal_separator_map",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
)
)
),
conditionalPanel(
condition = "input.parsed_reads_fluorescence.length > 0 && input.parsed_reads_fluorescence != 'Ignore'",
selectInput(inputId = 'norm_type_parse',
label = 'Select Read for fluorescence normalization',
choices = "")
),
tags$div(title="Shall blank values (the mean of samples identified by 'Blank' IDs) be subtracted from values within the same experiment?",
checkboxInput(inputId = 'subtract_blank_plate_reader',
label = 'Subtract blank',
value = TRUE)
),
tags$div(title=HTML(paste("Provide an equation in the form 'y = function(x)' to convert time values. For example, type 'y = x / 60' to convert minutes to hours.\n", "Note: the time unit will affect calculated parameters (e.g., the growth rate in 1/h, 1/min, or 1/s) as well as the time displayed in all plots.")),
checkboxInput(inputId = 'convert_time_values_plate_reader',
label = 'Convert time values',
value = TRUE)
),
conditionalPanel(
condition = 'input.convert_time_values_plate_reader',
tags$div(title=HTML(paste("Provide an equation in the form 'y = function(x)' to convert time values. For example, type 'y = x / 60' to convert minutes to hours.\n", "Note: the time unit will affect calculated parameters (e.g., the growth rate in 1/h, 1/min, or 1/s) as well as the time displayed in all plots.")),
div(style = "margin-bottom: -10px"),
textInput(inputId = "convert_time_equation_plate_reader",
label = "Type equation in the form 'y = function(x)'",
placeholder = 'y = x / 24')
)
),
fluidRow(
column(12,
div(
actionButton(inputId = "parse_data",
label = "Parse data",
icon=icon("file-lines"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
),# sidebar panel
), # Plate reader tabPanel
), # tabSet Panel
##____DATA - MAIN PANEL____####
mainPanel(
div(
id = "data_instruction",
conditionalPanel(
condition = "input.tabs_data == 'Custom'",
img(src = 'data_instruction.png',
width = '100%')
)
),
bsPopover(id = "data_instruction", title = "Custom data layout",
content = paste("Please format your data in the format shown in the figure:",
paste0(
"<ul>",
"<li>The first row contains \\'Time\\' and \\'Blank\\', as well as sample identifiers \\(identical for replicates\\).</li>",
"<li>The second row contains replicate numbers for identical conditions. If technical replicates were used in addition to biological replicates, indicate technical replicates with the same replicate number. Samples with identical IDs, concentrations, and replicate <i>numbers</i> will be combined by their <i>average</i>.</li>",
"<li>The third row contains \\(optional\\) concentration values to perform a dose-response analysis, if different concentrations of a compound were used in the experiment.</li>",
"</ul>"
),
"Details:",
paste0(
"<ul>",
"<li>Different experiments with differing time values and experiment-specific blanks are distinguished by an individual \\'Time\\' column to the left of each dataset.</li>",
"<li>Blank values \\(for each experiment\\) are combined as their average and subtracted from all remaining values if option \\[Subtract blank\\] is selected.</li>",
"<li>The metadata in the second and third rows are optional to perform the analysis and can be left empty.</li>",
"</ul>"
),
sep = "<br>"),
trigger = "hover", options = list(container = "body", template = widePopover)
),
div(
id = "mapping_layout",
conditionalPanel(
condition = "input.tabs_data != 'Custom' && output.parsefileUploaded",
img(src = 'mapping_layout.png',
width = '60%')
)
),
bsPopover(id = "mapping_layout", title = "Mapping layout",
content = paste("Please format a table providing sample information in the format shown in the figure:",
paste0(
"<ul>",
"<li>The first column contains the <i>well</i> position/name in the plate.</li>",
"<li>The second column contains the <i>ID</i> \\(i.e., organism, condition, etc.\\) of each sample. The ID needs to be identical for replicates.</li>",
"<li>The third column row contains replicate numbers for the same conditions. If technical replicates were used in addition to biological replicates, indicate technical replicates with the same replicate number. Samples with identical IDs, concentrations, and replicate <i>numbers</i> will be combined by their <i>average</i>.</li>",
"<li>The fourth column contains \\(optional\\) concentration values to perform a dose-response analysis, if different concentrations of a compound were used in the experiment.</li>",
"</ul>"
),
"Details:",
paste0(
"The values in \\'Blank\\' samples are combined as their average and subtracted from all remaining values if option \\[Subtract blank\\] is selected. The metadata in the third and fourth columns are optional to perform the analysis and can be left empty."
),
sep = "<br>"),
trigger = "hover", options = list(container = "body", template = widePopover)
),
div(id = 'Custom_Data_Tables',
h1("Your Data"),
tabsetPanel(type = "tabs", id = "tabsetPanel_custom_tables",
tabPanel(title = "Growth plot", value = "tabPanel_custom_plots_growth",
withSpinner(
plotOutput("custom_raw_growth_plot",
width = "100%", height = "1000px"),
)
),
tabPanel(title = "Fluorescence plot", value = "tabPanel_custom_plots_fluorescence",
withSpinner(
plotOutput("custom_raw_fluorescence_plot",
width = "100%", height = "1000px"),
)
),
tabPanel(title = "norm. Fluorescence plot", value = "tabPanel_custom_plots_norm_fluorescence",
withSpinner(
plotOutput("custom_raw_norm_fluorescence_plot",
width = "100%", height = "1000px"),
)
),
tabPanel(title = "Growth", value = "tabPanel_custom_tables_growth_processed",
withSpinner(
DT::dataTableOutput("growth_data_custom_processed")
),
downloadButton('download_custom_tables_growth_processed',"Download table")
),
tabPanel(title = "Fluorescence", value = "tabPanel_custom_tables_fluorescence_processed",
withSpinner(
DT::dataTableOutput("custom_table_fluorescence_processed")
),
downloadButton('download_custom_tables_fluorescence_processed',"Download table")
),
tabPanel(title = "Normalized fluorescence", value = "tabPanel_custom_tables_norm_fluorescence_processed",
withSpinner(
DT::dataTableOutput("custom_table_norm_fluorescence_processed")
),
downloadButton('download_custom_tables_norm_fluorescence_processed',"Download table")
),
tabPanel(title = "Experimental Design", value = "tabPanel_custom_tables_expdesign",
DT::dataTableOutput('custom_data_table_expdesign'),
downloadButton('download_custom_tables_expdesign',"Download table")
),
)
),
div(id = 'Parsed_Data_Tables',
h1("Parsed Data"),
tabsetPanel(type = "tabs", id = "tabsetPanel_parsed_tables",
tabPanel(title = "Growth plot", value = "tabPanel_parsed_plots_growth",
withSpinner(
plotOutput("parsed_raw_growth_plot",
width = "100%", height = "1000px"),
)
),
tabPanel(title = "Fluorescence plot", value = "tabPanel_parsed_plots_fluorescence",
withSpinner(
plotOutput("parsed_raw_fluorescence_plot",
width = "100%", height = "1000px"),
)
),
tabPanel(title = "norm. Fluorescence plot", value = "tabPanel_parsed_plots_norm_fluorescence",
withSpinner(
plotOutput("parsed_raw_norm_fluorescence_plot",
width = "100%", height = "1000px"),
)
),
tabPanel(title = "Growth", value = "tabPanel_parsed_tables_growth",
DT::dataTableOutput('parsed_data_table_growth'),
downloadButton('download_parsed_tables_growth',"Download table")
),
tabPanel(title = "Fluorescence", value = "tabPanel_parsed_tables_fluorescence",
DT::dataTableOutput('parsed_data_table_fluorescence'),
downloadButton('download_parsed_tables_fluorescence',"Download table")
),
tabPanel(title = "Normalized fluorescence", value = "tabPanel_parsed_tables_norm_fluorescence",
withSpinner(
DT::dataTableOutput("parsed_data_table_norm_fluorescence")
),
downloadButton('download_parsed_data_table_norm_fluorescence',"Download table")
),
tabPanel(title = "Experimental Design", value = "tabPanel_parsed_tables_expdesign",
DT::dataTableOutput('parsed_data_table_expdesign'),
downloadButton('download_parsed_tables_expdesign',"Download table")
)
)
)
) # main panel
), # Navbar 1
#____COMPUTATION____####
navbarMenu(span("Computation", title = "Run a complete data analysis workflow."),
menuName = "navbarMenu_Computation", icon=icon("gears"),
##____Computation_Growth____####
tabPanel("Growth", value = "tabPanel_Computation_Growth",
fluidRow(
sidebarLayout(
column(4,
sidebarPanel( width = 12,
style='border-color: #ADADAD',
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-top: 0; padding-bottom: 0',
h2(strong('Growth fit')),
h4('Global fit options'),
tags$div(title="Perform linear regression on (log-transformed) growth data.",
checkboxInput(inputId = 'linear_regression_growth',
label = 'Linear regression',
value = TRUE)
),
tags$div(title="Fit a selection of growth models to the data.",
checkboxInput(inputId = 'parametric_fit_growth',
label = 'Parametric fit',
value = FALSE)
),
tags$div(title="Perform a nonparametric fit to the data using the smooth.spline() function.",
checkboxInput(inputId = 'nonparametric_fit_growth',
label = 'Non-parametric fit',
value = TRUE)
),
tags$div(title="Apply a ln(x+1) transformation to the time data for linear and nonparametric fits.",
checkboxInput(inputId = 'log_transform_time_growth',
label = 'Log-transform time')
),
tags$div(title="Only for linear and nonparametric fits:\nExtract growth parameters for two different growth phases (as observed with, e.g., diauxic shifts).",
checkboxInput(inputId = 'biphasic_growth',
label = 'Biphasic growth')
),
QurvE:::numberInput(
inputId = 'growth_threshold_growth',
label = 'Growth threshold',
value = 1.5,
min = NA,
max = NA,
placeholder = 1.5
),
bsPopover(id = "growth_threshold_growth", title = HTML("<em>growth.thresh</em>"), content = "A sample will be considered to have no growth if no growth value is greater than [growth threshold] \\* start growth."),
QurvE:::numberInput(
inputId = 'minimum_growth_growth',
label = 'Minimum growth measurement',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "minimum_growth_growth", title = HTML("<em>min.growth</em>"), content = "Consider only growth values above [Minimum growth] for the fits."),
QurvE:::numberInput(
inputId = 'maximum_growth_growth',
label = 'Maximum growth measurement',
value = NULL,
min = NA,
max = NA
),
bsPopover(id = "maximum_growth_growth", title = HTML("<em>max.growth</em>"), content = "Consider only growth values below and including [Maximum growth measurement] for linear and spline fits."),
QurvE:::numberInput(
inputId = 't0_growth',
label = 't0',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "t0_growth", title = HTML("<em>t0</em>"), content = "Consider only time values above [t0] for the fits."),
QurvE:::numberInput(
inputId = 'tmax_growth',
label = 'tmax',
value = NULL,
min = NA,
max = NA
),
bsPopover(id = "tmax_growth", title = HTML("<em>tmax</em>"), content = "Consider only time values below and including [tmax] for linear and spline fits."),
), # Growth fit
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-top: 0; padding-bottom: 0',
h2(strong('Dose-response Analysis')),
checkboxInput(inputId = 'perform_ec50_growth',
label = 'Perform EC50 Analysis',
value = FALSE),
conditionalPanel(condition = "input.perform_ec50_growth",
selectInput(inputId = "dr_method_growth",
label = "Method",
choices = c("Dose-response models" = "model",
"Response spline fit" = "spline")
),
bsPopover(id = "dr_method_growth",
title = HTML("<em>dr.method</em>"),
placement = "right",
content = "Fit either various dose-response models (Ritz et al., 2015) to response-vs.-concentration data and select the best model based on the lowest AIC, or apply a nonparametric (spline) fit.",
trigger = "hover", options = list(container = "body", template = widePopover)
),
selectInput(inputId = "response_parameter_growth",
label = "Response Parameter",
choices = ""),
bsPopover(id = "response_parameter_growth", title = HTML("<em>dr.parameter</em>"), content = "Choose the response parameter to be used for creating a dose response curve.", placement = "top"),
conditionalPanel(
condition = 'input.dr_method_growth == "spline"',
tags$div(title="Perform a log(x+1) transformation on concentration values.",
checkboxInput(inputId = 'log_transform_concentration_growth',
label = 'Log transform concentration')
),
tags$div(title="Perform a log(y+1) transformation on response values.",
checkboxInput(inputId = 'log_transform_response_growth',
label = 'Log transform response')
),
textInput(
inputId = 'smoothing_factor_growth_dr',
label = 'Smoothing factor dose-response splines',
value = "",
placeholder = "NULL (choose automatically)"
),
bsPopover(id = "smoothing_factor_growth_dr", title = HTML("<em>smooth.dr</em>"), content = "\\'spar\\' argument in the R function smooth.spline() used to create the dose response curve."),
QurvE:::numberInput(
inputId = 'number_of_bootstrappings_dr_growth',
label = 'Number of bootstrappings',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "number_of_bootstrappings_dr_growth", title = HTML("<em>nboot.dr</em>"), content = "Optional: Define the number of bootstrap samples for EC50 estimation. Bootstrapping resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to determine the EC50.")
), #conditionalPanel(condition = 'input.dr_method_growth == "spline"')
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_growth_dr",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
HTML("<br>"),
) # conditionalPanel(condition = "input.perform_ec50_growth"
), # wellPanel
fluidRow(
column(12,
div(
actionButton(inputId = "run_growth",
label = "Run computation",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
style="float:right"),
div(
actionButton(inputId = "tooltip_growth_workflow",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
) # sidebarPanel
), # column
column(8,
conditionalPanel(
condition = "input.linear_regression_growth",
sidebarPanel(
width = 4,
style='border-color: #ADADAD; padding-top: 0',
h3(strong('Linear fit')),
tags$div(title="Perform a Ln(y/y0) transformation on growth values.",
checkboxInput(inputId = 'log_transform_data_linear_growth',
label = 'Log-transform data',
value = TRUE)
),
QurvE:::numberInput(
inputId = 'R2_threshold_growth',
label = 'R2 threshold',
value = 0.95,
placeholder = 0.95
),
bsPopover(id = "R2_threshold_growth", title = HTML("<em>lin.R2</em>"), content = "R2 threshold for calculated slopes of linear regression windows to be considered for the maximum growth rate."),
QurvE:::numberInput(
inputId = 'RSD_threshold_growth',
label = 'RSD threshold',
value = 0.1,
placeholder = 0.1
),
bsPopover(id = "RSD_threshold_growth", title = HTML("<em>lin.RSD</em>"), content = "Relative standard deviation (RSD) threshold for calculated slopes of linear regression windows to be considered for the maximum growth rate."),
QurvE:::numberInput(
inputId = 'dY_threshold_growth',
label = 'dY threshold',
value = 0.05,
placeholder = 0.05
),
bsPopover(id = "dY_threshold_growth", title = HTML("<em>lin.dY</em>"), content = "Threshold for the minimum fraction of growth increase a linear regression window should cover to be considered."),
checkboxInput(inputId = 'custom_sliding_window_size_growth',
label = 'Custom sliding window size',
value = FALSE),
conditionalPanel(
condition = "input.custom_sliding_window_size_growth",
numericInput(
inputId = 'custom_sliding_window_size_value_growth',
label = NULL,
value = "NULL",
min = NA,
max = NA,
),
bsPopover(id = "custom_sliding_window_size_value_growth", title = HTML("<em>lin.h</em>"), content = "If NULL, the sliding windows size (h) is chosen based on the number of data points within the growth phase (until maximum growth measurement)."),
),
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_growth.gcFitLinear",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
) # sidebarPanel
), # conditionalPanel
conditionalPanel(
condition = "input.parametric_fit_growth",
sidebarPanel(
style='border-color: #ADADAD; padding-top: 0',
h3(strong('Parametric fit')),
tags$div(title="Perform a Ln(y/y0) transformation on growth values.",
checkboxInput(inputId = 'log_transform_data_parametric_growth',
label = 'Log-transform data',
value = TRUE)
),
wellPanel(
h4(strong('Models:')),
style='border-color: #ADADAD; padding: 1; padding-top: 0; padding-bottom: 0',
tags$div(title="Reference: Zwietering MH, Jongenburger I, Rombouts FM, van 't Riet K. Modeling of the bacterial growth curve. Appl Environ Microbiol. 1990 Jun;56(6):1875-81. doi: 10.1128/aem.56.6.1875-1881.1990",
checkboxInput(inputId = 'logistic_growth',
label = 'logistic',
value = TRUE)
),
tags$div(title="Reference: Zwietering MH, Jongenburger I, Rombouts FM, van 't Riet K. Modeling of the bacterial growth curve. Appl Environ Microbiol. 1990 Jun;56(6):1875-81. doi: 10.1128/aem.56.6.1875-1881.1990",
checkboxInput(inputId = 'richards_growth',
label = 'Richards',
value = TRUE)
),
tags$div(title="Reference: Zwietering MH, Jongenburger I, Rombouts FM, van 't Riet K. Modeling of the bacterial growth curve. Appl Environ Microbiol. 1990 Jun;56(6):1875-81. doi: 10.1128/aem.56.6.1875-1881.1990",
checkboxInput(inputId = 'gompertz_growth',
label = 'Gompertz',
value = TRUE)
),
tags$div(title="Reference: Kahm, M., Hasenbrink, G., Lichtenberg-Fraté, H., Ludwig, J., & Kschischo, M. (2010). grofit: Fitting Biological Growth Curves with R. Journal of Statistical Software, 33(7), 1–21. https://doi.org/10.18637/jss.v033.i07",
checkboxInput(inputId = 'extended_gompertz_growth',
label = 'extended Gompertz',
value = TRUE)
),
tags$div(title="Reference: Huang, Lihan (2011) A new mechanistic growth model for simultaneous determination of lag phase duration and exponential growth rate and a new Belehdradek-type model for evaluating the effect of temperature on growth rate. Food Microbiology 28, 770 – 776. doi: 10.1016/j.fm.2010.05.019",
checkboxInput(inputId = 'huang_growth',
label = 'Huang',
value = TRUE)
),
tags$div(title="Reference: Baranyi and Roberts (1994) Mathematics of predictive food microbiology. Food Microbiology 26(2), 199 – 218. doi: 10.1016/0168-1605(94)00121-L",
checkboxInput(inputId = 'baranyi_growth',
label = 'Baranyi and Roberts',
value = TRUE)
)
),
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_growth.gcFitModel",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
)
), # conditionalPanel
conditionalPanel(
condition = "input.nonparametric_fit_growth",
sidebarPanel(
width = 4,
style='border-color: #ADADAD; padding-top: 0',
h3(strong('Nonparametric fit')),
tags$div(title="Perform a Ln(y/y0) transformation on growth values.",
checkboxInput(inputId = 'log_transform_data_nonparametric_growth',
label = 'Log-transform data',
value = TRUE)
),
QurvE:::numberInput(
inputId = 'smoothing_factor_nonparametric_growth',
label = 'Smoothing factor',
value = 0.55,
min = NA,
max = NA,
placeholder = 0.55
),
bsPopover(id = "smoothing_factor_nonparametric_growth", title = HTML("<em>smooth.gc</em>"), content = "\\'spar\\' argument within the R function smooth\\.spline\\(\\)."),
QurvE:::numberInput(
inputId = 'number_of_bootstrappings_growth',
label = 'Number of bootstrappings',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "number_of_bootstrappings_growth", title = HTML("<em>nboot.gc</em>"), content = "Optional: Define the number of bootstrap samples. Bootstrapping resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to yield a statistic distribution of growth parameters."),
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_growth.gcFitSpline",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
)
) # conditionalPanel
) # column
) # sidebarLayout
), # fluidRow
textOutput("text")
), # Growth Tab Panel
"----",
##____Computation_Fluorescence____####
tabPanel("Fluorescence", value = "tabPanel_Computation_Fluorescence",
fluidRow(
sidebarLayout(
column(4,
sidebarPanel( width = 12,
style = 'border-color: #ADADAD',
wellPanel(
style = 'padding: 1; border-color: #ADADAD; padding-top: 0; padding-bottom: 0',
h2(strong('Fluorescence fit')),
h4('Options'),
tags$div(title="Perform linear regression on log-transformed growth data.",
checkboxInput(
inputId = 'linear_regression_fluorescence',
label = 'linear regression',
value = TRUE
)
),
tags$div(title="Perform a nonparametric fit to the data using the smooth.spline() function.",
checkboxInput(
inputId = 'nonparametric_fit_fluorescence',
label = 'nonparametric fit',
value = TRUE
)
),
tags$div(title="Extract kinetic parameters for two different phases (as observed with, e.g., regulator-promoter systems with varying response in different growth stages).",
checkboxInput(inputId = 'biphasic_fluorescence',
label = 'Biphasic')
),
selectInput(
inputId = 'data_type_x_fluorescence',
label = 'Independent variable (x)',
choices = ""
),
bsPopover(id = "data_type_x_fluorescence", title = HTML("<em>x_type</em>"), content = "Select the data type that is used as the independent variable for all fits."),
conditionalPanel(
condition = "input.data_type_x_fluorescence == 'time' && output.normalized_fl_present",
tags$div(title="Use normalized fluorescence (divided by growth values) for all fits.",
checkboxInput(inputId = 'normalize_fluorescence',
label = 'Use normalized fluorescence'
)
)
),
conditionalPanel(
condition = 'input.data_type_x_fluorescence.includes("growth")',
QurvE:::numberInput(
inputId = 'growth_threshold_in_percent_fluorescence',
label = 'Growth threshold (in %)',
value = 1.5,
min = NA,
max = NA,
placeholder = 1.5
),
bsPopover(id = "growth_threshold_in_percent_fluorescence", title = HTML("<em>growth.thresh</em>"), content = "A sample will be considered to have no growth if no growth value is greater than [growth threshold] \\* start growth."),
),
conditionalPanel(
condition = 'input.data_type_x_fluorescence.includes("growth")',
QurvE:::numberInput(
inputId = 'minimum_growth_fluorescence',
label = 'Minimum growth measurement',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "minimum_growth_fluorescence", title = HTML("<em>min.growth</em>"), content = "Consider only growth values above [Minimum growth measurement] for the fits."),
),
conditionalPanel(
condition = 'input.data_type_x_fluorescence.includes("growth")',
QurvE:::numberInput(
inputId = 'maximum_growth_fluorescence',
label = 'Maximum growth measurement',
value = NULL,
min = NA,
max = NA
),
bsPopover(id = "maximum_growth_fluorescence", title = HTML("<em>max.growth</em>"), content = "Consider only growth values below and including [Maximum growth] for linear and spline fits."),
),
conditionalPanel(
condition = 'input.data_type_x_fluorescence.includes("time")',
QurvE:::numberInput(
inputId = 't0_fluorescence',
label = 't0',
value = 0,
min = NA,
max = NA,
placeholder = 0
)
),
bsPopover(id = "t0_fluorescence", title = HTML("<em>t0</em>"), content = "Consider only time values above [t0] for the fits."),
conditionalPanel(
condition = 'input.data_type_x_fluorescence.includes("time")',
QurvE:::numberInput(
inputId = 'tmax_fluorescence',
label = 'tmax',
value = NULL,
min = NA,
max = NA
),
bsPopover(id = "tmax_fluorescence", title = HTML("<em>tmax</em>"), content = "Consider only time values below and including [tmax] for linear and spline fits."),
),
), # wellPanel
wellPanel(style='padding: 1; border-color: #ADADAD; padding-top: 0; padding-bottom: 0',
h2(strong('Dose-response Analysis')),
checkboxInput(inputId = 'perform_ec50_fluorescence',
label = 'Perform dose-response analysis',
value = FALSE),
conditionalPanel(condition = 'input.perform_ec50_fluorescence',
selectInput(inputId = "dr_method_fluorescence",
label = "Method",
choices = c("Biosensor response model" = "model",
"Response spline fit" = "spline")
),
bsPopover(id = "dr_method_fluorescence",
placement = "right",
title = HTML("<em>dr.method</em>"),
content = "Fit either a biosensor response model (Meyer et al., 2019) to response-vs.-concentration data, or apply a nonparametric (spline) fit."
),
selectInput(inputId = "response_parameter_fluorescence",
label = "Response Parameter",
choices = ""),
bsPopover(id = "response_parameter_fluorescence", title = HTML("<em>dr.parameter</em>"), content = "Choose the response parameter to be used for creating a dose response curve.", placement = "top"),
tags$div(title="Perform a log(x+1) transformation on concentration values.",
checkboxInput(inputId = 'log_transform_concentration_fluorescence',
label = 'log transform concentration')
),
tags$div(title="Perform a log(x+1) transformation on response values.",
checkboxInput(inputId = 'log_transform_response_fluorescence',
label = 'log transform response')
),
conditionalPanel(
condition = 'input.dr_method_fluorescence == "spline"',
QurvE:::numberInput(
inputId = 'number_of_bootstrappings_dr_fluorescence',
label = 'Number of bootstrappings',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "number_of_bootstrappings_dr_fluorescence", title = HTML("<em>nboot.dr</em>"), content = "Optional: Define the number of bootstrap samples for EC50 estimation. Bootstrapping resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to determine the EC50."),
),
conditionalPanel(
condition = 'input.dr_method_fluorescence == "spline"',
textInput(
inputId = 'smoothing_factor_fluorescence_dr',
label = 'Smoothing factor dose-response splines',
value = "",
placeholder = "NULL (choose automatically)"
),
bsPopover(id = "smoothing_factor_fluorescence_dr", title = HTML("<em>smooth.dr</em>"), content = "\\'spar\\' argument in the R function smooth.spline() used to create the dose response curve."),
),
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_fl_dr",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
HTML("<br>"),
) # conditionalPanel(condition = "input.perform_ec50_fluorescence"
), # wellPanel
# [Run Computation] button
conditionalPanel(
condition = 'output.fluorescence_present',
fluidRow(
column(12,
div(
actionButton(inputId = "run_fluorescence",
label = "Run computation",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
style="float:right"),
div(
actionButton(inputId = "tooltip_fl_workflow",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
) # sidebarPanel
), # column
column(8,
conditionalPanel(
condition = "input.linear_regression_fluorescence",
sidebarPanel(
width = 4,
style='border-color: #ADADAD; padding-top: 0',
h3(strong('Linear fit')),
QurvE:::numberInput(
inputId = 'R2_threshold_fluorescence',
label = 'R2 threshold',
value = 0.95,
min = NA,
max = NA,
placeholder = 0.95
),
bsPopover(id = "R2_threshold_fluorescence", title = HTML("<em>lin.R2</em>"), content = "R2 threshold for calculated slopes of linear regression windows to be considered for the maximum slope."),
QurvE:::numberInput(
inputId = 'RSD_threshold_fluorescence',
label = 'RSD threshold',
value = 0.1,
min = NA,
max = NA,
placeholder = 0.1
),
bsPopover(id = "RSD_threshold_fluorescence", title = HTML("<em>lin.RSD</em>"), content = "Relative standard deviation (RSD) threshold for calculated slopes of linear regression windows to be considered for the maximum slope."),
QurvE:::numberInput(
inputId = 'dY_threshold_fluorescence',
label = 'dY threshold',
value = 0.05,
min = NA,
max = NA,
placeholder = 0.05
),
bsPopover(id = "dY_threshold_fluorescence", title = HTML("<em>lin.dY</em>"), content = "Threshold for the minimum fraction of fluorescence increase a linear regression window should cover to be considered."),
tags$div(title="Perform a Ln(y/y0) transformation on fluorescence values.",
checkboxInput(inputId = 'log_transform_data_linear_fluorescence',
label = 'Log-transform fluorescence data')
),
tags$div(title="Perform a Ln(y/y0) transformation on the independent variable",
checkboxInput(inputId = 'log_transform_x_linear_fluorescence',
label = 'Log-transform x data')
),
checkboxInput(inputId = 'custom_sliding_window_size_fluorescence',
label = 'custom sliding window size',
value = FALSE),
conditionalPanel(
condition = "input.custom_sliding_window_size_fluorescence",
numericInput(
inputId = 'custom_sliding_window_size_value_fluorescence',
label = NULL,
value = "NULL",
min = NA,
max = NA,
),
bsPopover(id = "custom_sliding_window_size_value_fluorescence", title = HTML("<em>lin.h</em>"), content = "If NULL, the sliding windows size (h) is chosen based on the number of data points within the phase of fluorescence increase (until maximum fluorescence or growth)."),
),
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_flFitLinear",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
)
), # conditionalPanel
conditionalPanel(
condition = "input.nonparametric_fit_fluorescence",
sidebarPanel(
width = 4,
style='border-color: #ADADAD; padding-top: 0',
h3(strong('Nonparametric fit')),
QurvE:::numberInput(
inputId = 'smoothing_factor_nonparametric_fluorescence',
label = 'Smoothing factor',
value = 0.75,
min = NA,
max = NA,
placeholder = 0.75
),
bsPopover(id = "smoothing_factor_nonparametric_fluorescence", title = HTML("<em>smooth.fl</em>"), content = "\\'spar\\' argument within the R function smooth\\.spline\\(\\)."),
QurvE:::numberInput(
inputId = 'number_of_bootstrappings_fluorescence',
label = 'Number of bootstrappings',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "number_of_bootstrappings_fluorescence", title = HTML("<em>nboot.fl</em>"), content = "Optional: Define the number of bootstrap samples. Bootstrapping resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to yield a statistic distribution of growth parameters."),
tags$div(title="Perform a Ln(y/y0) transformation on fluorescence values.",
checkboxInput(inputId = 'log_transform_data_nonparametric_fluorescence',
label = 'Log-transform fluorescence data')
),
tags$div(title="Perform a Ln(y/y0) transformation on the independent variable",
checkboxInput(inputId = 'log_transform_x_nonparametric_fluorescence',
label = 'Log-transform x data')
),
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_flFitSpline",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
)
) # conditionalPanel
) # column
) # sidebarLayout
) # fluidRow
), # tabPanel("Fluorescence"
), # navbarMenu('Computation'
#____VALIDATE____####
navbarMenu(span("Validation", title = "Graphical display for each fit."),
menuName = "navbarMenu_Validate", icon = icon("user-check"),
##____Validate_Growth____####
tabPanel(title = "Growth Fits", value = "tabPanel_Validate_Growth",
h1("Growth Fits"),
tabsetPanel(type = "tabs", id = "tabsetPanel_Validate_Growth",
###___Linear Fits___####
tabPanel(title = "Linear Fits", value = "tabPanel_Validate_Growth_Linear",
sidebarPanel(width = 5,
selectizeInput(inputId = "sample_validate_growth_linear",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
checkboxInput(inputId = 'logy_validate_growth_plot_linear',
label = 'Log-transform y axis',
value = TRUE),
checkboxInput(inputId = 'diagnostics_validate_growth_plot_linear',
label = 'Show diagnostics',
value = FALSE),
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_validate_growth_plot_linear',
label = 'Shape type',
min = 1,
max = 25,
value = 21),
sliderInput(inputId = 'shape_size_validate_growth_plot_linear',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_validate_growth_plot_linear',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.9,
step = 0.1),
sliderInput(inputId = 'lab_size_validate_growth_plot_linear',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.7,
step = 0.1),
sliderInput(inputId = 'line_width_validate_growth_plot_linear',
label = 'Line width',
min = 0.01,
max = 10,
value = 3),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_validate_growth_plot_linear",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_validate_growth_plot_linear",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_validate_growth_plot_linear",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_validate_growth_plot_linear",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(
inputId = 'color_validate_growth_plot_linear',
label = 'Change color',
value = "firebrick3"
),
bsPopover(id = "color_validate_growth_plot_linear",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(width = 7,
plotOutput("validate_growth_plot_linear", width = "100%", height = "600px"),
HTML("<br>"),
HTML("<br>"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_growth_linear",
label = "Re-run with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_growth_linear",
label = "Restore fit",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_validate_linear",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_validate_linear",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_validate_linear",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_growth_validate_linear',"Download Plot"),
radioButtons("format_download_growth_validate_linear",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) #mainPanel
), #tabPanel(title = "Linear Fits", value = "tabPanel_Validate_Growth_linearFits",
###___Spline Fits___####
tabPanel(title = "Nonparametric fits", value = "tabPanel_Validate_Growth_Spline",
sidebarPanel(width = 5,
selectizeInput(inputId = "sample_validate_growth_spline",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
sliderInput(inputId = 'shape_type_validate_growth_plot_spline',
label = 'Shape type',
min = 1,
max = 25,
value = 21),
checkboxInput(inputId = 'logy_validate_growth_plot_spline',
label = 'Log-transform y axis',
value = TRUE),
checkboxInput(inputId = "plot_derivative_validate_growth_plot_spline",
label = "Plot derivative",
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_validate_growth_plot_spline",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_validate_growth_plot_spline",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_validate_growth_plot_spline",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_validate_growth_plot_spline",
label = NULL,
value = "", placeholder = "max"
)
)
),
conditionalPanel(
condition = "input.logy_validate_growth_plot_spline",
strong("y-Range (derivative)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_derivative_validate_growth_plot_spline",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_derivative_validate_growth_plot_spline",
label = NULL,
value = "", placeholder = "max"
)
)
)
),
sliderInput(inputId = 'shape_size_validate_growth_plot_spline',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = "line_width_validate_growth_plot_spline",
label = "Line width",
min = 0.01,
max = 10,
value = 1),
sliderInput(inputId = 'base_size_validate_growth_plot_spline',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "nbreaks_validate_growth_plot_spline",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
textInput(
inputId = 'color_validate_growth_plot_spline',
label = 'Change color',
value = "dodgerblue3"
),
bsPopover(id = "color_validate_growth_plot_spline",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(width = 7,
withSpinner(
plotOutput("validate_growth_plot_spline",
width = "100%", height = "700px")
),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_growth_spline",
label = "Re-run with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_growth_spline",
label = "Restore fit",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_validate_spline",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_validate_spline",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_validate_spline",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_growth_validate_spline',"Download Plot"),
radioButtons("format_download_growth_validate_spline",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
), # fluidRow
h3(strong("Export spline values")),
fluidRow(
column(width = 4,
downloadButton('download_growth_validate_spline_values',"Download value table (x-y)")
),
column(width = 4,
downloadButton('download_growth_validate_spline_deriv_values',"Download derivative value table (x-y)")
),
)
) # mainPanel
), # tabPanel(title = "Nonparametric fits", value = "tabPanel_Validate_Growth_splineFits",
###___Model Fits___####
tabPanel(title = "Parametric fits", value = "tabPanel_Validate_Growth_Model",
sidebarPanel(width = 5,
wellPanel(
style='padding: 1; padding-top: 0; padding-bottom: 0',
selectizeInput(inputId = "sample_validate_growth_model",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
),
sliderInput(inputId = 'shape_type_validate_growth_plot_model',
label = 'Shape type',
min = 1,
max = 25,
value = 21),
sliderInput(inputId = 'shape_size_validate_growth_plot_model',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = "line_width_validate_growth_plot_model",
label = "Line width",
min = 0.01,
max = 10,
value = 1),
sliderInput(inputId = 'base_size_validate_growth_plot_model',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "nbreaks_validate_growth_plot_model",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
sliderInput(inputId = "eqsize_validate_growth_plot_model",
label = "Equation font size",
min = 0.1,
max = 10,
step = 0.1,
value = 1.9),
textInput(
inputId = 'color_validate_growth_plot_model',
label = 'Change color',
value = "forestgreen"
),
bsPopover(id = "color_validate_growth_plot_model",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(width = 7,
withSpinner(
plotOutput("validate_growth_plot_model",
width = "100%", height = "600px")
),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_growth_model",
label = "Re-run with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_growth_model",
label = "Restore fit",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_validate_model",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_validate_model",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_validate_model",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_growth_validate_model',"Download Plot"),
radioButtons("format_download_growth_validate_model",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) # mainPanel
), # tabPanel(title = "Parametric fits", value = "tabPanel_Validate_Growth_modelFits",
### Growth Boostrapping Spline Plots ####
tabPanel(title = "Bootstrapping Spline", value = "tabPanel_Validate_Growth_Spline_bt",
sidebarPanel(width = 4,
selectizeInput(inputId = "sample_validate_growth_spline_bt",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
checkboxInput(inputId = "plot_derivative_growth_spline_bt",
label = "Plot derivative",
value = TRUE),
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_validate_growth_spline_bt',
label = 'Shape type',
min = 1,
max = 25,
value = 1),
sliderInput(inputId = 'shape_size_validate_growth_spline_bt',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_validate_growth_spline_bt',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.9,
step = 0.1),
sliderInput(inputId = 'lab_size_validate_growth_spline_bt',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.7,
step = 0.1),
sliderInput(inputId = 'line_width_validate_growth_spline_bt',
label = 'Line width',
min = 0.01,
max = 10,
value = 0.5),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_validate_growth_spline_bt",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_validate_growth_spline_bt",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_validate_growth_spline_bt",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_validate_growth_spline_bt",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range (derivative)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_derivative_validate_growth_spline_bt",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_derivative_validate_growth_spline_bt",
label = NULL,
value = "", placeholder = "max"
)
),
textInput(
inputId = 'color_validate_growth_plot_spline_bt',
label = 'Change color',
value = "dodgerblue3"
),
bsPopover(id = "color_validate_growth_plot_spline_bt",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
), # sidebarPanel
mainPanel(width = 8,
plotOutput("validate_growth_plot_spline_bt",
width = "100%", height = "1000px"),
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_validate_spline_bt",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_validate_spline_bt",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_validate_spline_bt",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_growth_validate_spline_bt',"Download Plot"),
radioButtons("format_download_growth_validate_spline_bt",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) # mainPanel
) # tabPanel(title = "Bootstrapping Spline"
) # tabsetPanel(type = "tabs",
), # tabPanel(title = "Growth Fits", value = "tabPanel_Validate_Growth",
##____Validate_Fluorescence____####
tabPanel(title = "Fluorescence Fits", value = "tabPanel_Validate_Fluorescence",
h1("Fluorescence Fits"),
tabsetPanel(type = "tabs", id = "tabsetPanel_Validate_Fluorescence",
###___Linear Fits___####
tabPanel(title = "Linear Fits", value = "tabPanel_Validate_Fluorescence_Linear",
sidebarPanel(width = 5,
selectizeInput(inputId = "sample_validate_fluorescence_linear",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
checkboxInput(inputId = 'logy_validate_fluorescence_plot_linear',
label = 'Log-transform y axis',
value = FALSE),
checkboxInput(inputId = 'diagnostics_validate_fluorescence_plot_linear',
label = 'Show diagnostics',
value = FALSE),
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_validate_fluorescence_plot_linear',
label = 'Shape type',
min = 1,
max = 25,
value = 21),
sliderInput(inputId = 'shape_size_validate_fluorescence_plot_linear',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_validate_fluorescence_plot_linear',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'lab_size_validate_fluorescence_plot_linear',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.8,
step = 0.1),
sliderInput(inputId = 'line_width_validate_fluorescence_plot_linear',
label = 'Line width',
min = 0.01,
max = 10,
value = 3),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_validate_fluorescence_plot_linear",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_validate_fluorescence_plot_linear",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_validate_fluorescence_plot_linear",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_validate_fluorescence_plot_linear",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(
inputId = 'color_validate_fluorescence_plot_linear',
label = 'Change color',
value = "firebrick3"
),
bsPopover(id = "color_validate_fluorescence_plot_linear",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(width = 7,
plotOutput("validate_fluorescence_plot_linear", width = "100%", height = "600px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_fluorescence_linear",
label = "Re-run with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_fluorescence_linear",
label = "Restore fit",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_validate_linear",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_validate_linear",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_validate_linear",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_fluorescence_validate_linear',"Download Plot"),
radioButtons("format_download_fluorescence_validate_linear",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
)
),
###___Spline Fits___####
tabPanel(title = "Nonparametric fits", value = "tabPanel_Validate_Fluorescence_Spline",
sidebarPanel(width = 5,
selectizeInput(inputId = "sample_validate_fluorescence_spline",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
checkboxInput(inputId = 'logy_validate_fluorescence_plot_spline',
label = 'Log-transform y axis',
value = FALSE),
checkboxInput(inputId = "plot_derivative_validate_fluorescence_plot_spline",
label = "Plot derivative",
value = TRUE),
sliderInput(inputId = 'shape_type_validate_fluorescence_plot_spline',
label = 'Shape type',
min = 1,
max = 25,
value = 21),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_validate_fluorescence_plot_spline",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_validate_fluorescence_plot_spline",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_validate_fluorescence_plot_spline",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_validate_fluorescence_plot_spline",
label = NULL,
value = "", placeholder = "max"
)
)
),
conditionalPanel(
condition = "input.logy_validate_growth_plot_spline",
strong("y-Range (derivative)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_derivative_validate_fluorescence_plot_spline",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_derivative_validate_fluorescence_plot_spline",
label = NULL,
value = "", placeholder = "max"
)
)
)
),
sliderInput(inputId = 'shape_size_validate_fluorescence_plot_spline',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = "line_width_validate_fluorescence_plot_spline",
label = "Line width",
min = 0.01,
max = 10,
value = 1.1),
sliderInput(inputId = 'base_size_validate_fluorescence_plot_spline',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "nbreaks__validate_fluorescence_plot_spline",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
textInput(
inputId = 'color_validate_fluorescence_plot_spline',
label = 'Change color',
value = "dodgerblue3"
),
bsPopover(id = "color_validate_fluorescence_plot_spline",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(width = 7,
#conditional if diagnostics
withSpinner(
plotOutput("validate_fluorescence_plot_spline",
width = "100%", height = "700px")
),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_fluorescence_spline",
label = "Re-run with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_fluorescence_spline",
label = "Restore fit",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
) # column
), # fluidRow
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_validate_spline",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_validate_spline",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_validate_spline",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_fluorescence_validate_spline',"Download Plot"),
radioButtons("format_download_fluorescence_validate_spline",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
), # fluidRow
h3(strong("Export spline values")),
fluidRow(
column(width = 4,
downloadButton('download_fluorescence_validate_spline_values',"Download value table (x-y)")
),
column(width = 4,
downloadButton('download_fluorescence_validate_spline_deriv_values',"Download derivative value table (x-y)")
),
)
) # mainPanel
), # tabPanel(title = "Nonparametric fits", value = "tabPanel_Validate_Fluorescence_splineFits",
# Bootstrapping spline fit ####
tabPanel(title = "Bootstrapping Spline", value = "tabPanel_Validate_Fluorescence_Spline_bt",
sidebarPanel(width = 4,
selectizeInput(inputId = "sample_validate_fluorescence_spline_bt",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
checkboxInput(inputId = "plot_derivative_fluorescence_spline_bt",
label = "Plot derivative",
value = TRUE),
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_validate_fluorescence_spline_bt',
label = 'Shape type',
min = 1,
max = 25,
value = 1),
sliderInput(inputId = 'shape_size_validate_fluorescence_spline_bt',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_validate_fluorescence_spline_bt',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 2.8,
step = 0.1),
sliderInput(inputId = 'lab_size_validate_fluorescence_spline_bt',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 2.4,
step = 0.1),
sliderInput(inputId = 'line_width_validate_fluorescence_spline_bt',
label = 'Line width',
min = 0.01,
max = 10,
value = 0.5),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_validate_fluorescence_spline_bt",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_validate_fluorescence_spline_bt",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_validate_fluorescence_spline_bt",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_validate_fluorescence_spline_bt",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range (derivative)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_derivative_validate_fluorescence_spline_bt",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_derivative_validate_fluorescence_spline_bt",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(
inputId = 'color_validate_fluorescence_spline_bt',
label = 'Change color',
value = "dodgerblue3"
),
bsPopover(id = "color_validate_fluorescence_spline_bt",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
), # sidebarPanel
mainPanel(width = 8,
plotOutput("validate_fluorescence_plot_spline_bt",
width = "100%", height = "1000px"),
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_validate_spline_bt",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_validate_spline_bt",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_validate_spline_bt",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_fluorescence_validate_spline_bt',"Download Plot"),
radioButtons("format_download_fluorescence_validate_spline_bt",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) # mainPanel
) # tabPanel(title = "Bootstrapping Spline"
) # tabsetPanel(type = "tabs",
) # tabPanel(title = "Fluorescence Fits", value = "tabPanel_Validate_Fluorescence",
), # navbarMenu("Validate", icon = icon("user-check"),
#____RESULTS____####
navbarMenu(span("Results", title = "Tabular overview of computation results."),
menuName = "navbarMenu_Results", icon = icon("magnifying-glass-chart"),
##____Results_Growth___####
tabPanel(title = "Growth", value = "tabPanel_Results_Growth",
tabsetPanel(type = "tabs", id = "tabsetPanel_Results_Growth",
tabPanel(title = "Linear Fit", value = "tabPanel_Results_Growth_Linear",
conditionalPanel(condition = "input.biphasic_growth",
h5("(Values in parentheses indicate parameters for secondary growth phase)")
),
checkboxInput(inputId = 'grouped_results_growth_linear',
label = 'Group averages',
value = TRUE),
conditionalPanel(
condition = "!input.grouped_results_growth_linear",
DT::dataTableOutput('results_table_growth_linear')
),
conditionalPanel(
condition = "input.grouped_results_growth_linear",
DT::dataTableOutput('results_table_growth_linear_group')
),
conditionalPanel(
condition = "!input.grouped_results_growth_linear",
downloadButton('download_table_growth_linear',"Download table")
),
conditionalPanel(
condition = "input.grouped_results_growth_linear",
downloadButton('download_table_growth_linear_group',"Download table")
),
),
tabPanel(title = "Nonparametric Fit", value = "tabPanel_Results_Growth_Spline",
conditionalPanel(condition = "input.biphasic_growth",
h5("(Values in parentheses indicate parameters for secondary growth phase)")
),
checkboxInput(inputId = 'grouped_results_growth_spline',
label = 'Group averages',
value = TRUE),
conditionalPanel(
condition = "!input.grouped_results_growth_spline",
DT::dataTableOutput('results_table_growth_spline')
),
conditionalPanel(
condition = "input.grouped_results_growth_spline",
DT::dataTableOutput('results_table_growth_spline_group')
),
conditionalPanel(
condition = "!input.grouped_results_growth_spline",
downloadButton('download_table_growth_spline',"Download table")
),
conditionalPanel(
condition = "input.grouped_results_growth_spline",
downloadButton('download_table_growth_spline_group',"Download table")
),
),
tabPanel(title = "Nonparametric Fit (Bootstrapping)", value = "tabPanel_Results_Growth_Spline_bt",
DT::dataTableOutput('results_table_growth_spline_bt'),
downloadButton('download_table_growth_spline_bt',"Download table")
),
tabPanel(title = "Parametric Fit", value = "tabPanel_Results_Growth_Model",
checkboxInput(inputId = 'grouped_results_growth_model',
label = 'Group averages',
value = TRUE),
conditionalPanel(
condition = "!input.grouped_results_growth_model",
DT::dataTableOutput('results_table_growth_model'),
downloadButton('download_table_growth_model',"Download table")
),
conditionalPanel(
condition = "input.grouped_results_growth_model",
DT::dataTableOutput('results_table_growth_model_group'),
downloadButton('download_table_growth_model_group',"Download table")
),
),
tabPanel(title = "Dose-response analysis", value = "tabPanel_Results_Growth_DR",
DT::dataTableOutput('results_table_growth_dr_spline'),
downloadButton('download_table_growth_dr',"Download table")
)
)
),
##____Results_Fluorescence___####
tabPanel(title = "Fluorescence", value = "tabPanel_Results_Fluorescence",
tabsetPanel(type = "tabs", id = "tabsetPanel_Results_Fluorescence",
tabPanel(title = "Linear Fit", value = "tabPanel_Results_Fluorescence_Linear",
conditionalPanel(condition = "input.biphasic_fluorescence",
h5("(Values in parentheses indicate parameters for secondary phase)")
),
checkboxInput(inputId = 'grouped_results_fluorescence_linear',
label = 'Group averages',
value = TRUE),
conditionalPanel(
condition = "!input.grouped_results_fluorescence_linear",
DT::dataTableOutput('results_table_fluorescence_linear')
),
conditionalPanel(
condition = "input.grouped_results_fluorescence_linear",
DT::dataTableOutput('results_table_fluorescence_linear_group')
),
conditionalPanel(
condition = "!input.grouped_results_fluorescence_linear",
downloadButton('download_table_fluorescence_linear',"Download table")
),
conditionalPanel(
condition = "input.grouped_results_fluorescence_linear",
downloadButton('download_table_fluorescence_linear_group',"Download table")
),
),
tabPanel(title = "Nonparametric Fit", value = "tabPanel_Results_Fluorescence_Spline",
conditionalPanel(condition = "input.biphasic_fluorescence",
h5("(Values in parentheses indicate parameters for secondary phase)")
),
checkboxInput(inputId = 'grouped_results_fluorescence_spline',
label = 'Group averages',
value = TRUE),
conditionalPanel(
condition = "!input.grouped_results_fluorescence_spline",
DT::dataTableOutput('results_table_fluorescence_spline')
),
conditionalPanel(
condition = "input.grouped_results_fluorescence_spline",
DT::dataTableOutput('results_table_fluorescence_spline_group')
),
conditionalPanel(
condition = "!input.grouped_results_fluorescence_spline",
downloadButton('download_table_fluorescence_spline',"Download table")
),
conditionalPanel(
condition = "input.grouped_results_fluorescence_spline",
downloadButton('download_table_fluorescence_spline_group',"Download table")
),
),
tabPanel(title = "Nonparametric Fit (Bootstrapping)", value = "tabPanel_Results_Fluorescence_Spline_bt",
DT::dataTableOutput('results_table_fluorescence_spline_bt'),
downloadButton('download_table_fluorescence_spline_bt',"Download table")
),
tabPanel(title = "Dose-response analysis", value = "tabPanel_Results_Fluorescence_DR_Spline",
DT::dataTableOutput('results_table_fluorescence_dr_spline'),
downloadButton('download_table_fluorescence_dr_spline',"Download table")
),
tabPanel(title = "Dose-response analysis", value = "tabPanel_Results_Fluorescence_DR_Model",
DT::dataTableOutput('results_table_fluorescence_dr_model'),
downloadButton('download_table_fluorescence_dr_model',"Download table")
)
)
)
),
#____Visualize____####
navbarMenu(span("Visualization", title = "Visualize computation results for the entire dataset."),
menuName = "navbarMenu_Visualize", icon = icon("chart-line"),
## Growth Plots ####
tabPanel(title = "Growth Plots", value = "tabPanel_Visualize_Growth",
h1("Growth Plots"),
tabsetPanel(type = "tabs", id = "tabsetPanel_Visualize_Growth",
### Growth Group Plots ####
tabPanel(title = "Group Plots",
sidebarPanel(
selectInput(inputId = "data_type_growth_group_plot",
label = "Data type",
choices = c("Raw growth" = "raw",
"Spline fits" = "spline")
),
checkboxInput(inputId = "select_string_visualize_growth_group",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_growth_group && !input.plot_group_averages_growth_group_plot",
selectizeInput(inputId = "samples_visualize_growth_group",
label = "Samples:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "!input.select_string_visualize_growth_group && input.plot_group_averages_growth_group_plot",
selectizeInput(inputId = "groups_visualize_growth_group",
label = "Conditions:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_growth_group",
textInput(inputId = "select_samples_based_on_string_growth_group_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_string_growth_group_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
conditionalPanel(
condition = "input.plot_group_averages_growth_group_plot || input.select_string_visualize_growth_group",
textInput(inputId = "select_samples_based_on_concentration_growth_group_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_concentration_growth_group_plot",
label = "Exclude sample based on concentration (separate by ;)"
)
),
checkboxInput(inputId = "plot_group_averages_growth_group_plot",
label = "Plot group averages",
value = TRUE),
conditionalPanel(
condition = "input.data_type_growth_group_plot == 'spline'",
checkboxInput(inputId = "plot_derivative_growth_group_plot",
label = "Plot derivative",
value = TRUE)
),
h3("Customize plot appearance"),
checkboxInput(inputId = "log_transform_y_axis_growth_group_plot",
label = "Log-transform y-axis",
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_growth_group_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_growth_group_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_growth_group_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_growth_group_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
conditionalPanel(
condition = "input.data_type_growth_group_plot == 'spline'",
strong("y-Range (derivative)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_derivative_growth_group_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_derivative_growth_group_plot",
label = NULL,
value = "", placeholder = "max"
)
)
)
),
textInput(inputId = "y_axis_title_growth_group_plot",
label = "y-axis title",
value = "Growth [y(t)]"
),
textInput(inputId = "x_axis_title_growth_group_plot",
label = "x-axis title",
value = "Time"
),
conditionalPanel(
condition = "input.data_type_growth_group_plot == 'spline'",
textInput(inputId = "y_axis_title_derivative_growth_group_plot",
label = "y-axis title derivative",
value = "Growth rate"
)
),
sliderInput(inputId = "nbreaks_growth_group_plot",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
sliderInput(inputId = "line_width_growth_group_plot",
label = "Line width",
min = 0.01,
max = 10,
value = 1.1),
sliderInput(inputId = 'base_size_growth_group_plot',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
selectInput(inputId = "legend_position_group_plot",
label = "Legend position",
choices = c("Bottom" = "bottom",
"Top" = "top",
"Left" = "left",
"Right" = "right")
),
sliderInput(inputId = 'legend_ncol_group_plot',
label = 'Number of legend columns',
min = 1,
max = 10,
value = 4,
step = 1
),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "color_groups_group_plot",
label = "Color samples by group",
value = TRUE)
),
textInput(
inputId = 'custom_colors_group_plot',
label = 'Custom colors'
),
bsPopover(id = "custom_colors_group_plot",
title = HTML("<em>Provide custom colors</em>"), placement = "top",
content = "Enter colors either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). Separate colors with a comma. A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
conditionalPanel(
condition = "output.more_than_two_conc && input.color_groups_group_plot",
selectizeInput(inputId = "color_palettes_group_plot",
label = "Change color palettes",
width = "100%",
choices = names(QurvE:::single_hue_palettes),
selected = names(QurvE:::single_hue_palettes),
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
),
bsPopover(id = "color_palettes_group_plot",
title = HTML("<em>Define the colors used to display sample groups with identical concentrations</em>"), placement = "top",
content = "The number of selected color palettes must be at least the number of displayed groups. The order of the chosen palettes corresponds to the oder of conditions in the legend."
),
)
), # Side panel growth group plots
mainPanel(
withSpinner(
plotOutput("growth_group_plot",
width = "100%", height = "1000px"),
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_group_plot",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_group_plot",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_group_plot",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_growth_group_plot',"Download Plot"),
radioButtons("format_download_growth_group_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) # mainPanel
),
### Growth Parameter Plots ####
tabPanel(title = "Parameter Plots",
sidebarPanel(
selectInput(inputId = "parameter_parameter_growth_plot",
label = "Parameter",
choices = ""
),
checkboxInput(inputId = "select_string_visualize_parameter_growth_plot",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_parameter_growth_plot",
selectizeInput(inputId = "samples_visualize_parameter_growth_plot",
label = "Conditions:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_parameter_growth_plot",
textInput(inputId = "select_sample_based_on_string_growth_parameter_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_sample_based_on_strings_growth_parameter_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
textInput(inputId = "select_sample_based_on_concentration_growth_parameter_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_sample_based_on_concentration_growth_parameter_plot",
label = "Exclude sample based on concentration (separate by ;)"
),
checkboxInput(inputId = 'normalize_to_reference_growth_parameter_plot',
label = 'normalize to reference',
value = FALSE),
h3("Customize plot appearance"),
# Conditional Panel
conditionalPanel(condition = "input.normalize_to_reference_growth_parameter_plot",
# reactive selection
selectInput(inputId = 'reference_condition_growth_parameter_plot',
label = 'Reference condition',
choices = ""
),
# reactive selection
selectInput(inputId = 'reference_concentration_growth_parameter_plot',
label = 'Reference concentration',
choices = ""
),
),
sliderInput(inputId = "shape.size_growth_parameter_plot",
label = "Shape size",
min = 0.1,
max = 10,
value = 3,
step = 0.1),
sliderInput(inputId = "basesize_growth_parameter_plot",
label = "Base font size",
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "label.size_growth_parameter_plot",
label = "Label font size",
min = 5,
max = 35,
value = 17,
step = 0.5),
selectInput(inputId = "legend_position_growth_parameter_plot",
label = "Legend position",
choices = c("Bottom" = "bottom",
"Top" = "top",
"Left" = "left",
"Right" = "right"),
selected = "right"
),
sliderInput(inputId = 'legend_ncol_growth_parameter_plot',
label = 'Number of legend columns',
min = 1,
max = 10,
value = 1,
step = 1
),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "sort_by_conc_growth_parameter_plot",
label = "Sort samples by concentration",
value = FALSE)
),
textInput(
inputId = 'custom_colors_growth_parameter_plot',
label = 'Custom colors'
),
bsPopover(id = "custom_colors_growth_parameter_plot",
title = HTML("<em>Provide custom colors</em>"), placement = "top",
content = "Enter colors either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). Separate colors with a comma. A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(
plotOutput("growth_parameter_plot",
width = "100%", height = "800px"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_parameter_plot",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_parameter_plot",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_parameter_plot",
label = "DPI",
value = 300)
), # column
column(width = 5,
downloadButton('download_growth_parameter_plot',"Download Plot"),
radioButtons("format_download_growth_parameter_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
),
### Growth Grid Plots ####
tabPanel(title = "Plot Grid",
sidebarPanel(
selectInput(inputId = "data_type_growth_grid_plot",
label = "Data type",
choices = c("Raw growth" = "raw",
"Spline fits" = "spline")
),
selectInput(inputId = "parameter_parameter_grid_plot",
label = "Parameter",
choices = ""
),
checkboxInput(inputId = "select_string_visualize_growth_grid",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_growth_grid && !input.plot_group_averages_growth_grid_plot",
selectizeInput(inputId = "samples_visualize_growth_grid",
label = "Samples:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
),
checkboxInput(inputId = "order_matters_visualize_growth_grid",
label = "Select order matters",
value = FALSE
),
),
conditionalPanel(
condition = "!input.select_string_visualize_growth_grid && input.plot_group_averages_growth_grid_plot",
selectizeInput(inputId = "groups_visualize_growth_grid",
label = "Conditions:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_growth_grid",
textInput(inputId = "select_samples_based_on_string_growth_grid_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_string_growth_grid_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
conditionalPanel(
condition = "input.plot_group_averages_growth_grid_plot || input.select_string_visualize_growth_grid",
textInput(inputId = "select_samples_based_on_concentration_growth_grid_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_concentration_growth_grid_plot",
label = "Exclude sample based on concentration (separate by ;)"
)
),
checkboxInput(inputId = "plot_group_averages_growth_grid_plot",
label = "Plot group averages",
value = TRUE),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "sort_by_conc_growth_grid_plot",
label = "Sort by concentration",
value = TRUE)
),
h3("Customize plot appearance"),
checkboxInput(inputId = "log_transform_y_axis_growth_grid_plot",
label = "Log-transform y-axis",
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_growth_grid_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_growth_grid_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_growth_grid_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_growth_grid_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("Color scale limits"),
fluidRow(
column(5,
textInput(inputId = "legend_lim_min_growth_grid_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "legend_lim_max_growth_grid_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(inputId = "y_axis_title_growth_grid_plot",
label = "y-axis title",
value = "Growth [y(t)]"
),
textInput(inputId = "x_axis_title_growth_grid_plot",
label = "x-axis title",
value = "Time"
),
sliderInput(inputId = "nbreaks_growth_grid_plot",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
sliderInput(inputId = "line_width_growth_grid_plot",
label = "Line width",
min = 0.01,
max = 10,
value = 1.1),
sliderInput(inputId = 'base_size_growth_grid_plot',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
conditionalPanel(
condition = "!input.sort_by_conc_growth_grid_plot",
sliderInput(inputId = "nrows_growth_grid_plot",
label = "Number of rows in grid",
min = 1,
max = 20,
value = 2)
),
selectInput(inputId = "color_palettes_grid_plot",
label = "Change color palette",
width = "100%",
choices = names(QurvE:::single_hue_palettes),
selected = names(QurvE:::single_hue_palettes)[1],
multiple = FALSE
),
bsPopover(id = "color_palettes_grid_plot",
title = HTML("<em>Define the colors used to visualize the value of the chosen parameter</em>"), placement = "top",
content = ""
),
checkboxInput(inputId = "invert_color_palette_grid_plot",
label = "Invert color palette",
value = FALSE)
), # Side panel growth group plots
mainPanel(
withSpinner(
plotOutput("growth_grid_plot",
width = "100%", height = "1000px"),
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_grid_plot",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_grid_plot",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_grid_plot",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_growth_grid_plot',"Download Plot"),
radioButtons("format_download_growth_grid_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) # mainPanel
),
### Growth DR Plots Spline ####
tabPanel(title = "Dose-Response Analysis", value = "tabPanel_Visualize_Growth_DoseResponse_Spline",
sidebarPanel(
conditionalPanel(
condition = "output.more_than_one_drfit_spline",
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-bottom: 0',
checkboxInput(inputId = 'combine_conditions_into_a_single_plot_dose_response_growth_plot',
label = 'Combine conditions into a single plot',
value = FALSE)
)
),
conditionalPanel(
condition = "input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
textInput(inputId = "select_samples_based_on_string_dose_response_growth_plot",
label = "Select sample based on string (separate by ;)"
)
),
conditionalPanel(
condition = "input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
textInput(inputId = "exclude_samples_based_on_string_dose_response_growth_plot",
label = "Exclude sample based on string (separate by ;)"
)
),
h3('Customize plot appearance'),
checkboxInput(inputId = "log_transform_y_axis_dose_response_growth_plot",
label = "Log-transform y-axis",
value = FALSE),
checkboxInput(inputId = "log_transform_x_axis_dose_response_growth_plot",
label = "Log-transform x-axis",
value = FALSE),
sliderInput(inputId = 'shape_type_dose_response_growth_plot',
label = 'Shape type',
min = 1,
max = 25,
value = 15),
sliderInput(inputId = 'shape_size_dose_response_growth_plot',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
conditionalPanel(
condition = "input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
sliderInput(inputId = 'base_size_dose_response_growth_plot',
label = 'Base size',
min = 10,
max = 35,
value = 15,
step = 0.5)
),
conditionalPanel(
condition = "!input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
sliderInput(inputId = 'axis_size_dose_response_growth_plot',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1)
),
conditionalPanel(
condition = "!input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
sliderInput(inputId = 'lab_size_dose_response_growth_plot',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1)
),
sliderInput(inputId = 'line_width_dose_response_growth_plot',
label = 'Line width',
min = 0.01,
max = 10,
value = 1),
checkboxInput(inputId = 'show_ec50_indicator_lines_dose_response_growth_plot',
label = 'Show EC50 indicator lines',
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_dose_response_growth_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_dose_response_growth_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_dose_response_growth_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_dose_response_growth_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(inputId = "y_axis_title_dose_response_growth_plot",
label = "y-axis title",
value = ""
),
textInput(inputId = "x_axis_title_dose_response_growth_plot",
label = "x-axis title",
value = ""
)
), # sidebarPanel
conditionalPanel(condition = "input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
mainPanel(
h3('Combined plots'),
plotOutput("dose_response_growth_plot_combined",
width = "100%", height = "800px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_dr_growth",
label = "Re-run dose-response analysis with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_dr_growth",
label = "Restore dose-response analysis",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_growth_plot_combined",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_growth_plot_combined",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_growth_plot_combined",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_growth_plot_combined',"Download Plot"),
radioButtons("format_download_dose_response_growth_plot_combined",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
),
conditionalPanel(condition = "!input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
mainPanel(
h3('Individual plots'),
selectInput(inputId = 'individual_plots_dose_response_growth_plot',
label = 'Select plot',
choices = "",
multiple = FALSE,
selectize = FALSE,
size = 3),
plotOutput("dose_response_growth_plot_individual",
width = "100%", height = "800px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_dr_growth2",
label = "Re-run dose-response analysis with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_dr_growth2",
label = "Restore dose-response analysis",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_growth_plot_individual",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_growth_plot_individual",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_growth_plot_individual",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_growth_plot_individual',"Download Plot"),
radioButtons("format_download_dose_response_growth_plot_individual",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
),
), # tabPanel(title = "Dose-response analysis"
### Growth DR Plots Model ####
tabPanel(title = "Dose-Response Analysis", value = "tabPanel_Visualize_Growth_DoseResponse_Model",
sidebarPanel(
h3('Customize plot appearance'),
checkboxInput(inputId = "log_transform_x_axis_dose_response_growth_plot_model",
label = "Log-transform x-axis",
value = TRUE),
sliderInput(inputId = 'shape_type_dose_response_growth_plot_model',
label = 'Shape type',
min = 1,
max = 25,
value = 15),
sliderInput(inputId = 'shape_size_dose_response_growth_plot_model',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_dose_response_growth_plot_model',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'lab_size_dose_response_growth_plot_model',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'line_width_dose_response_growth_plot_model',
label = 'Line width',
min = 0.01,
max = 10,
value = 1),
conditionalPanel(
condition = "input.log_transform_x_axis_dose_response_growth_plot_model",
sliderInput(inputId = "nbreaks_x_growth_dose_response_plot_model",
label = "Number of breaks on x-axis",
min = 1,
max = 20,
value = 6)
),
conditionalPanel(
condition = "input.log_transform_y_axis_dose_response_growth_plot_model",
sliderInput(inputId = "nbreaks_y_growth_dose_response_plot_model",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6)
),
checkboxInput(inputId = 'show_ec50_indicator_lines_dose_response_growth_plot_model',
label = 'Show EC50 indicator lines',
value = TRUE),
conditionalPanel(
condition = "input.log_transform_x_axis_dose_response_growth_plot_model",
checkboxInput(inputId = 'show_break_dose_response_growth_plot_model',
label = 'Show x axis break',
value = TRUE)
),
conditionalPanel(
condition = "input.show_break_dose_response_growth_plot_model && input.log_transform_x_axis_dose_response_growth_plot_model",
QurvE:::numberInput(inputId = 'bp_dose_response_growth_plot_model',
label = 'Break point position',
value = ""),
),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_dose_response_growth_plot_model",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_dose_response_growth_plot_model",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_dose_response_growth_plot_model",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_dose_response_growth_plot_model",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(inputId = "y_axis_title_dose_response_growth_plot_model",
label = "y-axis title",
value = ""
),
textInput(inputId = "x_axis_title_dose_response_growth_plot_model",
label = "x-axis title",
value = ""
)
), # sidebarPanel
mainPanel(
selectInput(inputId = 'individual_plots_dose_response_growth_plot_model',
label = 'Select plot',
choices = "",
multiple = FALSE,
selectize = FALSE,
size = 3),
plotOutput("dose_response_growth_plot_model",
width = "100%", height = "800px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_dr_growth3",
label = "Re-run dose-response analysis with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_dr_growth3",
label = "Restore dose-response analysis",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_growth_plot_model",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_growth_plot_model",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_growth_plot_model",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_growth_plot_model',"Download Plot"),
radioButtons("format_download_dose_response_growth_plot_model",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
), # mainPanel
), # tabPanel(title = "Dose-response analysis"
### Growth DR Plots Bootstrap ####
tabPanel(title = "Dose-Response Analysis (Bootstrap)", value = "tabPanel_Visualize_Growth_DoseResponse_Spline_bt",
sidebarPanel(
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_dose_response_growth_plot_bt',
label = 'Shape type',
min = 1,
max = 25,
value = 15),
sliderInput(inputId = 'shape_size_dose_response_growth_plot_bt',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_dose_response_growth_plot_bt',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'lab_size_dose_response_growth_plot_bt',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'line_width_dose_response_growth_plot_bt',
label = 'Line width',
min = 0.01,
max = 10,
value = 1),
), # sidebarPanel
mainPanel(
h3('Individual plots'),
selectInput(inputId = 'individual_plots_dose_response_growth_plot_bt',
label = 'Select plot',
choices = "",
multiple = FALSE,
selectize = FALSE,
size = 3),
plotOutput("dose_response_growth_plot_individual_bt",
width = "100%", height = "800px"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_growth_plot_individual_bt",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_growth_plot_individual_bt",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_growth_plot_individual_bt",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_growth_plot_individual_bt',"Download Plot"),
radioButtons("format_download_dose_response_growth_plot_individual_bt",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel(title = "Dose-response analysis (Bootstrap)"
### Growth DR Parameters ####
tabPanel(title = "DR Parameter Plots",value = "tabPanel_Visualize_Growth_DoseResponseParameters",
sidebarPanel(
selectInput(inputId = "parameter_dr_parameter_growth_plot",
label = "Parameter",
choices = ""
),
textInput(inputId = "select_sample_based_on_string_growth_dr_parameter_plot",
label = "Select sample based on string (separated by ;)"
),
textInput(inputId = "exclude_sample_based_on_strings_growth_dr_parameter_plot",
label = "Exclude sample based on strings (separated by ;)"
),
checkboxInput(inputId = 'normalize_to_reference_growth_dr_parameter_plot',
label = 'normalize to reference',
value = FALSE),
h3("Customize plot appearance"),
# Conditional Panel
conditionalPanel(condition = "input.normalize_to_reference_growth_dr_parameter_plot",
# reactive selection
selectInput(inputId = 'reference_condition_growth_dr_parameter_plot',
label = 'Reference condition',
choices = ""
)
),
sliderInput(inputId = "basesize_growth_dr_parameter_plot",
label = "Base font size",
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "label.size_growth_dr_parameter_plot",
label = "Label font size",
min = 5,
max = 35,
value = 20,
step = 0.5)
),
mainPanel(
plotOutput("growth_dr_parameter_plot",
width = "100%", height = "800px"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_dr_parameter_plot",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_dr_parameter_plot",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_dr_parameter_plot",
label = "DPI",
value = 300)
), # column
column(width = 5,
downloadButton('download_growth_dr_parameter_plot',"Download Plot"),
radioButtons("format_download_growth_dr_parameter_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel Growth_Parameter_Plots
)
),
## Fluorescence Plots ####
tabPanel(title = "Fluorescence Plots", value = "tabPanel_Visualize_Fluorescence",
h1("Fluorescence Plots"),
tabsetPanel(type = "tabs",id = "tabsetPanel_Visualize_Fluorescence",
### Fluorescence Group Plots ####
tabPanel(title = "Group plots",
sidebarPanel(
selectInput(inputId = "data_type_fluorescence_group_plot",
label = "Data type",
choices = c("Raw fluorescence" = "raw",
"Spline fits FL" = "spline",
"Normalized FL" = "norm.fl"
)
),
checkboxInput(inputId = "select_string_visualize_fluorescence_group",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_fluorescence_group && !input.plot_group_averages_fluorescence_group_plot",
selectizeInput(inputId = "samples_visualize_fluorescence_group",
label = "Samples:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "!input.select_string_visualize_fluorescence_group && input.plot_group_averages_fluorescence_group_plot",
selectizeInput(inputId = "groups_visualize_fluorescence_group",
label = "Conditions:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_fluorescence_group",
textInput(inputId = "select_samples_based_on_string_fluorescence_group_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_string_fluorescence_group_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
conditionalPanel(
condition = "input.plot_group_averages_fluorescence_group_plot || input.select_string_visualize_fluorescence_group",
textInput(inputId = "select_samples_based_on_concentration_fluorescence_group_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_concentration_fluorescence_group_plot",
label = "Exclude sample based on concentration (separate by ;)"
)
),
checkboxInput(inputId = "plot_group_averages_fluorescence_group_plot",
label = "Plot group averages",
value = TRUE),
conditionalPanel(
condition = "input.data_type_fluorescence_group_plot == 'spline' ",
checkboxInput(inputId = "plot_derivative_fluorescence_group_plot",
label = "Plot derivative",
value = TRUE)
),
h3("Customize plot appearance"),
checkboxInput(inputId = "log_transform_y_axis_fluorescence_group_plot",
label = "Log-transform y-axis",
value = FALSE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_fluorescence_group_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_fluorescence_group_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_fluorescence_group_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_fluorescence_group_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
conditionalPanel(
condition = "input.data_type_fluorescence_group_plot == 'spline'",
strong("y-Range (derivative)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_derivative_fluorescence_group_plot",
label = NULL,
value = "min", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_derivative_fluorescence_group_plot",
label = NULL,
value = "", placeholder = "max"
)
)
)
),
textInput(inputId = "y_axis_title_fluorescence_group_plot",
label = "y-axis title",
value = ""
),
textInput(inputId = "x_axis_title_fluorescence_group_plot",
label = "x-axis title",
value = ""
),
conditionalPanel(
condition = "input.data_type_fluorescence_group_plot == 'spline' ",
textInput(inputId = "y_axis_title_derivative_fluorescence_group_plot",
label = "y-axis title derivative",
value = ""
)
),
sliderInput(inputId = "nbreaks_fluorescence_group_plot",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
sliderInput(inputId = "line_width_fluorescence_group_plot",
label = "Line width",
min = 0.01,
max = 10,
value = 1.1),
sliderInput(inputId = 'base_size_fluorescence_group_plot',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = 'legend_ncol_fluorescence_group_plot',
label = 'Number of legend columns',
min = 1,
max = 10,
value = 4,
step = 1),
selectInput(inputId = "legend_position_fluorescence_group_plot",
label = "Legend position",
choices = c("Bottom" = "bottom",
"Top" = "top",
"Left" = "left",
"Right" = "right")
),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "color_groups_fluorescence_group_plot",
label = "Color samples by group",
value = TRUE)
),
textInput(
inputId = 'custom_colors_fluorescence_group_plot',
label = 'Custom colors'
),
bsPopover(id = "custom_colors_fluorescence_group_plot",
title = HTML("<em>Provide custom colors</em>"), placement = "top",
content = "Enter colors either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). Separate colors with a comma. A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
conditionalPanel(
condition = "output.more_than_two_conc && input.color_groups_fluorescence_group_plot",
selectizeInput(inputId = "color_palettes_fluorescence_group_plot",
label = "Change color palettes",
width = "100%",
choices = names(QurvE:::single_hue_palettes),
selected = names(QurvE:::single_hue_palettes),
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
),
bsPopover(id = "color_palettes_fluorescence_group_plot",
title = HTML("<em>Define the colors used to display sample groups with identical concentrations</em>"), placement = "top",
content = "The number of selected color palettes must be at least the number of displayed groups. The order of the chosen palettes corresponds to the oder of conditions in the legend."
),
),
), # Side panel growth group plots
mainPanel(
withSpinner(
plotOutput("fluorescence_group_plot",
width = "100%", height = "1000px")
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_group_plot",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_group_plot",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_group_plot",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_fluorescence_group_plot',"Download Plot"),
radioButtons("format_download_fluorescence_group_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel Group_Plots
### Dual Plots ####
tabPanel(title = "Growth & Flourescence Plot", value = "tabPabel_Visualize_Dual",
h1("Growth & Flourescence Plot"),
sidebarPanel(
selectInput(inputId = "fluorescence_type_dual_plot",
label = "Fluorescence type",
choices = c("Fluorescence" = "fl",
"Normalized fluorescence" = "norm.fl"
)
),
checkboxInput(inputId = "select_string_visualize_dual_plot",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_dual_plot",
selectizeInput(inputId = "samples_visualize_dual_plot",
label = "Samples:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_dual_plot",
textInput(inputId = "select_samples_based_on_string_dual_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_string_dual_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
conditionalPanel(
condition = "input.plot_group_averages_dual_plot || input.select_string_visualize_dual_plot",
textInput(inputId = "select_samples_based_on_concentration_dual_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_concentration_dual_plot",
label = "Exclude sample based on concentration (separate by ;)"
)
),
checkboxInput(inputId = "plot_group_averages_dual_plot",
label = "Plot group averages",
value = TRUE),
h3("Customize plot appearance"),
checkboxInput(inputId = "log_transform_y_axis_growth_dual_plot",
label = "Log-transform y-axis (growth)",
value = FALSE),
checkboxInput(inputId = "log_transform_y_axis_fluorescence_dual_plot",
label = "Log-transform y-axis (Fluorescence)",
value = FALSE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_dual_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_dual_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range (growth)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_growth_dual_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_growth_dual_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range (Fluorescence"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_fluorescence_dual_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_fluorescence_dual_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(inputId = "y_axis_title_growth_dual_plot",
label = "y-axis title (growth)",
value = ""
),
textInput(inputId = "y_axis_title_fluorescence_dual_plot",
label = "y-axis title (Fluorescence)",
value = ""
),
textInput(inputId = "x_axis_title_dual_plot",
label = "x-axis title",
value = ""
),
sliderInput(inputId = "nbreaks_dual_plot",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
sliderInput(inputId = "line_width_dual_plot",
label = "Line width",
min = 0.01,
max = 10,
value = 1.1),
sliderInput(inputId = 'base_size_dual_plot',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
selectInput(inputId = "legend_position_dual_plot",
label = "Legend position",
choices = c("Bottom" = "bottom",
"Top" = "top",
"Left" = "left",
"Right" = "right")
),
sliderInput(inputId = 'legend_ncol_dual_plot',
label = 'Number of legend columns',
min = 1,
max = 10,
value = 4,
step = 1
),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "color_groups_dual_plot",
label = "Color samples by group",
value = TRUE)
),
textInput(
inputId = 'custom_colors_dual_plot',
label = 'Custom colors'
),
bsPopover(id = "custom_colors_dual_plot",
title = HTML("<em>Provide custom colors</em>"), placement = "top",
content = "Enter colors either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). Separate colors with a comma. A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
conditionalPanel(
condition = "output.more_than_two_conc && input.color_groups_dual_plot",
selectizeInput(inputId = "color_palettes_dual_plot",
label = "Change color palettes",
width = "100%",
choices = names(QurvE:::single_hue_palettes),
selected = names(QurvE:::single_hue_palettes),
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
),
bsPopover(id = "color_palettes_dual_plot",
title = HTML("<em>Define the colors used to display sample groups with identical concentrations</em>"), placement = "top",
content = "The number of selected color palettes must be at least the number of displayed groups. The order of the chosen palettes corresponds to the oder of conditions in the legend."
),
),
),
mainPanel(
withSpinner(
plotOutput("dual_plot",
width = "100%", height = "1000px")
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dual_plot",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dual_plot",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dual_plot",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dual_plot',"Download Plot"),
radioButtons("format_download_dual_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel(title = "Growth & Flourescence Plot")
### Fluorescence Parameter Plots ####
tabPanel(title = "Parameter plots",
sidebarPanel(
selectInput(inputId = "parameter_fluorescence_parameter_fluorescence_plot",
label = "Parameter",
choices = ""
),
checkboxInput(inputId = "select_string_visualize_parameter_fluorescence_plot",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_parameter_fluorescence_plot",
selectizeInput(inputId = "samples_visualize_parameter_fluorescence_plot",
label = "Conditions:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_parameter_fluorescence_plot",
textInput(inputId = "select_sample_based_on_string_fluorescence_parameter_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_sample_based_on_strings_fluorescence_parameter_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
textInput(inputId = "select_sample_based_on_concentration_fluorescence_parameter_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_sample_based_on_concentration_fluorescence_parameter_plot",
label = "Exclude sample based on concentration (separate by ;)"
),
checkboxInput(inputId = 'normalize_to_reference_fluorescence_parameter_plot',
label = 'normalize to reference',
value = FALSE),
# Conditional Panel
conditionalPanel(condition = "input.normalize_to_reference_fluorescence_parameter_plot",
# reactive selection
selectInput(inputId = 'reference_condition_fluorescence_parameter_plot',
label = 'Reference condition',
choices = ""
),
# reactive selection
selectInput(inputId = 'reference_concentration_fluorescence_parameter_plot',
label = 'Reference concentration',
choices = ""
),
),
h3("Customize plot appearance"),
sliderInput(inputId = "shape.size_fluorescence_parameter_plot",
label = "Shape size",
min = 0.1,
max = 10,
value = 3,
step = 0.1),
sliderInput(inputId = "basesize_fluorescence_parameter_plot",
label = "Base font size",
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "label.size_fluorescence_parameter_plot",
label = "Label font size",
min = 5,
max = 35,
value = 17,
step = 0.5),
selectInput(inputId = "legend_position_fluorescence_parameter_plot",
label = "Legend position",
choices = c("Bottom" = "bottom",
"Top" = "top",
"Left" = "left",
"Right" = "right"),
selected = "right"
),
sliderInput(inputId = 'legend_ncol_fluorescence_parameter_plot',
label = 'Number of legend columns',
min = 1,
max = 10,
value = 1,
step = 1
),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "sort_by_conc_fluorescence_parameter_plot",
label = "Sort samples by concentration",
value = FALSE)
),
textInput(
inputId = 'custom_colors_fluorescence_parameter_plot',
label = 'Custom colors'
),
bsPopover(id = "custom_colors_fluorescence_parameter_plot",
title = HTML("<em>Provide custom colors</em>"), placement = "top",
content = "Enter colors either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). Separate colors with a comma. A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(
plotOutput("fluorescence_parameter_plot",
width = "100%", height = "800px"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_parameter_plot",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_parameter_plot",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_parameter_plot",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_fluorescence_parameter_plot',"Download Plot"),
radioButtons("format_download_fluorescence_parameter_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel(title = "Parameter plots"
### Fluorescence Grid Plots ####
tabPanel(title = "Plot Grid",
sidebarPanel(
selectInput(inputId = "data_type_fluorescence_grid_plot",
label = "Data type",
choices = c("Raw growth" = "raw",
"Spline fits" = "spline")
),
selectInput(inputId = "parameter_parameter_grid_plot_fluorescence",
label = "Parameter",
choices = ""
),
checkboxInput(inputId = "select_string_visualize_fluorescence_grid",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_fluorescence_grid && !input.plot_group_averages_fluorescence_grid_plot",
selectizeInput(inputId = "samples_visualize_fluorescence_grid",
label = "Samples:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
),
checkboxInput(inputId = "order_matters_visualize_fluorescence_grid",
label = "Select order matters",
value = FALSE
),
),
conditionalPanel(
condition = "!input.select_string_visualize_fluorescence_grid && input.plot_group_averages_fluorescence_grid_plot",
selectizeInput(inputId = "groups_visualize_fluorescence_grid",
label = "Conditions:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_fluorescence_grid",
textInput(inputId = "select_samples_based_on_string_fluorescence_grid_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_string_fluorescence_grid_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
conditionalPanel(
condition = "input.plot_group_averages_fluorescence_grid_plot || input.select_string_visualize_fluorescence_grid",
textInput(inputId = "select_samples_based_on_concentration_fluorescence_grid_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_concentration_fluorescence_grid_plot",
label = "Exclude sample based on concentration (separate by ;)"
)
),
checkboxInput(inputId = "plot_group_averages_fluorescence_grid_plot",
label = "Plot group averages",
value = TRUE),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "sort_by_conc_fluorescence_grid_plot",
label = "Sort by concentration",
value = TRUE)
),
h3("Customize plot appearance"),
checkboxInput(inputId = "log_transform_y_axis_fluorescence_grid_plot",
label = "Log-transform y-axis",
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_fluorescence_grid_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_fluorescence_grid_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_fluorescence_grid_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_fluorescence_grid_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("Color scale limits"),
fluidRow(
column(5,
textInput(inputId = "legend_lim_min_fluorescence_grid_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "legend_lim_max_fluorescence_grid_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(inputId = "y_axis_title_fluorescence_grid_plot",
label = "y-axis title",
value = ""
),
textInput(inputId = "x_axis_title_fluorescence_grid_plot",
label = "x-axis title",
value = ""
),
sliderInput(inputId = "nbreaks_fluorescence_grid_plot",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
sliderInput(inputId = "line_width_fluorescence_grid_plot",
label = "Line width",
min = 0.01,
max = 10,
value = 1.1),
sliderInput(inputId = 'base_size_fluorescence_grid_plot',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
conditionalPanel(
condition = "!input.sort_by_conc_fluorescence_grid_plot",
sliderInput(inputId = "nrows_fluorescence_grid_plot",
label = "Number of rows in grid",
min = 1,
max = 20,
value = 2)
),
selectInput(inputId = "color_palettes_grid_plot_fluorescence",
label = "Change color palette",
width = "100%",
choices = names(QurvE:::single_hue_palettes),
selected = names(QurvE:::single_hue_palettes)[1],
multiple = FALSE
),
bsPopover(id = "color_palettes_grid_plot_fluorescence",
title = HTML("<em>Define the colors used to visualize the value of the chosen parameter</em>"), placement = "top",
content = ""
),
checkboxInput(inputId = "invert_color_palette_grid_plot_fluorescence",
label = "Invert color palette",
value = FALSE)
), # Side panel fluorescence group plots
mainPanel(
withSpinner(
plotOutput("fluorescence_grid_plot",
width = "100%", height = "1000px"),
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_grid_plot",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_grid_plot",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_grid_plot",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_fluorescence_grid_plot',"Download Plot"),
radioButtons("format_download_fluorescence_grid_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) # mainPanel
),
### Fluorescence DR Plots Spline ####
tabPanel(title = "Dose-response analysis", value = "tabPanel_Visualize_Fluorescence_DoseResponse_spline",
sidebarPanel(
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-bottom: 0',
checkboxInput(inputId = 'combine_conditions_into_a_single_plot_dose_response_fluorescence_plot',
label = 'Combine conditions into a single plot',
value = TRUE)
),
conditionalPanel(
condition = "input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
textInput(inputId = "select_samples_based_on_string_dose_response_fluorescence_plot",
label = "Select sample based on string (separate by ;)"
)
),
conditionalPanel(
condition = "input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
textInput(inputId = "exclude_samples_based_on_string_dose_response_fluorescence_plot",
label = "Exclude sample based on string (separate by ;)"
)
),
h3('Customize plot appearance'),
checkboxInput(inputId = "log_transform_y_axis_dose_response_fluorescence_plot",
label = "Log-transform y-axis",
value = FALSE),
checkboxInput(inputId = "log_transform_x_axis_dose_response_fluorescence_plot",
label = "Log-transform x-axis",
value = FALSE),
sliderInput(inputId = 'shape_type_dose_response_fluorescence_plot',
label = 'Shape type',
min = 1,
max = 25,
value = 15),
sliderInput(inputId = 'shape_size_dose_response_fluorescence_plot',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
conditionalPanel(
condition = "input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
sliderInput(inputId = 'base_size_dose_response_fluorescence_plot',
label = 'Base font size',
min = 10,
max = 35,
value = 15,
step = 0.5)
),
conditionalPanel(
condition = "!input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
sliderInput(inputId = 'axis_size_dose_response_fluorescence_plot',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.5,
step = 0.1)
),
conditionalPanel(
condition = "!input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
sliderInput(inputId = 'lab_size_dose_response_fluorescence_plot',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1)
),
sliderInput(inputId = 'line_width_dose_response_fluorescence_plot',
label = 'Line width',
min = 0.01,
max = 10,
value = 1),
checkboxInput(inputId = 'show_ec50_indicator_lines_dose_response_fluorescence_plot',
label = 'Show EC50 indicator lines',
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_dose_response_fluorescence_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_dose_response_fluorescence_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_dose_response_fluorescence_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_dose_response_fluorescence_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(inputId = "y_axis_title_dose_response_fluorescence_plot",
label = "y-axis title",
value = ""
),
textInput(inputId = "x_axis_title_dose_response_fluorescence_plot",
label = "x-axis title",
value = ""
)
), # sidebarPanel
conditionalPanel(condition = "input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
mainPanel(
h3('Combined plots'),
plotOutput("dose_response_plot_fluorescence_combined",
width = "100%", height = "800px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_dr_fluorescence",
label = "Re-run dose-response analysis with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_dr_fluorescence",
label = "Restore dose-response analysis",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_plot_fluorescence_combined",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_plot_fluorescence_combined",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_plot_fluorescence_combined",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_plot_fluorescence_combined',"Download Plot"),
radioButtons("format_download_dose_response_plot_fluorescence_combined",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
),
conditionalPanel(condition = "!input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
mainPanel(
h3('Individual plots'),
selectInput(inputId = 'individual_plots_dose_response_fluorescence_plot',
label = 'Select plot',
choices = "",
multiple = FALSE,
selectize = FALSE,
size = 3),
plotOutput("dose_response_fluorescence_plot_individual",
width = "100%", height = "800px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_dr_fluorescence2",
label = "Re-run dose-response analysis with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_dr_fluorescence2",
label = "Restore dose-response analysis",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_fluorescence_plot_individual",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_fluorescence_plot_individual",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_fluorescence_plot_individual",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_fluorescence_plot_individual',"Download Plot"),
radioButtons("format_download_dose_response_fluorescence_plot_individual",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # conditionalPanel
), # tabPanel(title = "Dose-response analysis", value = "tabPanel_Visualize_Fluorescence_DoseResponse_spline",
### Fluorescence DR Plots Model ####
tabPanel(title = "Dose-response analysis", value = "tabPanel_Visualize_Fluorescence_DoseResponse_model",
sidebarPanel(
checkboxInput(inputId = "log_transform_y_axis_dose_response_model_fluorescence_plot",
label = "Log-transform y-axis",
value = TRUE),
checkboxInput(inputId = "log_transform_x_axis_dose_response_model_fluorescence_plot",
label = "Log-transform x-axis",
value = TRUE),
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_dose_response_model_fluorescence_plot',
label = 'Shape type',
min = 1,
max = 25,
value = 15),
sliderInput(inputId = 'shape_size_dose_response_model_fluorescence_plot',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_dose_response_model_fluorescence_plot',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.5,
step = 0.1),
sliderInput(inputId = 'lab_size_dose_response_model_fluorescence_plot',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'line_width_dose_response_model_fluorescence_plot',
label = 'Line width',
min = 0.01,
max = 10,
value = 1),
checkboxInput(inputId = 'show_ec50_indicator_lines_dose_response_model_fluorescence_plot',
label = 'Show EC50 indicator lines',
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_dose_response_model_fluorescence_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_dose_response_model_fluorescence_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_dose_response_model_fluorescence_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_dose_response_model_fluorescence_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
), # sidebarPanel
mainPanel(
selectInput(inputId = 'individual_plots_dose_response_model_fluorescence_plot',
label = 'Select plot',
choices = "",
multiple = FALSE,
selectize = FALSE,
size = 3),
plotOutput("dose_response_model_fluorescence_plot_individual",
width = "100%", height = "800px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_dr_fluorescence3",
label = "Re-run dose-response analysis with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_dr_fluorescence3",
label = "Restore dose-response analysis",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_model_fluorescence_plot_individual",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_model_fluorescence_plot_individual",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_model_fluorescence_plot_individual",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_model_fluorescence_plot_individual',"Download Plot"),
radioButtons("format_download_dose_response_model_fluorescence_plot_individual",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel(title = "Dose-response analysis",
### Fluorescence DR Plots Bootstrap ####
tabPanel(title = "Dose-Response Analysis (Bootstrap)", value = "tabPanel_Visualize_Fluorescence_DoseResponse_bt",
sidebarPanel(
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_dose_response_fluorescence_plot_bt',
label = 'Shape type',
min = 1,
max = 25,
value = 15),
sliderInput(inputId = 'shape_size_dose_response_fluorescence_plot_bt',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_dose_response_fluorescence_plot_bt',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'lab_size_dose_response_fluorescence_plot_bt',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'line_width_dose_response_fluorescence_plot_bt',
label = 'Line width',
min = 0.01,
max = 10,
value = 1),
), # sidebarPanel
mainPanel(
h3('Individual plots'),
selectInput(inputId = 'individual_plots_dose_response_fluorescence_plot_bt',
label = 'Select plot',
choices = "",
multiple = FALSE,
selectize = FALSE,
size = 3),
plotOutput("dose_response_fluorescence_plot_individual_bt",
width = "100%", height = "800px"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_fluorescence_plot_individual_bt",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_fluorescence_plot_individual_bt",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_fluorescence_plot_individual_bt",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_fluorescence_plot_individual_bt',"Download Plot"),
radioButtons("format_download_dose_response_fluorescence_plot_individual_bt",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel(title = "Dose-response analysis (Bootstrap)"
### Fluorescence DR Parameters ####
tabPanel(title = "DR Parameter Plots",value = "tabPanel_Visualize_Fluorescence_DoseResponseParameters",
sidebarPanel(
selectInput(inputId = "parameter_dr_parameter_fluorescence_plot",
label = "Parameter",
choices = ""
),
textInput(inputId = "select_sample_based_on_string_fluorescence_dr_parameter_plot",
label = "Select sample based on string (separated by ;)"
),
textInput(inputId = "exclude_sample_based_on_strings_fluorescence_dr_parameter_plot",
label = "Exclude sample based on strings (separated by ;)"
),
checkboxInput(inputId = 'normalize_to_reference_fluorescence_dr_parameter_plot',
label = 'normalize to reference',
value = FALSE),
h3("Customize plot appearance"),
# Conditional Panel
conditionalPanel(condition = "input.normalize_to_reference_fluorescence_dr_parameter_plot",
# reactive selection
selectInput(inputId = 'reference_condition_fluorescence_dr_parameter_plot',
label = 'Reference condition',
choices = ""
)
),
sliderInput(inputId = "basesize_fluorescence_dr_parameter_plot",
label = "Base font size",
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "label.size_fluorescence_dr_parameter_plot",
label = "Label font size",
min = 5,
max = 35,
value = 20,
step = 0.5)
),
mainPanel(
plotOutput("fluorescence_dr_parameter_plot",
width = "100%", height = "800px"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_dr_parameter_plot",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_dr_parameter_plot",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_dr_parameter_plot",
label = "DPI",
value = 300)
), # column
column(width = 5,
downloadButton('download_fluorescence_dr_parameter_plot',"Download Plot"),
radioButtons("format_download_fluorescence_dr_parameter_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel Fluorescence_Parameter_Plots
) # tabsetPanel(type = "tabs",
), # tabPanel(title = "Fluorescence Plots"
), # navbarMenu("Visualize"
#____REPORT____####
tabPanel(span("Report", title = "Generate a PDF or HTML report summarizing the results."),
value = "tabPanel_Report", icon=icon("file-contract"),
tabsetPanel(type = "tabs", id = "tabsetPanel_Report",
##____Growth report___####
tabPanel(title = "Growth", value = "tabPanel_report_growth",
sidebarPanel(width = 6,
selectInput(inputId = 'report_filetype_growth',
label = 'Choose file type',
choices = c('PDF' = 'pdf', 'HTML' = 'html')),
conditionalPanel(condition = "input.report_filetype_growth == 'pdf'",
fluidRow(
column(12,
div(
downloadButton(outputId = 'download_report_growth_pdf',
label = "Render Report",
icon = icon("file-pdf"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
),
conditionalPanel(condition = "input.report_filetype_growth == 'html'",
fluidRow(
column(12,
div(
downloadButton(outputId = 'download_report_growth_html',
label = "Render Report",
icon = icon("file-code"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
),
) # sidebarPanel
), # tabPanel(title = "Growth", value = "tabs_export_data_growth",
tabPanel(title = "Fluorescence", value = "tabPanel_report_fluorescence",
sidebarPanel(width = 6,
selectInput(inputId = 'report_filetype_fluorescence',
label = 'Choose file type',
choices = c('PDF' = 'pdf', 'HTML' = 'html')),
conditionalPanel(condition = "input.report_filetype_fluorescence == 'pdf'",
fluidRow(
column(12,
div(
downloadButton(outputId = 'download_report_fluorescence_pdf',
label = "Render Report",
icon = icon("file-pdf"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
),
conditionalPanel(condition = "input.report_filetype_fluorescence == 'html'",
fluidRow(
column(12,
div(
downloadButton(outputId = 'download_report_fluorescence_html',
label = "Render Report",
icon = icon("file-code"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
),
) # sidebarPanel
) # tabPanel(title = "fluorescence", value = "tabs_export_data_fluorescence",
), # tabsetPanel(type = "tabs", id = "tabs_report",
checkboxInput(inputId = "report_issues",
label = "Creating reports does not work",
value = FALSE),
conditionalPanel(condition = "input.report_issues",
actionButton(inputId = "run_report_fix",
label = "Run Report Troubleshooting"),
bsPopover(id = "run_report_fix", title = HTML("<em>Run Report Troubleshooting</em>"), content = HTML("This process will take several minutes!<br><br>Reinstalls TinyTeX, updates tlmgr and ensures that Pandoc is recognized correctly.")),
)
), # tabPanel("Report", value = "tabPanel_Report", icon=icon("file-contract"),
#___Export RData___####
tabPanel(span("Data Export", title = "Export all computation results as RData file."),
icon = icon("download"),
value = "tabPanel_Export_RData",
tabsetPanel(type = "tabs", id = "tabsetPanel_Export_Data",
##____Growth results export____####
tabPanel(title = "Growth", value = "tabPanel_export_data_growth",
sidebarPanel(width = 3,
fluidRow(
column(12,
div(
downloadButton(outputId = 'export_RData_growth',
label = "Export RData file",
icon = icon("file-export"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
)
),
## Fluorescence results export____####
tabPanel(title = "Fluorescence", value = "tabPanel_export_data_fluorescence",
sidebarPanel(width = 3,
fluidRow(
column(12,
div(
downloadButton(outputId = 'export_RData_fluorescence',
label = "Export RData file",
icon = icon("file-export"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
)
),
)
),
#___Import RData___####
tabPanel(span("Data Import", title = "Import and RData file with results from a previous QurvE analysis."),
icon = icon("upload"),
value = "tabPanel_Import_RData",
tabsetPanel(type = "tabs", id = "tabsetPanel_Import_Data",
##____Growth results export____####
tabPanel(title = "Growth", value = "tabPanel_import_data_growth",
sidebarPanel(width = 3,
fluidRow(
column(12,
fileInput(inputId = 'import_RData_growth',
label = 'Choose growth RData file',
accept = c('.rdata')
),
conditionalPanel(
condition = 'output.RData_growth_uploaded',
div(
actionButton(inputId = "read_RData_growth",
label = "Read data",
icon=icon("upload"),
style="padding:5px; font-size:120%"),
style="float:right")
),
)
)
)
),
## Fluorescence results export____####
tabPanel(title = "Fluorescence", value = "tabPanel_import_data_fluorescence",
sidebarPanel(width = 3,
fluidRow(
column(12,
fileInput(inputId = 'import_RData_fluorescence',
label = 'Choose fluorescence RData file',
accept = c('.rdata')
),
conditionalPanel(
condition = 'output.RData_fluorescence_uploaded',
div(
actionButton(inputId = "read_RData_fluorescence",
label = "Read data",
icon=icon("upload"),
style="padding:5px; font-size:120%"),
style="float:right")
),
)
)
)
),
)
),
#____ABOUT US____####
tabPanel("About Us",
mainPanel(
h2("Creators"),
'Nicolas Wirth', tags$a(icon("twitter"), href="https://twitter.com/The_NiWi"), br(),
'Jonathan Funk', tags$a(icon("twitter"), href="https://twitter.com/JonathanFunk12"),
h2("Bug reports"),
uiOutput("bug_report"),
h2("Cite QurvE"),
"Wirth, N.T., Funk, J., Donati, S. et al. QurvE: user-friendly software for the analysis of biological growth and fluorescence data. Nat Protoc (2023). https://doi.org/10.1038/s41596-023-00850-7"
# h2("Publications"),
# ''
)
),
) # navbarPage
) # div(
) # hidden(
) # tagList(
)
#____SERVER____####
server <- function(input, output, session){
# Disable navbar menus before running computations
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Export_RData]")
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Report]")
shinyjs::disable(selector = "#navbar li a[data-value=navbarMenu_Visualize]")
shinyjs::disable(selector = "#navbar li a[data-value=navbarMenu_Computation]")
shinyjs::disable(selector = "#navbar li a[data-value=navbarMenu_Results]")
shinyjs::disable(selector = "#navbar li a[data-value=navbarMenu_Validate]")
# Disable menus based on the type of computations run
observe({
if(is.null(results$growth)){
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Results_Growth]")
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Validate_Growth]")
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Visualize_Growth]")
} else {
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Results_Growth]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Validate_Growth]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Visualize_Growth]")
}
if(is.null(results$fluorescence)){
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Results_Fluorescence]")
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Validate_Fluorescence]")
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Visualize_Fluorescence]")
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Visualize_GrowthandFluorescence]")
} else {
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Results_Fluorescence]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Validate_Fluorescence]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Visualize_Fluorescence]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Visualize_GrowthandFluorescence]")
}
if(!is.null(results$custom_data)){
grodata <- results$custom_data
} else if(!is.null(results$parsed_data)){
grodata <- results$parsed_data
} else {
grodata <- NULL
}
if(!is.null(grodata)){
if(length(grodata$fluorescence) < 2){
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Computation_Fluorescence]")
} else {
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Computation_Fluorescence]")
}
if(length(grodata$growth) < 2){
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Computation_Growth]")
} else {
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Computation_Growth]")
}
}
})
# sidePanel_width <- reactive({
# if(as.numeric(input$dimension[1]) < 1000) return(TRUE)
# else return(6)
# })
results <- reactiveValues()
### Function help modals ####
output$gcFitLinear_tooltip <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/growth.gcFitLinear.Rd", out = paste0(tempfile("docs_gcFitLinear"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_growth.gcFitLinear,{
showModal(QurvE:::help_modal(size = "l", idcss = "gcFitLinear",
htmlOutput("gcFitLinear_tooltip"), easyClose = T )
)
})
observeEvent(input$tooltip_growth.gcFitLinear_validate,{
showModal(QurvE:::help_modal(size = "l", idcss = "gcFitLinear",
htmlOutput("gcFitLinear_tooltip"), easyClose = T )
)
})
output$rdm.data_tooltip <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/rdm.data.Rd", out = paste0(tempfile("docs_rdm.data"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_rdm.data,{
showModal(QurvE:::help_modal(size = "l", idcss = "rdm.data",
htmlOutput("rdm.data_tooltip"), easyClose = T )
)
})
output$gcFitSpline_tooltip <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/growth.gcFitSpline.Rd", out = paste0(tempfile("docs_gcFitSpline"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_growth.gcFitSpline,{
showModal(QurvE:::help_modal(size = "l", idcss = "gcFitSpline",
htmlOutput("gcFitSpline_tooltip"), easyClose = T )
)
})
observeEvent(input$tooltip_growth.gcFitSpline_validate,{
showModal(QurvE:::help_modal(size = "l", idcss = "gcFitSpline",
htmlOutput("gcFitSpline_tooltip"), easyClose = T )
)
})
output$gcFitModel_tooltip <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/growth.gcFitModel.Rd", out = paste0(tempfile("docs_gcFitModel"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_growth.gcFitModel,{
showModal(QurvE:::help_modal(size = "l", idcss = "gcFitModel",
htmlOutput("gcFitModel_tooltip"), easyClose = T )
)
})
observeEvent(input$tooltip_growth.gcFitModel_validate,{
showModal(QurvE:::help_modal(size = "l", idcss = "gcFitModel",
htmlOutput("gcFitModel_tooltip"), easyClose = T )
)
})
output$tooltip_growth_workflow <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/growth.workflow.Rd", out = paste0(tempfile("docs_growth_workflow"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_growth_workflow,{
showModal(QurvE:::help_modal(size = "l", idcss = "growthworkflow",
htmlOutput("tooltip_growth_workflow"), easyClose = T )
)
})
output$tooltip_growth_dr <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/growth.drFit.Rd", out = paste0(tempfile("docs_growth_drFit"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_growth_dr,{
showModal(QurvE:::help_modal(size = "l", idcss = "growthdr",
htmlOutput("tooltip_growth_dr"), easyClose = T )
)
})
output$flFitLinear_tooltip <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/flFitLinear.Rd", out = paste0(tempfile("docs_flFitLinear"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_flFitLinear,{
showModal(QurvE:::help_modal(size = "l", idcss = "flFitLinear",
htmlOutput("flFitLinear_tooltip"), easyClose = T )
)
})
observeEvent(input$tooltip_flFitLinear_validate,{
showModal(QurvE:::help_modal(size = "l", idcss = "flFitLinear",
htmlOutput("flFitLinear_tooltip"), easyClose = T )
)
})
output$flFitSpline_tooltip <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/flFitSpline.Rd", out = paste0(tempfile("docs_flFitSpline"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_flFitSpline,{
showModal(QurvE:::help_modal(size = "l", idcss = "flFitSpline",
htmlOutput("flFitSpline_tooltip"), easyClose = T )
)
})
observeEvent(input$tooltip_flFitSpline_validate,{
showModal(QurvE:::help_modal(size = "l", idcss = "flFitSpline",
htmlOutput("flFitSpline_tooltip"), easyClose = T )
)
})
output$tooltip_fl_workflow <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/fl.workflow.Rd", out = paste0(tempfile("docs_fl_workflow"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
output$tooltip_fl_dr <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/fl.drFit.Rd", out = paste0(tempfile("docs_fl_dr"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_fl_workflow,{
showModal(QurvE:::help_modal(size = "l", idcss = "flworkflow",
htmlOutput("tooltip_fl_workflow"), easyClose = T )
)
})
observeEvent(input$tooltip_fl_dr,{
showModal(QurvE:::help_modal(size = "l", idcss = "fldr",
htmlOutput("tooltip_fl_dr"), easyClose = T )
)
})
#____Read data____####
# Test if fluorescence data is contained in custom/parsed object
output$fluorescence_present <- reactive({
if(!is.null(results$custom_data)){
grodata <- results$custom_data
} else if(!is.null(results$parsed_data)){
grodata <- results$parsed_data
} else return(FALSE)
if(length(grodata$fluorescence) > 1 || length(grodata$fluorescence2) > 1){
return(TRUE)
} else return(FALSE)
})
outputOptions(output, 'fluorescence_present', suspendWhenHidden=FALSE)
# Test if more than one two concentrations are being tested
output$more_than_two_conc <- reactive({
if(!is.null(results$custom_data)){
grodata <- results$custom_data
} else if(!is.null(results$parsed_data)){
grodata <- results$parsed_data
} else if(!is.null(results$growth)){
grodata <- results$growth
} else if(!is.null(results$fluorescence)){
grodata <- results$fluorescence
}
else return(FALSE)
if(length(unique(grodata$expdesign$concentration)) > 2 )
return(TRUE)
else
return(FALSE)
})
outputOptions(output, 'more_than_two_conc', suspendWhenHidden=FALSE)
##___Custom____####
hide("Custom_Data_Tables")
hide("norm_type_custom")
### Test if custom_file_growth was loaded
output$growthfileUploaded <- reactive({
if(is.null(input$custom_file_growth)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'growthfileUploaded', suspendWhenHidden=FALSE)
### Test if custom_file_fluorescence was loaded
output$fluorescencefileUploaded <- reactive({
if(is.null(input$custom_file_fluorescence)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'fluorescencefileUploaded', suspendWhenHidden=FALSE)
## Test if custom_file_fluorescence was loaded
output$fluorescence2fileUploaded <- reactive({
if(is.null(input$custom_file_fluorescence2)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'fluorescence2fileUploaded', suspendWhenHidden=FALSE)
observe({
if(!is.null(input$custom_file_fluorescence) && (!is.null(input$custom_file_growth) | !is.null(input$custom_file_fluorescence2))){
show("norm_type_custom")
} else {
hide("norm_type_custom")
return(NULL)
}
norm.types <- c()
if(!is.null(input$custom_file_growth)){
norm.types <- c(norm.types, "growth")
}
if(!is.null(input$custom_file_fluorescence2)){
norm.types <- c(norm.types, "fluorescence 2")
}
if(length(norm.types) > 0){
show("norm_type_custom")
} else {
hide("norm_type_custom")
}
updateSelectInput(inputId = "norm_type_custom",
choices = norm.types
)
})
# Read data upon click on [Read data]
observeEvent(input$read_custom,{
showModal(modalDialog("Reading data input...", footer=NULL))
growth.file <- input$custom_file_growth
fl.file <- input$custom_file_fluorescence
fl2.file <- input$custom_file_fluorescence2
if(is.null(growth.file) && is.null(fl.file) ) return(NULL)
if(input$convert_time_equation_custom == "" || is.na(input$convert_time_equation_custom)) convert.time <- NULL
else convert.time <- input$convert_time_equation_custom
fl.normtype <- ifelse(input$load_fl2_data_custom, "fl2", "growth")
## Read data
try(
results$custom_data <- read_data(data.growth = growth.file$datapath,
data.fl = fl.file$datapath,
data.fl2 = fl2.file$datapath,
data.format = "col",
sheet.growth = input$custom_growth_sheets,
sheet.fl = input$custom_fluorescence_sheets,
sheet.fl2 = input$custom_fluorescence2_sheets,
csvsep = input$separator_custom_growth,
dec = input$decimal_separator_custom_growth,
csvsep.fl = input$separator_custom_fluorescence,
dec.fl = input$decimal_separator_custom_fluorescence,
csvsep.fl2 = input$separator_custom_fluorescence2,
dec.fl2 = input$decimal_separator_custom_fluorescence2,
subtract.blank = input$subtract_blank_custom,
convert.time = convert.time,
calib.growth = ifelse(input$calibration_growth_custom, input$calibration_equation_growth_custom, ""),
calib.fl = ifelse(input$calibration_fluorescence_custom, input$calibration_equation_fluorescence_custom, ""),
calib.fl2 = ifelse(input$calibration_fluorescence2_custom, input$calibration_equation_fluorescence2_custom, ""),
fl.normtype = fl.normtype
)
)
if(length(results$custom_data)<2){
showModal(modalDialog("Data could not be extracted from the provided file. Did you correctly format your custom data?", easyClose = T, footer=NULL))
} else {
hide("data_instruction")
show("Custom_Data_Tables")
hide("Parsed_Data_Tables")
if("growth" %in% names(results$custom_data) && length(results$custom_data$growth)>1){
# show [Run Computation] button in Computation-Growth
show("run_growth")
}
# if("growth" %in% names(results$custom_data) && length(results$custom_data$fluorescence2)>1){
# showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence2")
# }
# Remove eventually pre-loaded parsed data
results$parsed_data <- NULL
hide("parsed_reads_growth")
hide("parsed_reads_fluorescence")
hide("parsed_reads_fluorescence2")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_norm_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_norm_fluorescence")
# hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence2")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Computation]")
removeModal()
}
})
### Render custom growth table
# growth_data_custom <- reactive({
# inFile <- input$custom_file_growth
#
# if(is.null(inFile))
# return(NULL)
#
# filename <- inFile$datapath
# dec <- input$decimal_separator_custom_growth
# csvsep <- input$separator_custom_growth
# if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "csv") {
# dat <-
# utils::read.csv(
# filename,
# dec = dec,
# sep = csvsep,
# header = FALSE,
# stringsAsFactors = FALSE,
# fill = T,
# na.strings = "",
# quote = "",
# comment.char = "",
# check.names = F
# )
# } else if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "xls" |
# stringr::str_replace(filename, ".{1,}\\.", "") == "xlsx") {
# showModal(modalDialog("Reading data file...", footer=NULL))
# try(
# dat <- data.frame(suppressMessages(readxl::read_excel(filename, col_names = FALSE, sheet = input$custom_growth_sheets, progress = T)))
# )
# removeModal()
# } else if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "tsv") {
# dat <-
# utils::read.csv(
# filename,
# dec = dec,
# sep = "\t",
# header = FALSE,
# stringsAsFactors = FALSE,
# fill = T,
# na.strings = "",
# quote = "",
# comment.char = "",
# check.names = F
# )
# } else if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "txt") {
# dat <-
# utils::read.table(
# filename,
# dec = dec,
# sep = "\t",
# header = FALSE,
# stringsAsFactors = FALSE,
# fill = T,
# na.strings = "",
# quote = "",
# comment.char = "",
# check.names = F
# )
# }
# if(exists("dat")){
# dat[-(1:3),] <- apply(dat[-(1:3),], 2, as.numeric) %>% apply(., 2, round, digits = 2)
#
# colnames(dat)[1] <- "Time"
# dat[1,1] <- ""
# return(dat)
# } else return(NULL)
# })
# output$growth_data_rendered <- DT::renderDT({
# dat <- growth_data_custom()
# DT::datatable(dat,
# options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
# escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(dat)-3)))
# })
### Render experimental design table
custom_data_table_expdesign <- reactive({
if(is.null(growth_data_custom_processed()) && is.null(custom_table_fluorescence_processed())) return(NULL)
else if(!is.null(growth_data_custom_processed())){
dat <- growth_data_custom_processed()
}
else if(!is.null(custom_table_fluorescence_processed())){
dat <- custom_table_fluorescence_processed()
}
dat.mat <- t(dat)
label <- unlist(lapply(1:nrow(dat.mat), function(x) paste(dat.mat[x,1], dat.mat[x,2], dat.mat[x,3], sep = " | ")))
condition <- dat.mat[, 1]
replicate <- dat.mat[, 2]
concentration <- dat.mat[, 3]
expdesign <- data.frame(label, condition, replicate, concentration, check.names = FALSE)
expdesign[-1, ]
})
output$custom_data_table_expdesign <- DT::renderDT({
expdesign <- custom_data_table_expdesign()
DT::datatable(expdesign,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
observe({
# if(exists("growth_data_custom") && !is.null(growth_data_custom()) && is.null(growth_data_custom_processed()) ){
# showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_growth")
# } else {
# hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_growth")
# }
# if(exists("custom_table_fluorescence") && !is.null(custom_table_fluorescence()) && is.null(custom_table_fluorescence_processed()) ){
# showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence", select = TRUE)
# } else {
# hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence")
# }
if(exists("custom_data_table_expdesign") && !is.null(custom_data_table_expdesign())){
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_expdesign")
} else {
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_expdesign")
}
if(exists("growth_data_custom_processed") && !is.null(growth_data_custom_processed())){
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_growth_processed", select = TRUE)
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_growth", select = FALSE)
} else {
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_growth_processed")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_growth")
}
if(exists("custom_table_fluorescence_processed") && !is.null(custom_table_fluorescence_processed()) ){
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence_processed", select = TRUE)
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_fluorescence", select = FALSE)
} else {
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence_processed")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_fluorescence")
}
if(exists("custom_table_norm_fluorescence_processed") && !is.null(custom_table_norm_fluorescence_processed()) ){
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_norm_fluorescence_processed", select = TRUE)
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_norm_fluorescence", select = FALSE)
} else {
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_norm_fluorescence_processed")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_norm_fluorescence")
}
if(exists("custom_table_fluorescence_processed") && !is.null(custom_table_fluorescence_processed()) &&
exists("growth_data_custom_processed") && !is.null(growth_data_custom_processed()) &&
all(results$custom_data$growth == results$custom_data$fluorescence, na.rm = T)){
showModal(modalDialog("growth and Fluorescence data are identical. Did you assign the correct files and/or sheets?", easyClose = T))
}
})
### Render custom fluorescence table
custom_table_fluorescence <- reactive({
inFile <- input$custom_file_fluorescence
if(is.null(inFile))
return(NULL)
filename <- inFile$datapath
dec <- input$decimal_separator_custom_fluorescence
csvsep <- input$separator_custom_fluorescence
if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "csv") {
f1 <-
utils::read.csv(
filename,
dec = dec,
sep = csvsep,
header = FALSE,
stringsAsFactors = FALSE,
fill = T,
na.strings = "",
quote = "",
comment.char = "",
check.names = F
)
} else if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "xls" |
stringr::str_replace(filename, ".{1,}\\.", "") == "xlsx") {
showModal(
modalDialog(
HTML("<strong>Reading data file...</strong><br><br><i>Note: Reading .xls/.xlsx files can take up to several minutes. Consider saving as .csv/.tsv/.txt before running QurvE.</i>"),
footer=NULL))
try(
f1 <- data.frame(suppressMessages(readxl::read_excel(filename, col_names = FALSE, sheet = input$custom_fluorescence_sheets, progress = T)))
)
removeModal()
} else if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "tsv") {
f1 <-
utils::read.csv(
filename,
dec = dec,
sep = "\t",
header = FALSE,
stringsAsFactors = FALSE,
fill = T,
na.strings = "",
quote = "",
comment.char = "",
check.names = F
)
} else if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "txt") {
f1 <-
utils::read.table(
filename,
dec = dec,
sep = "\t",
header = FALSE,
stringsAsFactors = FALSE,
fill = T,
na.strings = "",
quote = "",
comment.char = "",
check.names = F
)
}
if(exists("f1")){
f1[-(1:3),] <- apply(apply(f1[-(1:3),], 2, as.numeric), 2, round, digits = 2)
colnames(f1)[1] <- "Time"
f1[1,1] <- ""
return(f1)
} else return(NULL)
})
output$custom_table_fluorescence <- DT::renderDT({
f1 <- custom_table_fluorescence()
DT::datatable(f1,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(f1)-3)))
})
# Render processed growth table
growth_data_custom_processed <- reactive({
if(is.null(results$custom_data) || length(results$custom_data$growth) < 2) return(NULL)
table_growth <- t(results$custom_data$growth)
table_growth[-(1:3), ] <- apply(apply(table_growth[-(1:3), ], 2, as.numeric), 2, round, digits = 3)
rownames(table_growth)[-(1:3)] <- ""
table_growth <- cbind(data.frame("Time" = c("","","", round(as.numeric(results$custom_data$time[1,]), digits = 2))),
table_growth)
table_growth
})
output$growth_data_custom_processed <- DT::renderDT({
table_growth <- growth_data_custom_processed()
DT::datatable(table_growth,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(table_growth)-3)))
})
# render processed fluorescence table
custom_table_fluorescence_processed <- reactive({
if(is.null(results$custom_data) || length(results$custom_data$fluorescence)<2) return(NULL)
table_fl <- t(results$custom_data$fluorescence)
table_fl[-(1:3), ] <- apply(apply(table_fl[-(1:3), ], 2, as.numeric), 2, round, digits = 1)
rownames(table_fl)[-(1:3)] <- ""
table_fl <- cbind(data.frame("Time" = c("","","", round(as.numeric(results$custom_data$time[1,]), digits = 2))),
table_fl)
table_fl
})
output$custom_table_fluorescence_processed <- DT::renderDT({
table_fl <- custom_table_fluorescence_processed()
DT::datatable(table_fl,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(table_fl)-3)))
})
# render processed normalized fluorescence table
custom_table_norm_fluorescence_processed <- reactive({
if(is.null(results$custom_data) || length(results$custom_data$norm.fluorescence)<2) return(NULL)
table_fl <- t(results$custom_data$norm.fluorescence)
table_fl[-(1:3), ] <- apply(apply(table_fl[-(1:3), ], 2, as.numeric), 2, round, digits = 1)
rownames(table_fl)[-(1:3)] <- ""
table_fl <- cbind(data.frame("Time" = c("","","", round(as.numeric(results$custom_data$time[1,]), digits = 2))),
table_fl)
table_fl
})
output$custom_table_norm_fluorescence_processed <- DT::renderDT({
table_fl <- custom_table_norm_fluorescence_processed()
DT::datatable(table_fl,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(table_fl)-3)))
})
custom_raw_growth_plot <- reactive({
if(is.null(results$custom_data) || length(results$custom_data$growth) < 2) return(NULL)
try(
suppressWarnings(
plot.grodata(x = results$custom_data,
data.type = "growth",
IDs = NULL
)
)
)
})
output$custom_raw_growth_plot <- renderPlot({
custom_raw_growth_plot()
})
custom_raw_fluorescence_plot <- reactive({
if(is.null(results$custom_data) || length(results$custom_data$fluorescence) < 2) return(NULL)
try(
suppressWarnings(
plot.grodata(x = results$custom_data,
data.type = "fl",
IDs = NULL
)
)
)
})
output$custom_raw_fluorescence_plot <- renderPlot({
custom_raw_fluorescence_plot()
})
custom_raw_norm_fluorescence_plot <- reactive({
if(is.null(results$custom_data) || length(results$custom_data$norm.fluorescence) < 2) return(NULL)
try(
suppressWarnings(
plot.grodata(x = results$custom_data,
data.type = "norm.fl",
IDs = NULL
)
)
)
})
output$custom_raw_norm_fluorescence_plot <- renderPlot({
custom_raw_norm_fluorescence_plot()
})
output$custom_growth_format <- reactive({
if(is.null(input$custom_file_growth)) return(NULL)
filename <- input$custom_file_growth$name
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
format
})
outputOptions(output, 'custom_growth_format', suspendWhenHidden=FALSE)
output$custom_fluorescence_format <- reactive({
if(is.null(input$custom_file_fluorescence)) return(NULL)
filename <- input$custom_file_fluorescence$name
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
format
})
outputOptions(output, 'custom_fluorescence_format', suspendWhenHidden=FALSE)
output$custom_fluorescence2_format <- reactive({
if(is.null(input$custom_file_fluorescence2)) return(NULL)
filename <- input$custom_file_fluorescence2$name
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
format
})
outputOptions(output, 'custom_fluorescence2_format', suspendWhenHidden=FALSE)
growth_excel_sheets <- reactive({
filename <- input$custom_file_growth$datapath
if(is.null(input$custom_file_growth)) return("")
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
if(format != "xlsx" && format != "xls") return("")
sheets <- readxl::excel_sheets(input$custom_file_growth$datapath)
sheets
})
fluorescence_excel_sheets <- reactive({
filename <- input$custom_file_fluorescence$datapath
if(is.null(input$custom_file_fluorescence)) return("")
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
if(format != "xlsx" && format != "xls") return("")
sheets <- readxl::excel_sheets(input$custom_file_fluorescence$datapath)
sheets
})
fluorescence2_excel_sheets <- reactive({
filename <- input$custom_file_fluorescence2$datapath
if(is.null(input$custom_file_fluorescence2)) return("")
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
if(format != "xlsx" && format != "xls") return("")
sheets <- readxl::excel_sheets(input$custom_file_fluorescence2$datapath)
sheets
})
observe({
updateSelectInput(inputId = "custom_growth_sheets",
choices = growth_excel_sheets()
)})
observe({
updateSelectInput(inputId = "custom_fluorescence_sheets",
choices = fluorescence_excel_sheets()
)})
observe({
updateSelectInput(inputId = "custom_fluorescence2_sheets",
choices = fluorescence2_excel_sheets()
)})
observeEvent(input$random_data_growth, {
# display a modal dialog with a header, textinput and action buttons
showModal(
modalDialog(
tags$h2('Please enter parameters to simulate growth curves'),
textInput('d.random.growth', 'Number of samples', placeholder = "35"),
textInput('y0.random.growth', 'Start growth', placeholder = "0.05"),
textInput('tmax.random.growth', 'Maximum time value', placeholder = "24"),
textInput('mu.random.growth', 'Maximum growth rate', placeholder = "0.6"),
textInput('lambda.random.growth', 'Minimum lag time', placeholder = "5"),
textInput('A.random.growth', 'Maximum growth', placeholder = "3"),
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.random.data.growth', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_rdm.data",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
) # footer
) # modalDialog
) # showModal
})
# Generate random dataset
observeEvent(input$submit.random.data.growth, {
d <- ifelse(input$d.random.growth == "", 35, as.numeric(input$d.random.growth))
y0 <- ifelse(input$y0.random.growth == "", 0.05, as.numeric(input$y0.random.growth))
tmax <- ifelse(input$tmax.random.growth == "", 24, as.numeric(input$tmax.random.growth))
mu <- ifelse(input$mu.random.growth == "", 0.6, as.numeric(input$mu.random.growth))
lambda <- ifelse(input$lambda.random.growth == "", 5, as.numeric(input$lambda.random.growth))
A <- ifelse(input$A.random.growth == "", 3, as.numeric(input$A.random.growth))
results$custom_data <- rdm.data(d, y0 = y0, tmax = tmax, mu = mu, lambda = lambda, A = A, label = "Test")
hide("data_instruction")
show("Custom_Data_Tables")
hide("Parsed_Data_Tables")
if("growth" %in% names(results$custom_data) && length(results$custom_data$growth)>1){
# show [Run Computation] button in Computation-Growth
show("run_growth")
}
# Remove eventually pre-loaded parsed data
results$parsed_data <- NULL
hide("parsed_reads_growth")
hide("parsed_reads_fluorescence")
hide("parsed_reads_fluorescence2")
hide("norm_type_parse")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_norm_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_norm_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Computation]")
results$df_random_data <- data.frame("time" = c("time", "","", results$custom_data$time[1,]),
"data" = t(results$custom_data$data))
colnames(results$df_random_data ) <- results$df_random_data [1,]
write.csv(results$df_random_data[-1,], file = paste0(tempdir(), "/random_data.csv"))
results$random_data_growth <- paste0(tempdir(), "/random_data.csv")
removeModal()
})
observe({
growth.file <- results$random_data_growth
if(is.null(growth.file)) return(NULL)
## Read data
try(
results$custom_data <- read_data(results$df_random_data,
# data.fluoro2 = fl2.file$datapath,
data.format = "col"
)
)
if(length(results$custom_data)<2){
showModal(modalDialog("Data could not be extracted from the provided file. Did you correctly format your custom data?", easyClose = T, footer=NULL))
} else {
hide("data_instruction")
show("Custom_Data_Tables")
hide("Parsed_Data_Tables")
if("growth" %in% names(results$custom_data) && length(results$custom_data$growth)>1){
# show [Run Computation] button in Computation-Growth
show("run_growth")
}
# if("growth" %in% names(results$custom_data) && length(results$custom_data$fluorescence2)>1){
# showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence2")
# }
# Remove eventually pre-loaded parsed data
results$parsed_data <- NULL
hide("parsed_reads_growth")
hide("parsed_reads_fluorescence")
hide("parsed_reads_norm_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_norm_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_norm_fluorescence")
# hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence2")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Computation]")
hide("random_data_growth")
removeModal()
}
})
##__Parse data____####
### Hide elements to guide user
hide("Parsed_Data_Tables")
hide("parsed_reads_growth")
hide("parsed_reads_fluorescence")
hide("parsed_reads_fluorescence2")
hide("parse_data")
hide("norm_type_parse")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence")
# hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence2")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
observe({
if(exists("parsed_data_table_expdesign") && !is.null(parsed_data_table_expdesign())){
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
} else {
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
}
if(exists("parsed_data_table_growth") && !is.null(parsed_data_table_growth())){
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth", select = TRUE)
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth", select = FALSE)
} else {
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth")
}
if(exists("parsed_data_table_fluorescence") && !is.null(parsed_data_table_fluorescence()) ){
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence", select = TRUE)
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence", select = FALSE)
} else {
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence")
}
if(exists("parsed_data_table_norm_fluorescence") && !is.null(parsed_data_table_norm_fluorescence()) ){
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_norm_fluorescence", select = TRUE)
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_norm_fluorescence", select = FALSE)
} else {
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_norm_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_norm_fluorescence")
}
})
# Update placeholder for time conversion based on selected software
observe({
if("Gen5" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = "y = x * 24")
}
if("Chi.Bio" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = "y = x / 3600")
}
if("GrowthProfiler" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = "y = x / 60")
}
if("Tecan" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = "y = x / 3600")
}
if("Biolector" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = NULL, placeholder = "")
}
if("VictorNivo" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = "y = x / 3600")
}
if("VictorX3" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = "y = x / 60")
}
})
### Test if parse_file was loaded
output$parsefileUploaded <- reactive({
if(is.null(input$parse_file)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'parsefileUploaded', suspendWhenHidden=FALSE)
parse_excel_sheets <- reactive({
filename <- input$parse_file$datapath
if(is.null(input$parse_file)) return("")
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
if(format != "xlsx" && format != "xls") return("")
sheets <- readxl::excel_sheets(input$parse_file$datapath)
sheets
})
map_excel_sheets <- reactive({
filename <- input$map_file$datapath
if(is.null(input$map_file)) return("")
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
if(format != "xlsx" && format != "xls") return("")
sheets <- readxl::excel_sheets(input$map_file$datapath)
sheets
})
### Test if map_file was loaded
output$mapfileUploaded <- reactive({
if(is.null(input$map_file)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'mapfileUploaded', suspendWhenHidden=FALSE)
### get file formats for parse_file and map_file
output$parse_file_format <- reactive({
if(is.null(input$parse_file)) return(NULL)
filename <- input$parse_file$name
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
format
})
outputOptions(output, 'parse_file_format', suspendWhenHidden=FALSE)
output$map_file_format <- reactive({
if(is.null(input$map_file)) return(NULL)
filename <- input$map_file$name
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
format
})
outputOptions(output, 'map_file_format', suspendWhenHidden=FALSE)
### Extract reads from data file
selected_inputs_parsed_reads <- reactive({
inFile <- input$parse_file
if(is.null(inFile))
return(NULL)
filename <- inFile$datapath
if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "xls" |
stringr::str_replace(filename, ".{1,}\\.", "") == "xlsx"){
showModal(modalDialog(HTML("<strong>Reading data file...</strong><br><br><i>Note: Reading .xls/.xlsx files can take up to several minutes. Consider saving as .csv/.tsv/.txt before running QurvE.</i>"), footer=NULL))
}else {
showModal(modalDialog("Reading data file...", footer=NULL))
}
if("Gen5" %in% input$platereader_software){
try(reads <- QurvE:::parse_properties_Gen5Gen6(file=filename,
csvsep = input$separator_parse,
dec = input$decimal_separator_parse,
sheet = ifelse(input$parse_data_sheets == "Sheet1", 1, input$parse_data_sheets) ),
silent = FALSE
)
}
if("Chi.Bio" %in% input$platereader_software){
try(reads <- QurvE:::parse_properties_chibio(file=filename,
csvsep = input$separator_parse,
dec = input$decimal_separator_parse,
sheet = ifelse(input$parse_data_sheets == "Sheet1", 1, input$parse_data_sheets) ),
silent = FALSE
)
}
if("Tecan" %in% input$platereader_software){
try(reads <- QurvE:::parse_properties_tecan(file=filename,
csvsep = input$separator_parse,
dec = input$decimal_separator_parse,
sheet = ifelse(input$parse_data_sheets == "Sheet1", 1, input$parse_data_sheets) ),
silent = FALSE
)
}
if("Biolector" %in% input$platereader_software){
try(reads <- QurvE:::parse_properties_biolector(file=filename,
csvsep = input$separator_parse,
dec = input$decimal_separator_parse,
sheet = ifelse(input$parse_data_sheets == "Sheet1", 1, input$parse_data_sheets) ),
silent = FALSE
)
}
if("VictorNivo" %in% input$platereader_software){
try(reads <- QurvE:::parse_properties_victornivo(file=filename,
csvsep = input$separator_parse,
dec = input$decimal_separator_parse,
sheet = ifelse(input$parse_data_sheets == "Sheet1", 1, input$parse_data_sheets) ),
silent = FALSE
)
}
if("VictorX3" %in% input$platereader_software){
try(reads <- QurvE:::parse_properties_victorx3(file=filename,
csvsep = input$separator_parse,
dec = input$decimal_separator_parse,
sheet = ifelse(input$parse_data_sheets == "Sheet1", 1, input$parse_data_sheets) ),
silent = FALSE
)
}
if(exists("reads") && length(reads) > 0){
show("parsed_reads_growth")
show("parsed_reads_fluorescence")
if(length(reads)>1){
show("parsed_reads_fluorescence2")
} else {
hide("parsed_reads_fluorescence2")
}
show("parse_data")
removeModal()
reads <- c(reads, "Ignore")
} else if(!("GrowthProfiler" %in% input$platereader_software) &&
!is.null(input$platereader_software)){
if(stringr::str_replace_all(filename, ".{1,}\\.", "") == "xlsx")
showModal(modalDialog("No read data could be extracted from the provided data file. Did you choose the correct software and sheet?", easyClose = T, footer=NULL))
if(stringr::str_replace_all(filename, ".{1,}\\.", "") == "csv")
showModal(modalDialog("No read data could be extracted from the provided data file. Did you choose the correct software and separator?", easyClose = T, footer=NULL))
else
showModal(modalDialog("No read data could be extracted from the provided data file. Did you choose the correct software?", easyClose = T, footer=NULL))
} else {
show("parse_data")
removeModal()
}
})
observe({
reads <- selected_inputs_parsed_reads()
if(length(reads) < 2)
return(NULL)
if(length(reads) > 1 && input$parsed_reads_fluorescence != "Ignore"){
show("parsed_reads_fluorescence2")
show("norm_type_parse")
} else {
hide("parsed_reads_fluorescence2")
hide("norm_type_parse")
}
norm.types <- c()
if(input$parsed_reads_growth != "Ignore"){
norm.types <- c(norm.types, "growth")
}
if(input$parsed_reads_fluorescence2 != "Ignore"){
norm.types <- c(norm.types, "fluorescence 2")
}
if(length(norm.types) > 0){
show("norm_type_parse")
} else {
hide("norm_type_parse")
}
updateSelectInput(inputId = "norm_type_parse",
choices = norm.types
)
})
observe({
updateSelectInput(inputId = "parsed_reads_growth",
choices = selected_inputs_parsed_reads()
)
})
observe({
updateSelectInput(inputId = "parsed_reads_fluorescence",
choices = selected_inputs_parsed_reads()
)
})
observe({
updateSelectInput(inputId = "parsed_reads_fluorescence2",
choices = selected_inputs_parsed_reads()
)
})
observeEvent(input$mapping_included_in_parse,{
if(input$mapping_included_in_parse) hide("map_file")
if(!input$mapping_included_in_parse) show("map_file")
}, ignoreInit = TRUE)
#### Parse data and extract read tabs
observeEvent(input$parse_data,{
if(input$norm_type_parse == "fluorescence 2"){
fl.normtype <- "fl2"
} else {
fl.normtype <- "growth"
}
showModal(modalDialog("Parsing data input...", footer=NULL))
if(input$convert_time_equation_plate_reader == "" || is.na(input$convert_time_equation_plate_reader)) convert.time <- NULL
else convert.time <- input$convert_time_equation_plate_reader
if(input$mapping_included_in_parse){
try(
results$parsed_data <- QurvE:::parse_data_shiny(
data.file = input$parse_file$datapath,
map.file = input$parse_file$datapath,
software = input$platereader_software,
convert.time = if(input$convert_time_values_plate_reader){
convert.time
} else {
NULL
},
sheet.data = input$parse_data_sheets,
sheet.map = input$map_data_sheets,
csvsep.data = input$separator_parse,
dec.data = input$decimal_separator_parse,
csvsep.map = input$separator_map,
dec.map = input$decimal_separator_map,
subtract.blank = input$subtract_blank_plate_reader,
growth.nm = input$parsed_reads_growth,
fl.nm = ifelse(
input$parsed_reads_fluorescence == input$parsed_reads_growth,
NA,
input$parsed_reads_fluorescence
),
calib.growth = ifelse(input$calibration_growth_plate_reader, input$calibration_equation_growth_plate_reader, ""),
calib.fl = ifelse(input$calibration_fluorescence_plate_reader, input$calibration_equation_fluorescence_plate_reader, ""),
calib.fl2 = ifelse(input$calibration_fluorescence2_plate_reader, input$calibration_equation_fluorescence2_plate_reader, ""),
fl2.nm = ifelse(
input$parsed_reads_fluorescence2 == input$parsed_reads_growth |
input$parsed_reads_fluorescence2 == input$parsed_reads_fluorescence,
NA,
input$parsed_reads_fluorescence2
),
fl.normtype = fl.normtype
)
)
} else {
if(is.null(input$map_file$datapath)){
showModal(
modalDialog(
HTML(
"<strong>Parsing data input...</strong><br><br>No mapping file was provided. The samples will be identified based on their well position (A1, A2, A3, etc.). Grouping options will not be available if you run any further analysis with QurvE."
),
easyClose = FALSE,
footer=NULL
)
)
}
try(
results$parsed_data <- QurvE:::parse_data_shiny(
data.file = input$parse_file$datapath,
map.file = input$map_file$datapath,
software = input$platereader_software,
convert.time = if(input$convert_time_values_plate_reader){
convert.time
} else {
NULL
},
sheet.data = input$parse_data_sheets,
sheet.map = input$map_data_sheets,
csvsep.data = input$separator_parse,
dec.data = input$decimal_separator_parse,
csvsep.map = input$separator_map,
dec.map = input$decimal_separator_map,
subtract.blank = input$subtract_blank_plate_reader,
growth.nm = input$parsed_reads_growth,
fl.nm = ifelse(
input$parsed_reads_fluorescence == input$parsed_reads_growth,
NA,
input$parsed_reads_fluorescence
),
calib.growth = ifelse(input$calibration_growth_plate_reader, input$calibration_equation_growth_plate_reader, ""),
calib.fl = ifelse(input$calibration_fluorescence_plate_reader, input$calibration_equation_fluorescence_plate_reader, ""),
calib.fl2 = ifelse(input$calibration_fluorescence2_plate_reader, input$calibration_equation_fluorescence2_plate_reader, ""),
fl2.nm = ifelse(
input$parsed_reads_fluorescence2 == input$parsed_reads_growth |
input$parsed_reads_fluorescence2 == input$parsed_reads_fluorescence,
NA,
input$parsed_reads_fluorescence2
),
fl.normtype = fl.normtype
)
)
}
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
hide("mapping_layout")
hide("Custom_Data_Tables")
show("Parsed_Data_Tables")
if("growth" %in% names(results$parsed_data) && length(results$parsed_data$growth)>1){
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth")
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth")
# show [Run Computation] button in Computation-Growth
show("run_growth")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Computation]")
}
if("fluorescence" %in% names(results$parsed_data) && length(results$parsed_data$fluorescence)>1){
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence")
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Computation]")
}
# Remove eventually pre-loaded custom data
results$custom_data <- NULL
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_growth_processed")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_growth")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence_processed")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_fluorescence")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_norm_fluorescence_processed")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_norm_fluorescence")
# hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence2")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_expdesign")
removeModal()
})
#### Generate parsed tables to display in [DATA] tab
parsed_data_table_growth <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$growth) < 2) return(NULL)
table_growth <- t(results$parsed_data$growth)
if(dim(table_growth)[2]<2){
table_growth[-(1:3), ] <- round(as.numeric(table_growth[-(1:3), ]), digits = 3)
}else{
table_growth[-(1:3), ] <- apply(apply(table_growth[-(1:3), ], 2, as.numeric), 2, round, digits = 3)
}
rownames(table_growth)[-(1:3)] <- ""
table_growth <- cbind(data.frame("Time" = c("","","", round(as.numeric(results$parsed_data$time[1,]), digits = 2))),
table_growth)
table_growth
})
output$parsed_data_table_growth <- DT::renderDT({
DT::datatable(parsed_data_table_growth(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(parsed_data_table_growth())-3)))
})
parsed_data_table_fluorescence <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$fluorescence)<2) return(NULL)
table_fl <- t(results$parsed_data$fluorescence)
table_fl[-(1:3), ] <- apply(apply(table_fl[-(1:3), ], 2, as.numeric), 2, round, digits = 1)
rownames(table_fl)[-(1:3)] <- ""
table_fl <- cbind(data.frame("Time" = c("","","", round(as.numeric(results$parsed_data$time[1,]), digits = 2))),
table_fl)
table_fl
})
output$parsed_data_table_fluorescence <- DT::renderDT({
DT::datatable(parsed_data_table_fluorescence(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(parsed_data_table_fluorescence())-3)))
})
# render processed normalized fluorescence table
parsed_data_table_norm_fluorescence <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$norm.fluorescence)<2) return(NULL)
table_fl <- t(results$parsed_data$norm.fluorescence)
table_fl[-(1:3), ] <- apply(apply(table_fl[-(1:3), ], 2, as.numeric), 2, round, digits = 1)
rownames(table_fl)[-(1:3)] <- ""
table_fl <- cbind(data.frame("Time" = c("","","", round(as.numeric(results$parsed_data$time[1,]), digits = 2))),
table_fl)
table_fl
})
output$parsed_data_table_norm_fluorescence <- DT::renderDT({
DT::datatable(parsed_data_table_norm_fluorescence(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(parsed_data_table_norm_fluorescence())-3)))
})
parsed_data_table_expdesign <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$expdesign)<2) return(NULL)
expdesign <- results$parsed_data$expdesign
expdesign
})
output$parsed_data_table_expdesign <- DT::renderDT({
expdesign <- parsed_data_table_expdesign()
DT::datatable(expdesign,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = T)
})
parsed_raw_growth_plot <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$growth) < 2) return(NULL)
try(
suppressWarnings(
plot.grodata(x = results$parsed_data,
data.type = "growth",
IDs = NULL
)
)
)
})
output$parsed_raw_growth_plot <- renderPlot({
parsed_raw_growth_plot()
})
parsed_raw_fluorescence_plot <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$fluorescence) < 2) return(NULL)
try(
suppressWarnings(
plot.grodata(x = results$parsed_data,
data.type = "fl",
IDs = NULL
)
)
)
})
output$parsed_raw_fluorescence_plot <- renderPlot({
parsed_raw_fluorescence_plot()
})
observe({
updateSelectInput(inputId = "parse_data_sheets",
choices = parse_excel_sheets()
)})
observe({
if(input$mapping_included_in_parse){
updateSelectInput(inputId = "map_data_sheets",
choices = parse_excel_sheets()
)
} else {
updateSelectInput(inputId = "map_data_sheets",
choices = map_excel_sheets()
)
}
})
parsed_raw_norm_fluorescence_plot <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$norm.fluorescence) < 2) return(NULL)
try(
suppressWarnings(
plot.grodata(x = results$parsed_data,
data.type = "norm.fl",
IDs = NULL
)
)
)
})
output$parsed_raw_norm_fluorescence_plot <- renderPlot({
parsed_raw_norm_fluorescence_plot()
})
##__Table_download____####
output$download_custom_tables_growth_processed <- downloadHandler(
filename = function() {
paste("custom_growth_data", ".csv", sep="")
},
content = function(file) {
table <- growth_data_custom_processed()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_custom_tables_fluorescence_processed <- downloadHandler(
filename = function() {
paste("custom_fluorescence_data", ".csv", sep="")
},
content = function(file) {
table <- custom_table_fluorescence_processed()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_custom_tables_norm_fluorescence_processed <- downloadHandler(
filename = function() {
paste("custom_norm_fluorescence_data", ".csv", sep="")
},
content = function(file) {
table <- custom_table_norm_fluorescence_processed()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_custom_tables_expdesign <- downloadHandler(
filename = function() {
paste("custom_expdesign", ".csv", sep="")
},
content = function(file) {
table <- custom_data_table_expdesign()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_parsed_tables_growth <- downloadHandler(
filename = function() {
paste("parsed_growth_data", ".csv", sep="")
},
content = function(file) {
table <- parsed_data_table_growth()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_parsed_tables_fluorescence <- downloadHandler(
filename = function() {
paste("parsed_fluorescence_data", ".csv", sep="")
},
content = function(file) {
table <- parsed_data_table_fluorescence()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_parsed_data_table_norm_fluorescence <- downloadHandler(
filename = function() {
paste("parsed_norm_fluorescence_data", ".csv", sep="")
},
content = function(file) {
table <- parsed_data_table_norm_fluorescence()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_parsed_tables_expdesign <- downloadHandler(
filename = function() {
paste("parsed_expdesign", ".csv", sep="")
},
content = function(file) {
if(is.null(results$parsed_data) || length(results$parsed_data$expdesign)<2) return(NULL)
table <- results$parsed_data$expdesign
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
# Computation ####
##____Growth____#####
hide("run_growth")
selected_inputs_response_parameter_growth <- reactive({
select_options <- c()
if(input$linear_regression_growth) select_options <- c(select_options, 'mu.linfit', 'lambda.linfit', 'dY.linfit',
'A.linfit')
if(input$nonparametric_fit_growth) select_options <- c(select_options, 'mu.spline', 'lambda.spline',
'A.spline', 'dY.spline', 'integral.spline')
if(input$parametric_fit_growth) select_options <- c(select_options, 'mu.model', 'lambda.model', 'A.model', 'integral.model')
select_options
})
observe({
updateSelectInput(session,
inputId = "response_parameter_growth",
choices = selected_inputs_response_parameter_growth()
)
})
observeEvent(input$run_growth,{
## Read data
# Choose data input
if(!is.null(results$custom_data)){
grodata <- results$custom_data
} else if(!is.null(results$parsed_data)){
grodata <- results$parsed_data
} else return(NULL)
if (is.null(input$number_of_bootstrappings_growth) || is.na(input$number_of_bootstrappings_growth) || input$number_of_bootstrappings_growth == "NULL" || input$number_of_bootstrappings_growth == "") {
nboot.gc <- 0.55
} else {
nboot.gc <- as.numeric(input$number_of_bootstrappings_growth)
}
if (is.null(input$smoothing_factor_nonparametric_growth) || is.na(input$smoothing_factor_nonparametric_growth) || input$smoothing_factor_nonparametric_growth == "NULL" || input$smoothing_factor_nonparametric_growth == "") {
smooth.gc <- 0.55
} else {
smooth.gc <- as.numeric(input$smoothing_factor_nonparametric_growth)
}
if (is.null(input$smoothing_factor_growth_dr) || is.na(input$smoothing_factor_growth_dr) || input$smoothing_factor_growth_dr == "NULL" || input$smoothing_factor_growth_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_growth_dr)
}
if (is.null(input$number_of_bootstrappings_dr_growth) || is.na(input$number_of_bootstrappings_dr_growth) || input$number_of_bootstrappings_dr_growth == "NULL" || input$number_of_bootstrappings_dr_growth == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_growth)
}
if (is.null(input$R2_threshold_growth) || is.na(input$R2_threshold_growth) || input$R2_threshold_growth == "NULL" || input$R2_threshold_growth == "") {
lin.R2 <- 0.95
} else {
lin.R2 <- as.numeric(input$R2_threshold_growth)
}
if (is.null(input$RSD_threshold_growth) || is.na(input$RSD_threshold_growth) || input$RSD_threshold_growth == "NULL" || input$RSD_threshold_growth == "") {
lin.RSD <- 0.1
} else {
lin.RSD <- as.numeric(input$RSD_threshold_growth)
}
if (is.null(input$dY_threshold_growth) || is.na(input$dY_threshold_growth) || input$dY_threshold_growth == "NULL" || input$dY_threshold_growth == "") {
lin.dY <- 0.05
} else {
lin.dY <- as.numeric(input$dY_threshold_growth)
}
if (is.null(input$minimum_growth_growth) || is.na(input$minimum_growth_growth) || input$minimum_growth_growth == "NULL" || input$minimum_growth_growth == "") {
min.growth <- 0
} else {
min.growth <- as.numeric(input$minimum_growth_growth)
}
if (is.null(input$maximum_growth_growth) || is.na(input$maximum_growth_growth) || input$maximum_growth_growth == "NULL" || input$maximum_growth_growth == "") {
max.growth <- NA
} else {
max.growth <- as.numeric(input$maximum_growth_growth)
}
if (is.null(input$t0_growth) || is.na(input$t0_growth) || input$t0_growth == "NULL" || input$t0_growth == "") {
t0 <- 0
} else {
t0 <- as.numeric(input$t0_growth)
}
if (is.null(input$tmax_growth) || is.na(input$tmax_growth) || input$tmax_growth == "NULL" || input$tmax_growth == "") {
tmax <- NA
} else {
tmax <- as.numeric(input$tmax_growth)
}
if (is.null(input$growth_threshold_growth) || is.na(input$growth_threshold_growth) || input$growth_threshold_growth == "NULL" || input$growth_threshold_growth == "") {
growth.thresh <- 1.5
} else {
growth.thresh <- as.numeric(input$growth_threshold_growth)
}
fit.opt <- c()
if(input$linear_regression_growth){
fit.opt <- c(fit.opt,
'l')
}
if(input$parametric_fit_growth){
fit.opt <- c(fit.opt,
'm')
# combine selected models into vector
models <- c()
if(input$logistic_growth == TRUE) models <- c(models, "logistic")
if(input$richards_growth == TRUE) models <- c(models, "richards")
if(input$gompertz_growth == TRUE) models <- c(models, "gompertz")
if(input$extended_gompertz_growth == TRUE) models <- c(models, "gompertz.exp")
if(input$huang_growth == TRUE) models <- c(models, "huang")
if(input$baranyi_growth == TRUE) models <- c(models, "baranyi")
} else {
models <- c("logistic")
}
if(input$nonparametric_fit_growth){
fit.opt <- c(fit.opt,
's')
}
showModal(modalDialog("Running computations...", footer=NULL))
# Run growth workflow
shiny::withProgress(message = "Computations completed",
try(
results$growth <-
suppressWarnings(
QurvE::growth.workflow(grodata = grodata,
ec50 = input$perform_ec50_growth,
fit.opt = fit.opt,
t0 = t0,
tmax = tmax,
min.growth = min.growth,
max.growth = max.growth,
log.x.gc = input$log_transform_time_growth,
log.y.lin = input$log_transform_data_linear_growth,
log.y.model = input$log_transform_data_parametric_growth,
log.y.spline = input$log_transform_data_nonparametric_growth,
biphasic = input$biphasic_growth,
lin.h = input$custom_sliding_window_size_value_growth,
lin.R2 = lin.R2,
lin.RSD = lin.RSD,
lin.dY = lin.dY,
interactive = FALSE,
nboot.gc = nboot.gc,
smooth.gc = smooth.gc,
model.type = models,
growth.thresh = growth.thresh,
dr.method = input$dr_method_growth,
dr.parameter = input$response_parameter_growth,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_growth,
log.y.dr = input$log_transform_response_growth,
nboot.dr = nboot.dr,
suppress.messages = T,
report = NULL,
shiny = TRUE
)
)
)
)
if(!is.null("results$growth")){
# ENABLE DISABLED PANELS AFTER RUNNING COMPUTATION
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Export_RData]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Report]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Visualize]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Results]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Validate]")
removeModal()
}
if(exists("results$growth") && !is.null("results$growth"))
showModal(modalDialog(geterrmessage()) )
})
##____Fluorescence____#####
# Create vector of x_types based on presence of data types
output$normalized_fl_present <- reactive({
if(!is.null(results$custom_data)){
grodata <- results$custom_data
} else if(!is.null(results$parsed_data)){
grodata <- results$parsed_data
if(input$parsed_reads_growth == input$parsed_reads_fluorescence)
showModal(modalDialog("Identical values for growth and fluorescence data. Did you choose the correct data identifiers?"))
} else return(FALSE)
if(length(grodata$norm.fluorescence) > 1){
return(TRUE)
} else {
return(FALSE)
}
})
outputOptions(output, 'normalized_fl_present', suspendWhenHidden=FALSE)
selected_inputs_fluorescence_x_types <- reactive({
if(is.null(results$custom_data) && is.null(results$parsed_data)) return(NULL)
if(!is.null(results$custom_data)) data <- results$custom_data
if(!is.null(results$parsed_data)) data <- results$parsed_data
x_types <- c()
if(length(data$growth) > 1) x_types <- c(x_types, 'growth')
if(length(data$time) > 1) x_types <- c(x_types, 'time')
x_types
})
observe({
updateSelectInput(session,
inputId = "data_type_x_fluorescence",
choices = selected_inputs_fluorescence_x_types()
)
})
selected_inputs_response_parameter_fluorescence <- reactive({
select_options <- c()
if(input$linear_regression_fluorescence) select_options <- c(select_options, 'max_slope.linfit', 'lambda.linfit', 'dY.linfit',
'A.linfit')
if(input$nonparametric_fit_fluorescence) select_options <- c(select_options, 'max_slope.spline', 'lambda.spline',
'A.spline', 'dY.spline', 'integral.spline')
select_options
})
observe({
updateSelectInput(session,
inputId = "response_parameter_fluorescence",
choices = selected_inputs_response_parameter_fluorescence()
)
})
observeEvent(input$run_fluorescence,{
# Choose data input
if(!is.null(results$custom_data)){
grodata <- results$custom_data
} else if(!is.null(results$parsed_data)){
grodata <- results$parsed_data
} else return(NULL)
if (is.null(input$smoothing_factor_nonparametric_fluorescence) || is.na(input$smoothing_factor_nonparametric_fluorescence) || input$smoothing_factor_nonparametric_fluorescence == "NULL" || input$smoothing_factor_nonparametric_fluorescence == "") {
smooth.fl = 0.75
} else {
smooth.fl <- as.numeric(input$smoothing_factor_nonparametric_fluorescence)
}
if (is.null(input$smoothing_factor_fluorescence_dr) || is.na(input$smoothing_factor_fluorescence_dr) || input$smoothing_factor_fluorescence_dr == "NULL" || input$smoothing_factor_fluorescence_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_fluorescence_dr)
}
if (is.null(input$number_of_bootstrappings_fluorescence) || is.na(input$number_of_bootstrappings_fluorescence) || input$number_of_bootstrappings_fluorescence == "NULL" || input$number_of_bootstrappings_fluorescence == "") {
nboot.fl <- 0
} else {
nboot.fl <- as.numeric(input$number_of_bootstrappings_fluorescence)
}
if (is.null(input$number_of_bootstrappings_dr_fluorescence) || is.na(input$number_of_bootstrappings_dr_fluorescence) || input$number_of_bootstrappings_dr_fluorescence == "NULL" || input$number_of_bootstrappings_dr_fluorescence == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_fluorescence)
}
if (is.null(input$R2_threshold_fluorescence) || is.na(input$R2_threshold_fluorescence) || input$R2_threshold_fluorescence == "NULL" || input$R2_threshold_fluorescence == "") {
lin.R2 <- 0.95
} else {
lin.R2 <- as.numeric(input$R2_threshold_fluorescence)
}
if (is.null(input$RSD_threshold_fluorescence) || is.na(input$RSD_threshold_fluorescence) || input$RSD_threshold_fluorescence == "NULL" || input$RSD_threshold_fluorescence == "") {
lin.RSD <- 0.1
} else {
lin.RSD <- as.numeric(input$RSD_threshold_fluorescence)
}
if (is.null(input$dY_threshold_fluorescence) || is.na(input$dY_threshold_fluorescence) || input$dY_threshold_fluorescence == "NULL" || input$dY_threshold_fluorescence == "") {
lin.dY <- 0.05
} else {
lin.dY <- as.numeric(input$dY_threshold_fluorescence)
}
if (is.null(input$growth_threshold_in_percent_fluorescence) || is.na(input$growth_threshold_in_percent_fluorescence) || input$growth_threshold_in_percent_fluorescence == "NULL" || input$growth_threshold_in_percent_fluorescence == "") {
growth.thresh <- 1.5
} else {
growth.thresh <- as.numeric(input$growth_threshold_in_percent_fluorescence)
}
if (is.null(input$t0_fluorescence) || is.na(input$t0_fluorescence) || input$t0_fluorescence == "NULL" || input$t0_fluorescence == "") {
t0 <- 0
} else {
t0 <- as.numeric(input$t0_fluorescence)
}
if (is.null(input$minimum_growth_fluorescence) || is.na(input$minimum_growth_fluorescence) || input$minimum_growth_fluorescence == "NULL" || input$minimum_growth_fluorescence == "") {
min.growth <- 0
} else {
min.growth <- as.numeric(input$minimum_growth_fluorescence)
}
if (is.null(input$maximum_growth_fluorescence) || is.na(input$maximum_growth_fluorescence) || input$maximum_growth_fluorescence == "NULL" || input$maximum_growth_fluorescence == "") {
max.growth <- NA
} else {
max.growth <- as.numeric(input$maximum_growth_fluorescence)
}
if (is.null(input$tmax_fluorescence) || is.na(input$tmax_fluorescence) || input$tmax_fluorescence == "NULL" || input$tmax_fluorescence == "") {
tmax <- NA
} else {
tmax <- as.numeric(input$tmax_fluorescence)
}
fit.opt <- c()
if(input$linear_regression_fluorescence){
fit.opt <- c(fit.opt,
'l')
}
if(input$nonparametric_fit_fluorescence){
fit.opt <- c(fit.opt,
's')
}
# removeModal()
showModal(modalDialog("Running computations...", footer=NULL))
# Run fluorescence workflow
try(
shiny::withProgress(message = "Computations completed",
results$fluorescence <-
suppressWarnings(
QurvE::fl.workflow(grodata = grodata,
ec50 = input$perform_ec50_fluorescence,
fit.opt = fit.opt,
x_type = input$data_type_x_fluorescence,
norm_fl = input$normalize_fluorescence,
t0 = t0,
min.growth = min.growth,
log.x.lin = input$log_transform_x_linear_fluorescence,
log.x.spline = input$log_transform_x_nonparametric_fluorescence,
log.y.lin = input$log_transform_data_linear_fluorescence,
log.y.spline = input$log_transform_data_nonparametric_fluorescence,
lin.h = as.numeric(input$custom_sliding_window_size_value_fluorescence),
lin.R2 = lin.R2,
lin.RSD = lin.RSD,
lin.dY = lin.dY,
biphasic = input$biphasic_fluorescence,
interactive = FALSE,
dr.parameter = input$response_parameter_fluorescence,
dr.method = input$dr_method_fluorescence,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_fluorescence,
log.y.dr = input$log_transform_response_fluorescence,
nboot.dr = nboot.dr,
nboot.fl = nboot.fl,
smooth.fl = smooth.fl,
growth.thresh = growth.thresh,
suppress.messages = T,
neg.nan.act = FALSE,
clean.bootstrap = TRUE,
report = NULL,
out.dir = NULL,
out.nm = NULL,
export.fig = FALSE,
shiny = TRUE,
tmax = tmax,
max.growth = max.growth
)
)
)
)
if(!is.null("results$fluorescence")){
## ENABLE DISABLED PANELS AFTER RUNNING COMPUTATION
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Export_RData]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Report]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Visualize]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Results]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Validate]")
removeModal()
}
if(exists("results$fluorescence") && !is.null("results$fluorescence"))
showModal(modalDialog(geterrmessage()) )
})
# Results ####
## Growth ####
observe({
if(!is.null(results$growth)){
if(!("s" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Spline")
hideTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Spline_bt")
} else{
showTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Spline")
}
if(!("l" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Linear")
} else {
showTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Linear")
}
if(!("m" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Model")
} else {
showTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Model")
}
if(!("s" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt) || results$growth$control$nboot.gc <= 1){
hideTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Spline_bt")
} else {
showTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Spline_bt")
}
if(length(results$growth$drFit) >1 && length(results$growth$drFit$drTable) > 1){
showTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_DR")
} else {
hideTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_DR")
}
}
})
### Linear ####
table_growth_linear <- reactive({
res.table.gc <- results$growth$gcFit$gcTable
table_linear <- data.frame("Sample|Replicate|Conc." = paste(res.table.gc$TestId, res.table.gc$AddId, res.table.gc$concentration, sep = "|"),
"µ<sub>max</sub>" = ifelse(res.table.gc$mu.linfit==0 | is.na(res.table.gc$mu.linfit), "", ifelse(is.na(res.table.gc$mu2.linfit), round(as.numeric(res.table.gc$mu.linfit), 3), paste0("<strong>", round(as.numeric(res.table.gc$mu.linfit), 3), "</strong>", " (", round(as.numeric(res.table.gc$mu2.linfit), 3), ")"))),
"t<sub>D</sub>" = ifelse(res.table.gc$mu.linfit==0 | is.na(res.table.gc$mu.linfit), "", ifelse(is.na(res.table.gc$mu2.linfit), round(log(2)/as.numeric(res.table.gc$mu.linfit), 2), paste0("<strong>", round(log(2)/as.numeric(res.table.gc$mu.linfit), 2), "</strong>", " (", round(log(2)/as.numeric(res.table.gc$mu2.linfit), 2), ")"))),
"λ" = round(as.numeric(res.table.gc$lambda.linfit), 2),
"ΔY" = round(as.numeric(res.table.gc$dY.linfit), 3),
"y<sub>max</sub>" = round(as.numeric(res.table.gc$A.linfit), 3),
"t<sub>start</sub><br>(µ<sub>max</sub>)" = ifelse(is.na(res.table.gc$mu2.linfit), round(as.numeric(res.table.gc$tmu.start.linfit), 2), paste0("<strong>", round(as.numeric(res.table.gc$tmu.start.linfit), 2), "</strong>", " (", round(as.numeric(res.table.gc$tmu2.start.linfit), 2), ")")),
"t<sub>end</sub><br>(µ<sub>max</sub>)" = ifelse(is.na(res.table.gc$mu2.linfit), round(as.numeric(res.table.gc$tmu.end.linfit), 2), paste0("<strong>", round(as.numeric(res.table.gc$tmu.end.linfit), 2), "</strong>", " (", round(as.numeric(res.table.gc$tmu2.end.linfit), 2), ")")),
"R<sup>2</sup><br>(linear fit)" = ifelse(is.na(res.table.gc$mu2.linfit), round(as.numeric(res.table.gc$r2mu.linfit), 3), paste0("<strong>", round(as.numeric(res.table.gc$r2mu.linfit), 3), "</strong>", " (", round(as.numeric(res.table.gc$r2mu2.linfit), 3), ")")),
stringsAsFactors = FALSE, check.names = F)
table_linear
})
output$results_table_growth_linear <- DT::renderDT({
DT::datatable(table_growth_linear(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_growth_linear_group <- reactive({
gcTable <- results$growth$gcFit$gcTable
table <- QurvE:::table_group_growth_linear(gcTable)
colnames(table) <- c("Sample|Conc.", "\u03bc<sub>max</sub>", "t<sub>D</sub>", "\u03bb",
"\u2206Y", "y<sub>max</sub>", "t<sub>start</sub><br>(\u03bc<sub>max</sub>)",
"t<sub>end</sub><br>(\u03bc<sub>max</sub>)")
table
})
output$results_table_growth_linear_group <- DT::renderDT({
DT::datatable(table_growth_linear_group(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
### Spline ####
table_growth_spline <- reactive({
res.table.gc <- results$growth$gcFit$gcTable
table_spline <- data.frame("Sample|Replicate|Conc." = paste(res.table.gc$TestId, res.table.gc$AddId, res.table.gc$concentration, sep = "|"),
"µ<sub>max</sub>" = ifelse(res.table.gc$mu.spline==0 | is.na(res.table.gc$mu.spline), "", ifelse(is.na(res.table.gc$mu2.spline), round(as.numeric(res.table.gc$mu.spline), 3), paste0("<strong>", round(as.numeric(res.table.gc$mu.spline), 3), "</strong>", " (", round(as.numeric(res.table.gc$mu2.spline), 3), ")"))),
"t<sub>D</sub>" = ifelse(res.table.gc$mu.spline==0 | is.na(res.table.gc$mu.spline), "", ifelse(is.na(res.table.gc$mu2.spline), round(log(2)/as.numeric(res.table.gc$mu.spline), 2), paste0("<strong>", round(log(2)/as.numeric(res.table.gc$mu.spline), 2), "</strong>", " (", round(log(2)/as.numeric(res.table.gc$mu2.spline), 2), ")"))),
"λ" = round(as.numeric(res.table.gc$lambda.spline), 2),
"y<sub>max</sub>" = round(as.numeric(res.table.gc$A.spline), 3),
"ΔY" = round(as.numeric(res.table.gc$dY.spline), 3),
"t<sub>max</sub>" = ifelse(is.na(res.table.gc$mu2.spline), round(as.numeric(res.table.gc$tmax.spline), 2), paste0("<strong>", round(as.numeric(res.table.gc$tmax.spline), 2), "</strong>", " (", round(as.numeric(res.table.gc$tmax2.spline), 2), ")")),
"smooth.<br>fac" = res.table.gc$smooth.spline, check.names = F)
table_spline
})
output$results_table_growth_spline <- DT::renderDT({
DT::datatable(table_growth_spline(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_growth_spline_group <- reactive({
gcTable <- results$growth$gcFit$gcTable
table <- QurvE:::table_group_growth_spline(gcTable)
colnames(table) <- c("Sample|Conc.", "\u03bc<sub>max</sub>", "t<sub>D</sub>", "\u03bb",
"\u2206Y", "y<sub>max</sub>", "t(\u03bc<sub>max</sub>)")
table
})
output$results_table_growth_spline_group <- DT::renderDT({
DT::datatable(table_growth_spline_group(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
### Spline BT ####
table_growth_spline_bt <- reactive({
res.table.gc <- results$growth$gcFit$gcTable
table_spline <- data.frame("Sample|Replicate|Conc." = paste(res.table.gc$TestId, res.table.gc$AddId, res.table.gc$concentration, sep = "|"),
"µ<sub>max</sub>" = ifelse(res.table.gc$mu.bt==0 | is.na(res.table.gc$mu.bt),
"",
paste0(round(as.numeric(res.table.gc$mu.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.gc$stdmu.bt), 3)
)
),
"t<sub>D</sub>" = ifelse(res.table.gc$mu.bt==0 | is.na(res.table.gc$mu.bt),
"",
paste0(round(log(2)/as.numeric(res.table.gc$mu.bt), 3) ,
" \u00B1 ",
round(log(2)/as.numeric(res.table.gc$stdmu.bt), 3)
)
),
"λ" = ifelse(res.table.gc$lambda.bt==0 | is.na(res.table.gc$lambda.bt),
"",
paste0(round(as.numeric(res.table.gc$lambda.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.gc$stdlambda.bt ), 3)
)
),
"y<sub>max</sub>" = ifelse(res.table.gc$lambda.bt==0 | is.na(res.table.gc$lambda.bt),
"",
paste0(round(as.numeric(res.table.gc$lambda.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.gc$stdlambda.bt ), 3)
)
),
"ΔY" = ifelse(res.table.gc$dY.bt==0 | is.na(res.table.gc$dY.bt),
"",
paste0(round(as.numeric(res.table.gc$dY.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.gc$stddY.bt ), 3)
)
),
"smooth.<br>fac" = res.table.gc$smooth.spline, check.names = F)
table_spline
})
output$results_table_growth_spline_bt <- DT::renderDT({
DT::datatable(table_growth_spline_bt(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
### Model ####
table_growth_model <- reactive({
try({
res.table.gc <- results$growth$gcFit$gcTable
table_model <- data.frame("Sample|Replicate|Conc." = paste(res.table.gc$TestId, res.table.gc$AddId, res.table.gc$concentration, sep = "|"),
"Model" = res.table.gc$used.model,
"µ<sub>max</sub>" = ifelse(res.table.gc$mu.model==0 | is.na(res.table.gc$mu.model), "", paste(round(as.numeric(res.table.gc$mu.model), 3),"\u00B1", round(as.numeric(res.table.gc$stdmu.model),3))),
"t<sub>D</sub>" = paste(ifelse(res.table.gc$mu.model==0 | is.na(res.table.gc$mu.model), "", paste(round(log(2)/as.numeric(res.table.gc$mu.model), 2), "\u00B1", round(sqrt(((-log(2)*as.numeric(res.table.gc$stdmu.model))/(as.numeric(res.table.gc$mu.model))^2)^2), 2)))),
"λ" = ifelse(res.table.gc$lambda.model==0 | is.na(res.table.gc$lambda.model), "", paste(round(as.numeric(res.table.gc$lambda.model), 2), "\u00B1", round(as.numeric(res.table.gc$stdlambda.model),3))),
"A" = ifelse(res.table.gc$A.model==0 | is.na(res.table.gc$A.model), "", paste(round(as.numeric(res.table.gc$A.model), 3), "\u00B1", round(as.numeric(res.table.gc$stdA.model),3))),
stringsAsFactors = FALSE, check.names = F)
if(!is.null(res.table.gc)){
if ( "richards" %in% res.table.gc$used.model ){
table_model <- suppressWarnings(cbind(table_model, data.frame("ν" = round(as.numeric(res.table.gc$parameter_nu.model), 3), stringsAsFactors = FALSE, check.names = F)))
}
if ( "gompertz" %in% res.table.gc$used.model ){
table_model <- suppressWarnings(cbind(table_model, data.frame("α" = round(as.numeric(res.table.gc$parameter_alpha.model), 3), stringsAsFactors = FALSE, check.names = F)))
}
if ( "gompertz.exp" %in% res.table.gc$used.model ){
table_model <- suppressWarnings(cbind(table_model, data.frame("t<sub>shift</sub>" = round(as.numeric(res.table.gc$parameter_t_shift.model), 3), stringsAsFactors = FALSE, check.names = F)))
}
if ("baranyi" %in% res.table.gc$used.model || "huang" %in% res.table.gc$used.model ){
table_model <- suppressWarnings(cbind(table_model, data.frame("y0" = round(as.numeric(res.table.gc$parameter_y0.model), 3), stringsAsFactors = FALSE, check.names = F)))
}
}
table_model
})
})
output$results_table_growth_model <- DT::renderDT({
table_model <- table_growth_model()
DT::datatable(table_model,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_growth_model_group <- reactive({
gcTable <- results$growth$gcFit$gcTable
table <- QurvE:::table_group_growth_model(gcTable)
colnames(table) <- c("Sample|Conc.", "\u03bc<sub>max</sub>", "t<sub>D</sub>", "\u03bb",
"y<sub>max</sub>", "\u2206Y")
table
})
output$results_table_growth_model_group <- DT::renderDT({
DT::datatable(table_growth_model_group(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
### DR Spline ####
table_growth_dr_spline <- reactive({
if(results$growth$drFit$control$dr.method == "spline"){
try({
res.table.dr <- results$growth$drFit$drTable
table_dr <- data.frame("Test" = res.table.dr$Test,
"EC50" = round(as.numeric(res.table.dr[["EC50"]]), 3),
"Response(EC50)" = round(as.numeric(res.table.dr[["yEC50"]]), 3),
"Test parameter" = res.table.dr[["test"]],
stringsAsFactors = FALSE, check.names = F)
if(!is.null(res.table.dr)){
if(results$growth$control$log.x.dr){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("EC50 (original)" = round(as.numeric(res.table.dr[["EC50.orig"]]), 3),
stringsAsFactors = FALSE, check.names = F)))
}
if(results$growth$control$log.y.dr){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("Response(EC50) (original)" = round(as.numeric(res.table.dr[["yEC50.orig"]]), 3),
stringsAsFactors = FALSE, check.names = F)))
}
if ( results$growth$control$nboot.dr > 2 ){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("Mean EC50 (bootstrap)" = paste(round(as.numeric(res.table.dr[["drboot.meanEC50"]]), 3), round(as.numeric(res.table.dr[["drboot.sdEC50"]]), 3), sep = " \u00B1 "),
"Response(EC50) (bootstrap)" = paste(round(as.numeric(res.table.dr[["drboot.meanEC50y"]]), 3), round(as.numeric(res.table.dr[["drboot.sdEC50y"]]), 3), sep = " \u00B1 "),
stringsAsFactors = FALSE, check.names = F)))
}
}
table_dr
})
}
else{
models <- invisible(drc:::getMeanFunctions(display = F))
model.names <- c(unlist(lapply(1:length(models), function(x) models[[x]][1])), "weibull2x", "gammadr", "multi2")
model.descr <- c(unlist(lapply(1:length(models), function(x) models[[x]][2])), "four-parameter Weibull model", "Gamma dose-response model", "Multistage dose-response model with quadratic terms")
try({
res.table.dr <- results$growth$drFit$drTable
table_dr <- data.frame("Test" = res.table.dr$Test,
"EC50 \u00B1 SE" = paste0(round(as.numeric(res.table.dr[["EC50.Estimate"]]), 3), " \u00B1 ", round(as.numeric(res.table.dr[["EC50.Std..Error"]]), 3)),
"Response(EC50)" = round(as.numeric(res.table.dr[["yEC50"]]), 3),
"Test parameter" = res.table.dr[["test"]],
"Model" = paste0(res.table.dr[["model"]], ": ", model.descr[match(res.table.dr[["model"]], model.names)]),
stringsAsFactors = FALSE, check.names = F)
table_dr
})
}
})
output$results_table_growth_dr_spline <- DT::renderDT({
table_dr <- table_growth_dr_spline()
DT::datatable(table_dr,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "All")) ),
escape = FALSE)
})
###____Table Download____####
output$download_table_growth_linear <- downloadHandler(
filename = function() {
paste("growth_results_linear_fits", ".csv", sep="")
},
content = function(file) {
table <- table_growth_linear()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_linear_group <- downloadHandler(
filename = function() {
paste("growth_results_linear_fits_grouped", ".csv", sep="")
},
content = function(file) {
table <- table_growth_linear_group()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_spline <- downloadHandler(
filename = function() {
paste("growth_results_spline_fits", ".csv", sep="")
},
content = function(file) {
table <- table_growth_spline()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_spline_group <- downloadHandler(
filename = function() {
paste("growth_results_spline_fits_grouped", ".csv", sep="")
},
content = function(file) {
table <- table_growth_spline_group()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_spline_bt <- downloadHandler(
filename = function() {
paste("growth_results_spline_bootstrappings", ".csv", sep="")
},
content = function(file) {
table <- table_growth_spline_bt()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_model <- downloadHandler(
filename = function() {
paste("growth_results_model_fits", ".csv", sep="")
},
content = function(file) {
table <- table_growth_model()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_model_group <- downloadHandler(
filename = function() {
paste("growth_results_model_fits_grouped", ".csv", sep="")
},
content = function(file) {
table <- table_growth_model_group()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_dr <- downloadHandler(
filename = function() {
paste("growth_results_dose-response_analysis", ".csv", sep="")
},
content = function(file) {
table <- table_growth_dr_spline()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
## Fluorescence ####
observe({
if(!is.null(results$fluorescence)){
if(!("s" %in% results$fluorescence$control$fit.opt || "a" %in% results$fluorescence$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_Spline")
hideTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Growth_Fluorescence_bt")
} else {
showTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_Spline")
}
if(!("l" %in% results$fluorescence$control$fit.opt || "a" %in% results$fluorescence$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_Linear")
} else {
showTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_Linear")
}
if(!("s" %in% results$fluorescence$control$fit.opt || "a" %in% results$fluorescence$control$fit.opt) || results$fluorescence$control$nboot.fl <=1){
hideTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_Spline_bt")
} else {
showTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_Spline_bt")
}
if(length(results$fluorescence$drFit) >1 && length(results$fluorescence$drFit$drTable) > 1 && results$fluorescence$drFit$control$dr.method == "spline"){
showTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_DR_Spline")
} else {
hideTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_DR_Spline")
}
if(length(results$fluorescence$drFit) >1 && length(results$fluorescence$drFit$drTable) > 1 && results$fluorescence$drFit$control$dr.method == "model"){
showTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_DR_Model")
} else {
hideTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_DR_Model")
}
}
})
table_fluorescence_linear <- reactive({
res.table.fl <- results$fluorescence$flFit$flTable
table_linear <- data.frame("Sample|Replicate|Conc." = paste(res.table.fl$TestId, res.table.fl$AddId, res.table.fl$concentration, sep = "|"),
"slope<sub>max</sub>" = ifelse(res.table.fl$max_slope.linfit==0 | is.na(res.table.fl$max_slope.linfit), "", ifelse(is.na(res.table.fl$max_slope2.linfit), round(as.numeric(res.table.fl$max_slope.linfit), 3), paste0("<strong>", round(as.numeric(res.table.fl$max_slope.linfit), 3), "</strong>", " (", round(as.numeric(res.table.fl$max_slope2.linfit), 3), ")"))),
"t<sub>D</sub>" = ifelse(res.table.fl$max_slope.linfit==0 | is.na(res.table.fl$max_slope.linfit), "", ifelse(is.na(res.table.fl$max_slope2.linfit), round(log(2)/as.numeric(res.table.fl$max_slope.linfit), 2), paste0("<strong>", round(log(2)/as.numeric(res.table.fl$max_slope.linfit), 2), "</strong>", " (", round(log(2)/as.numeric(res.table.fl$max_slope2.linfit), 2), ")"))),
"λ" = round(as.numeric(res.table.fl$lambda.linfit), 2),
"ΔY" = round(as.numeric(res.table.fl$dY.linfit), 3),
"y<sub>max</sub>" = round(as.numeric(res.table.fl$A.linfit), 3),
"x<sub>start</sub><br>(slope<sub>max</sub>)" = ifelse((is.na(res.table.fl$max_slope2.linfit)), round(as.numeric(res.table.fl$x.mu.start.linfit), 2), paste0("<strong>", round(as.numeric(res.table.fl$x.mu.start.linfit), 2), "</strong>", " (", round(as.numeric(res.table.fl$x.mu2.start.linfit), 2), ")")),
"x<sub>end</sub><br>(slope<sub>max</sub>)" = ifelse((is.na(res.table.fl$max_slope2.linfit)), round(as.numeric(res.table.fl$x.mu.end.linfit), 2), paste0("<strong>", round(as.numeric(res.table.fl$x.mu.end.linfit), 2), "</strong>", " (", round(as.numeric(res.table.fl$x.mu2.end.linfit), 2), ")")),
"R<sup>2</sup><br>(linear fit)" = ifelse((is.na(res.table.fl$max_slope2.linfit)), round(as.numeric(res.table.fl$r2mu.linfit), 3), paste0("<strong>", round(as.numeric(res.table.fl$r2mu.linfit), 3), "</strong>", " (", round(as.numeric(res.table.fl$r2mu.linfit), 3), ")")),
stringsAsFactors = FALSE, check.names = F)
table_linear
})
output$results_table_fluorescence_linear <- DT::renderDT({
DT::datatable(table_fluorescence_linear(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_fluorescence_linear_group <- reactive({
flTable <- results$fluorescence$flFit$flTable
table <- QurvE:::table_group_fluorescence_linear(flTable)
colnames(table) <- c("Sample|Conc.", "slope<sub>max</sub>", "\u03bb",
"\u0394Y", "y<sub>max</sub>", "x<sub>start</sub><br>(\u03bc<sub>max</sub>)",
"x<sub>end</sub><br>(\u03bc<sub>max</sub>)")
table
})
output$results_table_fluorescence_linear_group <- DT::renderDT({
DT::datatable(table_fluorescence_linear_group(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_fluorescence_spline <- reactive({
res.table.fl <- results$fluorescence$flFit$flTable
table_spline <- data.frame("Sample|Replicate|Conc." = paste(res.table.fl$TestId, res.table.fl$AddId, res.table.fl$concentration, sep = "|"),
"slope<sub>max</sub>" = ifelse(res.table.fl$max_slope.spline==0 | is.na(res.table.fl$max_slope.spline), "", ifelse(is.na(res.table.fl$max_slope2.spline), round(as.numeric(res.table.fl$max_slope.spline), 3), paste0("<strong>", round(as.numeric(res.table.fl$max_slope.spline), 3), "</strong>", " (", round(as.numeric(res.table.fl$max_slope2.spline), 3), ")"))),
"λ" = round(as.numeric(res.table.fl$lambda.spline), 2),
"y<sub>max</sub>" = round(as.numeric(res.table.fl$A.spline), 3),
"ΔY" = round(as.numeric(res.table.fl$dY.spline), 3),
"x<sub>max</sub>" = ifelse(is.na(res.table.fl$max_slope2.spline), round(as.numeric(res.table.fl$x.max.spline), 2), paste0("<strong>", round(as.numeric(res.table.fl$x.max.spline), 2), "</strong>", " (", round(as.numeric(res.table.fl$x.max2.spline), 2), ")")),
"smooth.<br>fac" = res.table.fl$smooth.spline, check.names = F)
table_spline
})
output$results_table_fluorescence_spline <- DT::renderDT({
DT::datatable(table_fluorescence_spline(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_fluorescence_spline_group <- reactive({
flTable <- results$fluorescence$flFit$flTable
QurvE:::table_group_fluorescence_spline(flTable)
colnames(table) <- c("Sample|Conc.", "slope<sub>max</sub>", "\u03bb",
"\u0394Y", "y<sub>max</sub>", "x(slope<sub>max</sub>)")
table
})
output$results_table_fluorescence_spline_group <- DT::renderDT({
DT::datatable(table_fluorescence_spline_group(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_fluorescence_spline_bt <- reactive({
res.table.fl <- results$fluorescence$flFit$flTable
table_spline <- data.frame("Sample|Replicate|Conc." = paste(res.table.fl$TestId, res.table.fl$AddId, res.table.fl$concentration, sep = "|"),
"µ<sub>max</sub>" = ifelse(res.table.fl$max_slope.bt==0 | is.na(res.table.fl$max_slope.bt),
"",
paste0(round(as.numeric(res.table.fl$max_slope.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.fl$stdmax_slope.bt), 3)
)
),
"t<sub>D</sub>" = ifelse(res.table.fl$max_slope.bt==0 | is.na(res.table.fl$max_slope.bt),
"",
paste0(round(log(2)/as.numeric(res.table.fl$max_slope.bt), 3) ,
" \u00B1 ",
round(log(2)/as.numeric(res.table.fl$stdmax_slope.bt), 3)
)
),
"λ" = ifelse(res.table.fl$lambda.bt==0 | is.na(res.table.fl$lambda.bt),
"",
paste0(round(as.numeric(res.table.fl$lambda.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.fl$stdlambda.bt ), 3)
)
),
"y<sub>max</sub>" = ifelse(res.table.fl$lambda.bt==0 | is.na(res.table.fl$lambda.bt),
"",
paste0(round(as.numeric(res.table.fl$lambda.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.fl$stdlambda.bt ), 3)
)
),
"ΔY" = ifelse(res.table.fl$dY.bt==0 | is.na(res.table.fl$dY.bt),
"",
paste0(round(as.numeric(res.table.fl$dY.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.fl$stddY.bt ), 3)
)
),
"smooth.<br>fac" = res.table.fl$smooth.spline, check.names = F)
table_spline
})
output$results_table_fluorescence_spline_bt <- DT::renderDT({
DT::datatable(table_fluorescence_spline_bt(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_fluorescence_dr_spline <- reactive({
try({
res.table.dr <- results$fluorescence$drFit$drTable
table_dr <- data.frame("Test" = res.table.dr$Test,
"EC50" = round(as.numeric(res.table.dr[["EC50"]]), 3),
"Response(EC50)" = round(as.numeric(res.table.dr[["yEC50"]]), 3),
"Test parameter" = res.table.dr[["test"]],
stringsAsFactors = FALSE, check.names = F)
if(!is.null(res.table.dr)){
if(results$fluorescence$control$log.x.dr){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("EC50 (original)" = round(as.numeric(res.table.dr[["EC50.orig"]]), 3),
stringsAsFactors = FALSE, check.names = F)))
}
if(results$fluorescence$control$log.y.dr){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("Response(EC50) (original)" = round(as.numeric(res.table.dr[["yEC50.orig"]]), 3),
stringsAsFactors = FALSE, check.names = F)))
}
if ( results$fluorescence$control$nboot.dr > 2 ){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("Mean EC50 (bootstrap)" = paste(round(as.numeric(res.table.dr[["drboot.meanEC50"]]), 3), round(as.numeric(res.table.dr[["drboot.sdEC50"]]), 3), sep = " \u00B1 "),
"Response(EC50) (bootstrap)" = paste(round(as.numeric(res.table.dr[["drboot.meanEC50y"]]), 3), round(as.numeric(res.table.dr[["drboot.sdEC50y"]]), 3), sep = " \u00B1 "),
stringsAsFactors = FALSE, check.names = F)))
}
}
table_dr
})
})
output$results_table_fluorescence_dr_spline <- DT::renderDT({
table_dr <- table_fluorescence_dr_spline()
DT::datatable(table_dr,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "All")) ),
escape = FALSE)
})
table_fluorescence_dr_model <- reactive({
try({
res.table.dr <- results$fluorescence$drFit$drTable
table_dr <- data.frame("Test" = res.table.dr$Test,
"Leakiness (min. FL)" = round(as.numeric(res.table.dr[["y.min"]]), 3),
"Sensitivity (K, EC50)" = round(as.numeric(res.table.dr[["K"]]), 3),
"Fold change (fc)" = round(as.numeric(res.table.dr[["fc"]]), 3),
"Cooperativity (n)" = round(as.numeric(res.table.dr[["n"]]), 3),
"Response(EC50)" = round(as.numeric(res.table.dr[["yEC50"]]), 3),
"Maximum FL" = round(as.numeric(res.table.dr[["y.max"]]), 3),
stringsAsFactors = FALSE, check.names = F)
if(!is.null(res.table.dr)){
if(results$fluorescence$control$log.x.dr){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("K (original)" = round(as.numeric(res.table.dr[["K.orig"]]), 3),
stringsAsFactors = FALSE, check.names = F)))
}
if(results$fluorescence$control$log.y.dr){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("Response(EC50) (original)" = round(as.numeric(res.table.dr[["yEC50.orig"]]), 3),
stringsAsFactors = FALSE, check.names = F)))
}
}
table_dr
})
})
output$results_table_fluorescence_dr_model <- DT::renderDT({
table_dr_model <- table_fluorescence_dr_model()
DT::datatable(table_dr_model,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "All")) ),
escape = FALSE)
})
###____Table Download____####
output$download_table_fluorescence_linear <- downloadHandler(
filename = function() {
paste("fluorescence_results_linear_fits", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_linear()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_fluorescence_linear_group <- downloadHandler(
filename = function() {
paste("fluorescence_results_linear_fits_grouped", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_linear_group()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_fluorescence_spline <- downloadHandler(
filename = function() {
paste("fluorescence_results_spline_fits", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_spline()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_fluorescence_spline_group <- downloadHandler(
filename = function() {
paste("fluorescence_results_spline_fits_grouped", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_spline_group()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_fluorescence_spline_bt <- downloadHandler(
filename = function() {
paste("fluorescence_results_spline_bootstrappings", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_spline_bt()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_fluorescence_dr_spline <- downloadHandler(
filename = function() {
paste("fluorescence_results_dose-response_analysis_spline", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_dr_spline()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_fluorescence_dr_model <- downloadHandler(
filename = function() {
paste("fluorescence_results_dose-response_analysis_model", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_dr_model()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
# Validate ####
## Growth #####
observe({
if(!is.null(results$growth)){
if(!("s" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Spline")
} else {
showTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Spline")
}
if(!("l" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Linear")
} else {
showTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Linear")
}
if(!("m" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Model")
} else {
showTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Model")
}
if(!("s" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt) || results$growth$control$nboot.gc <=1){
hideTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Spline_bt")
} else {
showTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Spline_bt")
}
}
})
#### Hide [Restore Fit] buttons when starting the app
hide("restore_growth_linear"); hide("restore_growth_spline"); hide("restore_growth_model")
hide("restore_dr_growth"); hide("restore_dr_growth2"); hide("restore_dr_growth3");
hide("restore_dr_fluorescence"); hide("restore_dr_fluorescence2"); hide("restore_dr_fluorescence3")
# hide("restore_dr_spline_individual_fluorescence")
#### Hide [Restore Fit] buttons whenever a sample is changed
observeEvent(input$sample_validate_growth_linear, {
hide("restore_growth_linear")
})
observeEvent(input$sample_validate_growth_spline, {
hide("restore_growth_spline")
})
observeEvent(input$sample_validate_growth_model, {
hide("restore_growth_model")
})
#------- Initialize the Memory to store settings
selected_vals_validate_growth <- reactiveValues(sample_validate_growth_linear = 1,
sample_validate_growth_spline = 1,
sample_validate_growth_model = 1,
sample_validate_growth_spline_bt = 1)
#------ Whenever any of the inputs are changed, it only modifies the memory
observe({
req(input$sample_validate_growth_linear)
selected_vals_validate_growth$sample_validate_growth_linear <- input$sample_validate_growth_linear
})
observe({
req(input$sample_validate_growth_spline)
selected_vals_validate_growth$sample_validate_growth_spline <- input$sample_validate_growth_spline
})
observe({
req(input$sample_validate_growth_model)
selected_vals_validate_growth$sample_validate_growth_model <- input$sample_validate_growth_model
})
observe({
req(input$sample_validate_growth_spline_bt)
selected_vals_validate_growth$sample_validate_growth_spline_bt <- input$sample_validate_growth_spline_bt
})
### Linear Fits ####
selected_inputs_validate_growth_linear_sample <- reactive({
results <- results$growth
if(is.null(results)) return("")
if("l" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
select_samples <- names(results$gcFit$gcFittedLinear)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_growth_linear",
choices = selected_inputs_validate_growth_linear_sample(),
selected = selected_vals_validate_growth$sample_validate_growth_linear
)})
logy_validate_growth_plot_linear <- reactive({
if(input$logy_validate_growth_plot_linear) return("y")
else return("")
})
validate_growth_plot_linear <- reactive({
results <- results$growth
# Define x- and y-axis limits
if(any(input$y_range_min_validate_growth_plot_linear == "",
input$y_range_max_validate_growth_plot_linear == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_growth_plot_linear),
as.numeric(input$y_range_max_validate_growth_plot_linear))
}
if(any(input$y_range_min_derivative_validate_growth_plot_linear == "",
input$y_range_max_derivative_validate_growth_plot_linear == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_growth_plot_linear),
as.numeric(input$y_range_max_derivative_validate_growth_plot_linear))
}
if(any(input$x_range_min_validate_growth_plot_linear == "",
input$x_range_max_validate_growth_plot_linear == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_growth_plot_linear),
as.numeric(input$x_range_max_validate_growth_plot_linear))
}
if (length(results$gcFit$gcFittedLinear[[ifelse(
selected_vals_validate_growth$sample_validate_growth_linear == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_linear
) ||
selected_vals_validate_growth$sample_validate_growth_linear == "" ,
1,
selected_vals_validate_growth$sample_validate_growth_linear
)]]) > 1) {
if(input$diagnostics_validate_growth_plot_linear){
try(
suppressWarnings(
plot.gcFitLinear(results$gcFit$gcFittedLinear[[ifelse(
selected_vals_validate_growth$sample_validate_growth_linear == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_linear
),
1,
selected_vals_validate_growth$sample_validate_growth_linear
)]],
which = "fit_diagnostics",
pch = input$shape_type_validate_growth_plot_linear,
log = logy_validate_growth_plot_linear(),
cex.point = input$shape_size_validate_growth_plot_linear,
cex.lab = input$axis_size_validate_growth_plot_linear,
cex.axis = input$lab_size_validate_growth_plot_linear,
lwd = input$line_width_validate_growth_plot_linear,
color = input$color_validate_growth_plot_linear
)
)
)
} else {
try(
suppressWarnings(
plot.gcFitLinear(
results$gcFit$gcFittedLinear[[ifelse(
selected_vals_validate_growth$sample_validate_growth_linear == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_linear
),
1,
selected_vals_validate_growth$sample_validate_growth_linear
)]],
pch = input$shape_type_validate_growth_plot_linear,
log = logy_validate_growth_plot_linear(),
cex.point = input$shape_size_validate_growth_plot_linear,
cex.lab = input$axis_size_validate_growth_plot_linear,
cex.axis = input$lab_size_validate_growth_plot_linear,
lwd = input$line_width_validate_growth_plot_linear,
y.lim = y.lim,
x.lim = x.lim,
color = input$color_validate_growth_plot_linear
)
)
)
}
}
}
)
output$validate_growth_plot_linear <- renderPlot({
validate_growth_plot_linear()
})
lin.rerun.param <- reactiveValues()
observeEvent(input$rerun_growth_linear, {
# display a modal dialog with a header, textinput and action buttons
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
checkboxInput('log.y.lin.rerun', 'log-transform growth values', value = results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$log.y.lin),
textInput('t0.lin.rerun', 'Minimum time (t0)', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$t0)),
textInput('tmax.lin.rerun', 'Maximum time (tmax)', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$tmax)),
textInput('min.growth.lin.rerun', 'Minimum growth measurement', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$min.growth)),
textInput('max.growth.lin.rerun', 'Maximum growth measurement', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$max.growth)),
textAreaInput('quota.rerun', 'Quota', placeholder = HTML(paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$quota,
"\n",
"include regression windows with slope = ", expression(µ[max]), " * quota into the final linear fit."))),
textInput('lin.h.rerun', 'Sliding window size (h)', placeholder = paste0("previously: ",
ifelse(!is.null(results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$lin.h),
results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$lin.h,
"NULL"))),
textInput('lin.R2.rerun', 'R2 threshold', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$lin.R2)),
textInput('lin.RSD.rerun', 'RSD threshold for slope', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$lin.RSD)),
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.linear.growth', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_growth.gcFitLinear_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
})
# Re-run selected linear fit with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.linear.growth, {
if(!is.null(results$growth$gcFit)){
showModal(modalDialog("Fitting sample data...", footer = NULL))
# store previous fit in memory
selected_vals_validate_growth$restore_growth_linear <- results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]
# Re-run fit and store in results object
actwell <- results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$raw.data
acttime <- results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$raw.time
control <- results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control
control_new <- control
gcID <- results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$gcID
lin.h.new <- ifelse(!is.na(as.numeric(input$lin.h.rerun)), as.numeric(input$lin.h.rerun), as.numeric(control$lin.h))
if(!is.na(lin.h.new)) control_new$lin.h <- lin.h.new
control_new$lin.R2 <- ifelse(!is.na(as.numeric(input$lin.R2.rerun)), as.numeric(input$lin.R2.rerun), control$lin.R2)
control_new$lin.RSD <- ifelse(!is.na(as.numeric(input$lin.RSD.rerun)), as.numeric(input$lin.RSD.rerun), control$lin.RSD)
control_new$t0 <- ifelse(!is.na(as.numeric(input$t0.lin.rerun)), as.numeric(input$t0.lin.rerun), control$t0)
control_new$tmax <- ifelse(!is.na(as.numeric(input$tmax.lin.rerun)), as.numeric(input$tmax.lin.rerun), control$tmax)
control_new$log.y.lin <- input$log.y.lin.rerun
min.growth.lin.new <- ifelse(!is.na(as.numeric(input$min.growth.lin.rerun)), as.numeric(input$min.growth.lin.rerun), control$min.growth)
if(is.numeric(min.growth.lin.new)){
if(!is.na(min.growth.lin.new) && all(as.vector(actwell) < min.growth.lin.new)){
message(paste0("Start growth values need to be greater than 'min.growth'.\nThe minimum start value in your dataset is: ",
min(as.vector(actwell)),". 'min.growth' was not adjusted."), call. = FALSE)
} else if(!is.na(min.growth.lin.new)){
control_new$min.growth <- min.growth.lin.new
}
}
control_new$max.growth <- ifelse(!is.na(as.numeric(input$max.growth.lin.rerun)), as.numeric(input$max.growth.lin.rerun), control$max.growth)
quota_new <- ifelse(!is.na(as.numeric(input$quota.rerun)), as.numeric(input$quota.rerun), 0.95)
try(
results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]] <-
growth.gcFitLinear(acttime, actwell,
gcID = gcID,
control = control_new,
quota = quota_new)
)
# Update gcTable with new results
res.table.gc <- results$growth$gcFit$gcTable
fit.summary <- QurvE:::summary.gcFitLinear(results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]])
sample.ndx <- ifelse(is.numeric(selected_vals_validate_growth$sample_validate_growth_linear),
selected_vals_validate_growth$sample_validate_growth_linear,
match(selected_vals_validate_growth$sample_validate_growth_linear, names(results$growth$gcFit$gcFittedLinear)))
results$growth$gcFit$gcTable[sample.ndx, colnames(res.table.gc) %in% colnames(fit.summary)] <- fit.summary
# Re-run drFit if respective fit type (linear/spline/model) was chosen as dr.parameter
dr.parameter.fit.method <- gsub(".+\\.", "", results$growth$control$dr.parameter)
if(length(results$growth$drFit) > 1 && dr.parameter.fit.method == "linfit"){
# Store previous drFit in memory
selected_vals_validate_growth$restore_growth_drFit <- results$growth$drFit
# Re-run drFit
if (is.null(input$smoothing_factor_growth_dr) || is.na(input$smoothing_factor_growth_dr) || input$smoothing_factor_growth_dr == "NULL" || input$smoothing_factor_growth_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_growth_dr)
}
if (is.null(input$number_of_bootstrappings_dr_growth) || is.na(input$number_of_bootstrappings_dr_growth) || input$number_of_bootstrappings_dr_growth == "NULL" || input$number_of_bootstrappings_dr_growth == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_growth)
}
showModal(modalDialog("Re-calculating dose-response analysis...", footer = NULL))
try(
results$growth$drFit <-
suppressWarnings(
growth.drFit(gcTable = results$growth$gcFit$gcTable,
control = growth.control(dr.method = input$dr_method_growth,
dr.parameter = results$growth$control$dr.parameter,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_growth,
log.y.dr = input$log_transform_response_growth,
nboot.dr = nboot.dr
)
)
)
)
}
# Show [Restore fit] button
show("restore_growth_linear")
}
removeModal()
})
# Restore previous linear fit upon click on [Restore Fit]
observeEvent(input$restore_growth_linear, {
# restore previous fit from memory
results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]] <- selected_vals_validate_growth$restore_growth_linear
dr.parameter.fit.method <- gsub(".+\\.", "", results$growth$control$dr.parameter)
if(length(results$growth$drFit) > 1 && dr.parameter.fit.method == "linfit"){
results$growth$drFit <- selected_vals_validate_growth$restore_growth_drFit
}
hide("restore_growth_linear")
})
output$download_growth_validate_linear <- downloadHandler(
filename = function() {
paste("linear_fit_", gsub(" \\| ", "_", selected_vals_validate_growth$sample_validate_growth_linear), input$format_download_growth_validate_linear, sep="")
},
content = function(file) {
if(input$format_download_growth_validate_linear == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_growth_validate_linear,
height = input$height_download_growth_validate_linear)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_growth_validate_linear,
height = input$height_download_growth_validate_linear)
}
} else {
grDevices::png(file = file,
width = input$width_download_growth_validate_linear,
height = input$height_download_growth_validate_linear,
units = "in",
res = input$dpi_download_growth_validate_linear)
}
if(input$logy_validate_growth_plot_linear) log <- "y"
else log <- ""
results <- results$growth
# Define x- and y-axis limits
if(any(input$y_range_min_validate_growth_plot_linear == "",
input$y_range_max_validate_growth_plot_linear == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_growth_plot_linear),
as.numeric(input$y_range_max_validate_growth_plot_linear))
}
if(any(input$y_range_min_derivative_validate_growth_plot_linear == "",
input$y_range_max_derivative_validate_growth_plot_linear == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_growth_plot_linear),
as.numeric(input$y_range_max_derivative_validate_growth_plot_linear))
}
if(any(input$x_range_min_validate_growth_plot_linear == "",
input$x_range_max_validate_growth_plot_linear == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_growth_plot_linear),
as.numeric(input$x_range_max_validate_growth_plot_linear))
}
if (length(results$gcFit$gcFittedLinear[[ifelse(
selected_vals_validate_growth$sample_validate_growth_linear == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_linear
),
1,
selected_vals_validate_growth$sample_validate_growth_linear
)]]) > 1) {
suppressWarnings(
plot.gcFitLinear(results$gcFit$gcFittedLinear[[ifelse(selected_vals_validate_growth$sample_validate_growth_linear == "1" || is.null(selected_vals_validate_growth$sample_validate_growth_linear), 1, selected_vals_validate_growth$sample_validate_growth_linear)]],
pch = input$shape_type_validate_growth_plot_linear,
log = log,
cex.point = input$shape_size_validate_growth_plot_linear,
cex.lab = input$axis_size_validate_growth_plot_linear,
cex.axis = input$lab_size_validate_growth_plot_linear,
lwd = input$line_width_validate_growth_plot_linear,
y.lim = y.lim,
x.lim = x.lim
)
)
if(input$diagnostics_validate_growth_plot_linear){
suppressWarnings(
plot.gcFitLinear(results$gcFit$gcFittedLinear[[ifelse(
selected_vals_validate_growth$sample_validate_growth_linear == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_linear
),
1,
selected_vals_validate_growth$sample_validate_growth_linear
)]],
which = "fit_diagnostics",
pch = input$shape_type_validate_growth_plot_linear,
log = log,
cex.point = input$shape_size_validate_growth_plot_linear,
cex.lab = input$axis_size_validate_growth_plot_linear,
cex.axis = input$lab_size_validate_growth_plot_linear,
lwd = input$line_width_validate_growth_plot_linear
)
)
}
}
dev.off()
},
contentType = ifelse(input$format_download_growth_validate_linear == ".pdf", "image/pdf", "image/png")
)
### Spline Fits ####
selected_inputs_validate_growth_spline_sample <- reactive({
results <- results$growth
if(is.null(results)) return("")
if("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
select_samples <- names(results$gcFit$gcFittedSplines)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_growth_spline",
choices = selected_inputs_validate_growth_spline_sample(),
selected = selected_vals_validate_growth$sample_validate_growth_spline
)
})
output$validate_growth_plot_spline <- renderPlot({
results <- results$growth
if(length(results$gcFit$gcFittedSplines[[ifelse(
selected_vals_validate_growth$sample_validate_growth_spline == "1" ||
selected_vals_validate_growth$sample_validate_growth_spline == "" ||
is.null(selected_vals_validate_growth$sample_validate_growth_spline),
1,
selected_vals_validate_growth$sample_validate_growth_spline
)]]) > 1) {
showModal(modalDialog("Creating plot...", footer = NULL))
try(
suppressWarnings(
plot.gcFitSpline(
results$gcFit$gcFittedSplines[[ifelse(
selected_vals_validate_growth$sample_validate_growth_spline == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_spline
),
1,
selected_vals_validate_growth$sample_validate_growth_spline
)]],
log.y = input$logy_validate_growth_plot_spline,
x.lim = c(
input$x_range_min_validate_growth_plot_spline,
input$x_range_max_validate_growth_plot_spline
),
y.lim = c(
input$y_range_min_validate_growth_plot_spline,
input$y_range_max_validate_growth_plot_spline
),
y.lim.deriv = c(
input$y_range_min_derivative_validate_growth_plot_spline,
input$y_range_max_derivative_validate_growth_plot_spline
),
lwd = input$line_width_validate_growth_plot_spline,
cex.point = input$shape_size_validate_growth_plot_spline,
basesize = input$base_size_validate_growth_plot_spline,
n.ybreaks = input$nbreaks_validate_growth_plot_spline,
pch = input$shape_type_validate_growth_plot_spline,
deriv = input$plot_derivative_validate_growth_plot_spline,
colSpline = input$color_validate_growth_plot_spline
)
)
)
removeModal()
}
})
spline.rerun.param <- reactiveValues()
observeEvent(input$rerun_growth_spline, {
# display a modal dialog with a header, textinput and action buttons
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
textInput('t0.spline.rerun', 'Minimum time (t0)', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$control$t0)),
textInput('tmax.spline.rerun', 'Maximum time (tmax)', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$control$tmax)),
textInput('min.growth.spline.rerun', 'Minimum growth measurement', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$control$min.growth)),
textInput('max.growth.spline.rerun', 'Maximum growth measurement', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$control$max.growth)),
textInput('smooth.gc.rerun', 'Smoothing factor', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$control$smooth.gc)),
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.spline.growth', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_growth.gcFitSpline_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
})
# Re-run selected spline fit with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.spline.growth, {
if(!is.null(results$growth$gcFit)){
# store previous fit in memory
selected_vals_validate_growth$restore_growth_spline <- results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]
# Re-run fit and store in results object
actwell <- results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$data.in
acttime <- results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$time.in
control <- results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$control
control_new <- control
gcID <- results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$gcID
control_new$smooth.gc <- ifelse(!is.na(as.numeric(input$smooth.gc.rerun)), as.numeric(input$smooth.gc.rerun), control$smooth.gc)
control_new$t0 <- ifelse(!is.na(as.numeric(input$t0.spline.rerun)), as.numeric(input$t0.spline.rerun), control$t0)
control_new$tmax <- ifelse(!is.na(as.numeric(input$tmax.spline.rerun)), as.numeric(input$tmax.spline.rerun), control$tmax)
min.growth.spline.new <- ifelse(!is.na(as.numeric(input$min.growth.spline.rerun)), as.numeric(input$min.growth.spline.rerun), control$min.growth)
if(is.numeric(min.growth.spline.new)){
if(!is.na(min.growth.spline.new) && all(as.vector(actwell) < min.growth.spline.new)){
message(paste0("Start growth values need to be greater than 'min.growth'.\nThe minimum start value in your dataset is: ",
min(as.vector(actwell)),". 'min.growth' was not adjusted."), call. = FALSE)
} else if(!is.na(min.growth.spline.new)){
control_new$min.growth <- min.growth.spline.new
}
}
control_new$max.growth <- ifelse(!is.na(as.numeric(input$max.growth.spline.rerun)), as.numeric(input$max.growth.spline.rerun), control$max.growth)
showModal(modalDialog("Fitting sample data...", footer = NULL))
try(
results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]] <-
growth.gcFitSpline(acttime, actwell,
gcID = gcID,
control = control_new)
)
# Update gcTable with new results
res.table.gc <- results$growth$gcFit$gcTable
fit.summary <- QurvE:::summary.gcFitSpline(results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]])
sample.ndx <- ifelse(is.numeric(selected_vals_validate_growth$sample_validate_growth_spline),
selected_vals_validate_growth$sample_validate_growth_spline,
match(selected_vals_validate_growth$sample_validate_growth_spline, names(results$growth$gcFit$gcFittedSplines)))
results$growth$gcFit$gcTable[sample.ndx, colnames(res.table.gc) %in% colnames(fit.summary)] <- fit.summary
# Re-run drFit if respective fit type (linear/spline/model) was chosen as dr.parameter
dr.parameter.fit.method <- gsub(".+\\.", "", results$growth$control$dr.parameter)
if(length(results$growth$drFit) > 1 && dr.parameter.fit.method == "spline"){
# Store previous drFit in memory
selected_vals_validate_growth$restore_growth_drFit <- results$growth$drFit
# Re-run drFit
if (is.null(input$smoothing_factor_growth_dr) || is.na(input$smoothing_factor_growth_dr) || input$smoothing_factor_growth_dr == "NULL" || input$smoothing_factor_growth_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_growth_dr)
}
if (is.null(input$number_of_bootstrappings_dr_growth) || is.na(input$number_of_bootstrappings_dr_growth) || input$number_of_bootstrappings_dr_growth == "NULL" || input$number_of_bootstrappings_dr_growth == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_growth)
}
showModal(modalDialog("Re-calculating dose-response analysis...", footer = NULL))
try(
results$growth$drFit <-
suppressWarnings(
growth.drFit(gcTable = results$growth$gcFit$gcTable,
control = growth.control(dr.method = input$dr_method_growth,
dr.parameter = results$growth$control$dr.parameter,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_growth,
log.y.dr = input$log_transform_response_growth,
nboot.dr = nboot.dr
)
)
)
)
}
# Show [Restore fit] button
show("restore_growth_spline")
}
removeModal()
})
# Restore previous spline fit upon click on [Restore Fit]
observeEvent(input$restore_growth_spline, {
# restore previous fit from memory
results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]] <- selected_vals_validate_growth$restore_growth_spline
hide("restore_growth_spline")
dr.parameter.fit.method <- gsub(".+\\.", "", results$growth$control$dr.parameter)
if(length(results$growth$drFit) > 1 && dr.parameter.fit.method == "spline"){
results$growth$drFit <- selected_vals_validate_growth$restore_growth_drFit
}
})
output$download_growth_validate_spline <- downloadHandler(
filename = function() {
paste("spline_fit_", gsub(" \\| ", "_", selected_vals_validate_growth$sample_validate_growth_spline), input$format_download_growth_validate_spline, sep="")
},
content = function(file) {
if(input$format_download_growth_validate_spline == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_growth_validate_spline,
height = input$height_download_growth_validate_spline,
dpi = input$dpi_download_growth_validate_spline,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_growth_validate_spline,
height = input$height_download_growth_validate_spline,
dpi = input$dpi_download_growth_validate_spline,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_growth_validate_spline,
height = input$height_download_growth_validate_spline,
dpi = input$dpi_download_growth_validate_spline,
device = png)
}
},
contentType = ifelse(input$format_download_growth_validate_spline == ".pdf", "image/pdf", "image/png")
)
output$download_growth_validate_spline_values <- downloadHandler(
filename = function() {
paste("values_spline_fit_", gsub(" \\| ", "_", selected_vals_validate_growth$sample_validate_growth_spline), ".csv", sep="")
},
content = function(file) {
results <- results$growth
if(length(results$gcFit$gcFittedSplines[[ifelse(
selected_vals_validate_growth$sample_validate_growth_spline == "1" ||
selected_vals_validate_growth$sample_validate_growth_spline == "" ||
is.null(selected_vals_validate_growth$sample_validate_growth_spline),
1,
selected_vals_validate_growth$sample_validate_growth_spline
)]]) > 1) {
try(p <-
suppressWarnings(
plot.gcFitSpline(
results$gcFit$gcFittedSplines[[ifelse(
selected_vals_validate_growth$sample_validate_growth_spline == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_spline
),
1,
selected_vals_validate_growth$sample_validate_growth_spline
)]],
export = FALSE,
plot = FALSE,
log.y = FALSE,
deriv = F
)
))
}
table <- data.frame("x" = as.numeric(p$data$fit.time), "y" = as.numeric(p$data$fit.data))
QurvE:::write.csv.utf8.BOM(table, file)
},
contentType = ".csv"
)
output$download_growth_validate_spline_deriv_values <- downloadHandler(
filename = function() {
paste("values_spline_fit_derivative_", gsub(" \\| ", "_", selected_vals_validate_growth$sample_validate_growth_spline), ".csv", sep="")
},
content = function(file) {
results <- results$growth
if (length(results$gcFit$gcFittedSplines[[ifelse(
selected_vals_validate_growth$sample_validate_growth_spline == "1" ||
selected_vals_validate_growth$sample_validate_growth_spline == "" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_spline
),
1,
selected_vals_validate_growth$sample_validate_growth_spline
)]]) > 1) {
table <- as.data.frame(results$gcFit$gcFittedSplines[[ifelse(
selected_vals_validate_growth$sample_validate_growth_spline == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_spline
),
1,
selected_vals_validate_growth$sample_validate_growth_spline
)]]$spline.deriv1)
}
QurvE:::write.csv.utf8.BOM(table, file)
},
contentType = ".csv"
)
### Model Fits ####
selected_inputs_validate_growth_model_sample <- reactive({
results <- results$growth
if(is.null(results)) return("")
if("m" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
select_samples <- names(results$gcFit$gcFittedModel)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_growth_model",
choices = selected_inputs_validate_growth_model_sample(),
selected = selected_vals_validate_growth$sample_validate_growth_model
)})
output$validate_growth_plot_model <- renderPlot({
results <- results$growth
if(length(results$gcFit$gcFittedModels[[ifelse(selected_vals_validate_growth$sample_validate_growth_model == "1"||
selected_vals_validate_growth$sample_validate_growth_model == "" ||
is.null(selected_vals_validate_growth$sample_validate_growth_model), 1, selected_vals_validate_growth$sample_validate_growth_model)]]) > 1){
showModal(modalDialog("Creating plot...", footer=NULL))
try(
suppressWarnings(
plot.gcFitModel(results$gcFit$gcFittedModels[[ifelse(selected_vals_validate_growth$sample_validate_growth_model == "1" || is.null(selected_vals_validate_growth$sample_validate_growth_model), 1, selected_vals_validate_growth$sample_validate_growth_model)]],
colModel = input$color_validate_growth_plot_model,
pch = input$shape_type_validate_growth_plot_model,
basesize = input$base_size_validate_growth_plot_model,
cex.point = input$shape_size_validate_growth_plot_model,
lwd = input$line_width_validate_growth_plot_model,
n.ybreaks = input$nbreaks_validate_growth_plot_model,
eq.size = input$eqsize_validate_growth_plot_model,
export = FALSE
)
)
)
removeModal()
}
})
model.rerun.param <- reactiveValues()
observeEvent(input$rerun_growth_model, {
# display a modal dialog with a header, textinput and action buttons
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
textInput('t0.model.rerun', 'Minimum time (t0)', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$t0)),
textInput('min.growth.model.rerun', 'Minimum growth measurement', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$min.growth)),
wellPanel(
h4(strong('Models:')),
style='padding: 1; padding-top: 0; padding-bottom: 0',
checkboxInput(inputId = 'logistic_growth_rerun',
label = 'logistic',
value = ("logistic" %in% results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$model.type)),
checkboxInput(inputId = 'richards_growth_rerun',
label = 'Richards',
value = ("richards" %in% results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$model.type)),
checkboxInput(inputId = 'gompertz_growth_rerun',
label = 'Gompertz',
value = ("gompertz" %in% results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$model.type)),
checkboxInput(inputId = 'extended_gompertz_growth_rerun',
label = 'extended Gompertz',
value = ("gompertz.exp" %in% results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$model.type)),
checkboxInput(inputId = 'huang_growth_rerun',
label = 'Huang',
value = ("huang" %in% results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$model.type)),
checkboxInput(inputId = 'baranyi_growth_rerun',
label = 'Baranyi and Roberts',
value = ("baranyi" %in% results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$model.type))
),
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.model', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_growth.gcFitModel_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
})
# Re-run selected model fit with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.model, {
if(!is.null(results$growth$gcFit)){
# store previous fit in memory
selected_vals_validate_growth$restore_growth_model <- results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]
# Re-run fit and store in results object
actwell <- results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$data.in
acttime <- results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$time.in
control <- results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control
control_new <- control
gcID <- results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$gcID
control_new$t0 <- ifelse(!is.na(as.numeric(input$t0.model.rerun)), as.numeric(input$t0.model.rerun), control$t0)
min.growth.model.new <- ifelse(!is.na(as.numeric(input$min.growth.model.rerun)), as.numeric(input$min.growth.model.rerun), control$min.growth)
if(is.numeric(min.growth.model.new)){
if(!is.na(min.growth.model.new) && all(as.vector(actwell) < min.growth.model.new)){
message(paste0("Start growth values need to be greater than 'min.growth'.\nThe minimum start value in your dataset is: ",
min(as.vector(actwell)),". 'min.growth' was not adjusted."), call. = FALSE)
} else if(!is.na(min.growth.model.new)){
control_new$min.growth <- min.growth.model.new
}
}
# combine selected models into vector
models <- c()
if(input$logistic_growth_rerun == TRUE) models <- c(models, "logistic")
if(input$richards_growth_rerun == TRUE) models <- c(models, "richards")
if(input$gompertz_growth_rerun == TRUE) models <- c(models, "gompertz")
if(input$extended_gompertz_growth_rerun == TRUE) models <- c(models, "gompertz.exp")
if(input$huang_growth_rerun == TRUE) models <- c(models, "huang")
if(input$baranyi_growth_rerun == TRUE) models <- c(models, "baranyi")
control_new$model.type <- models
showModal(modalDialog("Fitting sample data...", footer = NULL))
try(results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]] <-
growth.gcFitModel(acttime, actwell,
gcID = gcID,
control = control_new))
# Update gcTable with new results
res.table.gc <- results$growth$gcFit$gcTable
fit.summary <- QurvE:::summary.gcFitModel(results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]])
sample.ndx <- ifelse(is.numeric(selected_vals_validate_growth$sample_validate_growth_model),
selected_vals_validate_growth$sample_validate_growth_model,
match(selected_vals_validate_growth$sample_validate_growth_model, names(results$growth$gcFit$gcFittedModels)))
results$growth$gcFit$gcTable[sample.ndx, colnames(res.table.gc) %in% colnames(fit.summary)] <- fit.summary
results$growth$gcFit$gcTable[sample.ndx, "used.model"] <- results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$model
# Re-run drFit if respective fit type (linear/spline/model) was chosen as dr.parameter
dr.parameter.fit.method <- gsub(".+\\.", "", results$growth$control$dr.parameter)
if(length(results$growth$drFit) > 1 && dr.parameter.fit.method == "model"){
# Store previous drFit in memory
selected_vals_validate_growth$restore_growth_drFit <- results$growth$drFit
# Re-run drFit
if (is.null(input$smoothing_factor_growth_dr) || is.na(input$smoothing_factor_growth_dr) || input$smoothing_factor_growth_dr == "NULL" || input$smoothing_factor_growth_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_growth_dr)
}
if (is.null(input$number_of_bootstrappings_dr_growth) || is.na(input$number_of_bootstrappings_dr_growth) || input$number_of_bootstrappings_dr_growth == "NULL" || input$number_of_bootstrappings_dr_growth == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_growth)
}
showModal(modalDialog("Re-calculating dose-response analysis...", footer = NULL))
try(
results$growth$drFit <-
suppressWarnings(
growth.drFit(gcTable = results$growth$gcFit$gcTable,
control = growth.control(dr.method = input$dr_method_growth,
dr.parameter = results$growth$control$dr.parameter,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_growth,
log.y.dr = input$log_transform_response_growth,
nboot.dr = nboot.dr
)
)
)
)
}
# Show [Restore fit] button
show("restore_growth_model")
}
removeModal()
})
# Restore previous model fit upon click on [Restore Fit]
observeEvent(input$restore_growth_model, {
# store previous fit from memory
results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]] <- selected_vals_validate_growth$restore_growth_model
dr.parameter.fit.method <- gsub(".+\\.", "", results$growth$control$dr.parameter)
if(length(results$growth$drFit) > 1 && dr.parameter.fit.method == "model"){
results$growth$drFit <- selected_vals_validate_growth$restore_growth_drFit
}
hide("restore_growth_model")
})
output$download_growth_validate_model <- downloadHandler(
filename = function() {
paste("model_fit_", gsub(" \\| ", "_", selected_vals_validate_growth$sample_validate_growth_model), input$format_download_growth_validate_model, sep="")
},
content = function(file) {
if(input$format_download_growth_validate_model == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_growth_validate_model,
height = input$height_download_growth_validate_model,
dpi = input$dpi_download_growth_validate_model,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_growth_validate_model,
height = input$height_download_growth_validate_model,
dpi = input$dpi_download_growth_validate_model,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_growth_validate_model,
height = input$height_download_growth_validate_model,
dpi = input$dpi_download_growth_validate_model,
device = png)
}
},
contentType = ifelse(input$format_download_growth_validate_model == ".pdf", "image/pdf", "image/png")
)
### Spline Fits BT ####
# observe({
# if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1){
# showTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse")
# } else {
# hideTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse")
# }
# })
selected_inputs_sample_validate_growth_spline_bt <- reactive({
results <- results$growth
if(is.null(results)) return("")
if(("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt) && results$control$nboot.gc > 1){
select_samples <- names(results$gcFit$gcBootSplines)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_growth_spline_bt",
choices = selected_inputs_sample_validate_growth_spline_bt(),
selected = selected_vals_validate_growth$sample_validate_growth_spline_bt
)})
validate_growth_plot_spline_bt <- reactive({
results <- results$growth
# Define x- and y-axis limits
if(any(input$y_range_min_validate_growth_spline_bt == "",
input$y_range_max_validate_growth_spline_bt == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_growth_spline_bt),
as.numeric(input$y_range_max_validate_growth_spline_bt))
}
if(any(input$y_range_min_derivative_validate_growth_spline_bt == "",
input$y_range_max_derivative_validate_growth_spline_bt == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_growth_spline_bt),
as.numeric(input$y_range_max_derivative_validate_growth_spline_bt))
}
if(any(input$x_range_min_validate_growth_spline_bt == "",
input$x_range_max_validate_growth_spline_bt == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_growth_spline_bt),
as.numeric(input$x_range_max_validate_growth_spline_bt))
}
if(length(results$gcFit$gcBootSplines[[ifelse(selected_vals_validate_growth$sample_validate_growth_spline_bt == "1"||
selected_vals_validate_growth$sample_validate_growth_spline_bt == ""||
is.null(selected_vals_validate_growth$sample_validate_growth_spline_bt), 1, selected_vals_validate_growth$sample_validate_growth_spline_bt)]]) > 1){
try(
plot.gcBootSpline(results$gcFit$gcBootSplines[[ifelse(selected_vals_validate_growth$sample_validate_growth_spline_bt == "1"||
selected_vals_validate_growth$sample_validate_growth_spline_bt == ""||
is.null(selected_vals_validate_growth$sample_validate_growth_spline_bt), 1, selected_vals_validate_growth$sample_validate_growth_spline_bt)]],
pch = input$shape_type_validate_growth_spline_bt,
cex.point = input$shape_size_validate_growth_spline_bt,
cex.lab = input$axis_size_validate_growth_spline_bt,
cex.axis = input$lab_size_validate_growth_spline_bt,
lwd = input$line_width_validate_growth_spline_bt,
y.lim = y.lim,
x.lim = x.lim,
y.lim.deriv = ylim.deriv,
deriv = input$plot_derivative_growth_spline_bt,
combine = TRUE,
colSpline = input$color_validate_growth_plot_spline_bt
)
)
}
})
output$validate_growth_plot_spline_bt <- renderPlot({
validate_growth_plot_spline_bt()
})
output$download_growth_validate_spline_bt <- downloadHandler(
filename = function() {
paste("spline_fit_bootstrap_", gsub(" \\| ", "_", input$sample_validate_growth_spline_bt), input$format_download_growth_validate_spline_bt, sep="")
},
content = function(file) {
if(input$format_download_growth_validate_spline_bt == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_growth_validate_spline_bt,
height = input$height_download_growth_validate_spline_bt)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_growth_validate_spline_bt,
height = input$height_download_growth_validate_spline_bt)
}
} else {
grDevices::png(file = file,
width = input$width_download_growth_validate_spline_bt,
height = input$height_download_growth_validate_spline_bt,
units = "in",
res = input$dpi_download_growth_validate_spline_bt)
}
# Generate plot
results <- results$growth$gcFit$gcBootSplines[[ifelse(selected_vals_validate_growth$sample_validate_growth_spline_bt == "1" ||
is.null(selected_vals_validate_growth$sample_validate_growth_spline_bt),
1,
selected_vals_validate_growth$sample_validate_growth_spline_bt)]]
## Define x- and y-axis limits
if(any(input$y_range_min_validate_growth_spline_bt == "",
input$y_range_max_validate_growth_spline_bt == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_growth_spline_bt),
as.numeric(input$y_range_max_validate_growth_spline_bt))
}
if(any(input$y_range_min_derivative_validate_growth_spline_bt == "",
input$y_range_max_derivative_validate_growth_spline_bt == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_growth_spline_bt),
as.numeric(input$y_range_max_derivative_validate_growth_spline_bt))
}
if(any(input$x_range_min_validate_growth_spline_bt == "",
input$x_range_max_validate_growth_spline_bt == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_growth_spline_bt),
as.numeric(input$x_range_max_validate_growth_spline_bt))
}
plot.gcBootSpline(results,
pch = input$shape_type_validate_growth_spline_bt,
cex.point = input$shape_size_validate_growth_spline_bt,
cex.lab = input$axis_size_validate_growth_spline_bt,
cex.axis = input$lab_size_validate_growth_spline_bt,
lwd = input$line_width_validate_growth_spline_bt,
y.lim = y.lim,
x.lim = x.lim,
y.lim.deriv = ylim.deriv,
deriv = input$plot_derivative_growth_spline_bt,
combine = TRUE
)
dev.off()
},
contentType = ifelse(input$format_download_growth_validate_spline_bt == ".pdf", "image/pdf", "image/png")
)
## Fluorescence #####
observe({
if(!is.null(results$fluorescence)){
if(!("s" %in% results$fluorescence$control$fit.opt || "a" %in% results$fluorescence$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Validate_Fluorescence", target = "tabPanel_Validate_Fluorescence_Spline")
} else {
showTab(inputId = "tabsetPanel_Validate_Fluorescence", target = "tabPanel_Validate_Fluorescence_Spline")
}
if(!("l" %in% results$fluorescence$control$fit.opt || "a" %in% results$fluorescence$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Validate_Fluorescence", target = "tabPanel_Validate_Fluorescence_Linear")
} else {
showTab(inputId = "tabsetPanel_Validate_Fluorescence", target = "tabPanel_Validate_Fluorescence_Linear")
}
if(!("s" %in% results$fluorescence$control$fit.opt || "a" %in% results$fluorescence$control$fit.opt) || results$fluorescence$control$nboot.fl <=1){
hideTab(inputId = "tabsetPanel_Validate_Fluorescence", target = "tabPanel_Validate_Fluorescence_Spline_bt")
} else {
showTab(inputId = "tabsetPanel_Validate_Fluorescence", target = "tabPanel_Validate_Fluorescence_Spline_bt")
}
}
})
#### Hide [Restore Fit] buttons when starting the app
hide("restore_fluorescence_linear"); hide("restore_fluorescence_spline")
#### Hide [Restore Fit] buttons whenever a sample is changed
observeEvent(input$sample_validate_fluorescence_linear, {
hide("restore_fluorescence_linear")
})
observeEvent(input$sample_validate_fluorescence_spline, {
hide("restore_fluorescence_spline")
})
#------- Initialize the Memory to store settings
selected_vals_validate_fluorescence <- reactiveValues(sample_validate_fluorescence_linear = 1,
sample_validate_fluorescence_spline = 1,
sample_validate_fluorescence_spline_bt = 1
)
#------ Whenever any of the inputs are changed, it only modifies the memory
observe({
req(input$sample_validate_fluorescence_linear)
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear <- input$sample_validate_fluorescence_linear
})
observe({
req(input$sample_validate_fluorescence_spline)
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline <- input$sample_validate_fluorescence_spline
})
observe({
req(input$sample_validate_fluorescence_spline_bt)
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt <- input$sample_validate_fluorescence_spline_bt
})
### Linear Fits ####
selected_inputs_validate_fluorescence_linear_sample <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
if("l" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
select_samples <- names(results$flFit$flFittedLinear)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_fluorescence_linear",
choices = selected_inputs_validate_fluorescence_linear_sample(),
selected = selected_vals_validate_fluorescence$sample_validate_fluorescence_linear
)})
logy_validate_fluorescence_plot_linear <- reactive({
if(input$logy_validate_fluorescence_plot_linear) return("y")
else return("")
})
# Render plot
output$validate_fluorescence_plot_linear <- renderPlot({
results <- results$fluorescence
# Define x- and y-axis limits
if(any(input$y_range_min_validate_fluorescence_plot_linear == "",
input$y_range_max_validate_fluorescence_plot_linear == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_fluorescence_plot_linear),
as.numeric(input$y_range_max_validate_fluorescence_plot_linear))
}
if(any(input$y_range_min_derivative_validate_fluorescence_plot_linear == "",
input$y_range_max_derivative_validate_fluorescence_plot_linear == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_fluorescence_plot_linear),
as.numeric(input$y_range_max_derivative_validate_fluorescence_plot_linear))
}
if(any(input$x_range_min_validate_fluorescence_plot_linear == "",
input$x_range_max_validate_fluorescence_plot_linear == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_fluorescence_plot_linear),
as.numeric(input$x_range_max_validate_fluorescence_plot_linear))
}
if(length(results$flFit$flFittedLinear[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1"||
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear), 1, selected_vals_validate_fluorescence$sample_validate_fluorescence_linear)]]) > 1){
try(
suppressWarnings(
plot.flFitLinear(results$flFit$flFittedLinear[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1" || is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear), 1, selected_vals_validate_fluorescence$sample_validate_fluorescence_linear)]],
log = logy_validate_fluorescence_plot_linear(),
pch = input$shape_type_validate_fluorescence_plot_linear,
cex.point = input$shape_size_validate_fluorescence_plot_linear,
cex.lab = input$axis_size_validate_fluorescence_plot_linear,
cex.axis = input$lab_size_validate_fluorescence_plot_linear,
lwd = input$line_width_validate_fluorescence_plot_linear,
y.lim = y.lim,
x.lim = x.lim,
color = input$color_validate_fluorescence_plot_linear
# ADD FURTHER INPUT (see Notion)
)
)
)
if(input$diagnostics_validate_fluorescence_plot_linear){
try(
suppressWarnings(
plot.flFitLinear(results$flFit$flFittedLinear[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear),
1,
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear)]],
which = "fit_diagnostics",
log = logy_validate_fluorescence_plot_linear(),
pch = input$shape_type_validate_fluorescence_plot_linear,
cex.point = input$shape_size_validate_fluorescence_plot_linear,
cex.lab = input$axis_size_validate_fluorescence_plot_linear,
cex.axis = input$lab_size_validate_fluorescence_plot_linear,
lwd = input$line_width_validate_fluorescence_plot_linear,
y.lim = y.lim,
x.lim = x.lim,
color = input$color_validate_fluorescence_plot_linear
# ADD FURTHER INPUT (see Notion)
)
)
)
}
}
})
# lin.rerun.param <- reactiveValues()
observeEvent(input$rerun_fluorescence_linear, {
# display a modal dialog with a header, textinput and action buttons
if(results$fluorescence$flFit$flFittedLinear[[ifelse(
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1" ||
is.null(
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear
),
1,
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear
)]]$control$x_type == "time"){
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
textInput('t0.lin.rerun.fluorescence', 'Minimum time (t0)', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$t0)),
textInput('tmax.lin.rerun.fluorescence', 'Maximum time (tmax)', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$tmax)),
textAreaInput('quota.rerun.fluorescence', 'Quota', placeholder = HTML(paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$quota,
"\n",
"include regression windows with slope = ", expression(µ[max]), " * quota into the final linear fit."))),
textInput('lin.h.rerun.fluorescence', 'Sliding window size (h)', placeholder = paste0("previously: ",
ifelse(!is.null(results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.h),
results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.h,
"NULL"))),
textInput('lin.R2.rerun.fluorescence', 'R2 threshold', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.R2)),
textInput('lin.RSD.rerun.fluorescence', 'RSD threshold for slope', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.RSD)),
footer=tagList(
fluidRow(
column(12,
div(
actionButton(inputId = "submit.rerun.linear.fluorescence",
label = "Submit"),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_flFitLinear_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
} else {
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
textInput('min.growth.lin.rerun.fluorescence', 'Minimum growth measurement', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$min.growth)),
textInput('max.growth.lin.rerun.fluorescence', 'Maximum growth measurement', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$max.growth)),
textAreaInput('quota.rerun.fluorescence', 'Quota', placeholder = HTML(paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$quota,
"\n",
"include regression windows with slope = ", expression(µ[max]), " * quota into the final linear fit."))),
textInput('lin.h.rerun.fluorescence', 'Sliding window size (h)', placeholder = paste0("previously: ",
ifelse(!is.null(results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.h),
results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.h,
"NULL"))),
textInput('lin.R2.rerun.fluorescence', 'R2 threshold', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.R2)),
textInput('lin.RSD.rerun.fluorescence', 'RSD threshold for slope', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.RSD)),
footer=tagList(
fluidRow(
column(12,
div(
actionButton(inputId = "submit.rerun.linear.fluorescence",
label = "Submit"),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_flFitLinear_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
}
})
# Re-run selected linear fit with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.linear.fluorescence, {
if(!is.null(results$fluorescence$flFit)){
showModal(modalDialog("Fitting sample data...", footer = NULL))
# store previous fit in memory
selected_vals_validate_fluorescence$restore_fluorescence_linear <- results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]
# Re-run fit and store in results object
actwell <- results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$raw.fl
acttime <- results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$raw.x
control <- results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control
control_new <- control
ID <- results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$ID
lin.h.new <- ifelse(!is.na(as.numeric(input$lin.h.rerun.fluorescence)), as.numeric(input$lin.h.rerun.fluorescence), control$lin.h)
if(!is.na(lin.h.new)) control_new$lin.h <- lin.h.new
control_new$lin.R2 <- ifelse(!is.na(as.numeric(input$lin.R2.rerun.fluorescence)), as.numeric(input$lin.R2.rerun.fluorescence), control$lin.R2)
control_new$lin.RSD <- ifelse(!is.na(as.numeric(input$lin.RSD.rerun.fluorescence)), as.numeric(input$lin.RSD.rerun.fluorescence), control$lin.RSD)
control_new$t0 <- ifelse(!is.na(input$t0.lin.rerun.fluorescence) && !is.null(input$t0.lin.rerun.fluorescence) && input$t0.lin.rerun.fluorescence != "",
as.numeric(input$t0.lin.rerun.fluorescence),
control$t0)
control_new$tmax <- ifelse(!is.na(input$tmax.lin.rerun.fluorescence) && !is.null(input$tmax.lin.rerun.fluorescence) && input$tmax.lin.rerun.fluorescence != "",
as.numeric(input$tmax.lin.rerun.fluorescence),
control$tmax)
min.growth.lin.new <- ifelse(!is.na(input$min.growth.lin.rerun.fluorescence) && !is.null(input$min.growth.lin.rerun.fluorescence) && input$min.growth.lin.rerun.fluorescence != "",
as.numeric(input$min.growth.lin.rerun.fluorescence),
control$min.growth)
if(is.numeric(min.growth.lin.new)){
if(!is.na(min.growth.lin.new) && all(as.vector(actwell) < min.growth.lin.new)){
showModal(
modalDialog(paste0("Start growth values need to be greater than 'min.growth'.\nThe minimum start value in your dataset is: ",
min(as.vector(actwell)),". 'min.growth' was not adjusted."), easyClose = T)
)
} else if(!is.na(min.growth.lin.new)){
control_new$min.growth <- min.growth.lin.new
}
}
control_new$max.growth <- ifelse(!is.na(as.numeric(input$max.growth.lin.rerun.fluorescence)), as.numeric(input$max.growth.lin.rerun.fluorescence), control$max.growth)
quota_new <- ifelse(!is.na(as.numeric(input$quota.rerun.fluorescence)), as.numeric(input$quota.rerun.fluorescence), 0.95)
if(control$x_type == "time"){
try(
results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]] <-
flFitLinear(time = acttime, fl_data = actwell,
ID = ID,
control = control_new,
quota = quota_new)
)
} else {
try(
results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]] <-
flFitLinear(growth = acttime, fl_data = actwell,
ID = ID,
control = control_new,
quota = quota_new)
)
}
# Update gcTable with new results
res.table.fl <- results$fluorescence$flFit$flTable
fit.summary <- QurvE:::summary.flFitLinear(results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]])
sample.ndx <- ifelse(is.numeric(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear),
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear,
match(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear, names(results$fluorescence$flFit$flFittedLinear)))
results$fluorescence$flFit$flTable[sample.ndx, colnames(res.table.fl) %in% colnames(fit.summary)] <- fit.summary
# Re-run drFit if respective fit type (linear/spline) was chosen as dr.parameter
dr.parameter.fit.method <- gsub(".+\\.", "", results$fluorescence$control$dr.parameter)
if(length(results$fluorescence$drFit) > 1 && dr.parameter.fit.method == "linfit"){
# Store previous drFit in memory
selected_vals_validate_fluorescence$restore_fluorescence_drFit <- results$fluorescence$drFit
# Re-run drFit
if (is.null(input$smoothing_factor_fluorescence_dr) || is.na(input$smoothing_factor_fluorescence_dr) || input$smoothing_factor_fluorescence_dr == "NULL" || input$smoothing_factor_fluorescence_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_fluorescence_dr)
}
if (is.null(input$number_of_bootstrappings_dr_fluorescence) || is.na(input$number_of_bootstrappings_dr_fluorescence) || input$number_of_bootstrappings_dr_fluorescence == "NULL" || input$number_of_bootstrappings_dr_fluorescence == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_fluorescence)
}
showModal(modalDialog("Re-calculating dose-response analysis...", footer = NULL))
try(
results$fluorescence$drFit <-
suppressWarnings(
fl.drFit(flTable = results$fluorescence$flFit$flTable,
control = fl.control(dr.method = input$dr_method_fluorescence,
dr.parameter = results$fluorescence$control$dr.parameter,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_fluorescence,
log.y.dr = input$log_transform_response_fluorescence,
nboot.dr = nboot.dr
)
)
)
)
}
# Show [Restore fit] button
show("restore_fluorescence_linear")
}
removeModal()
})
# Restore previous linear fit upon click on [Restore Fit]
observeEvent(input$restore_fluorescence_linear, {
# store previous fit from memory
results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]] <- selected_vals_validate_fluorescence$restore_fluorescence_linear
dr.parameter.fit.method <- gsub(".+\\.", "", results$fluorescence$control$dr.parameter)
if(length(results$fluorescence$drFit) > 1 && dr.parameter.fit.method == "linfit"){
results$fluorescence$drFit <- selected_vals_validate_fluorescence$restore_fluorescence_drFit
}
hide("restore_fluorescence_linear")
})
output$download_fluorescence_validate_linear <- downloadHandler(
filename = function() {
paste("linear_fit_", gsub(" \\| ", "_", selected_vals_validate_fluorescence$sample_validate_fluorescence_linear), input$format_download_fluorescence_validate_linear, sep="")
},
content = function(file) {
if(input$format_download_fluorescence_validate_linear == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_fluorescence_validate_linear,
height = input$height_download_fluorescence_validate_linear)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_fluorescence_validate_linear,
height = input$height_download_fluorescence_validate_linear)
}
} else {
grDevices::png(file = file,
width = input$width_download_fluorescence_validate_linear,
height = input$height_download_fluorescence_validate_linear,
units = "in",
res = input$dpi_download_fluorescence_validate_linear)
}
if(input$logy_validate_fluorescence_plot_linear) log <- "y"
else log <- ""
results <- results$fluorescence
# Define x- and y-axis limits
if(any(input$y_range_min_validate_fluorescence_plot_linear == "",
input$y_range_max_validate_fluorescence_plot_linear == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_fluorescence_plot_linear),
as.numeric(input$y_range_max_validate_fluorescence_plot_linear))
}
if(any(input$y_range_min_derivative_validate_fluorescence_plot_linear == "",
input$y_range_max_derivative_validate_fluorescence_plot_linear == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_fluorescence_plot_linear),
as.numeric(input$y_range_max_derivative_validate_fluorescence_plot_linear))
}
if(any(input$x_range_min_validate_fluorescence_plot_linear == "",
input$x_range_max_validate_fluorescence_plot_linear == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_fluorescence_plot_linear),
as.numeric(input$x_range_max_validate_fluorescence_plot_linear))
}
if(length(results$flFit$flFittedLinear[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1" ||
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear),
1,
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear)]]) > 1){
suppressWarnings(
plot.flFitLinear(results$flFit$flFittedLinear[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1" || is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear), 1, selected_vals_validate_fluorescence$sample_validate_fluorescence_linear)]],
log = log,
pch = input$shape_type_validate_fluorescence_plot_linear,
cex.point = input$shape_size_validate_fluorescence_plot_linear,
cex.lab = input$axis_size_validate_fluorescence_plot_linear,
cex.axis = input$lab_size_validate_fluorescence_plot_linear,
lwd = input$line_width_validate_fluorescence_plot_linear,
y.lim = y.lim,
x.lim = x.lim
# ADD FURTHER INPUT (see Notion)
)
)
if(input$diagnostics_validate_fluorescence_plot_linear){
suppressWarnings(
plot.flFitLinear(results$flFit$flFittedLinear[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1"||
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == ""||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear), 1, selected_vals_validate_fluorescence$sample_validate_fluorescence_linear)]],
which = "fit_diagnostics",
log = log,
pch = input$shape_type_validate_fluorescence_plot_linear,
cex.point = input$shape_size_validate_fluorescence_plot_linear,
cex.lab = input$axis_size_validate_fluorescence_plot_linear,
cex.axis = input$lab_size_validate_fluorescence_plot_linear,
lwd = input$line_width_validate_fluorescence_plot_linear,
y.lim = y.lim,
x.lim = x.lim
# ADD FURTHER INPUT (see Notion)
)
)
}
}
dev.off()
},
contentType = ifelse(input$format_download_fluorescence_validate_linear == ".pdf", "image/pdf", "image/png")
)
### Spline Fits ####
selected_inputs_validate_fluorescence_spline_sample <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
if("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
select_samples <- names(results$flFit$flFittedSplines)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_fluorescence_spline",
choices = selected_inputs_validate_fluorescence_spline_sample(),
selected = selected_vals_validate_fluorescence$sample_validate_fluorescence_spline
)})
output$validate_fluorescence_plot_spline <- renderPlot({
results <- results$fluorescence
if(length(results$flFit$flFittedSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "1"||
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
1,
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline)]]) > 1){
showModal(modalDialog("Creating plot...", footer=NULL))
try(
suppressWarnings(
plot.flFitSpline(results$flFit$flFittedSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "1" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
1,
input$sample_validate_fluorescence_spline)]],
log.y = input$logy_validate_fluorescence_plot_spline,
x.lim = c(input$x_range_min_validate_fluorescence_plot_spline, input$x_range_max_validate_fluorescence_plot_spline),
y.lim = c(input$y_range_min_validate_fluorescence_plot_spline,input$y_range_max_validate_fluorescence_plot_spline),
y.lim.deriv = c(input$y_range_min_derivative_validate_fluorescence_plot_spline, input$y_range_max_derivative_validate_fluorescence_plot_spline),
lwd = input$line_width_validate_fluorescence_plot_spline,
basesize = input$base_size_validate_fluorescence_plot_spline,
cex.point = input$shape_size_validate_fluorescence_plot_spline,
n.ybreaks = input$nbreaks__validate_fluorescence_plot_spline,
deriv = input$plot_derivative_validate_fluorescence_plot_spline,
pch = input$shape_type_validate_fluorescence_plot_spline,
colSpline = input$color_validate_fluorescence_plot_spline
)
)
)
removeModal()
}
})
spline.rerun.param.fluorescence <- reactiveValues()
observeEvent(input$rerun_fluorescence_spline, {
# display a modal dialog with a header, textinput and action buttons
if (results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$x_type == "time") {
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
textInput('t0.spline.rerun.fluorescence', 'Minimum time (t0)', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$t0)),
textInput('tmax.spline.rerun.fluorescence', 'Maximum time (tmax)', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$tmax)),
textInput('smooth.fl.rerun.fluorescence', 'Smoothing factor', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$smooth.fl)),
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.spline.fluorescence', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_flFitSpline_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
} else {
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
textInput('min.growth.spline.rerun.fluorescence', 'Minimum growth measurement', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$min.growth)),
textInput('max.growth.spline.rerun.fluorescence', 'Maximum growth measurement', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$max.growth)),
textInput('smooth.fl.rerun.fluorescence', 'Smoothing factor', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$smooth.fl)),
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.spline.fluorescence', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_flFitSpline_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
}
})
# Re-run selected spline fit with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.spline.fluorescence, {
if(!is.null(results$fluorescence$flFit)){
showModal(modalDialog("Fitting sample data...", footer = NULL))
# store previous fit in memory
selected_vals_validate_fluorescence$restore_fluorescence_spline <- results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]
# Re-run fit and store in results object
actwell <- results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$fl.in
acttime <- results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$x.in
control <- results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control
control_new <- control
ID <- results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$ID
control_new$smooth.fl <- ifelse(!is.na(as.numeric(input$smooth.fl.rerun.fluorescence)), as.numeric(input$smooth.fl.rerun.fluorescence), control$smooth.fl)
control_new$t0 <- ifelse(!is.na(input$t0.spline.rerun.fluorescence) && !is.null(input$t0.spline.rerun.fluorescence) && input$t0.spline.rerun.fluorescence != "",
as.numeric(input$t0.spline.rerun.fluorescence),
control$t0)
control_new$tmax <- ifelse(!is.na(input$tmax.spline.rerun.fluorescence) && !is.null(input$tmax.spline.rerun.fluorescence) && input$tmax.spline.rerun.fluorescence != "",
as.numeric(input$tmax.spline.rerun.fluorescence),
control$tmax)
min.growth.spline.new <- ifelse(!is.na(as.numeric(input$min.growth.spline.rerun.fluorescence)) && !is.null(input$min.growth.spline.rerun.fluorescence),
as.numeric(input$min.growth.spline.rerun.fluorescence),
control$min.growth)
control_new$max.growth <- ifelse(!is.na(as.numeric(input$max.growth.spline.rerun.fluorescence)) && !is.null(input$max.growth.spline.rerun.fluorescence),
as.numeric(input$max.growth.spline.rerun.fluorescence),
control$max.growth)
if(is.numeric(min.growth.spline.new)){
if(!is.na(min.growth.spline.new) && all(as.vector(actwell) < min.growth.spline.new)){
message(paste0("Start growth values need to be greater than 'min.growth'.\nThe minimum start value in your dataset is: ",
min(as.vector(actwell)),". 'min.growth' was not adjusted."), call. = FALSE)
} else if(!is.na(min.growth.spline.new)){
control_new$min.growth <- min.growth.spline.new
}
}
if(control$x_type == "time"){
try(
results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]] <-
flFitSpline(time = acttime, fl_data = actwell,
ID = ID,
control = control_new)
)
} else {
try(
results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]] <-
flFitSpline(growth = acttime, fl_data = actwell,
ID = ID,
control = control_new)
)
}
# Update gcTable with new results
res.table.fl <- results$fluorescence$flFit$flTable
fit.summary <- QurvE:::summary.flFitSpline(results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]])
sample.ndx <- ifelse(is.numeric(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline,
match(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline, names(results$fluorescence$flFit$flFittedSplines)))
results$fluorescence$flFit$flTable[sample.ndx, colnames(res.table.fl) %in% colnames(fit.summary)] <- fit.summary
# Re-run drFit if respective fit type (linear/spline) was chosen as dr.parameter
dr.parameter.fit.method <- gsub(".+\\.", "", results$fluorescence$control$dr.parameter)
if(length(results$fluorescence$drFit) > 1 && dr.parameter.fit.method == "spline"){
# Store previous drFit in memory
selected_vals_validate_fluorescence$restore_fluorescence_drFit <- results$fluorescence$drFit
# Re-run drFit
if (is.null(input$smoothing_factor_fluorescence_dr) || is.na(input$smoothing_factor_fluorescence_dr) || input$smoothing_factor_fluorescence_dr == "NULL" || input$smoothing_factor_fluorescence_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_fluorescence_dr)
}
if (is.null(input$number_of_bootstrappings_dr_fluorescence) || is.na(input$number_of_bootstrappings_dr_fluorescence) || input$number_of_bootstrappings_dr_fluorescence == "NULL" || input$number_of_bootstrappings_dr_fluorescence == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_fluorescence)
}
showModal(modalDialog("Re-calculating dose-response analysis...", footer = NULL))
try(
results$fluorescence$drFit <-
suppressWarnings(
fl.drFit(flTable = results$fluorescence$flFit$flTable,
control = fl.control(dr.method = input$dr_method_fluorescence,
dr.parameter = results$fluorescence$control$dr.parameter,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_fluorescence,
log.y.dr = input$log_transform_response_fluorescence,
nboot.dr = nboot.dr
)
)
)
)
}
# Show [Restore fit] button
show("restore_fluorescence_spline")
}
removeModal()
})
# Restore previous spline fit upon click on [Restore Fit]
observeEvent(input$restore_fluorescence_spline, {
# store previous fit from memory
results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]] <- selected_vals_validate_fluorescence$restore_fluorescence_spline
dr.parameter.fit.method <- gsub(".+\\.", "", results$fluorescence$control$dr.parameter)
if(length(results$fluorescence$drFit) > 1 && dr.parameter.fit.method == "spline"){
results$fluorescence$drFit <- selected_vals_validate_fluorescence$restore_fluorescence_drFit
}
hide("restore_fluorescence_spline")
})
output$download_fluorescence_validate_spline <- downloadHandler(
filename = function() {
paste("spline_fit_", gsub(" \\| ", "_", selected_vals_validate_fluorescence$sample_validate_fluorescence_spline), input$format_download_fluorescence_validate_spline, sep="")
},
content = function(file) {
if(input$format_download_fluorescence_validate_spline == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_fluorescence_validate_spline,
height = input$height_download_fluorescence_validate_spline,
dpi = input$dpi_download_fluorescence_validate_spline,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_fluorescence_validate_spline,
height = input$height_download_fluorescence_validate_spline,
dpi = input$dpi_download_fluorescence_validate_spline,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_fluorescence_validate_spline,
height = input$height_download_fluorescence_validate_spline,
dpi = input$dpi_download_fluorescence_validate_spline,
device = png)
}
},
contentType = ifelse(input$format_download_fluorescence_validate_spline == ".pdf", "image/pdf", "image/png")
)
output$download_fluorescence_validate_spline_values <- downloadHandler(
filename = function() {
paste("values_spline_fit_", gsub(" \\| ", "_", selected_vals_validate_fluorescence$sample_validate_fluorescence_spline), ".csv", sep="")
},
content = function(file) {
results <- results$fluorescence
if(length(results$flFit$flFittedSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "1" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
1,
input$sample_validate_fluorescence_spline)]]) > 1) {
try(p <-
suppressWarnings(
plot.flFitSpline(
results$flFit$flFittedSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "1" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
1,
input$sample_validate_fluorescence_spline)]],
export = FALSE,
plot = FALSE,
log.y = FALSE,
deriv = F
)
))
}
table <- data.frame("x" = as.numeric(p$data$fit.x), "y" = as.numeric(p$data$fit.fl))
QurvE:::write.csv.utf8.BOM(table, file)
},
contentType = ".csv"
)
output$download_fluorescence_validate_spline_deriv_values <- downloadHandler(
filename = function() {
paste("values_spline_fit_derivative_", gsub(" \\| ", "_", selected_vals_validate_fluorescence$sample_validate_fluorescence_spline), ".csv", sep="")
},
content = function(file) {
results <- results$fluorescence
if (length(results$flFit$flFittedSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "1" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
1,
input$sample_validate_fluorescence_spline)]]) > 1) {
table <- as.data.frame(results$flFit$flFittedSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "1" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
1,
input$sample_validate_fluorescence_spline)]]$spline.deriv1)
}
QurvE:::write.csv.utf8.BOM(table, file)
},
contentType = ".csv"
)
### Spline Fits BT ####
selected_inputs_sample_validate_fluorescence_spline_bt <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
if(("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt) && results$control$nboot.fl > 1){
select_samples <- names(results$flFit$flBootSplines)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_fluorescence_spline_bt",
choices = selected_inputs_sample_validate_fluorescence_spline_bt(),
selected = selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt
)})
validate_fluorescence_plot_spline_bt <- reactive({
results <- results$fluorescence$flFit$flBootSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt == "1"||
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt == "" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt),
1,
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt)]]
# Define x- and y-axis limits
if(any(input$y_range_min_validate_fluorescence_spline_bt == "",
input$y_range_max_validate_fluorescence_spline_bt == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_fluorescence_spline_bt),
as.numeric(input$y_range_max_validate_fluorescence_spline_bt))
}
if(any(input$y_range_min_derivative_validate_fluorescence_spline_bt == "",
input$y_range_max_derivative_validate_fluorescence_spline_bt == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_fluorescence_spline_bt),
as.numeric(input$y_range_max_derivative_validate_fluorescence_spline_bt))
}
if(any(input$x_range_min_validate_fluorescence_spline_bt == "",
input$x_range_max_validate_fluorescence_spline_bt == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_fluorescence_spline_bt),
as.numeric(input$x_range_max_validate_fluorescence_spline_bt))
}
try(
plot.flBootSpline(results,
pch = input$shape_type_validate_fluorescence_spline_bt,
cex.point = input$shape_size_validate_fluorescence_spline_bt,
cex.lab = input$axis_size_validate_fluorescence_spline_bt,
cex.axis = input$lab_size_validate_fluorescence_spline_bt,
lwd = input$line_width_validate_fluorescence_spline_bt,
y.lim = y.lim,
x.lim = x.lim,
y.lim.deriv = ylim.deriv,
deriv = input$plot_derivative_fluorescence_spline_bt,
combine = T,
colSpline = input$color_validate_fluorescence_spline_bt
)
)
})
output$validate_fluorescence_plot_spline_bt <- renderPlot({
validate_fluorescence_plot_spline_bt()
})
output$download_fluorescence_validate_spline_bt <- downloadHandler(
filename = function() {
paste("spline_fit_bootstrap_", gsub(" \\| ", "_", input$sample_validate_fluorescence_spline_bt), input$format_download_fluorescence_validate_spline_bt, sep="")
},
content = function(file) {
if(input$format_download_fluorescence_validate_spline_bt == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_fluorescence_validate_spline_bt,
height = input$height_download_fluorescence_validate_spline_bt)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_fluorescence_validate_spline_bt,
height = input$height_download_fluorescence_validate_spline_bt)
}
} else {
grDevices::png(file = file,
width = input$width_download_fluorescence_validate_spline_bt,
height = input$height_download_fluorescence_validate_spline_bt,
units = "in",
res = input$dpi_download_fluorescence_validate_spline_bt)
}
# Generate plot
results <-
results$fluorescence$flFit$flBootSplines[[ifelse(
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt == "1" ||
is.null(
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt
),
1,
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt
)]]
# Define x- and y-axis limits
if(any(input$y_range_min_validate_fluorescence_spline_bt == "",
input$y_range_max_validate_fluorescence_spline_bt == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_fluorescence_spline_bt),
as.numeric(input$y_range_max_validate_fluorescence_spline_bt))
}
if(any(input$y_range_min_derivative_validate_fluorescence_spline_bt == "",
input$y_range_max_derivative_validate_fluorescence_spline_bt == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_fluorescence_spline_bt),
as.numeric(input$y_range_max_derivative_validate_fluorescence_spline_bt))
}
if(any(input$x_range_min_validate_fluorescence_spline_bt == "",
input$x_range_max_validate_fluorescence_spline_bt == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_fluorescence_spline_bt),
as.numeric(input$x_range_max_validate_fluorescence_spline_bt))
}
plot.flBootSpline(results,
pch = input$shape_type_validate_fluorescence_spline_bt,
cex.point = input$shape_size_validate_fluorescence_spline_bt,
cex.lab = input$axis_size_validate_fluorescence_spline_bt,
cex.axis = input$lab_size_validate_fluorescence_spline_bt,
lwd = input$line_width_validate_fluorescence_spline_bt,
y.lim = y.lim,
x.lim = x.lim,
y.lim.deriv = ylim.deriv,
deriv = input$plot_derivative_fluorescence_spline_bt
)
dev.off()
},
contentType = ifelse(input$format_download_fluorescence_validate_spline_bt == ".pdf", "image/pdf", "image/png")
)
# Visualize ####
## Growth Plots: #####
### Group Plots ####
selected_inputs_visualize_growth_group <- reactive({
results <- results$growth
if(is.null(results)) return("")
if(input$plot_group_averages_growth_group_plot){
select_samples <- results$expdesign$condition
} else {
select_samples <- results$expdesign$label
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_growth_group",
choices = selected_inputs_visualize_growth_group()
)
})
observe({
updateSelectInput(session,
inputId = "groups_visualize_growth_group",
choices = selected_inputs_visualize_growth_group()
)
})
growth_group_plot <- reactive({
results <- results$growth
if(is.null(input$custom_colors_group_plot) || is.na(input$custom_colors_group_plot) || input$custom_colors_group_plot == ""){
cols <- NULL
} else {
cols <- toupper(unlist(str_split(input$custom_colors_group_plot, ", |; |,|;")))
}
if(input$select_string_visualize_growth_group){
suppressWarnings(
plot.grofit(results,
data.type = input$data_type_growth_group_plot,
IDs = NULL,
names = input$select_samples_based_on_string_growth_group_plot,
conc = input$select_samples_based_on_concentration_growth_group_plot,
exclude.nm = input$exclude_samples_based_on_string_growth_group_plot,
exclude.conc = input$exclude_samples_based_on_concentration_growth_group_plot,
mean = input$plot_group_averages_growth_group_plot,
deriv = input$plot_derivative_growth_group_plot,
log.y = input$log_transform_y_axis_growth_group_plot,
x.lim = c(input$x_range_min_growth_group_plot, input$x_range_max_growth_group_plot),
y.lim = c(input$y_range_min_growth_group_plot,input$y_range_max_growth_group_plot),
y.lim.deriv = c(input$y_range_min_derivative_growth_group_plot, input$y_range_max_derivative_growth_group_plot),
y.title = input$y_axis_title_growth_group_plot,
x.title = input$x_axis_title_growth_group_plot,
y.title.deriv = input$y_axis_title_derivative_growth_group_plot,
n.ybreaks = input$nbreaks_growth_group_plot,
lwd = input$line_width_growth_group_plot,
basesize = input$base_size_growth_group_plot,
legend.position = input$legend_position_group_plot,
legend.ncol = input$legend_ncol_group_plot,
color_groups = input$color_groups_group_plot,
group_pals = input$color_palettes_group_plot,
colors = cols
)
)
}
else{
suppressWarnings(
plot.grofit(results,
data.type = input$data_type_growth_group_plot,
IDs = if(input$plot_group_averages_growth_group_plot){
input$groups_visualize_growth_group
}else{
input$samples_visualize_growth_group
},
names = NULL,
conc = input$select_samples_based_on_concentration_growth_group_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_samples_based_on_concentration_growth_group_plot,
mean = input$plot_group_averages_growth_group_plot,
deriv = input$plot_derivative_growth_group_plot,
log.y = input$log_transform_y_axis_growth_group_plot,
x.lim = c(input$x_range_min_growth_group_plot, input$x_range_max_growth_group_plot),
y.lim = c(input$y_range_min_growth_group_plot,input$y_range_max_growth_group_plot),
y.lim.deriv = c(input$y_range_min_derivative_growth_group_plot, input$y_range_max_derivative_growth_group_plot),
y.title = input$y_axis_title_growth_group_plot,
x.title = input$x_axis_title_growth_group_plot,
y.title.deriv = input$y_axis_title_derivative_growth_group_plot,
n.ybreaks = input$nbreaks_growth_group_plot,
lwd = input$line_width_growth_group_plot,
basesize = input$base_size_growth_group_plot,
legend.position = input$legend_position_group_plot,
color_groups = input$color_groups_group_plot,
group_pals = input$color_palettes_group_plot,
legend.ncol = input$legend_ncol_group_plot,
colors = cols
)
)
}
})
output$growth_group_plot <- renderPlot({
growth_group_plot()
})
observe({
if(input$plot_derivative_growth_group_plot && input$data_type_growth_group_plot == 'spline') h <- 9
else h <- 6
updateSelectInput(inputId = "height_download_growth_group_plot",
selected = h
)
})
output$download_growth_group_plot <- downloadHandler(
filename = function() {
paste("growth_group_plot", input$format_download_growth_group_plot, sep="")
},
content = function(file) {
if(input$format_download_growth_group_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_growth_group_plot,
height = input$height_download_growth_group_plot,
dpi = input$dpi_download_growth_group_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_growth_group_plot,
height = input$height_download_growth_group_plot,
dpi = input$dpi_download_growth_group_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_growth_group_plot,
height = input$height_download_growth_group_plot,
dpi = input$dpi_download_growth_group_plot,
device = png)
}
},
contentType = ifelse(input$format_download_growth_group_plot == ".pdf", "image/pdf", "image/png")
)
### DR Plots Spline ####
observe({
if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1 && results$growth$drFit$control$dr.method == "spline"){
showTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse_Spline")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse_Spline")
}
})
output$more_than_one_drfit_spline <- reactive({
if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drFittedSplines) > 1) return(TRUE)
else return(FALSE)
})
outputOptions(output, 'more_than_one_drfit_spline', suspendWhenHidden=FALSE)
dose_response_growth_plot_combined <- reactive({
results <- results$growth$drFit
plot.drFit(results,
combine = TRUE,
pch = input$shape_type_dose_response_growth_plot,
names = input$select_samples_based_on_string_dose_response_growth_plot,
exclude.nm = input$exclude_samples_based_on_string_dose_response_growth_plot,
y.lim = c(as.numeric(input$y_range_min_dose_response_growth_plot), as.numeric(input$y_range_max_dose_response_growth_plot)),
x.lim = c(as.numeric(input$x_range_min_dose_response_growth_plot), as.numeric(input$x_range_max_dose_response_growth_plot)),
y.title = input$y_axis_title_dose_response_growth_plot,
x.title = input$x_axis_title_dose_response_growth_plot,
cex.point = input$shape_size_dose_response_growth_plot,
basesize = input$base_size_dose_response_growth_plot,
lwd = input$line_width_dose_response_growth_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_growth_plot,
log.y = input$log_transform_y_axis_dose_response_growth_plot,
log.x = input$log_transform_x_axis_dose_response_growth_plot
)
})
rerun_dr_growth_buttons <- reactive({
list(input$rerun_dr_growth, input$rerun_dr_growth2, input$rerun_dr_growth3)
})
observeEvent(rerun_dr_growth_buttons(), {
if(input$rerun_dr_growth==0 && input$rerun_dr_growth2==0 && input$rerun_dr_growth3==0){
return()
}
select_options <- c()
if(any("l" %in% results$growth$control$fit.opt)) select_options <- c(select_options, 'mu.linfit', 'lambda.linfit', 'dY.linfit',
'A.linfit')
if(any("s" %in% results$growth$control$fit.opt)) select_options <- c(select_options, 'mu.spline', 'lambda.spline',
'A.spline', 'dY.spline', 'integral.spline')
if(any("m" %in% results$growth$control$fit.opt)) select_options <- c(select_options, 'mu.model', 'lambda.model', 'A.model', 'integral.model')
select_options
# display a modal dialog with a header, textinput and action buttons
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
selectInput(inputId = "dr_method_growth_rerun",
label = "Method",
choices = c("Dose-response models" = "model",
"Response spline fit" = "spline")
),
bsPopover(id = "dr_method_growth_rerun",
title = HTML("<em>dr.method</em>"),
placement = "right",
content = "Fit either a various dose-response models (Ritz et al., 2015) to response-vs.-concentration data and select the best model based on the lowest AIC, or apply a nonparametric (spline) fit."),
selectInput(inputId = "response_parameter_growth_rerun",
label = "Response Parameter",
choices = select_options),
bsPopover(id = "response_parameter_growth_rerun", title = HTML("<em>dr.parameter</em>"), content = "Choose the response parameter to be used for creating a dose response curve.", placement = "top"),
conditionalPanel(
condition = 'input.dr_method_growth_rerun == "spline"',
tags$div(title="Perform a log(x+1) transformation on concentration values.",
checkboxInput(inputId = 'log_transform_concentration_growth_rerun',
label = 'Log transform concentration')
),
tags$div(title="Perform a log(y+1) transformation on response values.",
checkboxInput(inputId = 'log_transform_response_growth_rerun',
label = 'Log transform response')
),
textInput(
inputId = 'smoothing_factor_growth_dr_rerun',
label = 'Smoothing factor dose-response splines',
value = "",
placeholder = "NULL (choose automatically)"
),
bsPopover(id = "smoothing_factor_growth_dr_rerun", title = HTML("<em>smooth.dr</em>"), content = "\\'spar\\' argument in the R function smooth.spline() used to create the dose response curve."),
QurvE:::numberInput(
inputId = 'number_of_bootstrappings_dr_growth_rerun',
label = 'Number of bootstrappings',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "number_of_bootstrappings_dr_growth_rerun", title = HTML("<em>nboot.dr</em>"), content = "Optional: Define the number of bootstrap samples for EC50 estimation. Bootstrapping resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to determine the EC50.")
), #conditionalPanel(condition = 'input.dr_method_growth_rerun == "spline"')
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.dr.growth', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right")
)
)
)
)
)
})
# Re-run dose-response analysis with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.dr.growth, {
if(!is.null(results$growth$drFit)){
showModal(modalDialog("Performing dose-reponse analysis...", footer = NULL))
# store previous fit in memory
selected_vals_validate_growth$restore_dr_growth <- results$growth$drFit
# Re-run fit and store in results object
gcTable <- results$growth$gcFit$gcTable
control <- results$growth$drFit$control
control_new <- results$growth$control <- control
control_new$dr.method <- input$dr_method_growth_rerun
control_new$dr.parameter <- input$response_parameter_growth_rerun
control_new$smooth.dr <- as.numeric(input$smoothing_factor_growth_dr_rerun)
if(control_new$smooth.dr == "" || is.na(control_new$smooth.dr))
control_new$smooth.dr <- NULL
control_new$nboot.dr <- input$number_of_bootstrappings_dr_growth_rerun
control_new$log.x.dr <- input$log_transform_concentration_growth_rerun
control_new$log.y.dr <- input$log_transform_response_growth_rerun
try(
results$growth$drFit <-
growth.drFit(gcTable, control = control_new)
)
# Show [Restore fit] button
show("restore_dr_growth")
show("restore_dr_growth2")
show("restore_dr_growth3")
}
removeModal()
})
restore_dr_growth_buttons <- reactive({
list(input$restore_dr_growth, input$restore_dr_growth2, input$restore_dr_growth3)
})
# Restore previous linear fit upon click on [Restore Fit]
observeEvent(restore_dr_growth_buttons(), {
if(input$restore_dr_growth==0 && input$restore_dr_growth2==0 && input$restore_dr_growth3==0){
return()
}
# store previous fit from memory
results$growth$drFit <- selected_vals_validate_growth$restore_dr_growth
hide("restore_dr_growth")
hide("restore_dr_growth2")
hide("restore_dr_growth3")
})
output$dose_response_growth_plot_combined <- renderPlot({
dose_response_growth_plot_combined()
})
output$download_dose_response_growth_plot_combined <- downloadHandler(
filename = function() {
paste("dose_response_growth_combined", input$format_download_dose_response_growth_plot_combined, sep="")
},
content = function(file) {
if(input$format_download_dose_response_growth_plot_combined == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_dose_response_growth_plot_combined,
height = input$height_download_dose_response_growth_plot_combined,
dpi = input$dpi_download_dose_response_growth_plot_combined,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_dose_response_growth_plot_combined,
height = input$height_download_dose_response_growth_plot_combined,
dpi = input$dpi_download_dose_response_growth_plot_combined,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_dose_response_growth_plot_combined,
height = input$height_download_dose_response_growth_plot_combined,
dpi = input$dpi_download_dose_response_growth_plot_combined,
device = png)
}
},
contentType = ifelse(input$format_download_dose_response_growth_plot_combined == ".pdf", "image/pdf", "image/png")
)
### DR Plots Model ####
observe({
if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1 && results$growth$drFit$control$dr.method == "model"){
showTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse_Model")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse_Model")
}
})
output$more_than_one_drfit_model <- reactive({
if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drFittedModels) > 1) return(TRUE)
else return(FALSE)
})
outputOptions(output, 'more_than_one_drfit_model', suspendWhenHidden=FALSE)
select_inputs_individual_plots_dose_response_growth_plot_model <- reactive({
if (length(results$growth$drFit)>1) names(results$growth$drFit$drFittedModels)
else return("")
})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_growth_plot_model",
choices = select_inputs_individual_plots_dose_response_growth_plot_model())
})
select_inputs_show_breaks_dose_response_growth_plot_model <- reactive({
if(!input$log_transform_x_axis_dose_response_growth_plot_model)
return(FALSE)
else
return(TRUE)
})
observe({
updateCheckboxInput(inputId = "show_break_dose_response_growth_plot_model",
value = select_inputs_show_breaks_dose_response_growth_plot_model())
})
dose_response_growth_plot_model <- reactive({
# Define log-transformation of axes
if(input$log_transform_x_axis_dose_response_growth_plot_model){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(!any(input$y_range_min_dose_response_growth_plot_model == "",
input$y_range_max_dose_response_growth_plot_model == "") &&
!any(is.null(input$y_range_min_dose_response_growth_plot_model),
is.null(input$y_range_max_dose_response_growth_plot_model))){
y.lim <- c(as.numeric(input$y_range_min_dose_response_growth_plot_model),
as.numeric(input$y_range_max_dose_response_growth_plot_model))
}
if(!any(input$x_range_min_dose_response_growth_plot_model == "",
input$x_range_max_dose_response_growth_plot_model == "") &&
!any(is.null(input$x_range_min_dose_response_growth_plot_model),
is.null(input$x_range_max_dose_response_growth_plot_model))){
x.lim <- c(as.numeric(input$x_range_min_dose_response_growth_plot_model),
as.numeric(input$x_range_max_dose_response_growth_plot_model))
}
results <- results$growth$drFit$drFittedModels[[ifelse(input$individual_plots_dose_response_growth_plot_model == "1" || is.null(input$individual_plots_dose_response_growth_plot_model), 1, input$individual_plots_dose_response_growth_plot_model)]]
try(
plot.drFitModel(results,
pch = input$shape_type_dose_response_growth_plot_model,
cex.point = input$shape_size_dose_response_growth_plot_model,
cex.lab = input$axis_size_dose_response_growth_plot_model,
cex.axis = input$lab_size_dose_response_growth_plot_model,
log = log,
lwd = input$line_width_dose_response_growth_plot_model,
ec50line = input$show_ec50_indicator_lines_dose_response_growth_plot_model,
y.lim = if(!exists("y.lim")){
substitute()
} else {
y.lim
},
x.lim = if(!exists("x.lim")){
substitute()
} else {
x.lim
},
broken = input$show_break_dose_response_growth_plot_model,
n.xbreaks = input$nbreaks_x_growth_dose_response_plot_model,
n.ybreaks = input$nbreaks_y_growth_dose_response_plot_model,
xlab = input$x_axis_title_dose_response_growth_plot_model,
ylab = input$y_axis_title_dose_response_growth_plot_model,
bp = ifelse(is.na(input$bp_dose_response_growth_plot_model)||(input$bp_dose_response_growth_plot_model == ""), rlang::missing_arg(), input$bp_dose_response_growth_plot_model)
)
)
})
output$dose_response_growth_plot_model <- renderPlot({
dose_response_growth_plot_model()
})
output$download_dose_response_growth_plot_model <- downloadHandler(
filename = function() {
paste("dose_response_growth_model", input$format_download_dose_response_growth_plot_model, sep="")
},
content = function(file) {
if(input$format_download_dose_response_growth_plot_individual == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_dose_response_growth_plot_model,
height = input$height_download_dose_response_growth_plot_model)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_dose_response_growth_plot_model,
height = input$height_download_dose_response_growth_plot_model)
}
} else {
grDevices::png(file = file,
width = input$width_download_dose_response_growth_plot_model,
height = input$height_download_dose_response_growth_plot_model,
units = "in",
res = input$dpi_download_dose_response_growth_plot_model)
}
# Define log-transformation of axes
if(input$log_transform_x_axis_dose_response_growth_plot_model){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(!any(input$y_range_min_dose_response_growth_plot_model == "",
input$y_range_max_dose_response_growth_plot_model == "") &&
!any(is.null(input$y_range_min_dose_response_growth_plot_model),
is.null(input$y_range_max_dose_response_growth_plot_model))){
y.lim <- c(as.numeric(input$y_range_min_dose_response_growth_plot_model),
as.numeric(input$y_range_max_dose_response_growth_plot_model))
}
if(!any(input$x_range_min_dose_response_growth_plot_model == "",
input$x_range_max_dose_response_growth_plot_model == "") &&
!any(is.null(input$x_range_min_dose_response_growth_plot_model),
is.null(input$x_range_max_dose_response_growth_plot_model))){
x.lim <- c(as.numeric(input$x_range_min_dose_response_growth_plot_model),
as.numeric(input$x_range_max_dose_response_growth_plot_model))
}
results <- results$growth$drFit$drFittedModels[[ifelse(input$individual_plots_dose_response_growth_plot_model == "1" || is.null(input$individual_plots_dose_response_growth_plot_model), 1, input$individual_plots_dose_response_growth_plot_model)]]
try(
plot.drFitModel(results,
pch = input$shape_type_dose_response_growth_plot_model,
cex.point = input$shape_size_dose_response_growth_plot_model,
cex.lab = input$axis_size_dose_response_growth_plot_model,
cex.axis = input$lab_size_dose_response_growth_plot_model,
log = log,
lwd = input$line_width_dose_response_growth_plot_model,
ec50line = input$show_ec50_indicator_lines_dose_response_growth_plot_model,
y.lim = ifelse(!exists("y.lim"), substitute(), y.lim),
x.lim = ifelse(!exists("x.lim"), substitute(), x.lim),
broken = input$show_break_dose_response_growth_plot_model,
n.xbreaks = input$nbreaks_x_growth_dose_response_plot_model,
n.ybreaks = input$nbreaks_y_growth_dose_response_plot_model,
xlab = input$x_axis_title_dose_response_growth_plot_model,
ylab = input$y_axis_title_dose_response_growth_plot_model,
bp = ifelse(is.na(input$bp_dose_response_growth_plot_model)||(input$bp_dose_response_growth_plot_model == ""), rlang::missing_arg(), input$bp_dose_response_growth_plot_model)
)
)
dev.off()
},
contentType = ifelse(input$format_download_dose_response_growth_plot_model == ".pdf", "image/pdf", "image/png")
)
### DR Plots Spline Individual ####
dose_response_growth_plot_individual <- reactive({
results <- results$growth$drFit$drFittedSplines[[ifelse(input$individual_plots_dose_response_growth_plot == "1" || is.null(input$individual_plots_dose_response_growth_plot), 1, input$individual_plots_dose_response_growth_plot)]]
# Define log-transformation of axes
if(input$log_transform_y_axis_dose_response_growth_plot &&
input$log_transform_x_axis_dose_response_growth_plot){
log <- "xy"
} else if(input$log_transform_y_axis_dose_response_growth_plot){
log <- "y"
} else if(input$log_transform_x_axis_dose_response_growth_plot){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(any(input$y_range_min_dose_response_growth_plot == "",
input$y_range_max_dose_response_growth_plot == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_dose_response_growth_plot),
as.numeric(input$y_range_max_dose_response_growth_plot))
}
if(any(input$x_range_min_dose_response_growth_plot == "",
input$x_range_max_dose_response_growth_plot == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_dose_response_growth_plot),
as.numeric(input$x_range_max_dose_response_growth_plot))
}
plot.drFitSpline(results,
combine = FALSE,
pch = input$shape_type_dose_response_growth_plot,
cex.point = input$shape_size_dose_response_growth_plot,
cex.lab = input$axis_size_dose_response_growth_plot,
cex.axis = input$lab_size_dose_response_growth_plot,
y.title = input$y_axis_title_dose_response_growth_plot,
x.title = input$x_axis_title_dose_response_growth_plot,
log = log,
lwd = input$line_width_dose_response_growth_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_growth_plot,
y.lim = y.lim,
x.lim = x.lim
)
})
output$dose_response_growth_plot_individual <- renderPlot({
dose_response_growth_plot_individual()
})
output$download_dose_response_growth_plot_individual <- downloadHandler(
filename = function() {
paste("dose_response_growth_", gsub(" \\| ", "_", input$individual_plots_dose_response_growth_plot), input$format_download_dose_response_growth_plot_individual, sep="")
},
content = function(file) {
if(input$format_download_dose_response_growth_plot_individual == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_dose_response_growth_plot_individual,
height = input$height_download_dose_response_growth_plot_individual)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_dose_response_growth_plot_individual,
height = input$height_download_dose_response_growth_plot_individual)
}
} else {
grDevices::png(file = file,
width = input$width_download_dose_response_growth_plot_individual,
height = input$height_download_dose_response_growth_plot_individual,
units = "in",
res = input$dpi_download_dose_response_growth_plot_individual)
}
results <- results$growth$drFit$drFittedSplines[[ifelse(input$individual_plots_dose_response_growth_plot == "1" || is.null(input$individual_plots_dose_response_growth_plot), 1, input$individual_plots_dose_response_growth_plot)]]
# Define log-transformation of axes
if(input$log_transform_y_axis_dose_response_growth_plot &&
input$log_transform_x_axis_dose_response_growth_plot){
log <- "xy"
} else if(input$log_transform_y_axis_dose_response_growth_plot){
log <- "y"
} else if(input$log_transform_x_axis_dose_response_growth_plot){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(any(input$y_range_min_dose_response_growth_plot == "",
input$y_range_max_dose_response_growth_plot == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_dose_response_growth_plot),
as.numeric(input$y_range_max_dose_response_growth_plot))
}
if(any(input$x_range_min_dose_response_growth_plot == "",
input$x_range_max_dose_response_growth_plot == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_dose_response_growth_plot),
as.numeric(input$x_range_max_dose_response_growth_plot))
}
plot.drFitSpline(results,
combine = FALSE,
pch = input$shape_type_dose_response_growth_plot,
cex.point = input$shape_size_dose_response_growth_plot,
cex.lab = input$axis_size_dose_response_growth_plot,
cex.axis = input$lab_size_dose_response_growth_plot,
y.title = input$y_axis_title_dose_response_growth_plot,
x.title = input$x_axis_title_dose_response_growth_plot,
log = log,
lwd = input$line_width_dose_response_growth_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_growth_plot,
y.lim = y.lim,
x.lim = x.lim
)
dev.off()
},
contentType = ifelse(input$format_download_dose_response_growth_plot_individual == ".pdf", "image/pdf", "image/png")
)
### DR Plots (Bootstrap) ####
observe({
if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1 && results$growth$control$nboot.dr > 1){
showTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse_Spline_bt")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse_Spline_bt")
}
})
dose_response_growth_plot_individual_bt <- reactive({
results <- results$growth$drFit$drBootSplines[[ifelse(input$individual_plots_dose_response_growth_plot_bt == "1" || is.null(input$individual_plots_dose_response_growth_plot_bt), 1, input$individual_plots_dose_response_growth_plot_bt)]]
plot.drBootSpline(results,
pch = input$shape_type_dose_response_growth_plot_bt,
cex.point = input$shape_size_dose_response_growth_plot_bt,
cex.lab = input$axis_size_dose_response_growth_plot_bt,
cex.axis = input$lab_size_dose_response_growth_plot_bt,
lwd = input$line_width_dose_response_growth_plot_bt,
combine = TRUE
)
})
output$dose_response_growth_plot_individual_bt <- renderPlot({
dose_response_growth_plot_individual_bt()
})
output$download_dose_response_growth_plot_individual_bt <- downloadHandler(
filename = function() {
paste("dose_response_boot_growth_", gsub(" \\| ", "_", input$individual_plots_dose_response_growth_plot_bt), input$format_download_dose_response_growth_plot_individual_bt, sep="")
},
content = function(file) {
if(input$format_download_dose_response_growth_plot_individual_bt == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_dose_response_growth_plot_individual_bt,
height = input$height_download_dose_response_growth_plot_individual_bt)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_dose_response_growth_plot_individual_bt,
height = input$height_download_dose_response_growth_plot_individual_bt)
}
} else {
grDevices::png(file = file,
width = input$width_download_dose_response_growth_plot_individual_bt,
height = input$height_download_dose_response_growth_plot_individual_bt,
units = "in",
res = input$dpi_download_dose_response_growth_plot_individual_bt)
}
results <- results$growth$drFit$drBootSplines[[input$individual_plots_dose_response_growth_plot_bt]]
plot.drBootSpline(results,
pch = input$shape_type_dose_response_growth_plot_bt,
cex.point = input$shape_size_dose_response_growth_plot_bt,
cex.lab = input$axis_size_dose_response_growth_plot_bt,
cex.axis = input$lab_size_dose_response_growth_plot_bt,
lwd = input$line_width_dose_response_growth_plot_bt,
combine = TRUE
)
dev.off()
},
contentType = ifelse(input$format_download_dose_response_growth_plot_individual_bt == ".pdf", "image/pdf", "image/png")
)
### Parameter Plots ####
selected_inputs_visualize_parameter_growth_plot <- reactive({
results <- results$growth
if(is.null(results)) return("")
select_samples <- results$expdesign$condition
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_parameter_growth_plot",
choices = selected_inputs_visualize_parameter_growth_plot()
)
})
growth_parameter_plot <- reactive({
results <- results$growth
if (input$normalize_to_reference_growth_parameter_plot){
reference.conc <- as.numeric(input$reference_concentration_growth_parameter_plot)
reference.nm <- input$reference_condition_growth_parameter_plot
} else {
reference.conc <- NULL
reference.nm <- NULL
}
if(is.null(input$custom_colors_growth_parameter_plot) || is.na(input$custom_colors_growth_parameter_plot) || input$custom_colors_growth_parameter_plot == ""){
cols <- NULL
} else {
cols <- toupper(unlist(str_split(input$custom_colors_growth_parameter_plot, ", |; |,|;")))
}
if(input$select_string_visualize_parameter_growth_plot){
suppressWarnings(
plot.parameter(results,
param = input$parameter_parameter_growth_plot,
IDs = NULL,
names = input$select_sample_based_on_string_growth_parameter_plot,
conc = input$select_sample_based_on_concentration_growth_parameter_plot,
exclude.nm = input$exclude_sample_based_on_strings_growth_parameter_plot,
exclude.conc = input$exclude_sample_based_on_concentration_growth_parameter_plot,
reference.nm = reference.nm,
reference.conc = reference.conc,
shape.size = input$shape.size_growth_parameter_plot,
basesize = input$basesize_growth_parameter_plot,
label.size = input$label.size_growth_parameter_plot,
legend.position = input$legend_position_growth_parameter_plot,
legend.ncol = input$legend_ncol_growth_parameter_plot,
order_by_conc = input$sort_by_conc_growth_parameter_plot,
colors = cols
)
)
} else {
suppressWarnings(
plot.parameter(results,
param = input$parameter_parameter_growth_plot,
IDs = input$samples_visualize_parameter_growth_plot,
names = NULL,
conc = input$select_sample_based_on_concentration_growth_parameter_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_sample_based_on_concentration_growth_parameter_plot,
reference.nm = reference.nm,
reference.conc = reference.conc,
shape.size = input$shape.size_growth_parameter_plot,
basesize = input$basesize_growth_parameter_plot,
label.size = input$label.size_growth_parameter_plot,
legend.position = input$legend_position_growth_parameter_plot,
legend.ncol = input$legend_ncol_growth_parameter_plot,
order_by_conc = input$sort_by_conc_growth_parameter_plot,
colors = cols
)
)
}
})
output$growth_parameter_plot <- renderPlot({
growth_parameter_plot()
})
output$download_growth_parameter_plot <- downloadHandler(
filename = function() {
paste("growth_parameter_plot", input$format_download_growth_parameter_plot, sep="")
},
content = function(file) {
if(input$format_download_growth_parameter_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_growth_parameter_plot,
height = input$height_download_growth_parameter_plot,
dpi = input$dpi_download_growth_parameter_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_growth_parameter_plot,
height = input$height_download_growth_parameter_plot,
dpi = input$dpi_download_growth_parameter_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_growth_parameter_plot,
height = input$height_download_growth_parameter_plot,
dpi = input$dpi_download_growth_parameter_plot,
device = png)
}
},
contentType = ifelse(input$format_download_growth_parameter_plot == ".pdf", "image/pdf", "image/png")
)
selected_inputs_parameter_growth_parameter_plot <- reactive({
results <- results$growth
gc_parameters <- c()
if("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Growth rate (Spline)' = 'mu.spline',
'Growth rate phase 2 (Spline)' = 'mu.spline',
"Doubling time (Spline)" = "tD.spline",
"Doubling time phase 2 (Spline)" = "tD.spline",
'Lag time (Spline)' = 'lambda.spline',
'Maximum growth measurement (Spline)' = 'A.spline',
'ΔGrowth measurement (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
} else {
gc_parameters <- c(gc_parameters,
'Growth rate (Spline)' = 'mu.spline',
"Doubling time (Spline)" = "tD.spline",
'Lag time (Spline)' = 'lambda.spline',
'Maximum growth measurement (Spline)' = 'A.spline',
'ΔGrowth measurement (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
}
}
if("l" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Growth rate (linear fit)' = 'mu.linfit',
'Growth rate phase 2 (linear fit)' = 'mu.linfit',
"Doubling time (linear fit)" = "tD.linfit",
"Doubling time phase 2 (linear fit)" = "tD.linfit",
'Lag time (linear fit)' = 'lambda.linfit',
'Maximum growth measurement (linear fit)' = 'A.linfit',
'ΔGrowth measurement (linear fit)' = 'dY.linfit')
} else {
gc_parameters <- c(gc_parameters,
'Growth rate (linear fit)' = 'mu.linfit',
"Doubling time (linear fit)" = "tD.linfit",
'Lag time (linear fit)' = 'lambda.linfit',
'Maximum growth measurement (linear fit)' = 'A.linfit',
'ΔGrowth measurement (linear fit)' = 'dY.linfit')
}
}
if("m" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
gc_parameters <- c(gc_parameters,
'Growth rate (model)' = 'mu.model',
'Doubling time (model)' = 'tD.model',
'Lag time (model)' = 'lambda.model',
'Maximum growth measurement (model)' = 'A.model')
}
gc_parameters
})
selected_inputs_reference_condition_growth_parameter_plot <- reactive({
results <- results$growth
results$expdesign$condition
})
select_inputs_reference_concentration_growth_parameter_plot <- reactive({
results <- results$growth
results$expdesign$concentration
})
select_inputs_individual_plots_dose_response_growth_plot <- reactive({
if (length(results$growth$drFit)>1) names(results$growth$drFit$drFittedSplines)
else return("")
})
observe({
updateSelectInput(inputId = "parameter_parameter_growth_plot",
choices = selected_inputs_parameter_growth_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_condition_growth_parameter_plot",
choices = selected_inputs_reference_condition_growth_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_concentration_growth_parameter_plot",
choices = select_inputs_reference_concentration_growth_parameter_plot()
)})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_growth_plot",
choices = select_inputs_individual_plots_dose_response_growth_plot())
})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_growth_plot_bt",
choices = select_inputs_individual_plots_dose_response_growth_plot_bt())
})
### Grid Plot ####
observe({
if(!exists("output$more_than_two_conc") || is.null(output$more_than_two_conc))
return(NULL)
if(!output$more_than_two_conc){
updateSelectInput(session = session,
inputId = "sort_by_conc_growth_grid_plot",
selected = FALSE)
}
})
observe({
if(input$select_string_visualize_growth_grid)
updateSelectInput(session = session,
inputId = "order_matters_visualize_growth_grid",
selected = FALSE)
})
selected_inputs_parameter_growth_grid_plot <- reactive({
results <- results$growth
gc_parameters <- c()
if("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Growth rate (Spline)' = 'mu.spline',
'Growth rate phase 2 (Spline)' = 'mu.spline',
"Doubling time (Spline)" = "tD.spline",
"Doubling time phase 2 (Spline)" = "tD.spline",
'Lag time (Spline)' = 'lambda.spline',
'Maximum growth measurement (Spline)' = 'A.spline',
'ΔGrowth measurement (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
} else {
gc_parameters <- c(gc_parameters,
'Growth rate (Spline)' = 'mu.spline',
"Doubling time (Spline)" = "tD.spline",
'Lag time (Spline)' = 'lambda.spline',
'Maximum growth measurement (Spline)' = 'A.spline',
'ΔGrowth measurement (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
}
}
if("l" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Growth rate (linear fit)' = 'mu.linfit',
'Growth rate phase 2 (linear fit)' = 'mu.linfit',
"Doubling time (linear fit)" = "tD.linfit",
"Doubling time phase 2 (linear fit)" = "tD.linfit",
'Lag time (linear fit)' = 'lambda.linfit',
'Maximum growth measurement (linear fit)' = 'A.linfit',
'ΔGrowth measurement (linear fit)' = 'dY.linfit')
} else {
gc_parameters <- c(gc_parameters,
'Growth rate (linear fit)' = 'mu.linfit',
"Doubling time (linear fit)" = "tD.linfit",
'Lag time (linear fit)' = 'lambda.linfit',
'Maximum growth measurement (linear fit)' = 'A.linfit',
'ΔGrowth measurement (linear fit)' = 'dY.linfit')
}
}
if("m" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
gc_parameters <- c(gc_parameters,
'Growth rate (model)' = 'mu.model',
'Doubling time (model)' = 'tD.model',
'Lag time (model)' = 'lambda.model',
'Maximum growth measurement (model)' = 'A.model')
}
gc_parameters
})
observe({
updateSelectInput(inputId = "parameter_parameter_grid_plot",
choices = selected_inputs_parameter_growth_grid_plot()
)})
selected_inputs_visualize_growth_grid <- reactive({
results <- results$growth
if(is.null(results)) return("")
if(input$plot_group_averages_growth_grid_plot){
select_samples <- results$expdesign$condition
} else {
select_samples <- results$expdesign$label
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_growth_grid",
choices = selected_inputs_visualize_growth_grid()
)
})
observe({
updateSelectInput(session,
inputId = "groups_visualize_growth_grid",
choices = selected_inputs_visualize_growth_grid()
)
})
growth_grid_plot <- reactive({
results <- results$growth
if(input$select_string_visualize_growth_grid){
suppressWarnings(
plot.grid(results,
data.type = input$data_type_growth_grid_plot,
IDs = NULL,
sort_by_ID = FALSE,
names = input$select_samples_based_on_string_growth_grid_plot,
conc = input$select_samples_based_on_concentration_growth_grid_plot,
exclude.nm = input$exclude_samples_based_on_string_growth_grid_plot,
exclude.conc = input$exclude_samples_based_on_concentration_growth_grid_plot,
mean = input$plot_group_averages_growth_grid_plot,
log.y = input$log_transform_y_axis_growth_grid_plot,
x.lim = c(input$x_range_min_growth_grid_plot, input$x_range_max_growth_grid_plot),
y.lim = c(input$y_range_min_growth_grid_plot,input$y_range_max_growth_grid_plot),
y.title = input$y_axis_title_growth_grid_plot,
x.title = input$x_axis_title_growth_grid_plot,
n.ybreaks = input$nbreaks_growth_grid_plot,
lwd = input$line_width_growth_grid_plot,
basesize = input$base_size_growth_grid_plot,
pal = input$color_palettes_grid_plot,
invert.pal = input$invert_color_palette_grid_plot,
sort_by_conc = input$sort_by_conc_growth_grid_plot,
legend.lim = c(input$legend_lim_min_growth_grid_plot, input$legend_lim_max_growth_grid_plot),
nrow = input$nrows_growth_grid_plot,
param = input$parameter_parameter_grid_plot
)
)
}
else{
suppressWarnings(
plot.grid(results,
data.type = input$data_type_growth_grid_plot,
IDs = if(input$plot_group_averages_growth_grid_plot){
input$groups_visualize_growth_grid
}else{
input$samples_visualize_growth_grid
},
sort_by_ID = input$order_matters_visualize_growth_grid,
names = NULL,
conc = input$select_samples_based_on_concentration_growth_grid_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_samples_based_on_concentration_growth_grid_plot,
mean = input$plot_group_averages_growth_grid_plot,
log.y = input$log_transform_y_axis_growth_grid_plot,
x.lim = c(input$x_range_min_growth_grid_plot, input$x_range_max_growth_grid_plot),
y.lim = c(input$y_range_min_growth_grid_plot,input$y_range_max_growth_grid_plot),
y.title = input$y_axis_title_growth_grid_plot,
x.title = input$x_axis_title_growth_grid_plot,
n.ybreaks = input$nbreaks_growth_grid_plot,
lwd = input$line_width_growth_grid_plot,
basesize = input$base_size_growth_grid_plot,
pal = input$color_palettes_grid_plot,
invert.pal = input$invert_color_palette_grid_plot,
sort_by_conc = input$sort_by_conc_growth_grid_plot,
legend.lim = c(input$legend_lim_min_growth_grid_plot, input$legend_lim_max_growth_grid_plot),
nrow = input$nrows_growth_grid_plot,
param = input$parameter_parameter_grid_plot
)
)
}
})
output$growth_grid_plot <- renderPlot({
growth_grid_plot()
})
output$download_growth_grid_plot <- downloadHandler(
filename = function() {
paste("growth_grid_plot", input$format_download_growth_grid_plot, sep="")
},
content = function(file) {
if(input$format_download_growth_grid_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_growth_grid_plot,
height = input$height_download_growth_grid_plot,
dpi = input$dpi_download_growth_grid_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_growth_grid_plot,
height = input$height_download_growth_grid_plot,
dpi = input$dpi_download_growth_grid_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_growth_grid_plot,
height = input$height_download_growth_grid_plot,
dpi = input$dpi_download_growth_grid_plot,
device = png)
}
},
contentType = ifelse(input$format_download_growth_grid_plot == ".pdf", "image/pdf", "image/png")
)
### DR Parameter Plots ####
observe({
if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1){
showTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponseParameters")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponseParameters")
}
})
growth_dr_parameter_plot <- reactive({
results <- results$growth
if (input$normalize_to_reference_growth_dr_parameter_plot){
reference.nm <- input$reference_condition_growth_dr_parameter_plot
} else {
reference.nm <- NULL
}
param <- input$parameter_dr_parameter_growth_plot
suppressWarnings(
plot.dr_parameter(results,
param = param,
names = input$select_sample_based_on_string_growth_dr_parameter_plot,
exclude.nm = input$exclude_sample_based_on_strings_growth_dr_parameter_plot,
reference.nm = reference.nm,
basesize = input$basesize_growth_dr_parameter_plot,
label.size = input$label.size_growth_dr_parameter_plot
)
)
})
output$growth_dr_parameter_plot <- renderPlot({
growth_dr_parameter_plot()
})
output$download_growth_dr_parameter_plot <- downloadHandler(
filename = function() {
paste("growth_dr_parameter_plot", input$format_download_growth_dr_parameter_plot, sep="")
},
content = function(file) {
if(input$format_download_growth_dr_parameter_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_growth_dr_parameter_plot,
height = input$height_download_growth_dr_parameter_plot,
dpi = input$dpi_download_growth_dr_parameter_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_growth_dr_parameter_plot,
height = input$height_download_growth_dr_parameter_plot,
dpi = input$dpi_download_growth_dr_parameter_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_growth_dr_parameter_plot,
height = input$height_download_growth_dr_parameter_plot,
dpi = input$dpi_download_growth_dr_parameter_plot,
device = png)
}
},
contentType = ifelse(input$format_download_growth_dr_parameter_plot == ".pdf", "image/pdf", "image/png")
)
selected_inputs_parameter_growth_dr_parameter_plot <- reactive({
results <- results$growth
if(!is.null(results)){
if(results$control$dr.method == "spline"){
dr_parameters <- c('Response(EC50)' = 'yEC50','EC50' = 'EC50')
if(!is.null(input$number_of_bootstrappings_dr_growth) && !is.na(input$number_of_bootstrappings_dr_growth) && input$number_of_bootstrappings_dr_growth != "" && as.numeric(input$number_of_bootstrappings_dr_growth) > 1){
dr_parameters <- c(dr_parameters, 'Response(EC50) - Bootstrap' = 'drboot.meanEC50', 'EC50 - Bootstrap' = 'drboot.meanEC50y')
}
if(input$log_transform_response_growth){
dr_parameters <- c(dr_parameters, 'Original response(EC50)' = 'yEC50.orig')
}
if(input$log_transform_concentration_growth){
dr_parameters <- c(dr_parameters, 'Original EC50' = 'EC50.orig')
}
} else {
dr_parameters <- c('Response(EC50)' = 'yEC50','EC50' = 'EC50.Estimate')
}
dr_parameters
}
})
selected_inputs_reference_condition_growth_dr_parameter_plot <- reactive({
results <- results$growth
results$expdesign$condition
})
select_inputs_individual_plots_dose_response_growth_plot_bt <- reactive({
if (length(results$growth$drFit)>1 && results$growth$control$nboot.dr > 1) names(results$growth$drFit$drBootSplines)
else return("")
})
observe({
updateSelectInput(inputId = "parameter_dr_parameter_growth_plot",
choices = selected_inputs_parameter_growth_dr_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_condition_growth_dr_parameter_plot",
choices = selected_inputs_reference_condition_growth_dr_parameter_plot()
)})
## Fluorescence Plots: #####
### Group Plots ####
selected_inputs_visualize_fluorescence_group <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
if(input$plot_group_averages_fluorescence_group_plot){
select_samples <- results$expdesign$condition
} else {
select_samples <- results$expdesign$label
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_fluorescence_group",
choices = selected_inputs_visualize_fluorescence_group()
)
})
observe({
updateSelectInput(session,
inputId = "groups_visualize_fluorescence_group",
choices = selected_inputs_visualize_fluorescence_group()
)
})
observe({
if (length(results$fluorescence$flFit)>1){
if(input$data_type_fluorescence_group_plot == "raw") y_axis <- "Fluorescence"
if(input$data_type_fluorescence_group_plot == "spline" && results$fluorescence$control$norm_fl){
y_axis <- "Normalized fluorescence"
}
if(input$data_type_fluorescence_group_plot == "spline" && !results$fluorescence$control$norm_fl){
y_axis <- "Fluorescence"
}
if(input$data_type_fluorescence_group_plot == "norm.fl") y_axis <- "Normalized fluorescence"
}
else {
y_axis <- ""
}
updateSelectInput(inputId = "y_axis_title_fluorescence_group_plot",
selected = y_axis
)
})
observe({
if (length(results$fluorescence$flFit)>1){
if(input$data_type_fluorescence_group_plot == "norm.fl" || input$data_type_fluorescence_group_plot == "raw" ){ # || data.type == "raw2" || data.type == "norm.fl2"
x_axis <- "Time"
} else if (results$fluorescence$control$x_type == "growth"){
x_axis <- "growth"
} else {
x_axis <- "Time"
}
}
else {
x_axis <- ""
}
updateSelectInput(inputId = "x_axis_title_fluorescence_group_plot",
selected = x_axis
)
})
observe({
if (length(results$fluorescence$flFit)>1){
if(input$data_type_fluorescence_group_plot == "spline" && results$fluorescence$control$x_type == "growth"){
shinyjs::hide(id = "plot_group_averages_fluorescence_group_plot")
} else {
shinyjs::show(id = "plot_group_averages_fluorescence_group_plot")
}
}
})
selected_inputs_fluorescence_group_plot_data_type <- reactive({
results <- results$fluorescence
selection <- c()
if(length(results$data$fluorescence) > 1){
selection <- c(selection, "Raw fluorescence" = "raw")
}
if(length(results$data$norm.fluorescence) > 1){
selection <- c(selection, "Normalized FL" = "norm.fl")
}
if(length(results$data$fluorescence) > 1 && "s" %in% results$control$fit.opt){
selection <- c(selection, "Spline fits FL" = "spline")
}
selection
})
output$fluorescence_group_plot <- renderPlot({
results <- results$fluorescence
if(is.null(input$custom_colors_fluorescence_group_plot) || is.na(input$custom_colors_fluorescence_group_plot) || input$custom_colors_fluorescence_group_plot == ""){
cols <- NULL
} else {
cols <- toupper(unlist(str_split(input$custom_colors_fluorescence_group_plot, ", |; |,|;")))
}
if(input$data_type_fluorescence_group_plot == "spline" && results$control$x_type == "growth"){
plot_mean <- FALSE
} else {
plot_mean <- input$plot_group_averages_fluorescence_group_plot
}
if(input$select_string_visualize_fluorescence_group){
suppressWarnings(
plot.flFitRes(
results,
data.type = input$data_type_fluorescence_group_plot,
IDs = NULL,
names = input$select_samples_based_on_string_fluorescence_group_plot,
conc = input$select_samples_based_on_concentration_fluorescence_group_plot,
exclude.nm = input$exclude_samples_based_on_string_fluorescence_group_plot,
exclude.conc = input$exclude_samples_based_on_concentration_fluorescence_group_plot,
mean = plot_mean,
deriv = input$plot_derivative_fluorescence_group_plot,
log.y = input$log_transform_y_axis_fluorescence_group_plot,
x.lim = c(input$x_range_min_fluorescence_group_plot, input$x_range_max_fluorescence_group_plot),
y.lim = c(input$y_range_min_fluorescence_group_plot,input$y_range_max_fluorescence_group_plot),
y.lim.deriv = c(input$y_range_min_derivative_fluorescence_group_plot, input$y_range_max_derivative_fluorescence_group_plot),
y.title = input$y_axis_title_fluorescence_group_plot,
x.title = input$x_axis_title_fluorescence_group_plot,
y.title.deriv = input$y_axis_title_derivative_fluorescence_group_plot,
lwd = input$line_width_fluorescence_group_plot,
basesize = input$base_size_fluorescence_group_plot,
color_groups = input$color_groups_fluorescence_group_plot,
n.ybreaks = input$nbreaks_fluorescence_group_plot,
legend.position = input$legend_position_fluorescence_group_plot,
legend.ncol = input$legend_ncol_fluorescence_group_plot,
group_pals = input$color_palettes_fluorescence_group_plot,
colors = cols
)
)
} else {
suppressWarnings(
plot.flFitRes(
results,
data.type = input$data_type_fluorescence_group_plot,
IDs = if(input$plot_group_averages_fluorescence_group_plot){
input$groups_visualize_fluorescence_group
}else{
input$samples_visualize_fluorescence_group
},
names = NULL,
conc = input$select_samples_based_on_concentration_fluorescence_group_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_samples_based_on_concentration_fluorescence_group_plot,
mean = plot_mean,
deriv = input$plot_derivative_fluorescence_group_plot,
log.y = input$log_transform_y_axis_fluorescence_group_plot,
x.lim = c(input$x_range_min_fluorescence_group_plot, input$x_range_max_fluorescence_group_plot),
y.lim = c(input$y_range_min_fluorescence_group_plot,input$y_range_max_fluorescence_group_plot),
y.lim.deriv = c(input$y_range_min_derivative_fluorescence_group_plot, input$y_range_max_derivative_fluorescence_group_plot),
y.title = input$y_axis_title_fluorescence_group_plot,
x.title = input$x_axis_title_fluorescence_group_plot,
y.title.deriv = input$y_axis_title_derivative_fluorescence_group_plot,
lwd = input$line_width_fluorescence_group_plot,
basesize = input$base_size_fluorescence_group_plot,
n.ybreaks = input$nbreaks_fluorescence_group_plot,
color_groups = input$color_groups_fluorescence_group_plot,
legend.position = input$legend_position_fluorescence_group_plot,
legend.ncol = input$legend_ncol_fluorescence_group_plot,
colors = cols,
group_pals = input$color_palettes_fluorescence_group_plot
)
)
}
})
observe({
if(input$plot_derivative_fluorescence_group_plot && (input$data_type_fluorescence_group_plot == 'spline' )) h <- 9
else h <- 6
updateSelectInput(inputId = "height_download_fluorescence_group_plot",
selected = h
)
})
output$download_fluorescence_group_plot <- downloadHandler(
filename = function() {
paste("fluorescence_group_plot", input$format_download_fluorescence_group_plot, sep="")
},
content = function(file) {
if(input$format_download_growth_dr_parameter_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_fluorescence_group_plot,
height = input$height_download_fluorescence_group_plot,
dpi = input$dpi_download_fluorescence_group_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_fluorescence_group_plot,
height = input$height_download_fluorescence_group_plot,
dpi = input$dpi_download_fluorescence_group_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_fluorescence_group_plot,
height = input$height_download_fluorescence_group_plot,
dpi = input$dpi_download_fluorescence_group_plot,
device = png)
}
},
contentType = ifelse(input$format_download_fluorescence_group_plot == ".pdf", "image/pdf", "image/png")
)
observe({
updateSelectInput(inputId = "data_type_fluorescence_group_plot",
choices = selected_inputs_fluorescence_group_plot_data_type())
})
### DR Plots Spline ####
# Hide Spline dose-response plot if dr.method != "spline"
observe({
if(length(results$fluorescence$drFit) > 1 &&
length(results$fluorescence$drFit$drTable) > 1 &&
results$fluorescence$drFit$control$dr.method == "spline"){
showTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponse_spline")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponse_spline")
}
})
output$dose_response_plot_fluorescence_combined <- renderPlot({
results <- results$fluorescence$drFit
plot.drFit(results,
combine=TRUE,
names = input$select_samples_based_on_string_dose_response_fluorescence_plot,
exclude.nm = input$exclude_samples_based_on_string_dose_response_fluorescence_plot,
pch = input$shape_type_dose_response_fluorescence_plot,
cex.point = input$shape_size_dose_response_fluorescence_plot,
basesize = input$base_size_dose_response_fluorescence_plot,
lwd = input$line_width_dose_response_fluorescence_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_fluorescence_plot,
y.lim = c(as.numeric(input$y_range_min_dose_response_fluorescence_plot), as.numeric(input$y_range_max_dose_response_fluorescence_plot)),
x.lim = c(as.numeric(input$x_range_min_dose_response_fluorescence_plot), as.numeric(input$x_range_max_dose_response_fluorescence_plot)),
y.title = input$y_axis_title_dose_response_fluorescence_plot,
x.title = input$x_axis_title_dose_response_fluorescence_plot,
log.y = input$log_transform_y_axis_dose_response_fluorescence_plot,
log.x = input$log_transform_x_axis_dose_response_fluorescence_plot
)
})
rerun_dr_fluorescence_buttons <- reactive({
list(input$rerun_dr_fluorescence, input$rerun_dr_fluorescence2, input$rerun_dr_fluorescence3)
})
observeEvent(rerun_dr_fluorescence_buttons(), {
if(input$rerun_dr_fluorescence==0 && input$rerun_dr_fluorescence2==0 && input$rerun_dr_fluorescence3==0){
return()
}
select_options <- c()
if(any("l" %in% results$fluorescence$control$fit.opt)) select_options <- c(select_options, 'max_slope.linfit', 'lambda.linfit', 'dY.linfit',
'A.linfit')
if(any("s" %in% results$fluorescence$control$fit.opt)) select_options <- c(select_options, 'max_slope.spline', 'lambda.spline',
'A.spline', 'dY.spline', 'integral.spline')
# display a modal dialog with a header, textinput and action buttons
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
selectInput(inputId = "dr_method_fluorescence_rerun",
label = "Method",
choices = c("Biosensor response model" = "model",
"Response spline fit" = "spline")
),
bsPopover(id = "dr_method_fluorescence_rerun",
placement = "right",
title = HTML("<em>dr.method</em>"),
content = "Fit either a biosensor response model (Meyer et al., 2019) to response-vs.-concentration data, or apply a nonparametric (spline) fit."
),
selectInput(inputId = "response_parameter_fluorescence_rerun",
label = "Response Parameter",
choices = select_options),
tags$div(title="Perform a log(x+1) transformation on concentration values.",
checkboxInput(inputId = 'log_transform_concentration_fluorescence_rerun',
label = 'Log transform concentration')
),
tags$div(title="Perform a log(y+1) transformation on response values.",
checkboxInput(inputId = 'log_transform_response_fluorescence_rerun',
label = 'Log transform response')
),
bsPopover(id = "response_parameter_fluorescence_rerun", title = HTML("<em>dr.parameter</em>"), content = "Choose the response parameter to be used for creating a dose response curve.", placement = "top"),
conditionalPanel(
condition = 'input.dr_method_fluorescence_rerun == "spline"',
textInput(
inputId = 'smoothing_factor_fluorescence_dr_rerun',
label = 'Smoothing factor dose-response splines',
value = "",
placeholder = "NULL (choose automatically)"
),
bsPopover(id = "smoothing_factor_fluorescence_dr_rerun", title = HTML("<em>smooth.dr</em>"), content = "\\'spar\\' argument in the R function smooth.spline() used to create the dose response curve."),
QurvE:::numberInput(
inputId = 'number_of_bootstrappings_dr_fluorescence_rerun',
label = 'Number of bootstrappings',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "number_of_bootstrappings_dr_fluorescence_rerun", title = HTML("<em>nboot.dr</em>"), content = "Optional: Define the number of bootstrap samples for EC50 estimation. Bootstrapping resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to determine the EC50.")
), #conditionalPanel(condition = 'input.dr_method_fluorescence_rerun == "spline"')
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.dr.fluorescence', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right")
)
)
)
)
)
})
# Re-run selected linear fit with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.dr.fluorescence, {
if(!is.null(results$fluorescence$drFit)){
showModal(modalDialog("Performing dose-reponse analysis...", footer = NULL))
# store previous fit in memory
selected_vals_validate_fluorescence$restore_dr_fluorescence <- results$fluorescence$drFit
# Re-run fit and store in results object
flTable <- results$fluorescence$flFit$flTable
control <- results$fluorescence$drFit$control
control_new <- results$fluorescence$control <- control
control_new$dr.method <- input$dr_method_fluorescence_rerun
control_new$dr.parameter <- input$response_parameter_fluorescence_rerun
control_new$smooth.dr <- input$smoothing_factor_fluorescence_dr_rerun
if(control_new$smooth.dr == "" || is.na(control_new$smooth.dr))
control_new$smooth.dr <- NULL
control_new$nboot.dr <- input$number_of_bootstrappings_dr_fluorescence_rerun
control_new$log.x.dr <- input$log_transform_concentration_fluorescence_rerun
control_new$log.y.dr <- input$log_transform_response_fluorescence_rerun
if(control_new$dr.method == "spline"){
try(
results$fluorescence$drFit <-
growth.drFit(flTable, control = control_new)
)
} else {
try(
results$fluorescence$drFit <-
fl.drFit(flTable, control = control_new)
)
}
# Show [Restore fit] button
show("restore_dr_fluorescence")
show("restore_dr_fluorescence2")
show("restore_dr_fluorescence3")
}
removeModal()
})
restore_dr_fluorescence_buttons <- reactive({
list(input$restore_dr_fluorescence, input$restore_dr_fluorescence2, input$restore_dr_fluorescence3)
})
# Restore previous linear fit upon click on [Restore Fit]
observeEvent(restore_dr_fluorescence_buttons(), {
if(input$restore_dr_fluorescence==0 && input$restore_dr_fluorescence2==0 && input$restore_dr_fluorescence3==0){
return()
}
# store previous fit from memory
results$fluorescence$drFit <- selected_vals_validate_fluorescence$restore_dr_fluorescence
hide("restore_dr_fluorescence")
hide("restore_dr_fluorescence2")
hide("restore_dr_fluorescence3")
})
output$download_dose_response_plot_fluorescence_combined <- downloadHandler(
filename = function() {
paste("dose_response_fluorescence_combined", input$format_download_dose_response_plot_fluorescence_combined, sep="")
},
content = function(file) {
if(input$format_download_dose_response_plot_fluorescence_combined == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_dose_response_plot_fluorescence_combined,
height = input$height_download_dose_response_plot_fluorescence_combined,
dpi = input$dpi_download_dose_response_plot_fluorescence_combined,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_dose_response_plot_fluorescence_combined,
height = input$height_download_dose_response_plot_fluorescence_combined,
dpi = input$dpi_download_dose_response_plot_fluorescence_combined,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_dose_response_plot_fluorescence_combined,
height = input$height_download_dose_response_plot_fluorescence_combined,
dpi = input$dpi_download_dose_response_plot_fluorescence_combined,
device = png)
}
},
contentType = ifelse(input$format_download_dose_response_plot_fluorescence_combined == ".pdf", "image/pdf", "image/png")
)
### DR Plots Spline Individual ####
output$dose_response_fluorescence_plot_individual <- renderPlot({
results <- results$fluorescence$drFit$drFittedSplines[[ifelse(input$individual_plots_dose_response_fluorescence_plot == "1" || is.null(input$individual_plots_dose_response_fluorescence_plot), 1, input$individual_plots_dose_response_fluorescence_plot)]]
# Define log-transformation of axes
if(input$log_transform_y_axis_dose_response_fluorescence_plot &&
input$log_transform_x_axis_dose_response_fluorescence_plot){
log <- "xy"
} else if(input$log_transform_y_axis_dose_response_fluorescence_plot){
log <- "y"
} else if(input$log_transform_x_axis_dose_response_fluorescence_plot){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(any(input$y_range_min_dose_response_fluorescence_plot == "",
input$y_range_max_dose_response_fluorescence_plot == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_dose_response_fluorescence_plot),
as.numeric(input$y_range_max_dose_response_fluorescence_plot))
}
if(any(input$x_range_min_dose_response_fluorescence_plot == "",
input$x_range_max_dose_response_fluorescence_plot == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_dose_response_fluorescence_plot),
as.numeric(input$x_range_max_dose_response_fluorescence_plot))
}
plot.drFitSpline(results,
combine=FALSE,
pch = input$shape_type_dose_response_fluorescence_plot,
cex.point = input$shape_size_dose_response_fluorescence_plot,
lwd = input$line_width_dose_response_fluorescence_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_fluorescence_plot,
cex.lab = input$axis_size_dose_response_fluorescence_plot,
cex.axis = input$lab_size_dose_response_fluorescence_plot,
y.title = input$y_axis_title_dose_response_fluorescence_plot,
x.title = input$x_axis_title_dose_response_fluorescence_plot,
log = log,
y.lim = y.lim,
x.lim = x.lim)
})
output$download_dose_response_fluorescence_plot_individual <- downloadHandler(
filename = function() {
paste("dose_response_fluorescence_", gsub(" \\| ", "_", input$individual_plots_dose_response_fluorescence_plot), input$format_download_dose_response_fluorescence_plot_individual, sep="")
},
content = function(file) {
if(input$format_download_dose_response_fluorescence_plot_individual == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_dose_response_fluorescence_plot_individual,
height = input$height_download_dose_response_fluorescence_plot_individual)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_dose_response_fluorescence_plot_individual,
height = input$height_download_dose_response_fluorescence_plot_individual)
}
} else {
grDevices::png(file = file,
width = input$width_download_dose_response_fluorescence_plot_individual,
height = input$height_download_dose_response_fluorescence_plot_individual,
units = "in",
res = input$dpi_download_dose_response_fluorescence_plot_individual)
}
results <- results$fluorescence$drFit$drFittedSplines[[ifelse(input$individual_plots_dose_response_fluorescence_plot == "1" || is.null(input$individual_plots_dose_response_fluorescence_plot), 1, input$individual_plots_dose_response_fluorescence_plot)]]
# Define log-transformation of axes
if(input$log_transform_y_axis_dose_response_fluorescence_plot &&
input$log_transform_x_axis_dose_response_fluorescence_plot){
log <- "xy"
} else if(input$log_transform_y_axis_dose_response_fluorescence_plot){
log <- "y"
} else if(input$log_transform_x_axis_dose_response_fluorescence_plot){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(any(input$y_range_min_dose_response_fluorescence_plot == "",
input$y_range_max_dose_response_fluorescence_plot == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_dose_response_fluorescence_plot),
as.numeric(input$y_range_max_dose_response_fluorescence_plot))
}
if(any(input$x_range_min_dose_response_fluorescence_plot == "",
input$x_range_max_dose_response_fluorescence_plot == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_dose_response_fluorescence_plot),
as.numeric(input$x_range_max_dose_response_fluorescence_plot))
}
plot.drFitSpline(results,
combine=FALSE,
pch = input$shape_type_dose_response_fluorescence_plot,
cex.point = input$shape_size_dose_response_fluorescence_plot,
lwd = input$line_width_dose_response_fluorescence_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_fluorescence_plot,
cex.lab = input$axis_size_dose_response_fluorescence_plot,
cex.axis = input$lab_size_dose_response_fluorescence_plot,
y.title = input$y_axis_title_dose_response_fluorescence_plot,
x.title = input$x_axis_title_dose_response_fluorescence_plot,
log = log,
y.lim = y.lim,
x.lim = x.lim)
dev.off()
},
contentType = ifelse(input$format_download_dose_response_fluorescence_plot_individual == ".pdf", "image/pdf", "image/png")
)
select_inputs_individual_plots_dose_response_fluorescence_plot<- reactive({
if (length(results$fluorescence$drFit)>1) names(results$fluorescence$drFit$drFittedSplines)
else return("")
})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_fluorescence_plot",
choices = select_inputs_individual_plots_dose_response_fluorescence_plot())
})
### DR Plots Model individual ####
# Hide Model dose-response plot if dr.method != "model"
observe({
if(length(results$fluorescence$drFit) > 1 &&
length(results$fluorescence$drFit$drTable) > 1 &&
results$fluorescence$drFit$control$dr.method == "model"){
showTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponse_model")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponse_model")
}
})
output$dose_response_model_fluorescence_plot_individual <- renderPlot({
results <- results$fluorescence$drFit$drFittedModels[[ifelse(input$individual_plots_dose_response_model_fluorescence_plot == "1" || is.null(input$individual_plots_dose_response_model_fluorescence_plot), 1, input$individual_plots_dose_response_model_fluorescence_plot)]]
# Define log-transformation of axes
if(input$log_transform_y_axis_dose_response_model_fluorescence_plot &&
input$log_transform_x_axis_dose_response_model_fluorescence_plot){
log <- "xy"
} else if(input$log_transform_y_axis_dose_response_model_fluorescence_plot){
log <- "y"
} else if(input$log_transform_x_axis_dose_response_model_fluorescence_plot){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(any(input$y_range_min_dose_response_model_fluorescence_plot == "",
input$y_range_max_dose_response_model_fluorescence_plot == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_dose_response_model_fluorescence_plot),
as.numeric(input$y_range_max_dose_response_model_fluorescence_plot))
}
if(any(input$x_range_min_dose_response_model_fluorescence_plot == "",
input$x_range_max_dose_response_model_fluorescence_plot == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_dose_response_model_fluorescence_plot),
as.numeric(input$x_range_max_dose_response_model_fluorescence_plot))
}
plot.drFitFLModel(results,
pch = input$shape_type_dose_response_model_fluorescence_plot,
cex.point = input$shape_size_dose_response_model_fluorescence_plot,
lwd = input$line_width_dose_response_model_fluorescence_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_model_fluorescence_plot,
log = log,
cex.lab = input$axis_size_dose_response_model_fluorescence_plot,
cex.axis = input$lab_size_dose_response_model_fluorescence_plot,
y.lim = y.lim,
x.lim = x.lim
)
})
output$download_dose_response_model_fluorescence_plot_individual <- downloadHandler(
filename = function() {
paste("biosensor_model_fluorescence_", gsub(" \\| ", "_", input$individual_plots_dose_response_model_fluorescence_plot), input$format_download_dose_response_model_fluorescence_plot_individual, sep="")
},
content = function(file) {
if(input$format_download_dose_response_model_fluorescence_plot_individual == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_dose_response_model_fluorescence_plot_individual,
height = input$height_download_dose_response_model_fluorescence_plot_individual,
dpi = input$dpi_download_dose_response_model_fluorescence_plot_individual,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_dose_response_model_fluorescence_plot_individual,
height = input$height_download_dose_response_model_fluorescence_plot_individual,
dpi = input$dpi_download_dose_response_model_fluorescence_plot_individual,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_dose_response_model_fluorescence_plot_individual,
height = input$height_download_dose_response_model_fluorescence_plot_individual,
dpi = input$dpi_download_dose_response_model_fluorescence_plot_individual,
device = png)
}
},
contentType = ifelse(input$format_download_dose_response_model_fluorescence_plot_individual == ".pdf", "image/pdf", "image/png")
)
select_inputs_individual_plots_dose_response_model_fluorescence_plot<- reactive({
if (length(results$fluorescence$drFit)>1) names(results$fluorescence$drFit$drFittedModels)
else return("")
})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_model_fluorescence_plot",
choices = select_inputs_individual_plots_dose_response_model_fluorescence_plot())
})
### Parameter Plots ####
selected_inputs_visualize_parameter_fluorescence_plot <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
select_samples <- results$expdesign$condition
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_parameter_fluorescence_plot",
choices = selected_inputs_visualize_parameter_fluorescence_plot()
)
})
fluorescence_parameter_plot <- reactive({
results <- results$fluorescence
if (input$normalize_to_reference_fluorescence_parameter_plot){
reference.conc <- as.numeric(input$reference_concentration_fluorescence_parameter_plot)
reference.nm <- input$reference_condition_fluorescence_parameter_plot
} else {
reference.conc <- NULL
reference.nm <- NULL
}
if(is.null(input$custom_colors_fluorescence_parameter_plot) || is.na(input$custom_colors_fluorescence_parameter_plot) || input$custom_colors_fluorescence_parameter_plot == ""){
cols <- NULL
} else {
cols <- toupper(unlist(str_split(input$custom_colors_fluorescence_parameter_plot, ", |; |,|;")))
}
if(input$select_string_visualize_parameter_fluorescence_plot){
suppressWarnings(
plot.parameter(results,
param = input$parameter_fluorescence_parameter_fluorescence_plot,
IDs = NULL,
names = input$select_sample_based_on_string_fluorescence_parameter_plot,
conc = input$select_sample_based_on_concentration_fluorescence_parameter_plot,
exclude.nm = input$exclude_sample_based_on_strings_fluorescence_parameter_plot,
exclude.conc = input$exclude_sample_based_on_concentration_fluorescence_parameter_plot,
reference.nm = reference.nm,
reference.conc = reference.conc,
shape.size = input$shape.size_fluorescence_parameter_plot,
basesize = input$basesize_fluorescence_parameter_plot,
label.size = input$label.size_fluorescence_parameter_plot,
legend.position = input$legend_position_fluorescence_parameter_plot,
legend.ncol = input$legend_ncol_fluorescence_parameter_plot,
order_by_conc = input$sort_by_conc_fluorescence_parameter_plot,
colors = cols
)
)
} else {
suppressWarnings(
plot.parameter(results,
param = input$parameter_fluorescence_parameter_fluorescence_plot,
IDs = input$samples_visualize_parameter_fluorescence_plot,
names = NULL,
conc = input$select_sample_based_on_concentration_fluorescence_parameter_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_sample_based_on_concentration_fluorescence_parameter_plot,
reference.nm = reference.nm,
reference.conc = reference.conc,
shape.size = input$shape.size_fluorescence_parameter_plot,
basesize = input$basesize_fluorescence_parameter_plot,
label.size = input$label.size_fluorescence_parameter_plot,
legend.position = input$legend_position_fluorescence_parameter_plot,
legend.ncol = input$legend_ncol_fluorescence_parameter_plot,
order_by_conc = input$sort_by_conc_fluorescence_parameter_plot,
colors = cols
)
)
}
})
output$fluorescence_parameter_plot <- renderPlot({
fluorescence_parameter_plot()
})
output$download_fluorescence_parameter_plot <- downloadHandler(
filename = function() {
paste("fluorescence_parameter_plot", input$format_download_fluorescence_parameter_plot, sep="")
},
content = function(file) {
if(input$format_download_fluorescence_parameter_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_fluorescence_parameter_plot,
height = input$height_download_fluorescence_parameter_plot,
dpi = input$dpi_download_fluorescence_parameter_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_fluorescence_parameter_plot,
height = input$height_download_fluorescence_parameter_plot,
dpi = input$dpi_download_fluorescence_parameter_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_fluorescence_parameter_plot,
height = input$height_download_fluorescence_parameter_plot,
dpi = input$dpi_download_fluorescence_parameter_plot,
device = png)
}
},
contentType = ifelse(input$format_download_fluorescence_parameter_plot == ".pdf", "image/pdf", "image/png")
)
selected_inputs_parameter_fluorescence_parameter_plot <- reactive({
results <- results$fluorescence
gc_parameters <- c()
if("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Increase rate (Spline)' = 'max_slope.spline',
'Increase rate phase 2 (Spline)' = 'max_slope.spline',
'Maximum fluorescence (Spline)' = 'A.spline',
'ΔFluorescence (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
} else {
gc_parameters <- c(gc_parameters,
'Increase rate (Spline)' = 'max_slope.spline',
'Maximum fluorescence (Spline)' = 'A.spline',
'ΔFluorescence (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
}
}
if("l" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Increase rate (linear fit)' = 'max_slope.linfit',
'Increase rate phase 2 (linear fit)' = 'max_slope.linfit',
'Maximum fluorescence (linear fit)' = 'A.linfit',
'ΔFluorescence (linear fit)' = 'dY.linfit')
} else {
gc_parameters <- c(gc_parameters,
'Increase rate (linear fit)' = 'max_slope.linfit',
'Maximum fluorescence (linear fit)' = 'A.linfit',
'ΔFluorescence (linear fit)' = 'dY.linfit')
}
}
gc_parameters
})
selected_inputs_reference_condition_fluorescence_parameter_plot <- reactive({
results <- results$fluorescence
results$expdesign$condition
})
select_inputs_reference_concentration_fluorescence_parameter_plot <- reactive({
results <- results$fluorescence
results$expdesign$concentration
})
observe({
updateSelectInput(inputId = "parameter_fluorescence_parameter_fluorescence_plot",
choices = selected_inputs_parameter_fluorescence_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_condition_fluorescence_parameter_plot",
choices = selected_inputs_reference_condition_fluorescence_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_concentration_fluorescence_parameter_plot",
choices = select_inputs_reference_concentration_fluorescence_parameter_plot()
)})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_fluorescence_plot",
choices = select_inputs_individual_plots_dose_response_fluorescence_plot())
})
selected_inputs_reference_condition_fluorescence_parameter_plot <- reactive({
results <- results$fluorescence
results$expdesign$condition
})
select_inputs_reference_concentration_fluorescence_parameter_plot <- reactive({
results <- results$fluorescence
results$expdesign$concentration
})
observe({
updateSelectInput(inputId = "reference_condition_fluorescence_parameter_plot",
choices = selected_inputs_reference_condition_fluorescence_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_concentration_fluorescence_parameter_plot",
choices = select_inputs_reference_concentration_fluorescence_parameter_plot()
)})
### Grid Plot ####
observe({
if(!exists("output$more_than_two_conc") || is.null(output$more_than_two_conc))
return(NULL)
if(!output$more_than_two_conc){
updateSelectInput(session = session,
inputId = "sort_by_conc_fluorescence_grid_plot",
selected = FALSE)
}
})
observe({
if(input$select_string_visualize_fluorescence_grid)
updateSelectInput(session = session,
inputId = "order_matters_visualize_fluorescence_grid",
selected = FALSE)
})
selected_inputs_fluorescence_grid_plot_data_type <- reactive({
results <- results$fluorescence
selection <- c()
if(length(results$data$fluorescence) > 1){
selection <- c(selection, "Raw fluorescence" = "raw")
}
if(length(results$data$norm.fluorescence) > 1){
selection <- c(selection, "Normalized FL" = "norm.fl")
}
if(length(results$data$fluorescence) > 1 && "s" %in% results$control$fit.opt){
selection <- c(selection, "Spline fits FL" = "spline")
}
selection
})
observe({
updateSelectInput(inputId = "data_type_fluorescence_grid_plot",
choices = selected_inputs_fluorescence_grid_plot_data_type())
})
selected_inputs_parameter_fluorescence_grid_plot <- reactive({
results <- results$fluorescence
gc_parameters <- c()
if("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Increase rate (Spline)' = 'max_slope.spline',
'Increase rate phase 2 (Spline)' = 'max_slope.spline',
'Maximum fluorescence (Spline)' = 'A.spline',
'ΔFluorescence (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
} else {
gc_parameters <- c(gc_parameters,
'Increase rate (Spline)' = 'max_slope.spline',
'Maximum fluorescence (Spline)' = 'A.spline',
'ΔFluorescence (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
}
}
if("l" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Increase rate (linear fit)' = 'max_slope.linfit',
'Increase rate phase 2 (linear fit)' = 'max_slope.linfit',
'Maximum fluorescence (linear fit)' = 'A.linfit',
'ΔFluorescence (linear fit)' = 'dY.linfit')
} else {
gc_parameters <- c(gc_parameters,
'Increase rate (linear fit)' = 'max_slope.linfit',
'Maximum fluorescence (linear fit)' = 'A.linfit',
'ΔFluorescence (linear fit)' = 'dY.linfit')
}
}
gc_parameters
})
observe({
updateSelectInput(inputId = "parameter_parameter_grid_plot_fluorescence",
choices = selected_inputs_parameter_fluorescence_grid_plot()
)})
selected_inputs_visualize_fluorescence_grid <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
if(input$plot_group_averages_fluorescence_grid_plot){
select_samples <- results$expdesign$condition
} else {
select_samples <- results$expdesign$label
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_fluorescence_grid",
choices = selected_inputs_visualize_fluorescence_grid()
)
})
observe({
updateSelectInput(session,
inputId = "groups_visualize_fluorescence_grid",
choices = selected_inputs_visualize_fluorescence_grid()
)
})
fluorescence_grid_plot <- reactive({
results <- results$fluorescence
if(input$y_axis_title_fluorescence_grid_plot == "")
y_axis_title <- NULL
else
y_axis_title <- input$y_axis_title_fluorescence_grid_plot
if(input$x_axis_title_fluorescence_grid_plot == "")
x_axis_title <- NULL
else
x_axis_title <- input$x_axis_title_fluorescence_grid_plot
if(input$select_string_visualize_fluorescence_grid){
suppressWarnings(
plot.grid(results,
data.type = input$data_type_fluorescence_grid_plot,
IDs = NULL,
sort_by_ID = FALSE,
names = input$select_samples_based_on_string_fluorescence_grid_plot,
conc = input$select_samples_based_on_concentration_fluorescence_grid_plot,
exclude.nm = input$exclude_samples_based_on_string_fluorescence_grid_plot,
exclude.conc = input$exclude_samples_based_on_concentration_fluorescence_grid_plot,
mean = input$plot_group_averages_fluorescence_grid_plot,
log.y = input$log_transform_y_axis_fluorescence_grid_plot,
x.lim = c(input$x_range_min_fluorescence_grid_plot, input$x_range_max_fluorescence_grid_plot),
y.lim = c(input$y_range_min_fluorescence_grid_plot,input$y_range_max_fluorescence_grid_plot),
y.title = y_axis_title,
x.title = x_axis_title,
n.ybreaks = input$nbreaks_fluorescence_grid_plot,
lwd = input$line_width_fluorescence_grid_plot,
basesize = input$base_size_fluorescence_grid_plot,
pal = input$color_palettes_grid_plot_fluorescence,
sort_by_conc = input$sort_by_conc_fluorescence_grid_plot,
invert.pal = input$invert_color_palette_grid_plot_fluorescence,
legend.lim = c(input$legend_lim_min_fluorescence_grid_plot, input$legend_lim_max_fluorescence_grid_plot),
nrow = input$nrows_fluorescence_grid_plot,
param = input$parameter_parameter_grid_plot_fluorescence
)
)
}
else{
suppressWarnings(
plot.grid(results,
data.type = input$data_type_fluorescence_grid_plot,
IDs = if(input$plot_group_averages_fluorescence_grid_plot){
input$groups_visualize_fluorescence_grid
}else{
input$samples_visualize_fluorescence_grid
},
sort_by_ID = input$order_matters_visualize_fluorescence_grid,
names = NULL,
conc = input$select_samples_based_on_concentration_fluorescence_grid_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_samples_based_on_concentration_fluorescence_grid_plot,
mean = input$plot_group_averages_fluorescence_grid_plot,
log.y = input$log_transform_y_axis_fluorescence_grid_plot,
x.lim = c(input$x_range_min_fluorescence_grid_plot, input$x_range_max_fluorescence_grid_plot),
y.lim = c(input$y_range_min_fluorescence_grid_plot,input$y_range_max_fluorescence_grid_plot),
y.title = y_axis_title,
x.title = x_axis_title,
n.ybreaks = input$nbreaks_fluorescence_grid_plot,
lwd = input$line_width_fluorescence_grid_plot,
basesize = input$base_size_fluorescence_grid_plot,
pal = input$color_palettes_grid_plot_fluorescence,
invert.pal = input$invert_color_palette_grid_plot_fluorescence,
sort_by_conc = input$sort_by_conc_fluorescence_grid_plot,
legend.lim = c(input$legend_lim_min_fluorescence_grid_plot, input$legend_lim_max_fluorescence_grid_plot),
nrow = input$nrows_fluorescence_grid_plot,
param = input$parameter_parameter_grid_plot_fluorescence
)
)
}
})
output$fluorescence_grid_plot <- renderPlot({
fluorescence_grid_plot()
})
output$download_fluorescence_grid_plot <- downloadHandler(
filename = function() {
paste("fluorescence_grid_plot", input$format_download_fluorescence_grid_plot, sep="")
},
content = function(file) {
if(input$format_download_fluorescence_grid_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_fluorescence_grid_plot,
height = input$height_download_fluorescence_grid_plot,
dpi = input$dpi_download_fluorescence_grid_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_fluorescence_grid_plot,
height = input$height_download_fluorescence_grid_plot,
dpi = input$dpi_download_fluorescence_grid_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_fluorescence_grid_plot,
height = input$height_download_fluorescence_grid_plot,
dpi = input$dpi_download_fluorescence_grid_plot,
device = png)
}
},
contentType = ifelse(input$format_download_fluorescence_grid_plot == ".pdf", "image/pdf", "image/png")
)
### DR Plots (Bootstrap) ####
observe({
if(length(results$fluorescence$drFit) > 1 && length(results$fluorescence$drFit$drTable) > 1 && results$fluorescence$control$nboot.dr > 1){
showTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponse_bt")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponse_bt")
}
})
dose_response_fluorescence_plot_individual_bt <- reactive({
results <- results$fluorescence$drFit$drBootSplines[[ifelse(input$individual_plots_dose_response_fluorescence_plot_bt == "1" || is.null(input$individual_plots_dose_response_fluorescence_plot_bt), 1, input$individual_plots_dose_response_fluorescence_plot_bt)]]
plot.drBootSpline(results,
pch = input$shape_type_dose_response_fluorescence_plot_bt,
cex.point = input$shape_size_dose_response_fluorescence_plot_bt,
cex.lab = input$axis_size_dose_response_fluorescence_plot_bt,
cex.axis = input$lab_size_dose_response_fluorescence_plot_bt,
lwd = input$line_width_dose_response_fluorescence_plot_bt,
combine = TRUE
)
})
output$dose_response_fluorescence_plot_individual_bt <- renderPlot({
dose_response_fluorescence_plot_individual_bt()
})
output$download_dose_response_fluorescence_plot_individual_bt <- downloadHandler(
filename = function() {
paste("dose_response_boot_fluorescence_", gsub(" \\| ", "_", input$individual_plots_dose_response_fluorescence_plot_bt), input$format_download_dose_response_fluorescence_plot_individual_bt, sep="")
},
content = function(file) {
if(input$format_download_dose_response_fluorescence_plot_individual_bt == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_dose_response_fluorescence_plot_individual_bt,
height = input$height_download_dose_response_fluorescence_plot_individual_bt)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_dose_response_fluorescence_plot_individual_bt,
height = input$height_download_dose_response_fluorescence_plot_individual_bt)
}
} else {
grDevices::png(file = file,
width = input$width_download_dose_response_fluorescence_plot_individual_bt,
height = input$height_download_dose_response_fluorescence_plot_individual_bt,
units = "in",
res = input$dpi_download_dose_response_fluorescence_plot_individual_bt)
}
results <- results$fluorescence$drFit$drBootSplines[[input$individual_plots_dose_response_fluorescence_plot_bt]]
plot.drBootSpline(results,
pch = input$shape_type_dose_response_fluorescence_plot_bt,
cex.point = input$shape_size_dose_response_fluorescence_plot_bt,
cex.lab = input$axis_size_dose_response_fluorescence_plot_bt,
cex.axis = input$lab_size_dose_response_fluorescence_plot_bt,
lwd = input$line_width_dose_response_fluorescence_plot_bt,
combine = TRUE
)
dev.off()
},
contentType = ifelse(input$format_download_dose_response_fluorescence_plot_individual_bt == ".pdf", "image/pdf", "image/png")
)
### Dual Plot ####
selected_inputs_visualize_dual_plot <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
if(input$plot_group_averages_dual_plot){
select_samples <- results$expdesign$condition
} else {
select_samples <- results$expdesign$label
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_dual_plot",
choices = selected_inputs_visualize_dual_plot()
)
})
observe({
if(length(results$fluorescence$data$growth) > 1 && length(results$fluorescence$data$fluorescence) > 1){
showTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPabel_Visualize_Dual")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPabel_Visualize_Dual")
}
})
dual_plot <- reactive({
results <- results$fluorescence
if(is.null(input$custom_colors_dual_plot) || is.na(input$custom_colors_dual_plot) || input$custom_colors_dual_plot == ""){
cols <- NULL
} else {
cols <- toupper(unlist(str_split(input$custom_colors_dual_plot, ", |; |,|;")))
}
if(input$select_string_visualize_dual_plot){
suppressWarnings(
plot.dual(results,
IDs = NULL,
fluorescence = input$fluorescence_type_dual_plot,
names = input$select_samples_based_on_string_dual_plot,
conc = input$select_samples_based_on_concentration_dual_plot,
exclude.nm = input$exclude_samples_based_on_string_dual_plot,
exclude.conc = input$exclude_samples_based_on_concentration_dual_plot,
mean = input$plot_group_averages_dual_plot,
log.y.growth = input$log_transform_y_axis_growth_dual_plot,
log.y.fl = input$log_transform_y_axis_fluorescence_dual_plot,
x.lim = c(input$x_range_min_dual_plot, input$x_range_max_dual_plot),
y.lim.growth = c(input$y_range_min_growth_dual_plot,input$y_range_max_growth_dual_plot),
y.lim.fl = c(input$y_range_min_fluorescence_dual_plot,input$y_range_max_fluorescence_dual_plot),
y.title.fl = input$y_axis_title_fluorescence_dual_plot,
y.title.growth = input$y_axis_title_growth_dual_plot,
x.title = input$x_axis_title_dual_plot,
n.ybreaks = input$nbreaks_dual_plot,
lwd = input$line_width_dual_plot,
basesize = input$base_size_dual_plot,
legend.position = input$legend_position_dual_plot,
legend.ncol = input$legend_ncol_dual_plot,
color_groups = input$color_groups_dual_plot,
colors = cols,
group_pals = input$color_palettes_dual_plot
)
)
} else {
suppressWarnings(
plot.dual(results,
fluorescence = input$fluorescence_type_dual_plot,
IDs = input$samples_visualize_dual_plot,
names = NULL,
conc = input$select_samples_based_on_concentration_dual_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_samples_based_on_concentration_dual_plot,
mean = input$plot_group_averages_dual_plot,
log.y.growth = input$log_transform_y_axis_growth_dual_plot,
log.y.fl = input$log_transform_y_axis_fluorescence_dual_plot,
x.lim = c(input$x_range_min_dual_plot, input$x_range_max_dual_plot),
y.lim.growth = c(input$y_range_min_growth_dual_plot,input$y_range_max_growth_dual_plot),
y.lim.fl = c(input$y_range_min_fluorescence_dual_plot,input$y_range_max_fluorescence_dual_plot),
y.title.fl = input$y_axis_title_fluorescence_dual_plot,
y.title.growth = input$y_axis_title_growth_dual_plot,
x.title = input$x_axis_title_dual_plot,
n.ybreaks = input$nbreaks_dual_plot,
lwd = input$line_width_dual_plot,
basesize = input$base_size_dual_plot,
color_groups = input$color_groups_dual_plot,
legend.position = input$legend_position_dual_plot,
legend.ncol = input$legend_ncol_dual_plot,
group_pals = input$color_palettes_dual_plot,
colors = cols
)
)
}
})
output$dual_plot <- renderPlot({
dual_plot()
})
output$download_dual_plot <- downloadHandler(
filename = function() {
paste("fluorescence_group_plot", input$format_download_dual_plot, sep="")
},
content = function(file) {
if(input$format_download_dual_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_dual_plot,
height = input$height_download_dual_plot,
dpi = input$dpi_download_dual_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_dual_plot,
height = input$height_download_dual_plot,
dpi = input$dpi_download_dual_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_dual_plot,
height = input$height_download_dual_plot,
dpi = input$dpi_download_dual_plot,
device = png)
}
},
contentType = ifelse(input$format_download_dual_plot == ".pdf", "image/pdf", "image/png")
)
selected_inputs_fluorescence_type_dual_plot <- reactive({
results <- results$fluorescence
selection <- c()
if(length(results$data$fluorescence) > 1){
selection <- c(selection, "Raw fluorescence" = "fl")
}
# if(length(results$data$fluorescence2) > 1){
# selection <- c(selection, "Raw fluorescence 2" = "fl2")
# }
if(length(results$data$norm.fluorescence) > 1){
selection <- c(selection, "Normalized FL" = "norm.fl")
}
# if(length(results$data$norm.fluorescence2) > 1){
# selection <- c(selection, "Normalized FL2" = "norm.fl2")
# }
selection
})
observe({
updateSelectInput(inputId = "fluorescence_type_dual_plot",
choices = selected_inputs_fluorescence_type_dual_plot())
})
### DR Parameter Plots ####
observe({
if(length(results$fluorescence$drFit) > 1 && length(results$fluorescence$drFit$drTable) > 1){
showTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponseParameters")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponseParameters")
}
})
fluorescence_dr_parameter_plot <- reactive({
results <- results$fluorescence
if (input$normalize_to_reference_fluorescence_dr_parameter_plot){
reference.nm <- input$reference_condition_fluorescence_dr_parameter_plot
} else {
reference.nm <- NULL
}
plot.dr_parameter(results,
param = input$parameter_dr_parameter_fluorescence_plot,
names = input$select_sample_based_on_string_fluorescence_dr_parameter_plot,
exclude.nm = input$exclude_sample_based_on_strings_fluorescence_dr_parameter_plot,
reference.nm = reference.nm,
basesize = input$basesize_fluorescence_dr_parameter_plot,
label.size = input$label.size_fluorescence_dr_parameter_plot
)
})
output$fluorescence_dr_parameter_plot <- renderPlot({
fluorescence_dr_parameter_plot()
})
output$download_fluorescence_dr_parameter_plot <- downloadHandler(
filename = function() {
paste("fluorescence_dr_parameter_plot", input$format_download_fluorescence_dr_parameter_plot, sep="")
},
content = function(file) {
if(input$format_download_fluorescence_dr_parameter_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_fluorescence_dr_parameter_plot,
height = input$height_download_fluorescence_dr_parameter_plot,
dpi = input$dpi_download_fluorescence_dr_parameter_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_fluorescence_dr_parameter_plot,
height = input$height_download_fluorescence_dr_parameter_plot,
dpi = input$dpi_download_fluorescence_dr_parameter_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_fluorescence_dr_parameter_plot,
height = input$height_download_fluorescence_dr_parameter_plot,
dpi = input$dpi_download_fluorescence_dr_parameter_plot,
device = png)
}
},
contentType = ifelse(input$format_download_fluorescence_dr_parameter_plot == ".pdf", "image/pdf", "image/png")
)
selected_inputs_parameter_fluorescence_dr_parameter_plot <- reactive({
results <- results$fluorescence
if(is.null(results)) return(NULL)
if(results$control$dr.method == "spline"){
dr_parameters <- c('Response(EC50)' = 'yEC50','EC50' = 'EC50')
if(results$control$nboot.dr > 1){
dr_parameters <- c(dr_parameters, 'Response(EC50) - Bootstrap' = 'drboot.meanEC50', 'EC50 - Bootstrap' = 'drboot.meanEC50y')
}
if(input$log_transform_response_fluorescence){
dr_parameters <- c(dr_parameters, 'Original response(EC50)' = 'yEC50.orig')
}
if(input$log_transform_concentration_fluorescence){
dr_parameters <- c(dr_parameters, 'Original EC50' = 'EC50.orig')
}
} else {
dr_parameters <- c('Leakiness' = 'y.min','Sensitivity' = 'K', 'Fold change' = 'fc', 'Maximum response' = 'y.max')
}
dr_parameters
})
selected_inputs_reference_condition_fluorescence_dr_parameter_plot <- reactive({
results <- results$fluorescence
results$expdesign$condition
})
select_inputs_individual_plots_dose_response_fluorescence_plot <- reactive({
if (length(results$fluorescence$drFit)>1) names(results$fluorescence$drFit$drFittedSplines)
else return("")
})
select_inputs_individual_plots_dose_response_fluorescence_plot_bt <- reactive({
if (length(results$fluorescence$drFit)>1 && results$fluorescence$control$nboot.dr > 1) names(results$fluorescence$drFit$drBootSplines)
else return("")
})
observe({
updateSelectInput(inputId = "parameter_dr_parameter_fluorescence_plot",
choices = selected_inputs_parameter_fluorescence_dr_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_condition_fluorescence_dr_parameter_plot",
choices = selected_inputs_reference_condition_fluorescence_dr_parameter_plot()
)})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_fluorescence_plot_bt",
choices = select_inputs_individual_plots_dose_response_fluorescence_plot_bt()
)})
# Report ####
volumes <- getVolumes() # this makes the directory at the base of your computer.
observe({
if(!is.null(results$growth)){
showTab(inputId = "tabsetPanel_Report", target = "tabPanel_report_growth")
} else {
hideTab(inputId = "tabsetPanel_Report", target = "tabPanel_report_growth")
}
if(!is.null(results$fluorescence)){
showTab(inputId = "tabsetPanel_Report", target = "tabPanel_report_fluorescence")
} else {
hideTab(inputId = "tabsetPanel_Report", target = "tabPanel_report_fluorescence")
}
})
observeEvent(input$run_report_fix, {
# check if pandoc is recognized
showModal(
modalDialog("Running report troubleshooting...", footer = NULL)
)
pandoc.path <- rmarkdown::find_pandoc()$dir
Sys.setenv(RSTUDIO_PANDOC = pandoc.path)
Sys.setenv(PANDOC_PATH = pandoc.path)
# reinstall tinytex
tinytex::tlmgr_update()
tinytex::reinstall_tinytex()
removeModal()
})
## Report Growth ####
output$download_report_growth_pdf <- downloadHandler(
filename = function() {
paste0("GrowthReport.", input$report_filetype_growth)
},
content = function(file) {
if (!requireNamespace("tinytex", quietly = TRUE)) {
showModal(
modalDialog("Please install package 'tinytex' to render PDF reports.", easyClose = T)
)
} else if(!tinytex::is_tinytex()){
# stop("TinyTex was not found on your system. To render PDF reports, please execute tinytex::install_tinytex().")
showModal(
modalDialog(HTML("TinyTeX is required to render PDF reports but was not found on your system. Installing TinyTeX...<br><br>(This requires and active internet connection and will take several minutes)"), footer = NULL)
)
update.packages(ask = FALSE, checkBuilt = TRUE, repos='http://cran.us.r-project.org')
tinytex::install_tinytex()
tinytex::tlmgr_update()
tinytex::reinstall_tinytex()
removeModal()
try(
suppressWarnings(
suppressMessages(
growth.report(grofit = results$growth,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file),
ec50 = ifelse(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1, TRUE, FALSE),
format = input$report_filetype_growth,
export = FALSE,
mean.grp = "all")
)
)
)
} else {
try(
suppressWarnings(
suppressMessages(
growth.report(grofit = results$growth,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file),
ec50 = ifelse(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1, TRUE, FALSE),
format = input$report_filetype_growth,
export = FALSE,
mean.grp = "all")
)
)
)
}
},
contentType = paste0(".", input$report_filetype_growth)
)
output$download_report_growth_html <- downloadHandler(
filename = function() {
paste0("GrowthReport.", input$report_filetype_growth)
},
content = function(file) {
try(
suppressWarnings(
suppressMessages(
growth.report(grofit = results$growth,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file),
ec50 = ifelse(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1, TRUE, FALSE),
format = input$report_filetype_growth,
export = FALSE,
mean.grp = "all")
)
)
)
},
contentType = paste0(".", input$report_filetype_growth)
)
## Report Fluorescence ####
output$download_report_fluorescence_pdf <- downloadHandler(
filename = function() {
paste0("FluorescenceReport.", input$report_filetype_fluorescence)
},
content = function(file) {
if (!requireNamespace("tinytex", quietly = TRUE)) {
showModal(
modalDialog("Please install package 'tinytex' to render PDF reports.", easyClose = T)
)
} else if(!tinytex::is_tinytex()){
# stop("TinyTex was not found on your system. To render PDF reports, please execute tinytex::install_tinytex().")
showModal(
modalDialog(HTML("TinyTeX is required to render PDF reports but was not found on your system. Installing TinyTeX...<br><br>(This requires and active internet connection and will take several minutes)"), footer = NULL)
)
update.packages(ask = FALSE, checkBuilt = TRUE, repos='http://cran.us.r-project.org')
tinytex::install_tinytex()
tinytex::tlmgr_update()
tinytex::reinstall_tinytex()
removeModal()
try(
suppressWarnings(
suppressMessages(
fl.report(flFitRes = results$fluorescence,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file),
ec50 = ifelse(length(results$fluorescence$drFit) > 1 && length(results$fluorescence$drFit$drTable) > 1, TRUE, FALSE),
format = input$report_filetype_fluorescence,
export = FALSE,
mean.grp = "all")
)
)
)
} else {
try(
suppressWarnings(
suppressMessages(
fl.report(flFitRes = results$fluorescence,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file),
ec50 = ifelse(length(results$fluorescence$drFit) > 1 && length(results$fluorescence$drFit$drTable) > 1, TRUE, FALSE),
format = input$report_filetype_fluorescence,
export = FALSE,
mean.grp = "all")
)
)
)
}
},
contentType = paste0(".", input$report_filetype_fluorescence)
)
output$download_report_fluorescence_html <- downloadHandler(
filename = function() {
paste0("FluorescenceReport.", input$report_filetype_fluorescence)
},
content = function(file) {
try(
suppressWarnings(
suppressMessages(
fl.report(flFitRes = results$fluorescence,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file),
ec50 = ifelse(length(results$fluorescence$drFit) > 1 && length(results$fluorescence$drFit$drTable) > 1, TRUE, FALSE),
format = input$report_filetype_fluorescence,
export = FALSE,
mean.grp = "all")
)
)
)
},
contentType = paste0(".", input$report_filetype_fluorescence)
)
# Bug report message ####
github_url <- a("QurvE Github", href="https://github.com/NicWir/QurvE/issues")
output$bug_report <- renderUI({
tagList("Please report bugs and user feedback at:", github_url)
})
# Export RData files ####
observe({
if(!is.null(results$growth)){
showTab(inputId = "tabsetPanel_Export_Data", target = "tabPanel_export_data_growth")
} else {
hideTab(inputId = "tabsetPanel_Export_Data", target = "tabPanel_export_data_growth")
}
if(!is.null(results$fluorescence)){
showTab(inputId = "tabsetPanel_Export_Data", target = "tabPanel_export_data_fluorescence")
} else {
hideTab(inputId = "tabsetPanel_Export_Data", target = "tabPanel_export_data_fluorescence")
}
})
## RData Growth ####
output$export_RData_growth <- downloadHandler(
filename = function() {
paste0("GrowthResults.RData")
},
content = function(file) {
try(
suppressWarnings(
suppressMessages(
QurvE:::export_RData(object = results$growth,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file)
)
)
)
)
},
contentType = paste0(".RData")
)
## RData Fluorescence ####
output$export_RData_fluorescence <- downloadHandler(
filename = function() {
paste0("FluorescenceResults.RData")
},
content = function(file) {
try(
suppressWarnings(
suppressMessages(
QurvE:::export_RData(object = results$fluorescence,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file)
)
)
)
)
},
contentType = paste0(".RData")
)
# Import RData files
## Growth
output$RData_growth_uploaded <- reactive({
if(is.null(input$import_RData_growth)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'RData_growth_uploaded', suspendWhenHidden=FALSE)
observeEvent(input$read_RData_growth,{
showModal(modalDialog("Reading data file...", footer=NULL))
try(load(input$import_RData_growth$datapath))
try(results$growth <- object)
if(!is.null("results$growth")){
# ENABLE DISABLED PANELS AFTER RUNNING COMPUTATION
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Export_RData]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Report]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Visualize]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Results]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Validate]")
removeModal()
} else showModal(modalDialog(geterrmessage(), footer=NULL, easyClose = T))
})
## Fluorescence
output$RData_fluorescence_uploaded <- reactive({
if(is.null(input$import_RData_fluorescence)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'RData_fluorescence_uploaded', suspendWhenHidden=FALSE)
observeEvent(input$read_RData_fluorescence,{
showModal(modalDialog("Reading data file...", footer=NULL))
try(load(input$import_RData_fluorescence$datapath))
try(results$fluorescence <- object)
if(!is.null("results$fluorescence")){
## ENABLE DISABLED PANELS AFTER RUNNING COMPUTATION
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Export_RData]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Report]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Visualize]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Results]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Validate]")
removeModal()
} else showModal(modalDialog(geterrmessage(), footer=NULL, easyClose = T))
})
# Show content after initializing ####
load_data()
# Close the app when the session completes ####
if(!interactive()) {
session$onSessionEnded(function() {
stopApp()
q("no")
})
}
}
shinyApp(ui = ui, server = server)
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options(shiny.maxRequestSize=60*1024^2)
#list of packages required
list.of.packages <- c("shiny", "shinythemes", "shinyFiles", "shinyjs", "shinyBS", "shinycssloaders", "QurvE")
#list of packages required
new_packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
#install missing packages
for( i in new_packages ){
# require returns TRUE invisibly if it was able to load package
if( ! require( i , character.only = TRUE ) ){
# If package was not able to be loaded then re-install
install.packages( i , dependencies = TRUE )
# Load package after installing
require( i , character.only = TRUE , quietly = T)
}
}
library(shiny, quietly = T)
library(QurvE, quietly = T)
library(shinyBS, quietly = T)
library(shinycssloaders, quietly = T)
library(shinyFiles, quietly = T)
library(shinyjs, quietly = T)
library(shinythemes, quietly = T)
library(ggplot2, quietly = T)
library(magrittr)
# Define icon set from custom SVG files
# iconset <- icons::icon_set("icons/")
jscode <- "
shinyjs.disableTab = function(name) {
var tab = $('.nav li a[data-value=' + name + ']');
tab.bind('click.tab', function(e) {
e.preventDefault();
return false;
});
tab.addClass('disabled');
}
shinyjs.enableTab = function(name) {
var tab = $('.nav li a[data-value=' + name + ']');
tab.unbind('click.tab');
tab.removeClass('disabled');
}
"
css <- "
.nav li a.disabled {
background-color: #65675F !important;
color: #333 !important;
cursor: not-allowed !important;
border-color: #aaa !important;
}"
load_data <- function() {
Sys.sleep(2)
hide("loading_page")
show("main_content")
}
widePopover <-
'<div class="popover popover-lg" role="tooltip"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'
ui <- fluidPage(theme = shinythemes::shinytheme(theme = "spacelab"),
tags$head(
tags$style(HTML(".popover.popover-lg {width: 600px; max-width: 600px;}"))
),
tags$style(
type = 'text/css',
'.modal-dialog.gcFitLinear { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.rdm.data { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.gcFitSpline { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.gcFitModel { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.flFitLinear { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.flFitSpline { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.flworkflow { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.fldr { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.growthworkflow { width: fit-content !important; }'
),
tags$style(
type = 'text/css',
'.modal-dialog.growthdr { width: fit-content !important; }'
),
tagList(
useShinyjs(),
shinyjs::extendShinyjs(text = jscode, functions = c("disableTab","enableTab")),
shinyjs::inlineCSS(css),
div(
id = "loading_page",
HTML("<br>"),
HTML("<br>"),
HTML('<center><img src="QurvE_logo.png" width="500" vertical-align="middle"></center>'),
HTML("<br>"),
HTML("<br>"),
h1("Initializing...", align = "center")
),
hidden(
div(
id = "main_content",
navbarPage(
'QurvE',
id = "navbar",
# load input file
#____DATA____####
tabPanel(span("Data", title = "Upload custom formatted data or parse results from plate readers and similar devices."),
icon = icon("file-lines"),
value = "tabPanel",
tabsetPanel(type = "tabs", id = "tabs_data",
##____CUSTOM____####
tabPanel(value = "Custom", span("Custom", title="Upload manually formatted data. Data from different experiments can be added. In column format, the first three table rows contain (see figure):\n1. sample description\n2. replicate number (optional: followed by a letter to indicate technical replicates)\n3. concentration value (optional, for dose-response analysis)"),
sidebarPanel(
style='border-color: #ADADAD',
# Growth data
wellPanel(
h4(strong("Growth data"), style = "line-height: 0.4;font-size: 150%; margin-bottom: 15px;"),
style='padding: 0.1; border-color: #ADADAD; padding: 1; padding-bottom: 0',
fileInput(inputId = 'custom_file_growth',
label = 'Choose growth data file',
accept = c('.xlsx', '.xls', '.csv', '.txt', '.tsv')
),
conditionalPanel(
condition = "output.growthfileUploaded && output.custom_growth_format == 'xlsx'",
div(style = "margin-bottom: -20px"),
selectInput(inputId = "custom_growth_sheets",
label = "Select Sheet",
choices = "Sheet1")
), # select sheet: conditional
conditionalPanel(
condition = "output.growthfileUploaded && output.custom_growth_format == 'csv'",
selectInput(inputId = "separator_custom_growth",
label = "Select separator",
choices = c("," = ",",
";" = ";")
),
selectInput(inputId = "decimal_separator_custom_growth",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
),
conditionalPanel(
condition = "output.growthfileUploaded && (output.custom_growth_format == 'tsv' || output.custom_growth_format == 'txt')",
selectInput(inputId = "decimal_separator_custom_growth",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
),
div(style = "margin-bottom: -15px"),
tags$div(title="Provide an equation in the form 'y = function(x)' (for example: 'y = x^2 * 0.3 - 0.5') to convert growth values. This can be used to, e.g., convert plate reader absorbance values into OD600.",
checkboxInput(inputId = 'calibration_growth_custom',
label = 'Apply calibration')
),
conditionalPanel(
condition = 'input.calibration_growth_custom',
div(style = "margin-bottom: -10px"),
textInput(inputId = "calibration_equation_growth_custom",
label = "Type equation in the form 'y = function(x)'",
placeholder = 'y = x * 0.5 - 1'
)
),
),
#____Fluorescence___________
wellPanel(
h4(strong("Fluorescence data"), style = "line-height: 1;font-size: 150%; margin-bottom: 15px;"),
style='padding: 0.1; border-color: #ADADAD; padding: 1; padding-bottom: 0',
fileInput(inputId = 'custom_file_fluorescence',
label = 'Choose fluorescence data file',
accept = c('.xlsx', '.xls', '.csv', '.txt', '.tsv')
),
conditionalPanel(
condition = "output.fluorescencefileUploaded && output.custom_fluorescence_format == 'xlsx'",
div(style = "margin-bottom: -20px"),
selectInput(inputId = "custom_fluorescence_sheets",
label = "Select Sheet",
choices = "Sheet1")
), # select sheet: conditional
conditionalPanel(
condition = "output.fluorescencefileUploaded && output.custom_fluorescence_format == 'csv'",
selectInput(inputId = "separator_custom_fluorescence",
label = "Select separator",
choices = c("," = ",",
";" = ";")
),
selectInput(inputId = "decimal_separator_custom_fluorescence",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
),
conditionalPanel(
condition = "output.fluorescencefileUploaded && (output.custom_fluorescence_format == 'tsv' || output.custom_fluorescence_format == 'txt')",
selectInput(inputId = "decimal_separator_custom_fluorescence",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
),
div(style = "margin-top: -15px"),
tags$div(title="Provide an equation in the form 'y = function(x)' (for example: 'y = x^2 * 0.3 - 0.5') to convert fluorescence values. This can be used to, e.g., convert fluorescence intensities into molecule concentrations.",
checkboxInput(inputId = 'calibration_fluorescence_custom',
label = 'Apply calibration')
),
conditionalPanel(
condition = 'input.calibration_fluorescence_custom',
div(style = "margin-bottom: -10px"),
textInput(inputId = "calibration_equation_fluorescence_custom",
label = "Type equation in the form 'y = function(x)'",
placeholder = 'y = x * 0.5 - 1'
)
),
), # wellpanel
conditionalPanel(
condition = "output.fluorescencefileUploaded",
checkboxInput(inputId = 'load_fl2_data_custom',
label = 'Use second fluorescence to normalize fluorescence.',
value = FALSE),
bsPopover("load_fl2_data_custom", title = "Provide a table file with fluorescence 2 data",
content = "Table layout must mimic that of growth data. Fluorescence 2 data is only used to normalize of fluorescence!")
),
# #_____Fluorescence 2___________
conditionalPanel(
condition = "input.load_fl2_data_custom",
wellPanel(
h4(strong("Fluorescence 2 data"), style = "line-height: 1;font-size: 150%; margin-bottom: 15px;"),
style='padding: 0.1; border-color: #ADADAD; padding: 1; padding-bottom: 0',
fileInput(inputId = 'custom_file_fluorescence2',
label = 'Choose fluorescence2 data file',
accept = c('.xlsx', '.xls', '.csv', '.txt', '.tsv')
),
conditionalPanel(
condition = "output.fluorescence2fileUploaded && output.custom_fluorescence2_format == 'xlsx'",
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-bottom: 0',
div(style = "margin-bottom: -20px"),
selectInput(inputId = "custom_fluorescence2_sheets",
label = "Select Sheet",
choices = "Sheet1")
)
), # select sheet: conditional
conditionalPanel(
condition = "output.fluorescence2fileUploaded && output.custom_fluorescence2_format == 'csv'",
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-bottom: 0',
selectInput(inputId = "separator_custom_fluorescence2",
label = "Select separator",
choices = c("," = ",",
";" = ";")
),
selectInput(inputId = "decimal_separator_custom_fluorescence2",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
)
),
conditionalPanel(
condition = "output.fluorescence2fileUploaded && (output.custom_fluorescence2_format == 'tsv' || output.custom_fluorescence2_format == 'txt')",
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-bottom: 0',
selectInput(inputId = "decimal_separator_custom_fluorescence2",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
)
),
div(style = "margin-top: -15px"),
tags$div(title="Provide an equation in the form 'y = function(x)' (for example: 'y = x^2 * 0.3 - 0.5') to convert fluorescence2 values. This can be used to, e.g., convert fluorescence intensities into molecule concentrations.",
checkboxInput(inputId = 'calibration_fluorescence2_custom',
label = 'Apply calibration')
),
conditionalPanel(
condition = 'input.calibration_fluorescence2_custom',
div(style = "margin-bottom: -10px"),
textInput(inputId = "calibration_equation_fluorescence2_custom",
label = "Type equation in the form 'y = function(x)'",
placeholder = 'y = x * 0.5 - 1'
)
),
) # wellPanel
),
selectInput(inputId = 'norm_type_custom',
label = 'Select data type for fluorescence normalization',
choices = ""),
tags$div(title="Shall blank values (the mean of samples identified by 'Blank' IDs) be subtracted from values within the same experiment?",
checkboxInput(inputId = 'subtract_blank_custom',
label = 'Subtract blank',
value = TRUE)
),
tags$div(title=HTML(paste("Provide an equation in the form 'y = function(x)' to convert time values. For example, type 'y = x / 60' to convert minutes to hours.\n", "Note: the time unit will affect calculated parameters (e.g., the growth rate in 1/h, 1/min, or 1/s) as well as the time displayed in all plots.")),
checkboxInput(inputId = 'convert_time_values_custom',
label = 'Convert time values',
value = FALSE)
),
conditionalPanel(
condition = 'input.convert_time_values_custom',
tags$div(title=HTML(paste("Provide an equation in the form 'y = function(x)' to convert time values. For example, type 'y = x / 60' to convert minutes to hours.\n", "Note: the time unit will affect calculated parameters (e.g., the growth rate in 1/h, 1/min, or 1/s) as well as the time displayed in all plots.")),
div(style = "margin-bottom: -10px"),
textInput(inputId = "convert_time_equation_custom",
label = "Type equation in the form 'y = function(x)'",
placeholder = 'y = x / 24')
)
),
conditionalPanel(
condition = 'output.growthfileUploaded || output.fluorescencefileUploaded',
fluidRow(
column(12,
div(
actionButton(inputId = "read_custom",
label = "Read data",
icon=icon("file-lines"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
),
HTML("<br>"),
HTML("<br>"),
tags$div(title="Simulate growth curves to generate a random demo dataset.",
actionButton(inputId = "random_data_growth",
label = "Create random growth dataset",
icon=icon("shuffle"),
style="padding:4px; font-size:80%; color: white; background-color: #35e51d")
),
),# sidebar panel
), # Custom tabPanel
##____PLATE READER____####
tabPanel(value = "Parse Raw Data", span("Parse Raw Data", title="Upload a results table file generated with the default export function of a plate reader (or similar device) software. Sample information need to be provided in a separate table."),
sidebarPanel(
style='border-color: #ADADAD',
wellPanel(
div(style = "margin-top: -10px"),
h3(strong("1. Load data"), style = "line-height: 0.4;font-size: 150%; margin-bottom: 15px;"),
style='padding: 5px; border-color: #ADADAD;',
# select file type
fileInput(inputId = 'parse_file',
label = 'Choose raw export file',
accept = c('.xlsx', '.xls', '.csv', '.tsv', '.txt')),
div(style = "margin-top: -10px"),
conditionalPanel(
condition = "output.parsefileUploaded && (output.parse_file_format == 'xlsx' | output.parse_file_format == 'xls')",
selectInput(inputId = "parse_data_sheets",
label = "Select sheet with read data",
choices = "Sheet1")
), # select sheet: conditional
conditionalPanel(
condition = "output.parsefileUploaded && output.parse_file_format == 'csv'",
selectInput(inputId = "separator_parse",
label = "Select separator",
choices = c("," = ",",
";" = ";")
),
selectInput(inputId = "decimal_separator_parse",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
),
conditionalPanel(
condition = "output.parsefileUploaded && (output.parse_file_format == 'tsv' || output.parse_file_format == 'txt')",
selectInput(inputId = "decimal_separator_parse",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
)
),
div(style = "margin-top: -15px"),
conditionalPanel(
condition = "output.parsefileUploaded",
wellPanel(
style='padding: 5px; border-color: #ADADAD; padding-bottom: 0',
div(style = "margin-top: -10px"),
h3(strong("2. Format"), style = "line-height: 0.4;font-size: 150%; margin-bottom: 15px;"),
selectizeInput(inputId = "platereader_software",
label = "Platereader software",
choices = c("Biotek - Gen5/Gen6" = "Gen5",
"Biolector" = "Biolector",
"Chi.Bio" = "Chi.Bio",
"Growth Profiler 960" = "GrowthProfiler",
"Tecan i-control" = "Tecan",
"PerkinElmer - Victor Nivo" = "VictorNivo",
"PerkinElmer - Victor X3" = "VictorX3"
),
multiple = TRUE,
options = list(maxItems = 1)
)
)
),
div(style = "margin-top: -15px"),
conditionalPanel(
condition = "output.parsefileUploaded",
wellPanel(
style='padding: 5px; border-color: #ADADAD; padding-bottom: 0',
div(style = "margin-top: -10px"),
h3(strong("3. Assign data type"), style = "line-height: 0.4; font-size: 150%; margin-bottom: 15px;"),
conditionalPanel(
condition = "input.platereader_software != 'GrowthProfiler'",
# Growth Read
selectInput(inputId = "parsed_reads_growth",
label = "Growth data",
choices = ""
),
conditionalPanel(
condition = "input.parsed_reads_growth.length > 0 && input.parsed_reads_growth != 'Ignore'",
div(style = "margin-top: -15px"),
tags$div(title="Provide an equation in the form 'y = function(x)' (for example: 'y = x^2 * 0.3 - 0.5') to convert growth values. This can be used to, e.g., convert plate reader absorbance values into OD600.",
checkboxInput(inputId = 'calibration_growth_plate_reader',
label = 'Apply calibration'),
),
conditionalPanel(
condition = "input.calibration_growth_plate_reader",
div(style = "margin-bottom: -10px"),
textInput(inputId = "calibration_equation_growth_plate_reader",
label = NULL,
placeholder = 'y = x * 0.5 - 1',
width = "85%")
)
),
# Fluorescence Read
selectInput(inputId = "parsed_reads_fluorescence",
label = "Fluorescence data",
choices = ""
),
conditionalPanel(
condition = "input.parsed_reads_fluorescence.length > 0 && input.parsed_reads_fluorescence != 'Ignore'",
div(style = "margin-top: -15px"),
tags$div(title="Provide an equation in the form 'y = function(x)' (for example: 'y = x^2 * 0.3 - 0.5') to convert fluorescence values. This can be used to, e.g., convert fluorescence intensities into molecule concentrations.",
checkboxInput(inputId = 'calibration_fluorescence_plate_reader',
label = 'Apply calibration')
),
conditionalPanel(
condition = "input.calibration_fluorescence_plate_reader",
div(style = "margin-bottom: -10px"),
textInput(inputId = "calibration_equation_fluorescence_plate_reader",
label = NULL,
placeholder = 'y = x * 0.5 - 1',
width = "85%"),
)
),
# Fluorescence2 Read
selectInput(inputId = "parsed_reads_fluorescence2",
label = "Fluorescence data 2 (used only for normalization)",
choices = ""
),
conditionalPanel(
condition = "input.parsed_reads_fluorescence2.length > 0 && input.parsed_reads_fluorescence2 != 'Ignore'",
div(style = "margin-top: -15px"),
tags$div(title="Provide an equation in the form 'y = function(x)' (for example: 'y = x^2 * 0.3 - 0.5') to convert fluorescence values. This can be used to, e.g., convert fluorescence intensities into molecule concentrations.",
checkboxInput(inputId = 'calibration_fluorescence2_plate_reader',
label = 'Apply calibration')
),
conditionalPanel(
condition = "input.calibration_fluorescence2_plate_reader",
div(style = "margin-bottom: -10px"),
textInput(inputId = "calibration_equation_fluorescence2_plate_reader",
label = NULL,
placeholder = 'y = x * 0.5 - 1',
width = "85%")
)
),
), # conditionalPanel
) # wellPanel
),
div(style = "margin-top: -15px"),
conditionalPanel(
condition = "output.parsefileUploaded",
wellPanel(
style='padding: 5px; border-color: #ADADAD; padding-bottom: 0',
div(style = "margin-top: -10px"),
h3(strong("4. Load mapping"), style = "line-height: 0.4; font-size: 150%; margin-bottom: 15px;"),
conditionalPanel(
condition = "output.parse_file_format == 'xlsx' | output.parse_file_format == 'xls'",
tags$div(title="A table with mapping information is stored within the same Excel file that contains experimental data as separate sheet.",
checkboxInput(inputId = 'mapping_included_in_parse',
label = 'Included in data file (xlsx/xls)',
value = FALSE)
)
),
tags$div(title = "Table with four columns: Well | Description | Replicate | Concentration",
fileInput(inputId = 'map_file',
label = 'Choose mapping file',
accept = c('.xlsx', '.xls', '.csv', '.tsv', '.txt'),
placeholder = "map file",
)
),
conditionalPanel(
condition = "(input.mapping_included_in_parse && (output.parse_file_format == 'xlsx' | output.parse_file_format == 'xls')) || (!input.mapping_included_in_parse && output.mapfileUploaded && (output.map_file_format == 'xlsx' | output.map_file_format == 'xls'))",
selectInput(inputId = "map_data_sheets",
label = "Select sheet with mapping information",
choices = "Sheet1")
),
conditionalPanel(
condition = "!input.mapping_included_in_parse && output.mapfileUploaded && output.map_file_format == 'csv'",
selectInput(inputId = "separator_map",
label = "Select separator",
choices = c("," = ",",
";" = ";")
),
selectInput(inputId = "decimal_separator_map",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
),
conditionalPanel(
condition = "!input.mapping_included_in_parse && output.mapfileUploaded && (output.map_file_format == 'tsv' || output.map_file_format == 'txt')",
style='padding: 5px; border-color: #ADADAD; padding-bottom: 0',
selectInput(inputId = "decimal_separator_map",
label = "Select Decimal separator",
choices = c("." = ".",
"," = ",")
)
)
)
),
conditionalPanel(
condition = "input.parsed_reads_fluorescence.length > 0 && input.parsed_reads_fluorescence != 'Ignore'",
selectInput(inputId = 'norm_type_parse',
label = 'Select Read for fluorescence normalization',
choices = "")
),
tags$div(title="Shall blank values (the mean of samples identified by 'Blank' IDs) be subtracted from values within the same experiment?",
checkboxInput(inputId = 'subtract_blank_plate_reader',
label = 'Subtract blank',
value = TRUE)
),
tags$div(title=HTML(paste("Provide an equation in the form 'y = function(x)' to convert time values. For example, type 'y = x / 60' to convert minutes to hours.\n", "Note: the time unit will affect calculated parameters (e.g., the growth rate in 1/h, 1/min, or 1/s) as well as the time displayed in all plots.")),
checkboxInput(inputId = 'convert_time_values_plate_reader',
label = 'Convert time values',
value = TRUE)
),
conditionalPanel(
condition = 'input.convert_time_values_plate_reader',
tags$div(title=HTML(paste("Provide an equation in the form 'y = function(x)' to convert time values. For example, type 'y = x / 60' to convert minutes to hours.\n", "Note: the time unit will affect calculated parameters (e.g., the growth rate in 1/h, 1/min, or 1/s) as well as the time displayed in all plots.")),
div(style = "margin-bottom: -10px"),
textInput(inputId = "convert_time_equation_plate_reader",
label = "Type equation in the form 'y = function(x)'",
placeholder = 'y = x / 24')
)
),
fluidRow(
column(12,
div(
actionButton(inputId = "parse_data",
label = "Parse data",
icon=icon("file-lines"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
),# sidebar panel
), # Plate reader tabPanel
), # tabSet Panel
##____DATA - MAIN PANEL____####
mainPanel(
div(
id = "data_instruction",
conditionalPanel(
condition = "input.tabs_data == 'Custom'",
img(src = 'data_instruction.png',
width = '100%')
)
),
bsPopover(id = "data_instruction", title = "Custom data layout",
content = paste("Please format your data in the format shown in the figure:",
paste0(
"<ul>",
"<li>The first row contains \\'Time\\' and \\'Blank\\', as well as sample identifiers \\(identical for replicates\\).</li>",
"<li>The second row contains replicate numbers for identical conditions. If technical replicates were used in addition to biological replicates, indicate technical replicates with the same replicate number. Samples with identical IDs, concentrations, and replicate <i>numbers</i> will be combined by their <i>average</i>.</li>",
"<li>The third row contains \\(optional\\) concentration values to perform a dose-response analysis, if different concentrations of a compound were used in the experiment.</li>",
"</ul>"
),
"Details:",
paste0(
"<ul>",
"<li>Different experiments with differing time values and experiment-specific blanks are distinguished by an individual \\'Time\\' column to the left of each dataset.</li>",
"<li>Blank values \\(for each experiment\\) are combined as their average and subtracted from all remaining values if option \\[Subtract blank\\] is selected.</li>",
"<li>The metadata in the second and third rows are optional to perform the analysis and can be left empty.</li>",
"</ul>"
),
sep = "<br>"),
trigger = "hover", options = list(container = "body", template = widePopover)
),
div(
id = "mapping_layout",
conditionalPanel(
condition = "input.tabs_data != 'Custom' && output.parsefileUploaded",
img(src = 'mapping_layout.png',
width = '60%')
)
),
bsPopover(id = "mapping_layout", title = "Mapping layout",
content = paste("Please format a table providing sample information in the format shown in the figure:",
paste0(
"<ul>",
"<li>The first column contains the <i>well</i> position/name in the plate.</li>",
"<li>The second column contains the <i>ID</i> \\(i.e., organism, condition, etc.\\) of each sample. The ID needs to be identical for replicates.</li>",
"<li>The third column row contains replicate numbers for the same conditions. If technical replicates were used in addition to biological replicates, indicate technical replicates with the same replicate number. Samples with identical IDs, concentrations, and replicate <i>numbers</i> will be combined by their <i>average</i>.</li>",
"<li>The fourth column contains \\(optional\\) concentration values to perform a dose-response analysis, if different concentrations of a compound were used in the experiment.</li>",
"</ul>"
),
"Details:",
paste0(
"The values in \\'Blank\\' samples are combined as their average and subtracted from all remaining values if option \\[Subtract blank\\] is selected. The metadata in the third and fourth columns are optional to perform the analysis and can be left empty."
),
sep = "<br>"),
trigger = "hover", options = list(container = "body", template = widePopover)
),
div(id = 'Custom_Data_Tables',
h1("Your Data"),
tabsetPanel(type = "tabs", id = "tabsetPanel_custom_tables",
tabPanel(title = "Growth plot", value = "tabPanel_custom_plots_growth",
withSpinner(
plotOutput("custom_raw_growth_plot",
width = "100%", height = "1000px"),
)
),
tabPanel(title = "Fluorescence plot", value = "tabPanel_custom_plots_fluorescence",
withSpinner(
plotOutput("custom_raw_fluorescence_plot",
width = "100%", height = "1000px"),
)
),
tabPanel(title = "norm. Fluorescence plot", value = "tabPanel_custom_plots_norm_fluorescence",
withSpinner(
plotOutput("custom_raw_norm_fluorescence_plot",
width = "100%", height = "1000px"),
)
),
tabPanel(title = "Growth", value = "tabPanel_custom_tables_growth_processed",
withSpinner(
DT::dataTableOutput("growth_data_custom_processed")
),
downloadButton('download_custom_tables_growth_processed',"Download table")
),
tabPanel(title = "Fluorescence", value = "tabPanel_custom_tables_fluorescence_processed",
withSpinner(
DT::dataTableOutput("custom_table_fluorescence_processed")
),
downloadButton('download_custom_tables_fluorescence_processed',"Download table")
),
tabPanel(title = "Normalized fluorescence", value = "tabPanel_custom_tables_norm_fluorescence_processed",
withSpinner(
DT::dataTableOutput("custom_table_norm_fluorescence_processed")
),
downloadButton('download_custom_tables_norm_fluorescence_processed',"Download table")
),
tabPanel(title = "Experimental Design", value = "tabPanel_custom_tables_expdesign",
DT::dataTableOutput('custom_data_table_expdesign'),
downloadButton('download_custom_tables_expdesign',"Download table")
),
)
),
div(id = 'Parsed_Data_Tables',
h1("Parsed Data"),
tabsetPanel(type = "tabs", id = "tabsetPanel_parsed_tables",
tabPanel(title = "Growth plot", value = "tabPanel_parsed_plots_growth",
withSpinner(
plotOutput("parsed_raw_growth_plot",
width = "100%", height = "1000px"),
)
),
tabPanel(title = "Fluorescence plot", value = "tabPanel_parsed_plots_fluorescence",
withSpinner(
plotOutput("parsed_raw_fluorescence_plot",
width = "100%", height = "1000px"),
)
),
tabPanel(title = "norm. Fluorescence plot", value = "tabPanel_parsed_plots_norm_fluorescence",
withSpinner(
plotOutput("parsed_raw_norm_fluorescence_plot",
width = "100%", height = "1000px"),
)
),
tabPanel(title = "Growth", value = "tabPanel_parsed_tables_growth",
DT::dataTableOutput('parsed_data_table_growth'),
downloadButton('download_parsed_tables_growth',"Download table")
),
tabPanel(title = "Fluorescence", value = "tabPanel_parsed_tables_fluorescence",
DT::dataTableOutput('parsed_data_table_fluorescence'),
downloadButton('download_parsed_tables_fluorescence',"Download table")
),
tabPanel(title = "Normalized fluorescence", value = "tabPanel_parsed_tables_norm_fluorescence",
withSpinner(
DT::dataTableOutput("parsed_data_table_norm_fluorescence")
),
downloadButton('download_parsed_data_table_norm_fluorescence',"Download table")
),
tabPanel(title = "Experimental Design", value = "tabPanel_parsed_tables_expdesign",
DT::dataTableOutput('parsed_data_table_expdesign'),
downloadButton('download_parsed_tables_expdesign',"Download table")
)
)
)
) # main panel
), # Navbar 1
#____COMPUTATION____####
navbarMenu(span("Computation", title = "Run a complete data analysis workflow."),
menuName = "navbarMenu_Computation", icon=icon("gears"),
##____Computation_Growth____####
tabPanel("Growth", value = "tabPanel_Computation_Growth",
fluidRow(
sidebarLayout(
column(4,
sidebarPanel( width = 12,
style='border-color: #ADADAD',
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-top: 0; padding-bottom: 0',
h2(strong('Growth fit')),
h4('Global fit options'),
tags$div(title="Perform linear regression on (log-transformed) growth data.",
checkboxInput(inputId = 'linear_regression_growth',
label = 'Linear regression',
value = TRUE)
),
tags$div(title="Fit a selection of growth models to the data.",
checkboxInput(inputId = 'parametric_fit_growth',
label = 'Parametric fit',
value = FALSE)
),
tags$div(title="Perform a nonparametric fit to the data using the smooth.spline() function.",
checkboxInput(inputId = 'nonparametric_fit_growth',
label = 'Non-parametric fit',
value = TRUE)
),
tags$div(title="Apply a ln(x+1) transformation to the time data for linear and nonparametric fits.",
checkboxInput(inputId = 'log_transform_time_growth',
label = 'Log-transform time')
),
tags$div(title="Only for linear and nonparametric fits:\nExtract growth parameters for two different growth phases (as observed with, e.g., diauxic shifts).",
checkboxInput(inputId = 'biphasic_growth',
label = 'Biphasic growth')
),
QurvE:::numberInput(
inputId = 'growth_threshold_growth',
label = 'Growth threshold',
value = 1.5,
min = NA,
max = NA,
placeholder = 1.5
),
bsPopover(id = "growth_threshold_growth", title = HTML("<em>growth.thresh</em>"), content = "A sample will be considered to have no growth if no growth value is greater than [growth threshold] \\* start growth."),
QurvE:::numberInput(
inputId = 'minimum_growth_growth',
label = 'Minimum growth measurement',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "minimum_growth_growth", title = HTML("<em>min.growth</em>"), content = "Consider only growth values above [Minimum growth] for the fits."),
QurvE:::numberInput(
inputId = 'maximum_growth_growth',
label = 'Maximum growth measurement',
value = NULL,
min = NA,
max = NA
),
bsPopover(id = "maximum_growth_growth", title = HTML("<em>max.growth</em>"), content = "Consider only growth values below and including [Maximum growth measurement] for linear and spline fits."),
QurvE:::numberInput(
inputId = 't0_growth',
label = 't0',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "t0_growth", title = HTML("<em>t0</em>"), content = "Consider only time values above [t0] for the fits."),
QurvE:::numberInput(
inputId = 'tmax_growth',
label = 'tmax',
value = NULL,
min = NA,
max = NA
),
bsPopover(id = "tmax_growth", title = HTML("<em>tmax</em>"), content = "Consider only time values below and including [tmax] for linear and spline fits."),
), # Growth fit
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-top: 0; padding-bottom: 0',
h2(strong('Dose-response Analysis')),
checkboxInput(inputId = 'perform_ec50_growth',
label = 'Perform EC50 Analysis',
value = FALSE),
conditionalPanel(condition = "input.perform_ec50_growth",
selectInput(inputId = "dr_method_growth",
label = "Method",
choices = c("Dose-response models" = "model",
"Response spline fit" = "spline")
),
bsPopover(id = "dr_method_growth",
title = HTML("<em>dr.method</em>"),
placement = "right",
content = "Fit either various dose-response models (Ritz et al., 2015) to response-vs.-concentration data and select the best model based on the lowest AIC, or apply a nonparametric (spline) fit.",
trigger = "hover", options = list(container = "body", template = widePopover)
),
selectInput(inputId = "response_parameter_growth",
label = "Response Parameter",
choices = ""),
bsPopover(id = "response_parameter_growth", title = HTML("<em>dr.parameter</em>"), content = "Choose the response parameter to be used for creating a dose response curve.", placement = "top"),
conditionalPanel(
condition = 'input.dr_method_growth == "spline"',
tags$div(title="Perform a log(x+1) transformation on concentration values.",
checkboxInput(inputId = 'log_transform_concentration_growth',
label = 'Log transform concentration')
),
tags$div(title="Perform a log(y+1) transformation on response values.",
checkboxInput(inputId = 'log_transform_response_growth',
label = 'Log transform response')
),
textInput(
inputId = 'smoothing_factor_growth_dr',
label = 'Smoothing factor dose-response splines',
value = "",
placeholder = "NULL (choose automatically)"
),
bsPopover(id = "smoothing_factor_growth_dr", title = HTML("<em>smooth.dr</em>"), content = "\\'spar\\' argument in the R function smooth.spline() used to create the dose response curve."),
QurvE:::numberInput(
inputId = 'number_of_bootstrappings_dr_growth',
label = 'Number of bootstrappings',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "number_of_bootstrappings_dr_growth", title = HTML("<em>nboot.dr</em>"), content = "Optional: Define the number of bootstrap samples for EC50 estimation. Bootstrapping resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to determine the EC50.")
), #conditionalPanel(condition = 'input.dr_method_growth == "spline"')
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_growth_dr",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
HTML("<br>"),
) # conditionalPanel(condition = "input.perform_ec50_growth"
), # wellPanel
fluidRow(
column(12,
div(
actionButton(inputId = "run_growth",
label = "Run computation",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
style="float:right"),
div(
actionButton(inputId = "tooltip_growth_workflow",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
) # sidebarPanel
), # column
column(8,
conditionalPanel(
condition = "input.linear_regression_growth",
sidebarPanel(
width = 4,
style='border-color: #ADADAD; padding-top: 0',
h3(strong('Linear fit')),
tags$div(title="Perform a Ln(y/y0) transformation on growth values.",
checkboxInput(inputId = 'log_transform_data_linear_growth',
label = 'Log-transform data',
value = TRUE)
),
QurvE:::numberInput(
inputId = 'R2_threshold_growth',
label = 'R2 threshold',
value = 0.95,
placeholder = 0.95
),
bsPopover(id = "R2_threshold_growth", title = HTML("<em>lin.R2</em>"), content = "R2 threshold for calculated slopes of linear regression windows to be considered for the maximum growth rate."),
QurvE:::numberInput(
inputId = 'RSD_threshold_growth',
label = 'RSD threshold',
value = 0.1,
placeholder = 0.1
),
bsPopover(id = "RSD_threshold_growth", title = HTML("<em>lin.RSD</em>"), content = "Relative standard deviation (RSD) threshold for calculated slopes of linear regression windows to be considered for the maximum growth rate."),
QurvE:::numberInput(
inputId = 'dY_threshold_growth',
label = 'dY threshold',
value = 0.05,
placeholder = 0.05
),
bsPopover(id = "dY_threshold_growth", title = HTML("<em>lin.dY</em>"), content = "Threshold for the minimum fraction of growth increase a linear regression window should cover to be considered."),
checkboxInput(inputId = 'custom_sliding_window_size_growth',
label = 'Custom sliding window size',
value = FALSE),
conditionalPanel(
condition = "input.custom_sliding_window_size_growth",
numericInput(
inputId = 'custom_sliding_window_size_value_growth',
label = NULL,
value = "NULL",
min = NA,
max = NA,
),
bsPopover(id = "custom_sliding_window_size_value_growth", title = HTML("<em>lin.h</em>"), content = "If NULL, the sliding windows size (h) is chosen based on the number of data points within the growth phase (until maximum growth measurement)."),
),
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_growth.gcFitLinear",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
) # sidebarPanel
), # conditionalPanel
conditionalPanel(
condition = "input.parametric_fit_growth",
sidebarPanel(
style='border-color: #ADADAD; padding-top: 0',
h3(strong('Parametric fit')),
tags$div(title="Perform a Ln(y/y0) transformation on growth values.",
checkboxInput(inputId = 'log_transform_data_parametric_growth',
label = 'Log-transform data',
value = TRUE)
),
wellPanel(
h4(strong('Models:')),
style='border-color: #ADADAD; padding: 1; padding-top: 0; padding-bottom: 0',
tags$div(title="Reference: Zwietering MH, Jongenburger I, Rombouts FM, van 't Riet K. Modeling of the bacterial growth curve. Appl Environ Microbiol. 1990 Jun;56(6):1875-81. doi: 10.1128/aem.56.6.1875-1881.1990",
checkboxInput(inputId = 'logistic_growth',
label = 'logistic',
value = TRUE)
),
tags$div(title="Reference: Zwietering MH, Jongenburger I, Rombouts FM, van 't Riet K. Modeling of the bacterial growth curve. Appl Environ Microbiol. 1990 Jun;56(6):1875-81. doi: 10.1128/aem.56.6.1875-1881.1990",
checkboxInput(inputId = 'richards_growth',
label = 'Richards',
value = TRUE)
),
tags$div(title="Reference: Zwietering MH, Jongenburger I, Rombouts FM, van 't Riet K. Modeling of the bacterial growth curve. Appl Environ Microbiol. 1990 Jun;56(6):1875-81. doi: 10.1128/aem.56.6.1875-1881.1990",
checkboxInput(inputId = 'gompertz_growth',
label = 'Gompertz',
value = TRUE)
),
tags$div(title="Reference: Kahm, M., Hasenbrink, G., Lichtenberg-Fraté, H., Ludwig, J., & Kschischo, M. (2010). grofit: Fitting Biological Growth Curves with R. Journal of Statistical Software, 33(7), 1–21. https://doi.org/10.18637/jss.v033.i07",
checkboxInput(inputId = 'extended_gompertz_growth',
label = 'extended Gompertz',
value = TRUE)
),
tags$div(title="Reference: Huang, Lihan (2011) A new mechanistic growth model for simultaneous determination of lag phase duration and exponential growth rate and a new Belehdradek-type model for evaluating the effect of temperature on growth rate. Food Microbiology 28, 770 – 776. doi: 10.1016/j.fm.2010.05.019",
checkboxInput(inputId = 'huang_growth',
label = 'Huang',
value = TRUE)
),
tags$div(title="Reference: Baranyi and Roberts (1994) Mathematics of predictive food microbiology. Food Microbiology 26(2), 199 – 218. doi: 10.1016/0168-1605(94)00121-L",
checkboxInput(inputId = 'baranyi_growth',
label = 'Baranyi and Roberts',
value = TRUE)
)
),
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_growth.gcFitModel",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
)
), # conditionalPanel
conditionalPanel(
condition = "input.nonparametric_fit_growth",
sidebarPanel(
width = 4,
style='border-color: #ADADAD; padding-top: 0',
h3(strong('Nonparametric fit')),
tags$div(title="Perform a Ln(y/y0) transformation on growth values.",
checkboxInput(inputId = 'log_transform_data_nonparametric_growth',
label = 'Log-transform data',
value = TRUE)
),
QurvE:::numberInput(
inputId = 'smoothing_factor_nonparametric_growth',
label = 'Smoothing factor',
value = 0.55,
min = NA,
max = NA,
placeholder = 0.55
),
bsPopover(id = "smoothing_factor_nonparametric_growth", title = HTML("<em>smooth.gc</em>"), content = "\\'spar\\' argument within the R function smooth\\.spline\\(\\)."),
QurvE:::numberInput(
inputId = 'number_of_bootstrappings_growth',
label = 'Number of bootstrappings',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "number_of_bootstrappings_growth", title = HTML("<em>nboot.gc</em>"), content = "Optional: Define the number of bootstrap samples. Bootstrapping resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to yield a statistic distribution of growth parameters."),
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_growth.gcFitSpline",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
)
) # conditionalPanel
) # column
) # sidebarLayout
), # fluidRow
textOutput("text")
), # Growth Tab Panel
"----",
##____Computation_Fluorescence____####
tabPanel("Fluorescence", value = "tabPanel_Computation_Fluorescence",
fluidRow(
sidebarLayout(
column(4,
sidebarPanel( width = 12,
style = 'border-color: #ADADAD',
wellPanel(
style = 'padding: 1; border-color: #ADADAD; padding-top: 0; padding-bottom: 0',
h2(strong('Fluorescence fit')),
h4('Options'),
tags$div(title="Perform linear regression on log-transformed growth data.",
checkboxInput(
inputId = 'linear_regression_fluorescence',
label = 'linear regression',
value = TRUE
)
),
tags$div(title="Perform a nonparametric fit to the data using the smooth.spline() function.",
checkboxInput(
inputId = 'nonparametric_fit_fluorescence',
label = 'nonparametric fit',
value = TRUE
)
),
tags$div(title="Extract kinetic parameters for two different phases (as observed with, e.g., regulator-promoter systems with varying response in different growth stages).",
checkboxInput(inputId = 'biphasic_fluorescence',
label = 'Biphasic')
),
selectInput(
inputId = 'data_type_x_fluorescence',
label = 'Independent variable (x)',
choices = ""
),
bsPopover(id = "data_type_x_fluorescence", title = HTML("<em>x_type</em>"), content = "Select the data type that is used as the independent variable for all fits."),
conditionalPanel(
condition = "input.data_type_x_fluorescence == 'time' && output.normalized_fl_present",
tags$div(title="Use normalized fluorescence (divided by growth values) for all fits.",
checkboxInput(inputId = 'normalize_fluorescence',
label = 'Use normalized fluorescence'
)
)
),
conditionalPanel(
condition = 'input.data_type_x_fluorescence.includes("growth")',
QurvE:::numberInput(
inputId = 'growth_threshold_in_percent_fluorescence',
label = 'Growth threshold (in %)',
value = 1.5,
min = NA,
max = NA,
placeholder = 1.5
),
bsPopover(id = "growth_threshold_in_percent_fluorescence", title = HTML("<em>growth.thresh</em>"), content = "A sample will be considered to have no growth if no growth value is greater than [growth threshold] \\* start growth."),
),
conditionalPanel(
condition = 'input.data_type_x_fluorescence.includes("growth")',
QurvE:::numberInput(
inputId = 'minimum_growth_fluorescence',
label = 'Minimum growth measurement',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "minimum_growth_fluorescence", title = HTML("<em>min.growth</em>"), content = "Consider only growth values above [Minimum growth measurement] for the fits."),
),
conditionalPanel(
condition = 'input.data_type_x_fluorescence.includes("growth")',
QurvE:::numberInput(
inputId = 'maximum_growth_fluorescence',
label = 'Maximum growth measurement',
value = NULL,
min = NA,
max = NA
),
bsPopover(id = "maximum_growth_fluorescence", title = HTML("<em>max.growth</em>"), content = "Consider only growth values below and including [Maximum growth] for linear and spline fits."),
),
conditionalPanel(
condition = 'input.data_type_x_fluorescence.includes("time")',
QurvE:::numberInput(
inputId = 't0_fluorescence',
label = 't0',
value = 0,
min = NA,
max = NA,
placeholder = 0
)
),
bsPopover(id = "t0_fluorescence", title = HTML("<em>t0</em>"), content = "Consider only time values above [t0] for the fits."),
conditionalPanel(
condition = 'input.data_type_x_fluorescence.includes("time")',
QurvE:::numberInput(
inputId = 'tmax_fluorescence',
label = 'tmax',
value = NULL,
min = NA,
max = NA
),
bsPopover(id = "tmax_fluorescence", title = HTML("<em>tmax</em>"), content = "Consider only time values below and including [tmax] for linear and spline fits."),
),
), # wellPanel
wellPanel(style='padding: 1; border-color: #ADADAD; padding-top: 0; padding-bottom: 0',
h2(strong('Dose-response Analysis')),
checkboxInput(inputId = 'perform_ec50_fluorescence',
label = 'Perform dose-response analysis',
value = FALSE),
conditionalPanel(condition = 'input.perform_ec50_fluorescence',
selectInput(inputId = "dr_method_fluorescence",
label = "Method",
choices = c("Biosensor response model" = "model",
"Response spline fit" = "spline")
),
bsPopover(id = "dr_method_fluorescence",
placement = "right",
title = HTML("<em>dr.method</em>"),
content = "Fit either a biosensor response model (Meyer et al., 2019) to response-vs.-concentration data, or apply a nonparametric (spline) fit."
),
selectInput(inputId = "response_parameter_fluorescence",
label = "Response Parameter",
choices = ""),
bsPopover(id = "response_parameter_fluorescence", title = HTML("<em>dr.parameter</em>"), content = "Choose the response parameter to be used for creating a dose response curve.", placement = "top"),
tags$div(title="Perform a log(x+1) transformation on concentration values.",
checkboxInput(inputId = 'log_transform_concentration_fluorescence',
label = 'log transform concentration')
),
tags$div(title="Perform a log(x+1) transformation on response values.",
checkboxInput(inputId = 'log_transform_response_fluorescence',
label = 'log transform response')
),
conditionalPanel(
condition = 'input.dr_method_fluorescence == "spline"',
QurvE:::numberInput(
inputId = 'number_of_bootstrappings_dr_fluorescence',
label = 'Number of bootstrappings',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "number_of_bootstrappings_dr_fluorescence", title = HTML("<em>nboot.dr</em>"), content = "Optional: Define the number of bootstrap samples for EC50 estimation. Bootstrapping resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to determine the EC50."),
),
conditionalPanel(
condition = 'input.dr_method_fluorescence == "spline"',
textInput(
inputId = 'smoothing_factor_fluorescence_dr',
label = 'Smoothing factor dose-response splines',
value = "",
placeholder = "NULL (choose automatically)"
),
bsPopover(id = "smoothing_factor_fluorescence_dr", title = HTML("<em>smooth.dr</em>"), content = "\\'spar\\' argument in the R function smooth.spline() used to create the dose response curve."),
),
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_fl_dr",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
HTML("<br>"),
) # conditionalPanel(condition = "input.perform_ec50_fluorescence"
), # wellPanel
# [Run Computation] button
conditionalPanel(
condition = 'output.fluorescence_present',
fluidRow(
column(12,
div(
actionButton(inputId = "run_fluorescence",
label = "Run computation",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
style="float:right"),
div(
actionButton(inputId = "tooltip_fl_workflow",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
) # sidebarPanel
), # column
column(8,
conditionalPanel(
condition = "input.linear_regression_fluorescence",
sidebarPanel(
width = 4,
style='border-color: #ADADAD; padding-top: 0',
h3(strong('Linear fit')),
QurvE:::numberInput(
inputId = 'R2_threshold_fluorescence',
label = 'R2 threshold',
value = 0.95,
min = NA,
max = NA,
placeholder = 0.95
),
bsPopover(id = "R2_threshold_fluorescence", title = HTML("<em>lin.R2</em>"), content = "R2 threshold for calculated slopes of linear regression windows to be considered for the maximum slope."),
QurvE:::numberInput(
inputId = 'RSD_threshold_fluorescence',
label = 'RSD threshold',
value = 0.1,
min = NA,
max = NA,
placeholder = 0.1
),
bsPopover(id = "RSD_threshold_fluorescence", title = HTML("<em>lin.RSD</em>"), content = "Relative standard deviation (RSD) threshold for calculated slopes of linear regression windows to be considered for the maximum slope."),
QurvE:::numberInput(
inputId = 'dY_threshold_fluorescence',
label = 'dY threshold',
value = 0.05,
min = NA,
max = NA,
placeholder = 0.05
),
bsPopover(id = "dY_threshold_fluorescence", title = HTML("<em>lin.dY</em>"), content = "Threshold for the minimum fraction of fluorescence increase a linear regression window should cover to be considered."),
tags$div(title="Perform a Ln(y/y0) transformation on fluorescence values.",
checkboxInput(inputId = 'log_transform_data_linear_fluorescence',
label = 'Log-transform fluorescence data')
),
tags$div(title="Perform a Ln(y/y0) transformation on the independent variable",
checkboxInput(inputId = 'log_transform_x_linear_fluorescence',
label = 'Log-transform x data')
),
checkboxInput(inputId = 'custom_sliding_window_size_fluorescence',
label = 'custom sliding window size',
value = FALSE),
conditionalPanel(
condition = "input.custom_sliding_window_size_fluorescence",
numericInput(
inputId = 'custom_sliding_window_size_value_fluorescence',
label = NULL,
value = "NULL",
min = NA,
max = NA,
),
bsPopover(id = "custom_sliding_window_size_value_fluorescence", title = HTML("<em>lin.h</em>"), content = "If NULL, the sliding windows size (h) is chosen based on the number of data points within the phase of fluorescence increase (until maximum fluorescence or growth)."),
),
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_flFitLinear",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
)
), # conditionalPanel
conditionalPanel(
condition = "input.nonparametric_fit_fluorescence",
sidebarPanel(
width = 4,
style='border-color: #ADADAD; padding-top: 0',
h3(strong('Nonparametric fit')),
QurvE:::numberInput(
inputId = 'smoothing_factor_nonparametric_fluorescence',
label = 'Smoothing factor',
value = 0.75,
min = NA,
max = NA,
placeholder = 0.75
),
bsPopover(id = "smoothing_factor_nonparametric_fluorescence", title = HTML("<em>smooth.fl</em>"), content = "\\'spar\\' argument within the R function smooth\\.spline\\(\\)."),
QurvE:::numberInput(
inputId = 'number_of_bootstrappings_fluorescence',
label = 'Number of bootstrappings',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "number_of_bootstrappings_fluorescence", title = HTML("<em>nboot.fl</em>"), content = "Optional: Define the number of bootstrap samples. Bootstrapping resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to yield a statistic distribution of growth parameters."),
tags$div(title="Perform a Ln(y/y0) transformation on fluorescence values.",
checkboxInput(inputId = 'log_transform_data_nonparametric_fluorescence',
label = 'Log-transform fluorescence data')
),
tags$div(title="Perform a Ln(y/y0) transformation on the independent variable",
checkboxInput(inputId = 'log_transform_x_nonparametric_fluorescence',
label = 'Log-transform x data')
),
fluidRow(
column(12,
div(
actionButton(inputId = "tooltip_flFitSpline",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
),
)
) # conditionalPanel
) # column
) # sidebarLayout
) # fluidRow
), # tabPanel("Fluorescence"
), # navbarMenu('Computation'
#____VALIDATE____####
navbarMenu(span("Validation", title = "Graphical display for each fit."),
menuName = "navbarMenu_Validate", icon = icon("user-check"),
##____Validate_Growth____####
tabPanel(title = "Growth Fits", value = "tabPanel_Validate_Growth",
h1("Growth Fits"),
tabsetPanel(type = "tabs", id = "tabsetPanel_Validate_Growth",
###___Linear Fits___####
tabPanel(title = "Linear Fits", value = "tabPanel_Validate_Growth_Linear",
sidebarPanel(width = 5,
selectizeInput(inputId = "sample_validate_growth_linear",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
checkboxInput(inputId = 'logy_validate_growth_plot_linear',
label = 'Log-transform y axis',
value = TRUE),
checkboxInput(inputId = 'diagnostics_validate_growth_plot_linear',
label = 'Show diagnostics',
value = FALSE),
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_validate_growth_plot_linear',
label = 'Shape type',
min = 1,
max = 25,
value = 21),
sliderInput(inputId = 'shape_size_validate_growth_plot_linear',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_validate_growth_plot_linear',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.9,
step = 0.1),
sliderInput(inputId = 'lab_size_validate_growth_plot_linear',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.7,
step = 0.1),
sliderInput(inputId = 'line_width_validate_growth_plot_linear',
label = 'Line width',
min = 0.01,
max = 10,
value = 3),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_validate_growth_plot_linear",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_validate_growth_plot_linear",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_validate_growth_plot_linear",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_validate_growth_plot_linear",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(
inputId = 'color_validate_growth_plot_linear',
label = 'Change color',
value = "firebrick3"
),
bsPopover(id = "color_validate_growth_plot_linear",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(width = 7,
plotOutput("validate_growth_plot_linear", width = "100%", height = "600px"),
HTML("<br>"),
HTML("<br>"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_growth_linear",
label = "Re-run with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_growth_linear",
label = "Restore fit",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_validate_linear",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_validate_linear",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_validate_linear",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_growth_validate_linear',"Download Plot"),
radioButtons("format_download_growth_validate_linear",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) #mainPanel
), #tabPanel(title = "Linear Fits", value = "tabPanel_Validate_Growth_linearFits",
###___Spline Fits___####
tabPanel(title = "Nonparametric fits", value = "tabPanel_Validate_Growth_Spline",
sidebarPanel(width = 5,
selectizeInput(inputId = "sample_validate_growth_spline",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
sliderInput(inputId = 'shape_type_validate_growth_plot_spline',
label = 'Shape type',
min = 1,
max = 25,
value = 21),
checkboxInput(inputId = 'logy_validate_growth_plot_spline',
label = 'Log-transform y axis',
value = TRUE),
checkboxInput(inputId = "plot_derivative_validate_growth_plot_spline",
label = "Plot derivative",
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_validate_growth_plot_spline",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_validate_growth_plot_spline",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_validate_growth_plot_spline",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_validate_growth_plot_spline",
label = NULL,
value = "", placeholder = "max"
)
)
),
conditionalPanel(
condition = "input.logy_validate_growth_plot_spline",
strong("y-Range (derivative)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_derivative_validate_growth_plot_spline",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_derivative_validate_growth_plot_spline",
label = NULL,
value = "", placeholder = "max"
)
)
)
),
sliderInput(inputId = 'shape_size_validate_growth_plot_spline',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = "line_width_validate_growth_plot_spline",
label = "Line width",
min = 0.01,
max = 10,
value = 1),
sliderInput(inputId = 'base_size_validate_growth_plot_spline',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "nbreaks_validate_growth_plot_spline",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
textInput(
inputId = 'color_validate_growth_plot_spline',
label = 'Change color',
value = "dodgerblue3"
),
bsPopover(id = "color_validate_growth_plot_spline",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(width = 7,
withSpinner(
plotOutput("validate_growth_plot_spline",
width = "100%", height = "700px")
),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_growth_spline",
label = "Re-run with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_growth_spline",
label = "Restore fit",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_validate_spline",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_validate_spline",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_validate_spline",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_growth_validate_spline',"Download Plot"),
radioButtons("format_download_growth_validate_spline",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
), # fluidRow
h3(strong("Export spline values")),
fluidRow(
column(width = 4,
downloadButton('download_growth_validate_spline_values',"Download value table (x-y)")
),
column(width = 4,
downloadButton('download_growth_validate_spline_deriv_values',"Download derivative value table (x-y)")
),
)
) # mainPanel
), # tabPanel(title = "Nonparametric fits", value = "tabPanel_Validate_Growth_splineFits",
###___Model Fits___####
tabPanel(title = "Parametric fits", value = "tabPanel_Validate_Growth_Model",
sidebarPanel(width = 5,
wellPanel(
style='padding: 1; padding-top: 0; padding-bottom: 0',
selectizeInput(inputId = "sample_validate_growth_model",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
),
sliderInput(inputId = 'shape_type_validate_growth_plot_model',
label = 'Shape type',
min = 1,
max = 25,
value = 21),
sliderInput(inputId = 'shape_size_validate_growth_plot_model',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = "line_width_validate_growth_plot_model",
label = "Line width",
min = 0.01,
max = 10,
value = 1),
sliderInput(inputId = 'base_size_validate_growth_plot_model',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "nbreaks_validate_growth_plot_model",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
sliderInput(inputId = "eqsize_validate_growth_plot_model",
label = "Equation font size",
min = 0.1,
max = 10,
step = 0.1,
value = 1.9),
textInput(
inputId = 'color_validate_growth_plot_model',
label = 'Change color',
value = "forestgreen"
),
bsPopover(id = "color_validate_growth_plot_model",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(width = 7,
withSpinner(
plotOutput("validate_growth_plot_model",
width = "100%", height = "600px")
),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_growth_model",
label = "Re-run with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_growth_model",
label = "Restore fit",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_validate_model",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_validate_model",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_validate_model",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_growth_validate_model',"Download Plot"),
radioButtons("format_download_growth_validate_model",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) # mainPanel
), # tabPanel(title = "Parametric fits", value = "tabPanel_Validate_Growth_modelFits",
### Growth Boostrapping Spline Plots ####
tabPanel(title = "Bootstrapping Spline", value = "tabPanel_Validate_Growth_Spline_bt",
sidebarPanel(width = 4,
selectizeInput(inputId = "sample_validate_growth_spline_bt",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
checkboxInput(inputId = "plot_derivative_growth_spline_bt",
label = "Plot derivative",
value = TRUE),
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_validate_growth_spline_bt',
label = 'Shape type',
min = 1,
max = 25,
value = 1),
sliderInput(inputId = 'shape_size_validate_growth_spline_bt',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_validate_growth_spline_bt',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.9,
step = 0.1),
sliderInput(inputId = 'lab_size_validate_growth_spline_bt',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.7,
step = 0.1),
sliderInput(inputId = 'line_width_validate_growth_spline_bt',
label = 'Line width',
min = 0.01,
max = 10,
value = 0.5),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_validate_growth_spline_bt",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_validate_growth_spline_bt",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_validate_growth_spline_bt",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_validate_growth_spline_bt",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range (derivative)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_derivative_validate_growth_spline_bt",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_derivative_validate_growth_spline_bt",
label = NULL,
value = "", placeholder = "max"
)
),
textInput(
inputId = 'color_validate_growth_plot_spline_bt',
label = 'Change color',
value = "dodgerblue3"
),
bsPopover(id = "color_validate_growth_plot_spline_bt",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
), # sidebarPanel
mainPanel(width = 8,
plotOutput("validate_growth_plot_spline_bt",
width = "100%", height = "1000px"),
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_validate_spline_bt",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_validate_spline_bt",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_validate_spline_bt",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_growth_validate_spline_bt',"Download Plot"),
radioButtons("format_download_growth_validate_spline_bt",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) # mainPanel
) # tabPanel(title = "Bootstrapping Spline"
) # tabsetPanel(type = "tabs",
), # tabPanel(title = "Growth Fits", value = "tabPanel_Validate_Growth",
##____Validate_Fluorescence____####
tabPanel(title = "Fluorescence Fits", value = "tabPanel_Validate_Fluorescence",
h1("Fluorescence Fits"),
tabsetPanel(type = "tabs", id = "tabsetPanel_Validate_Fluorescence",
###___Linear Fits___####
tabPanel(title = "Linear Fits", value = "tabPanel_Validate_Fluorescence_Linear",
sidebarPanel(width = 5,
selectizeInput(inputId = "sample_validate_fluorescence_linear",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
checkboxInput(inputId = 'logy_validate_fluorescence_plot_linear',
label = 'Log-transform y axis',
value = FALSE),
checkboxInput(inputId = 'diagnostics_validate_fluorescence_plot_linear',
label = 'Show diagnostics',
value = FALSE),
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_validate_fluorescence_plot_linear',
label = 'Shape type',
min = 1,
max = 25,
value = 21),
sliderInput(inputId = 'shape_size_validate_fluorescence_plot_linear',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_validate_fluorescence_plot_linear',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'lab_size_validate_fluorescence_plot_linear',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.8,
step = 0.1),
sliderInput(inputId = 'line_width_validate_fluorescence_plot_linear',
label = 'Line width',
min = 0.01,
max = 10,
value = 3),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_validate_fluorescence_plot_linear",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_validate_fluorescence_plot_linear",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_validate_fluorescence_plot_linear",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_validate_fluorescence_plot_linear",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(
inputId = 'color_validate_fluorescence_plot_linear',
label = 'Change color',
value = "firebrick3"
),
bsPopover(id = "color_validate_fluorescence_plot_linear",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(width = 7,
plotOutput("validate_fluorescence_plot_linear", width = "100%", height = "600px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_fluorescence_linear",
label = "Re-run with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_fluorescence_linear",
label = "Restore fit",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_validate_linear",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_validate_linear",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_validate_linear",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_fluorescence_validate_linear',"Download Plot"),
radioButtons("format_download_fluorescence_validate_linear",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
)
),
###___Spline Fits___####
tabPanel(title = "Nonparametric fits", value = "tabPanel_Validate_Fluorescence_Spline",
sidebarPanel(width = 5,
selectizeInput(inputId = "sample_validate_fluorescence_spline",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
checkboxInput(inputId = 'logy_validate_fluorescence_plot_spline',
label = 'Log-transform y axis',
value = FALSE),
checkboxInput(inputId = "plot_derivative_validate_fluorescence_plot_spline",
label = "Plot derivative",
value = TRUE),
sliderInput(inputId = 'shape_type_validate_fluorescence_plot_spline',
label = 'Shape type',
min = 1,
max = 25,
value = 21),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_validate_fluorescence_plot_spline",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_validate_fluorescence_plot_spline",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_validate_fluorescence_plot_spline",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_validate_fluorescence_plot_spline",
label = NULL,
value = "", placeholder = "max"
)
)
),
conditionalPanel(
condition = "input.logy_validate_growth_plot_spline",
strong("y-Range (derivative)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_derivative_validate_fluorescence_plot_spline",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_derivative_validate_fluorescence_plot_spline",
label = NULL,
value = "", placeholder = "max"
)
)
)
),
sliderInput(inputId = 'shape_size_validate_fluorescence_plot_spline',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = "line_width_validate_fluorescence_plot_spline",
label = "Line width",
min = 0.01,
max = 10,
value = 1.1),
sliderInput(inputId = 'base_size_validate_fluorescence_plot_spline',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "nbreaks__validate_fluorescence_plot_spline",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
textInput(
inputId = 'color_validate_fluorescence_plot_spline',
label = 'Change color',
value = "dodgerblue3"
),
bsPopover(id = "color_validate_fluorescence_plot_spline",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(width = 7,
#conditional if diagnostics
withSpinner(
plotOutput("validate_fluorescence_plot_spline",
width = "100%", height = "700px")
),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_fluorescence_spline",
label = "Re-run with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_fluorescence_spline",
label = "Restore fit",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
) # column
), # fluidRow
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_validate_spline",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_validate_spline",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_validate_spline",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_fluorescence_validate_spline',"Download Plot"),
radioButtons("format_download_fluorescence_validate_spline",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
), # fluidRow
h3(strong("Export spline values")),
fluidRow(
column(width = 4,
downloadButton('download_fluorescence_validate_spline_values',"Download value table (x-y)")
),
column(width = 4,
downloadButton('download_fluorescence_validate_spline_deriv_values',"Download derivative value table (x-y)")
),
)
) # mainPanel
), # tabPanel(title = "Nonparametric fits", value = "tabPanel_Validate_Fluorescence_splineFits",
# Bootstrapping spline fit ####
tabPanel(title = "Bootstrapping Spline", value = "tabPanel_Validate_Fluorescence_Spline_bt",
sidebarPanel(width = 4,
selectizeInput(inputId = "sample_validate_fluorescence_spline_bt",
label = "Sample:",
width = "100%",
choices = "",
multiple = FALSE,
options = list(closeAfterSelect = FALSE)
),
checkboxInput(inputId = "plot_derivative_fluorescence_spline_bt",
label = "Plot derivative",
value = TRUE),
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_validate_fluorescence_spline_bt',
label = 'Shape type',
min = 1,
max = 25,
value = 1),
sliderInput(inputId = 'shape_size_validate_fluorescence_spline_bt',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_validate_fluorescence_spline_bt',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 2.8,
step = 0.1),
sliderInput(inputId = 'lab_size_validate_fluorescence_spline_bt',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 2.4,
step = 0.1),
sliderInput(inputId = 'line_width_validate_fluorescence_spline_bt',
label = 'Line width',
min = 0.01,
max = 10,
value = 0.5),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_validate_fluorescence_spline_bt",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_validate_fluorescence_spline_bt",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_validate_fluorescence_spline_bt",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_validate_fluorescence_spline_bt",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range (derivative)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_derivative_validate_fluorescence_spline_bt",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_derivative_validate_fluorescence_spline_bt",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(
inputId = 'color_validate_fluorescence_spline_bt',
label = 'Change color',
value = "dodgerblue3"
),
bsPopover(id = "color_validate_fluorescence_spline_bt",
title = HTML("<em>Define the colors used to highlight data points used in linear regression and determined slope</em>"), placement = "top",
content = "Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
), # sidebarPanel
mainPanel(width = 8,
plotOutput("validate_fluorescence_plot_spline_bt",
width = "100%", height = "1000px"),
HTML("<br>"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_validate_spline_bt",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_validate_spline_bt",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_validate_spline_bt",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_fluorescence_validate_spline_bt',"Download Plot"),
radioButtons("format_download_fluorescence_validate_spline_bt",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) # mainPanel
) # tabPanel(title = "Bootstrapping Spline"
) # tabsetPanel(type = "tabs",
) # tabPanel(title = "Fluorescence Fits", value = "tabPanel_Validate_Fluorescence",
), # navbarMenu("Validate", icon = icon("user-check"),
#____RESULTS____####
navbarMenu(span("Results", title = "Tabular overview of computation results."),
menuName = "navbarMenu_Results", icon = icon("magnifying-glass-chart"),
##____Results_Growth___####
tabPanel(title = "Growth", value = "tabPanel_Results_Growth",
tabsetPanel(type = "tabs", id = "tabsetPanel_Results_Growth",
tabPanel(title = "Linear Fit", value = "tabPanel_Results_Growth_Linear",
conditionalPanel(condition = "input.biphasic_growth",
h5("(Values in parentheses indicate parameters for secondary growth phase)")
),
checkboxInput(inputId = 'grouped_results_growth_linear',
label = 'Group averages',
value = TRUE),
conditionalPanel(
condition = "!input.grouped_results_growth_linear",
DT::dataTableOutput('results_table_growth_linear')
),
conditionalPanel(
condition = "input.grouped_results_growth_linear",
DT::dataTableOutput('results_table_growth_linear_group')
),
conditionalPanel(
condition = "!input.grouped_results_growth_linear",
downloadButton('download_table_growth_linear',"Download table")
),
conditionalPanel(
condition = "input.grouped_results_growth_linear",
downloadButton('download_table_growth_linear_group',"Download table")
),
),
tabPanel(title = "Nonparametric Fit", value = "tabPanel_Results_Growth_Spline",
conditionalPanel(condition = "input.biphasic_growth",
h5("(Values in parentheses indicate parameters for secondary growth phase)")
),
checkboxInput(inputId = 'grouped_results_growth_spline',
label = 'Group averages',
value = TRUE),
conditionalPanel(
condition = "!input.grouped_results_growth_spline",
DT::dataTableOutput('results_table_growth_spline')
),
conditionalPanel(
condition = "input.grouped_results_growth_spline",
DT::dataTableOutput('results_table_growth_spline_group')
),
conditionalPanel(
condition = "!input.grouped_results_growth_spline",
downloadButton('download_table_growth_spline',"Download table")
),
conditionalPanel(
condition = "input.grouped_results_growth_spline",
downloadButton('download_table_growth_spline_group',"Download table")
),
),
tabPanel(title = "Nonparametric Fit (Bootstrapping)", value = "tabPanel_Results_Growth_Spline_bt",
DT::dataTableOutput('results_table_growth_spline_bt'),
downloadButton('download_table_growth_spline_bt',"Download table")
),
tabPanel(title = "Parametric Fit", value = "tabPanel_Results_Growth_Model",
checkboxInput(inputId = 'grouped_results_growth_model',
label = 'Group averages',
value = TRUE),
conditionalPanel(
condition = "!input.grouped_results_growth_model",
DT::dataTableOutput('results_table_growth_model'),
downloadButton('download_table_growth_model',"Download table")
),
conditionalPanel(
condition = "input.grouped_results_growth_model",
DT::dataTableOutput('results_table_growth_model_group'),
downloadButton('download_table_growth_model_group',"Download table")
),
),
tabPanel(title = "Dose-response analysis", value = "tabPanel_Results_Growth_DR",
DT::dataTableOutput('results_table_growth_dr_spline'),
downloadButton('download_table_growth_dr',"Download table")
)
)
),
##____Results_Fluorescence___####
tabPanel(title = "Fluorescence", value = "tabPanel_Results_Fluorescence",
tabsetPanel(type = "tabs", id = "tabsetPanel_Results_Fluorescence",
tabPanel(title = "Linear Fit", value = "tabPanel_Results_Fluorescence_Linear",
conditionalPanel(condition = "input.biphasic_fluorescence",
h5("(Values in parentheses indicate parameters for secondary phase)")
),
checkboxInput(inputId = 'grouped_results_fluorescence_linear',
label = 'Group averages',
value = TRUE),
conditionalPanel(
condition = "!input.grouped_results_fluorescence_linear",
DT::dataTableOutput('results_table_fluorescence_linear')
),
conditionalPanel(
condition = "input.grouped_results_fluorescence_linear",
DT::dataTableOutput('results_table_fluorescence_linear_group')
),
conditionalPanel(
condition = "!input.grouped_results_fluorescence_linear",
downloadButton('download_table_fluorescence_linear',"Download table")
),
conditionalPanel(
condition = "input.grouped_results_fluorescence_linear",
downloadButton('download_table_fluorescence_linear_group',"Download table")
),
),
tabPanel(title = "Nonparametric Fit", value = "tabPanel_Results_Fluorescence_Spline",
conditionalPanel(condition = "input.biphasic_fluorescence",
h5("(Values in parentheses indicate parameters for secondary phase)")
),
checkboxInput(inputId = 'grouped_results_fluorescence_spline',
label = 'Group averages',
value = TRUE),
conditionalPanel(
condition = "!input.grouped_results_fluorescence_spline",
DT::dataTableOutput('results_table_fluorescence_spline')
),
conditionalPanel(
condition = "input.grouped_results_fluorescence_spline",
DT::dataTableOutput('results_table_fluorescence_spline_group')
),
conditionalPanel(
condition = "!input.grouped_results_fluorescence_spline",
downloadButton('download_table_fluorescence_spline',"Download table")
),
conditionalPanel(
condition = "input.grouped_results_fluorescence_spline",
downloadButton('download_table_fluorescence_spline_group',"Download table")
),
),
tabPanel(title = "Nonparametric Fit (Bootstrapping)", value = "tabPanel_Results_Fluorescence_Spline_bt",
DT::dataTableOutput('results_table_fluorescence_spline_bt'),
downloadButton('download_table_fluorescence_spline_bt',"Download table")
),
tabPanel(title = "Dose-response analysis", value = "tabPanel_Results_Fluorescence_DR_Spline",
DT::dataTableOutput('results_table_fluorescence_dr_spline'),
downloadButton('download_table_fluorescence_dr_spline',"Download table")
),
tabPanel(title = "Dose-response analysis", value = "tabPanel_Results_Fluorescence_DR_Model",
DT::dataTableOutput('results_table_fluorescence_dr_model'),
downloadButton('download_table_fluorescence_dr_model',"Download table")
)
)
)
),
#____Visualize____####
navbarMenu(span("Visualization", title = "Visualize computation results for the entire dataset."),
menuName = "navbarMenu_Visualize", icon = icon("chart-line"),
## Growth Plots ####
tabPanel(title = "Growth Plots", value = "tabPanel_Visualize_Growth",
h1("Growth Plots"),
tabsetPanel(type = "tabs", id = "tabsetPanel_Visualize_Growth",
### Growth Group Plots ####
tabPanel(title = "Group Plots",
sidebarPanel(
selectInput(inputId = "data_type_growth_group_plot",
label = "Data type",
choices = c("Raw growth" = "raw",
"Spline fits" = "spline")
),
checkboxInput(inputId = "select_string_visualize_growth_group",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_growth_group && !input.plot_group_averages_growth_group_plot",
selectizeInput(inputId = "samples_visualize_growth_group",
label = "Samples:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "!input.select_string_visualize_growth_group && input.plot_group_averages_growth_group_plot",
selectizeInput(inputId = "groups_visualize_growth_group",
label = "Conditions:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_growth_group",
textInput(inputId = "select_samples_based_on_string_growth_group_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_string_growth_group_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
conditionalPanel(
condition = "input.plot_group_averages_growth_group_plot || input.select_string_visualize_growth_group",
textInput(inputId = "select_samples_based_on_concentration_growth_group_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_concentration_growth_group_plot",
label = "Exclude sample based on concentration (separate by ;)"
)
),
checkboxInput(inputId = "plot_group_averages_growth_group_plot",
label = "Plot group averages",
value = TRUE),
conditionalPanel(
condition = "input.data_type_growth_group_plot == 'spline'",
checkboxInput(inputId = "plot_derivative_growth_group_plot",
label = "Plot derivative",
value = TRUE)
),
h3("Customize plot appearance"),
checkboxInput(inputId = "log_transform_y_axis_growth_group_plot",
label = "Log-transform y-axis",
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_growth_group_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_growth_group_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_growth_group_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_growth_group_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
conditionalPanel(
condition = "input.data_type_growth_group_plot == 'spline'",
strong("y-Range (derivative)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_derivative_growth_group_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_derivative_growth_group_plot",
label = NULL,
value = "", placeholder = "max"
)
)
)
),
textInput(inputId = "y_axis_title_growth_group_plot",
label = "y-axis title",
value = "Growth [y(t)]"
),
textInput(inputId = "x_axis_title_growth_group_plot",
label = "x-axis title",
value = "Time"
),
conditionalPanel(
condition = "input.data_type_growth_group_plot == 'spline'",
textInput(inputId = "y_axis_title_derivative_growth_group_plot",
label = "y-axis title derivative",
value = "Growth rate"
)
),
sliderInput(inputId = "nbreaks_growth_group_plot",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
sliderInput(inputId = "line_width_growth_group_plot",
label = "Line width",
min = 0.01,
max = 10,
value = 1.1),
sliderInput(inputId = 'base_size_growth_group_plot',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
selectInput(inputId = "legend_position_group_plot",
label = "Legend position",
choices = c("Bottom" = "bottom",
"Top" = "top",
"Left" = "left",
"Right" = "right")
),
sliderInput(inputId = 'legend_ncol_group_plot',
label = 'Number of legend columns',
min = 1,
max = 10,
value = 4,
step = 1
),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "color_groups_group_plot",
label = "Color samples by group",
value = TRUE)
),
textInput(
inputId = 'custom_colors_group_plot',
label = 'Custom colors'
),
bsPopover(id = "custom_colors_group_plot",
title = HTML("<em>Provide custom colors</em>"), placement = "top",
content = "Enter colors either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). Separate colors with a comma. A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
conditionalPanel(
condition = "output.more_than_two_conc && input.color_groups_group_plot",
selectizeInput(inputId = "color_palettes_group_plot",
label = "Change color palettes",
width = "100%",
choices = names(QurvE:::single_hue_palettes),
selected = names(QurvE:::single_hue_palettes),
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
),
bsPopover(id = "color_palettes_group_plot",
title = HTML("<em>Define the colors used to display sample groups with identical concentrations</em>"), placement = "top",
content = "The number of selected color palettes must be at least the number of displayed groups. The order of the chosen palettes corresponds to the oder of conditions in the legend."
),
)
), # Side panel growth group plots
mainPanel(
withSpinner(
plotOutput("growth_group_plot",
width = "100%", height = "1000px"),
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_group_plot",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_group_plot",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_group_plot",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_growth_group_plot',"Download Plot"),
radioButtons("format_download_growth_group_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) # mainPanel
),
### Growth Parameter Plots ####
tabPanel(title = "Parameter Plots",
sidebarPanel(
selectInput(inputId = "parameter_parameter_growth_plot",
label = "Parameter",
choices = ""
),
checkboxInput(inputId = "select_string_visualize_parameter_growth_plot",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_parameter_growth_plot",
selectizeInput(inputId = "samples_visualize_parameter_growth_plot",
label = "Conditions:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_parameter_growth_plot",
textInput(inputId = "select_sample_based_on_string_growth_parameter_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_sample_based_on_strings_growth_parameter_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
textInput(inputId = "select_sample_based_on_concentration_growth_parameter_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_sample_based_on_concentration_growth_parameter_plot",
label = "Exclude sample based on concentration (separate by ;)"
),
checkboxInput(inputId = 'normalize_to_reference_growth_parameter_plot',
label = 'normalize to reference',
value = FALSE),
h3("Customize plot appearance"),
# Conditional Panel
conditionalPanel(condition = "input.normalize_to_reference_growth_parameter_plot",
# reactive selection
selectInput(inputId = 'reference_condition_growth_parameter_plot',
label = 'Reference condition',
choices = ""
),
# reactive selection
selectInput(inputId = 'reference_concentration_growth_parameter_plot',
label = 'Reference concentration',
choices = ""
),
),
sliderInput(inputId = "shape.size_growth_parameter_plot",
label = "Shape size",
min = 0.1,
max = 10,
value = 3,
step = 0.1),
sliderInput(inputId = "basesize_growth_parameter_plot",
label = "Base font size",
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "label.size_growth_parameter_plot",
label = "Label font size",
min = 5,
max = 35,
value = 17,
step = 0.5),
selectInput(inputId = "legend_position_growth_parameter_plot",
label = "Legend position",
choices = c("Bottom" = "bottom",
"Top" = "top",
"Left" = "left",
"Right" = "right"),
selected = "right"
),
sliderInput(inputId = 'legend_ncol_growth_parameter_plot',
label = 'Number of legend columns',
min = 1,
max = 10,
value = 1,
step = 1
),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "sort_by_conc_growth_parameter_plot",
label = "Sort samples by concentration",
value = FALSE)
),
textInput(
inputId = 'custom_colors_growth_parameter_plot',
label = 'Custom colors'
),
bsPopover(id = "custom_colors_growth_parameter_plot",
title = HTML("<em>Provide custom colors</em>"), placement = "top",
content = "Enter colors either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). Separate colors with a comma. A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(
plotOutput("growth_parameter_plot",
width = "100%", height = "800px"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_parameter_plot",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_parameter_plot",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_parameter_plot",
label = "DPI",
value = 300)
), # column
column(width = 5,
downloadButton('download_growth_parameter_plot',"Download Plot"),
radioButtons("format_download_growth_parameter_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
),
### Growth Grid Plots ####
tabPanel(title = "Plot Grid",
sidebarPanel(
selectInput(inputId = "data_type_growth_grid_plot",
label = "Data type",
choices = c("Raw growth" = "raw",
"Spline fits" = "spline")
),
selectInput(inputId = "parameter_parameter_grid_plot",
label = "Parameter",
choices = ""
),
checkboxInput(inputId = "select_string_visualize_growth_grid",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_growth_grid && !input.plot_group_averages_growth_grid_plot",
selectizeInput(inputId = "samples_visualize_growth_grid",
label = "Samples:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
),
checkboxInput(inputId = "order_matters_visualize_growth_grid",
label = "Select order matters",
value = FALSE
),
),
conditionalPanel(
condition = "!input.select_string_visualize_growth_grid && input.plot_group_averages_growth_grid_plot",
selectizeInput(inputId = "groups_visualize_growth_grid",
label = "Conditions:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_growth_grid",
textInput(inputId = "select_samples_based_on_string_growth_grid_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_string_growth_grid_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
conditionalPanel(
condition = "input.plot_group_averages_growth_grid_plot || input.select_string_visualize_growth_grid",
textInput(inputId = "select_samples_based_on_concentration_growth_grid_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_concentration_growth_grid_plot",
label = "Exclude sample based on concentration (separate by ;)"
)
),
checkboxInput(inputId = "plot_group_averages_growth_grid_plot",
label = "Plot group averages",
value = TRUE),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "sort_by_conc_growth_grid_plot",
label = "Sort by concentration",
value = TRUE)
),
h3("Customize plot appearance"),
checkboxInput(inputId = "log_transform_y_axis_growth_grid_plot",
label = "Log-transform y-axis",
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_growth_grid_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_growth_grid_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_growth_grid_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_growth_grid_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("Color scale limits"),
fluidRow(
column(5,
textInput(inputId = "legend_lim_min_growth_grid_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "legend_lim_max_growth_grid_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(inputId = "y_axis_title_growth_grid_plot",
label = "y-axis title",
value = "Growth [y(t)]"
),
textInput(inputId = "x_axis_title_growth_grid_plot",
label = "x-axis title",
value = "Time"
),
sliderInput(inputId = "nbreaks_growth_grid_plot",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
sliderInput(inputId = "line_width_growth_grid_plot",
label = "Line width",
min = 0.01,
max = 10,
value = 1.1),
sliderInput(inputId = 'base_size_growth_grid_plot',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
conditionalPanel(
condition = "!input.sort_by_conc_growth_grid_plot",
sliderInput(inputId = "nrows_growth_grid_plot",
label = "Number of rows in grid",
min = 1,
max = 20,
value = 2)
),
selectInput(inputId = "color_palettes_grid_plot",
label = "Change color palette",
width = "100%",
choices = names(QurvE:::single_hue_palettes),
selected = names(QurvE:::single_hue_palettes)[1],
multiple = FALSE
),
bsPopover(id = "color_palettes_grid_plot",
title = HTML("<em>Define the colors used to visualize the value of the chosen parameter</em>"), placement = "top",
content = ""
),
checkboxInput(inputId = "invert_color_palette_grid_plot",
label = "Invert color palette",
value = FALSE)
), # Side panel growth group plots
mainPanel(
withSpinner(
plotOutput("growth_grid_plot",
width = "100%", height = "1000px"),
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_grid_plot",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_grid_plot",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_grid_plot",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_growth_grid_plot',"Download Plot"),
radioButtons("format_download_growth_grid_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) # mainPanel
),
### Growth DR Plots Spline ####
tabPanel(title = "Dose-Response Analysis", value = "tabPanel_Visualize_Growth_DoseResponse_Spline",
sidebarPanel(
conditionalPanel(
condition = "output.more_than_one_drfit_spline",
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-bottom: 0',
checkboxInput(inputId = 'combine_conditions_into_a_single_plot_dose_response_growth_plot',
label = 'Combine conditions into a single plot',
value = FALSE)
)
),
conditionalPanel(
condition = "input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
textInput(inputId = "select_samples_based_on_string_dose_response_growth_plot",
label = "Select sample based on string (separate by ;)"
)
),
conditionalPanel(
condition = "input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
textInput(inputId = "exclude_samples_based_on_string_dose_response_growth_plot",
label = "Exclude sample based on string (separate by ;)"
)
),
h3('Customize plot appearance'),
checkboxInput(inputId = "log_transform_y_axis_dose_response_growth_plot",
label = "Log-transform y-axis",
value = FALSE),
checkboxInput(inputId = "log_transform_x_axis_dose_response_growth_plot",
label = "Log-transform x-axis",
value = FALSE),
sliderInput(inputId = 'shape_type_dose_response_growth_plot',
label = 'Shape type',
min = 1,
max = 25,
value = 15),
sliderInput(inputId = 'shape_size_dose_response_growth_plot',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
conditionalPanel(
condition = "input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
sliderInput(inputId = 'base_size_dose_response_growth_plot',
label = 'Base size',
min = 10,
max = 35,
value = 15,
step = 0.5)
),
conditionalPanel(
condition = "!input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
sliderInput(inputId = 'axis_size_dose_response_growth_plot',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1)
),
conditionalPanel(
condition = "!input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
sliderInput(inputId = 'lab_size_dose_response_growth_plot',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1)
),
sliderInput(inputId = 'line_width_dose_response_growth_plot',
label = 'Line width',
min = 0.01,
max = 10,
value = 1),
checkboxInput(inputId = 'show_ec50_indicator_lines_dose_response_growth_plot',
label = 'Show EC50 indicator lines',
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_dose_response_growth_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_dose_response_growth_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_dose_response_growth_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_dose_response_growth_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(inputId = "y_axis_title_dose_response_growth_plot",
label = "y-axis title",
value = ""
),
textInput(inputId = "x_axis_title_dose_response_growth_plot",
label = "x-axis title",
value = ""
)
), # sidebarPanel
conditionalPanel(condition = "input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
mainPanel(
h3('Combined plots'),
plotOutput("dose_response_growth_plot_combined",
width = "100%", height = "800px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_dr_growth",
label = "Re-run dose-response analysis with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_dr_growth",
label = "Restore dose-response analysis",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_growth_plot_combined",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_growth_plot_combined",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_growth_plot_combined",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_growth_plot_combined',"Download Plot"),
radioButtons("format_download_dose_response_growth_plot_combined",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
),
conditionalPanel(condition = "!input.combine_conditions_into_a_single_plot_dose_response_growth_plot",
mainPanel(
h3('Individual plots'),
selectInput(inputId = 'individual_plots_dose_response_growth_plot',
label = 'Select plot',
choices = "",
multiple = FALSE,
selectize = FALSE,
size = 3),
plotOutput("dose_response_growth_plot_individual",
width = "100%", height = "800px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_dr_growth2",
label = "Re-run dose-response analysis with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_dr_growth2",
label = "Restore dose-response analysis",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_growth_plot_individual",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_growth_plot_individual",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_growth_plot_individual",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_growth_plot_individual',"Download Plot"),
radioButtons("format_download_dose_response_growth_plot_individual",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
),
), # tabPanel(title = "Dose-response analysis"
### Growth DR Plots Model ####
tabPanel(title = "Dose-Response Analysis", value = "tabPanel_Visualize_Growth_DoseResponse_Model",
sidebarPanel(
h3('Customize plot appearance'),
checkboxInput(inputId = "log_transform_x_axis_dose_response_growth_plot_model",
label = "Log-transform x-axis",
value = TRUE),
sliderInput(inputId = 'shape_type_dose_response_growth_plot_model',
label = 'Shape type',
min = 1,
max = 25,
value = 15),
sliderInput(inputId = 'shape_size_dose_response_growth_plot_model',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_dose_response_growth_plot_model',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'lab_size_dose_response_growth_plot_model',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'line_width_dose_response_growth_plot_model',
label = 'Line width',
min = 0.01,
max = 10,
value = 1),
conditionalPanel(
condition = "input.log_transform_x_axis_dose_response_growth_plot_model",
sliderInput(inputId = "nbreaks_x_growth_dose_response_plot_model",
label = "Number of breaks on x-axis",
min = 1,
max = 20,
value = 6)
),
conditionalPanel(
condition = "input.log_transform_y_axis_dose_response_growth_plot_model",
sliderInput(inputId = "nbreaks_y_growth_dose_response_plot_model",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6)
),
checkboxInput(inputId = 'show_ec50_indicator_lines_dose_response_growth_plot_model',
label = 'Show EC50 indicator lines',
value = TRUE),
conditionalPanel(
condition = "input.log_transform_x_axis_dose_response_growth_plot_model",
checkboxInput(inputId = 'show_break_dose_response_growth_plot_model',
label = 'Show x axis break',
value = TRUE)
),
conditionalPanel(
condition = "input.show_break_dose_response_growth_plot_model && input.log_transform_x_axis_dose_response_growth_plot_model",
QurvE:::numberInput(inputId = 'bp_dose_response_growth_plot_model',
label = 'Break point position',
value = ""),
),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_dose_response_growth_plot_model",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_dose_response_growth_plot_model",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_dose_response_growth_plot_model",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_dose_response_growth_plot_model",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(inputId = "y_axis_title_dose_response_growth_plot_model",
label = "y-axis title",
value = ""
),
textInput(inputId = "x_axis_title_dose_response_growth_plot_model",
label = "x-axis title",
value = ""
)
), # sidebarPanel
mainPanel(
selectInput(inputId = 'individual_plots_dose_response_growth_plot_model',
label = 'Select plot',
choices = "",
multiple = FALSE,
selectize = FALSE,
size = 3),
plotOutput("dose_response_growth_plot_model",
width = "100%", height = "800px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_dr_growth3",
label = "Re-run dose-response analysis with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_dr_growth3",
label = "Restore dose-response analysis",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_growth_plot_model",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_growth_plot_model",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_growth_plot_model",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_growth_plot_model',"Download Plot"),
radioButtons("format_download_dose_response_growth_plot_model",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
), # mainPanel
), # tabPanel(title = "Dose-response analysis"
### Growth DR Plots Bootstrap ####
tabPanel(title = "Dose-Response Analysis (Bootstrap)", value = "tabPanel_Visualize_Growth_DoseResponse_Spline_bt",
sidebarPanel(
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_dose_response_growth_plot_bt',
label = 'Shape type',
min = 1,
max = 25,
value = 15),
sliderInput(inputId = 'shape_size_dose_response_growth_plot_bt',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_dose_response_growth_plot_bt',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'lab_size_dose_response_growth_plot_bt',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'line_width_dose_response_growth_plot_bt',
label = 'Line width',
min = 0.01,
max = 10,
value = 1),
), # sidebarPanel
mainPanel(
h3('Individual plots'),
selectInput(inputId = 'individual_plots_dose_response_growth_plot_bt',
label = 'Select plot',
choices = "",
multiple = FALSE,
selectize = FALSE,
size = 3),
plotOutput("dose_response_growth_plot_individual_bt",
width = "100%", height = "800px"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_growth_plot_individual_bt",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_growth_plot_individual_bt",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_growth_plot_individual_bt",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_growth_plot_individual_bt',"Download Plot"),
radioButtons("format_download_dose_response_growth_plot_individual_bt",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel(title = "Dose-response analysis (Bootstrap)"
### Growth DR Parameters ####
tabPanel(title = "DR Parameter Plots",value = "tabPanel_Visualize_Growth_DoseResponseParameters",
sidebarPanel(
selectInput(inputId = "parameter_dr_parameter_growth_plot",
label = "Parameter",
choices = ""
),
textInput(inputId = "select_sample_based_on_string_growth_dr_parameter_plot",
label = "Select sample based on string (separated by ;)"
),
textInput(inputId = "exclude_sample_based_on_strings_growth_dr_parameter_plot",
label = "Exclude sample based on strings (separated by ;)"
),
checkboxInput(inputId = 'normalize_to_reference_growth_dr_parameter_plot',
label = 'normalize to reference',
value = FALSE),
h3("Customize plot appearance"),
# Conditional Panel
conditionalPanel(condition = "input.normalize_to_reference_growth_dr_parameter_plot",
# reactive selection
selectInput(inputId = 'reference_condition_growth_dr_parameter_plot',
label = 'Reference condition',
choices = ""
)
),
sliderInput(inputId = "basesize_growth_dr_parameter_plot",
label = "Base font size",
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "label.size_growth_dr_parameter_plot",
label = "Label font size",
min = 5,
max = 35,
value = 20,
step = 0.5)
),
mainPanel(
plotOutput("growth_dr_parameter_plot",
width = "100%", height = "800px"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_growth_dr_parameter_plot",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_growth_dr_parameter_plot",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_growth_dr_parameter_plot",
label = "DPI",
value = 300)
), # column
column(width = 5,
downloadButton('download_growth_dr_parameter_plot',"Download Plot"),
radioButtons("format_download_growth_dr_parameter_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel Growth_Parameter_Plots
)
),
## Fluorescence Plots ####
tabPanel(title = "Fluorescence Plots", value = "tabPanel_Visualize_Fluorescence",
h1("Fluorescence Plots"),
tabsetPanel(type = "tabs",id = "tabsetPanel_Visualize_Fluorescence",
### Fluorescence Group Plots ####
tabPanel(title = "Group plots",
sidebarPanel(
selectInput(inputId = "data_type_fluorescence_group_plot",
label = "Data type",
choices = c("Raw fluorescence" = "raw",
"Spline fits FL" = "spline",
"Normalized FL" = "norm.fl"
)
),
checkboxInput(inputId = "select_string_visualize_fluorescence_group",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_fluorescence_group && !input.plot_group_averages_fluorescence_group_plot",
selectizeInput(inputId = "samples_visualize_fluorescence_group",
label = "Samples:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "!input.select_string_visualize_fluorescence_group && input.plot_group_averages_fluorescence_group_plot",
selectizeInput(inputId = "groups_visualize_fluorescence_group",
label = "Conditions:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_fluorescence_group",
textInput(inputId = "select_samples_based_on_string_fluorescence_group_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_string_fluorescence_group_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
conditionalPanel(
condition = "input.plot_group_averages_fluorescence_group_plot || input.select_string_visualize_fluorescence_group",
textInput(inputId = "select_samples_based_on_concentration_fluorescence_group_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_concentration_fluorescence_group_plot",
label = "Exclude sample based on concentration (separate by ;)"
)
),
checkboxInput(inputId = "plot_group_averages_fluorescence_group_plot",
label = "Plot group averages",
value = TRUE),
conditionalPanel(
condition = "input.data_type_fluorescence_group_plot == 'spline' ",
checkboxInput(inputId = "plot_derivative_fluorescence_group_plot",
label = "Plot derivative",
value = TRUE)
),
h3("Customize plot appearance"),
checkboxInput(inputId = "log_transform_y_axis_fluorescence_group_plot",
label = "Log-transform y-axis",
value = FALSE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_fluorescence_group_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_fluorescence_group_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_fluorescence_group_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_fluorescence_group_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
conditionalPanel(
condition = "input.data_type_fluorescence_group_plot == 'spline'",
strong("y-Range (derivative)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_derivative_fluorescence_group_plot",
label = NULL,
value = "min", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_derivative_fluorescence_group_plot",
label = NULL,
value = "", placeholder = "max"
)
)
)
),
textInput(inputId = "y_axis_title_fluorescence_group_plot",
label = "y-axis title",
value = ""
),
textInput(inputId = "x_axis_title_fluorescence_group_plot",
label = "x-axis title",
value = ""
),
conditionalPanel(
condition = "input.data_type_fluorescence_group_plot == 'spline' ",
textInput(inputId = "y_axis_title_derivative_fluorescence_group_plot",
label = "y-axis title derivative",
value = ""
)
),
sliderInput(inputId = "nbreaks_fluorescence_group_plot",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
sliderInput(inputId = "line_width_fluorescence_group_plot",
label = "Line width",
min = 0.01,
max = 10,
value = 1.1),
sliderInput(inputId = 'base_size_fluorescence_group_plot',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = 'legend_ncol_fluorescence_group_plot',
label = 'Number of legend columns',
min = 1,
max = 10,
value = 4,
step = 1),
selectInput(inputId = "legend_position_fluorescence_group_plot",
label = "Legend position",
choices = c("Bottom" = "bottom",
"Top" = "top",
"Left" = "left",
"Right" = "right")
),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "color_groups_fluorescence_group_plot",
label = "Color samples by group",
value = TRUE)
),
textInput(
inputId = 'custom_colors_fluorescence_group_plot',
label = 'Custom colors'
),
bsPopover(id = "custom_colors_fluorescence_group_plot",
title = HTML("<em>Provide custom colors</em>"), placement = "top",
content = "Enter colors either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). Separate colors with a comma. A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
conditionalPanel(
condition = "output.more_than_two_conc && input.color_groups_fluorescence_group_plot",
selectizeInput(inputId = "color_palettes_fluorescence_group_plot",
label = "Change color palettes",
width = "100%",
choices = names(QurvE:::single_hue_palettes),
selected = names(QurvE:::single_hue_palettes),
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
),
bsPopover(id = "color_palettes_fluorescence_group_plot",
title = HTML("<em>Define the colors used to display sample groups with identical concentrations</em>"), placement = "top",
content = "The number of selected color palettes must be at least the number of displayed groups. The order of the chosen palettes corresponds to the oder of conditions in the legend."
),
),
), # Side panel growth group plots
mainPanel(
withSpinner(
plotOutput("fluorescence_group_plot",
width = "100%", height = "1000px")
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_group_plot",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_group_plot",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_group_plot",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_fluorescence_group_plot',"Download Plot"),
radioButtons("format_download_fluorescence_group_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel Group_Plots
### Dual Plots ####
tabPanel(title = "Growth & Flourescence Plot", value = "tabPabel_Visualize_Dual",
h1("Growth & Flourescence Plot"),
sidebarPanel(
selectInput(inputId = "fluorescence_type_dual_plot",
label = "Fluorescence type",
choices = c("Fluorescence" = "fl",
"Normalized fluorescence" = "norm.fl"
)
),
checkboxInput(inputId = "select_string_visualize_dual_plot",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_dual_plot",
selectizeInput(inputId = "samples_visualize_dual_plot",
label = "Samples:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_dual_plot",
textInput(inputId = "select_samples_based_on_string_dual_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_string_dual_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
conditionalPanel(
condition = "input.plot_group_averages_dual_plot || input.select_string_visualize_dual_plot",
textInput(inputId = "select_samples_based_on_concentration_dual_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_concentration_dual_plot",
label = "Exclude sample based on concentration (separate by ;)"
)
),
checkboxInput(inputId = "plot_group_averages_dual_plot",
label = "Plot group averages",
value = TRUE),
h3("Customize plot appearance"),
checkboxInput(inputId = "log_transform_y_axis_growth_dual_plot",
label = "Log-transform y-axis (growth)",
value = FALSE),
checkboxInput(inputId = "log_transform_y_axis_fluorescence_dual_plot",
label = "Log-transform y-axis (Fluorescence)",
value = FALSE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_dual_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_dual_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range (growth)"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_growth_dual_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_growth_dual_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range (Fluorescence"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_fluorescence_dual_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_fluorescence_dual_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(inputId = "y_axis_title_growth_dual_plot",
label = "y-axis title (growth)",
value = ""
),
textInput(inputId = "y_axis_title_fluorescence_dual_plot",
label = "y-axis title (Fluorescence)",
value = ""
),
textInput(inputId = "x_axis_title_dual_plot",
label = "x-axis title",
value = ""
),
sliderInput(inputId = "nbreaks_dual_plot",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
sliderInput(inputId = "line_width_dual_plot",
label = "Line width",
min = 0.01,
max = 10,
value = 1.1),
sliderInput(inputId = 'base_size_dual_plot',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
selectInput(inputId = "legend_position_dual_plot",
label = "Legend position",
choices = c("Bottom" = "bottom",
"Top" = "top",
"Left" = "left",
"Right" = "right")
),
sliderInput(inputId = 'legend_ncol_dual_plot',
label = 'Number of legend columns',
min = 1,
max = 10,
value = 4,
step = 1
),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "color_groups_dual_plot",
label = "Color samples by group",
value = TRUE)
),
textInput(
inputId = 'custom_colors_dual_plot',
label = 'Custom colors'
),
bsPopover(id = "custom_colors_dual_plot",
title = HTML("<em>Provide custom colors</em>"), placement = "top",
content = "Enter colors either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). Separate colors with a comma. A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
conditionalPanel(
condition = "output.more_than_two_conc && input.color_groups_dual_plot",
selectizeInput(inputId = "color_palettes_dual_plot",
label = "Change color palettes",
width = "100%",
choices = names(QurvE:::single_hue_palettes),
selected = names(QurvE:::single_hue_palettes),
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
),
bsPopover(id = "color_palettes_dual_plot",
title = HTML("<em>Define the colors used to display sample groups with identical concentrations</em>"), placement = "top",
content = "The number of selected color palettes must be at least the number of displayed groups. The order of the chosen palettes corresponds to the oder of conditions in the legend."
),
),
),
mainPanel(
withSpinner(
plotOutput("dual_plot",
width = "100%", height = "1000px")
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dual_plot",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dual_plot",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dual_plot",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dual_plot',"Download Plot"),
radioButtons("format_download_dual_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel(title = "Growth & Flourescence Plot")
### Fluorescence Parameter Plots ####
tabPanel(title = "Parameter plots",
sidebarPanel(
selectInput(inputId = "parameter_fluorescence_parameter_fluorescence_plot",
label = "Parameter",
choices = ""
),
checkboxInput(inputId = "select_string_visualize_parameter_fluorescence_plot",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_parameter_fluorescence_plot",
selectizeInput(inputId = "samples_visualize_parameter_fluorescence_plot",
label = "Conditions:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_parameter_fluorescence_plot",
textInput(inputId = "select_sample_based_on_string_fluorescence_parameter_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_sample_based_on_strings_fluorescence_parameter_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
textInput(inputId = "select_sample_based_on_concentration_fluorescence_parameter_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_sample_based_on_concentration_fluorescence_parameter_plot",
label = "Exclude sample based on concentration (separate by ;)"
),
checkboxInput(inputId = 'normalize_to_reference_fluorescence_parameter_plot',
label = 'normalize to reference',
value = FALSE),
# Conditional Panel
conditionalPanel(condition = "input.normalize_to_reference_fluorescence_parameter_plot",
# reactive selection
selectInput(inputId = 'reference_condition_fluorescence_parameter_plot',
label = 'Reference condition',
choices = ""
),
# reactive selection
selectInput(inputId = 'reference_concentration_fluorescence_parameter_plot',
label = 'Reference concentration',
choices = ""
),
),
h3("Customize plot appearance"),
sliderInput(inputId = "shape.size_fluorescence_parameter_plot",
label = "Shape size",
min = 0.1,
max = 10,
value = 3,
step = 0.1),
sliderInput(inputId = "basesize_fluorescence_parameter_plot",
label = "Base font size",
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "label.size_fluorescence_parameter_plot",
label = "Label font size",
min = 5,
max = 35,
value = 17,
step = 0.5),
selectInput(inputId = "legend_position_fluorescence_parameter_plot",
label = "Legend position",
choices = c("Bottom" = "bottom",
"Top" = "top",
"Left" = "left",
"Right" = "right"),
selected = "right"
),
sliderInput(inputId = 'legend_ncol_fluorescence_parameter_plot',
label = 'Number of legend columns',
min = 1,
max = 10,
value = 1,
step = 1
),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "sort_by_conc_fluorescence_parameter_plot",
label = "Sort samples by concentration",
value = FALSE)
),
textInput(
inputId = 'custom_colors_fluorescence_parameter_plot',
label = 'Custom colors'
),
bsPopover(id = "custom_colors_fluorescence_parameter_plot",
title = HTML("<em>Provide custom colors</em>"), placement = "top",
content = "Enter colors either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). Separate colors with a comma. A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf"
),
),
mainPanel(
plotOutput("fluorescence_parameter_plot",
width = "100%", height = "800px"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_parameter_plot",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_parameter_plot",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_parameter_plot",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_fluorescence_parameter_plot',"Download Plot"),
radioButtons("format_download_fluorescence_parameter_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel(title = "Parameter plots"
### Fluorescence Grid Plots ####
tabPanel(title = "Plot Grid",
sidebarPanel(
selectInput(inputId = "data_type_fluorescence_grid_plot",
label = "Data type",
choices = c("Raw growth" = "raw",
"Spline fits" = "spline")
),
selectInput(inputId = "parameter_parameter_grid_plot_fluorescence",
label = "Parameter",
choices = ""
),
checkboxInput(inputId = "select_string_visualize_fluorescence_grid",
label = "(De-)select samples based on string",
value = FALSE),
conditionalPanel(
condition = "!input.select_string_visualize_fluorescence_grid && !input.plot_group_averages_fluorescence_grid_plot",
selectizeInput(inputId = "samples_visualize_fluorescence_grid",
label = "Samples:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
),
checkboxInput(inputId = "order_matters_visualize_fluorescence_grid",
label = "Select order matters",
value = FALSE
),
),
conditionalPanel(
condition = "!input.select_string_visualize_fluorescence_grid && input.plot_group_averages_fluorescence_grid_plot",
selectizeInput(inputId = "groups_visualize_fluorescence_grid",
label = "Conditions:",
width = "100%",
choices = "",
multiple = TRUE,
options = list(closeAfterSelect = FALSE,
plugins= list('remove_button'))
)
),
conditionalPanel(
condition = "input.select_string_visualize_fluorescence_grid",
textInput(inputId = "select_samples_based_on_string_fluorescence_grid_plot",
label = "Select sample based on string (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_string_fluorescence_grid_plot",
label = "Exclude sample based on string (separate by ;)"
),
),
conditionalPanel(
condition = "input.plot_group_averages_fluorescence_grid_plot || input.select_string_visualize_fluorescence_grid",
textInput(inputId = "select_samples_based_on_concentration_fluorescence_grid_plot",
label = "Select sample based on concentration (separate by ;)"
),
textInput(inputId = "exclude_samples_based_on_concentration_fluorescence_grid_plot",
label = "Exclude sample based on concentration (separate by ;)"
)
),
checkboxInput(inputId = "plot_group_averages_fluorescence_grid_plot",
label = "Plot group averages",
value = TRUE),
conditionalPanel(
condition = "output.more_than_two_conc",
checkboxInput(inputId = "sort_by_conc_fluorescence_grid_plot",
label = "Sort by concentration",
value = TRUE)
),
h3("Customize plot appearance"),
checkboxInput(inputId = "log_transform_y_axis_fluorescence_grid_plot",
label = "Log-transform y-axis",
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_fluorescence_grid_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_fluorescence_grid_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_fluorescence_grid_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_fluorescence_grid_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("Color scale limits"),
fluidRow(
column(5,
textInput(inputId = "legend_lim_min_fluorescence_grid_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "legend_lim_max_fluorescence_grid_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(inputId = "y_axis_title_fluorescence_grid_plot",
label = "y-axis title",
value = ""
),
textInput(inputId = "x_axis_title_fluorescence_grid_plot",
label = "x-axis title",
value = ""
),
sliderInput(inputId = "nbreaks_fluorescence_grid_plot",
label = "Number of breaks on y-axis",
min = 1,
max = 20,
value = 6),
sliderInput(inputId = "line_width_fluorescence_grid_plot",
label = "Line width",
min = 0.01,
max = 10,
value = 1.1),
sliderInput(inputId = 'base_size_fluorescence_grid_plot',
label = 'Base font size',
min = 10,
max = 35,
value = 23,
step = 0.5),
conditionalPanel(
condition = "!input.sort_by_conc_fluorescence_grid_plot",
sliderInput(inputId = "nrows_fluorescence_grid_plot",
label = "Number of rows in grid",
min = 1,
max = 20,
value = 2)
),
selectInput(inputId = "color_palettes_grid_plot_fluorescence",
label = "Change color palette",
width = "100%",
choices = names(QurvE:::single_hue_palettes),
selected = names(QurvE:::single_hue_palettes)[1],
multiple = FALSE
),
bsPopover(id = "color_palettes_grid_plot_fluorescence",
title = HTML("<em>Define the colors used to visualize the value of the chosen parameter</em>"), placement = "top",
content = ""
),
checkboxInput(inputId = "invert_color_palette_grid_plot_fluorescence",
label = "Invert color palette",
value = FALSE)
), # Side panel fluorescence group plots
mainPanel(
withSpinner(
plotOutput("fluorescence_grid_plot",
width = "100%", height = "1000px"),
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_grid_plot",
label = "Width (in inches)",
value = 10)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_grid_plot",
label = "Height (in inches)",
value = 9)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_grid_plot",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_fluorescence_grid_plot',"Download Plot"),
radioButtons("format_download_fluorescence_grid_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
) # column
) # fluidRow
) # mainPanel
),
### Fluorescence DR Plots Spline ####
tabPanel(title = "Dose-response analysis", value = "tabPanel_Visualize_Fluorescence_DoseResponse_spline",
sidebarPanel(
wellPanel(
style='padding: 1; border-color: #ADADAD; padding-bottom: 0',
checkboxInput(inputId = 'combine_conditions_into_a_single_plot_dose_response_fluorescence_plot',
label = 'Combine conditions into a single plot',
value = TRUE)
),
conditionalPanel(
condition = "input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
textInput(inputId = "select_samples_based_on_string_dose_response_fluorescence_plot",
label = "Select sample based on string (separate by ;)"
)
),
conditionalPanel(
condition = "input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
textInput(inputId = "exclude_samples_based_on_string_dose_response_fluorescence_plot",
label = "Exclude sample based on string (separate by ;)"
)
),
h3('Customize plot appearance'),
checkboxInput(inputId = "log_transform_y_axis_dose_response_fluorescence_plot",
label = "Log-transform y-axis",
value = FALSE),
checkboxInput(inputId = "log_transform_x_axis_dose_response_fluorescence_plot",
label = "Log-transform x-axis",
value = FALSE),
sliderInput(inputId = 'shape_type_dose_response_fluorescence_plot',
label = 'Shape type',
min = 1,
max = 25,
value = 15),
sliderInput(inputId = 'shape_size_dose_response_fluorescence_plot',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
conditionalPanel(
condition = "input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
sliderInput(inputId = 'base_size_dose_response_fluorescence_plot',
label = 'Base font size',
min = 10,
max = 35,
value = 15,
step = 0.5)
),
conditionalPanel(
condition = "!input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
sliderInput(inputId = 'axis_size_dose_response_fluorescence_plot',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.5,
step = 0.1)
),
conditionalPanel(
condition = "!input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
sliderInput(inputId = 'lab_size_dose_response_fluorescence_plot',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1)
),
sliderInput(inputId = 'line_width_dose_response_fluorescence_plot',
label = 'Line width',
min = 0.01,
max = 10,
value = 1),
checkboxInput(inputId = 'show_ec50_indicator_lines_dose_response_fluorescence_plot',
label = 'Show EC50 indicator lines',
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_dose_response_fluorescence_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_dose_response_fluorescence_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_dose_response_fluorescence_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_dose_response_fluorescence_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
textInput(inputId = "y_axis_title_dose_response_fluorescence_plot",
label = "y-axis title",
value = ""
),
textInput(inputId = "x_axis_title_dose_response_fluorescence_plot",
label = "x-axis title",
value = ""
)
), # sidebarPanel
conditionalPanel(condition = "input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
mainPanel(
h3('Combined plots'),
plotOutput("dose_response_plot_fluorescence_combined",
width = "100%", height = "800px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_dr_fluorescence",
label = "Re-run dose-response analysis with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_dr_fluorescence",
label = "Restore dose-response analysis",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_plot_fluorescence_combined",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_plot_fluorescence_combined",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_plot_fluorescence_combined",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_plot_fluorescence_combined',"Download Plot"),
radioButtons("format_download_dose_response_plot_fluorescence_combined",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
),
conditionalPanel(condition = "!input.combine_conditions_into_a_single_plot_dose_response_fluorescence_plot",
mainPanel(
h3('Individual plots'),
selectInput(inputId = 'individual_plots_dose_response_fluorescence_plot',
label = 'Select plot',
choices = "",
multiple = FALSE,
selectize = FALSE,
size = 3),
plotOutput("dose_response_fluorescence_plot_individual",
width = "100%", height = "800px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_dr_fluorescence2",
label = "Re-run dose-response analysis with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_dr_fluorescence2",
label = "Restore dose-response analysis",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_fluorescence_plot_individual",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_fluorescence_plot_individual",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_fluorescence_plot_individual",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_fluorescence_plot_individual',"Download Plot"),
radioButtons("format_download_dose_response_fluorescence_plot_individual",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # conditionalPanel
), # tabPanel(title = "Dose-response analysis", value = "tabPanel_Visualize_Fluorescence_DoseResponse_spline",
### Fluorescence DR Plots Model ####
tabPanel(title = "Dose-response analysis", value = "tabPanel_Visualize_Fluorescence_DoseResponse_model",
sidebarPanel(
checkboxInput(inputId = "log_transform_y_axis_dose_response_model_fluorescence_plot",
label = "Log-transform y-axis",
value = TRUE),
checkboxInput(inputId = "log_transform_x_axis_dose_response_model_fluorescence_plot",
label = "Log-transform x-axis",
value = TRUE),
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_dose_response_model_fluorescence_plot',
label = 'Shape type',
min = 1,
max = 25,
value = 15),
sliderInput(inputId = 'shape_size_dose_response_model_fluorescence_plot',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_dose_response_model_fluorescence_plot',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.5,
step = 0.1),
sliderInput(inputId = 'lab_size_dose_response_model_fluorescence_plot',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'line_width_dose_response_model_fluorescence_plot',
label = 'Line width',
min = 0.01,
max = 10,
value = 1),
checkboxInput(inputId = 'show_ec50_indicator_lines_dose_response_model_fluorescence_plot',
label = 'Show EC50 indicator lines',
value = TRUE),
strong("x-Range"),
fluidRow(
column(5,
textInput(inputId = "x_range_min_dose_response_model_fluorescence_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "x_range_max_dose_response_model_fluorescence_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
strong("y-Range"),
fluidRow(
column(5,
textInput(inputId = "y_range_min_dose_response_model_fluorescence_plot",
label = NULL,
value = "", placeholder = "min"
)
),
column(5,
textInput(inputId = "y_range_max_dose_response_model_fluorescence_plot",
label = NULL,
value = "", placeholder = "max"
)
)
),
), # sidebarPanel
mainPanel(
selectInput(inputId = 'individual_plots_dose_response_model_fluorescence_plot',
label = 'Select plot',
choices = "",
multiple = FALSE,
selectize = FALSE,
size = 3),
plotOutput("dose_response_model_fluorescence_plot_individual",
width = "100%", height = "800px"),
fluidRow(
column(6, align = "center", offset = 3,
actionButton(inputId = "rerun_dr_fluorescence3",
label = "Re-run dose-response analysis with modified parameters",
icon=icon("gears"),
style="padding:5px; font-size:120%"),
actionButton(inputId = "restore_dr_fluorescence3",
label = "Restore dose-response analysis",
# icon=icon("gears"),
style="padding:5px; font-size:120%")
)
),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_model_fluorescence_plot_individual",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_model_fluorescence_plot_individual",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_model_fluorescence_plot_individual",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_model_fluorescence_plot_individual',"Download Plot"),
radioButtons("format_download_dose_response_model_fluorescence_plot_individual",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel(title = "Dose-response analysis",
### Fluorescence DR Plots Bootstrap ####
tabPanel(title = "Dose-Response Analysis (Bootstrap)", value = "tabPanel_Visualize_Fluorescence_DoseResponse_bt",
sidebarPanel(
h3('Customize plot appearance'),
sliderInput(inputId = 'shape_type_dose_response_fluorescence_plot_bt',
label = 'Shape type',
min = 1,
max = 25,
value = 15),
sliderInput(inputId = 'shape_size_dose_response_fluorescence_plot_bt',
label = 'Shape size',
min = 0.1,
max = 10,
value = 2,
step = 0.1),
sliderInput(inputId = 'axis_size_dose_response_fluorescence_plot_bt',
label = 'Axis title font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'lab_size_dose_response_fluorescence_plot_bt',
label = 'Axis label font size',
min = 0.1,
max = 10,
value = 1.3,
step = 0.1),
sliderInput(inputId = 'line_width_dose_response_fluorescence_plot_bt',
label = 'Line width',
min = 0.01,
max = 10,
value = 1),
), # sidebarPanel
mainPanel(
h3('Individual plots'),
selectInput(inputId = 'individual_plots_dose_response_fluorescence_plot_bt',
label = 'Select plot',
choices = "",
multiple = FALSE,
selectize = FALSE,
size = 3),
plotOutput("dose_response_fluorescence_plot_individual_bt",
width = "100%", height = "800px"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_dose_response_fluorescence_plot_individual_bt",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_dose_response_fluorescence_plot_individual_bt",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_dose_response_fluorescence_plot_individual_bt",
label = "DPI",
value = 300)
), # column
column(width = 4,
downloadButton('download_dose_response_fluorescence_plot_individual_bt',"Download Plot"),
radioButtons("format_download_dose_response_fluorescence_plot_individual_bt",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel(title = "Dose-response analysis (Bootstrap)"
### Fluorescence DR Parameters ####
tabPanel(title = "DR Parameter Plots",value = "tabPanel_Visualize_Fluorescence_DoseResponseParameters",
sidebarPanel(
selectInput(inputId = "parameter_dr_parameter_fluorescence_plot",
label = "Parameter",
choices = ""
),
textInput(inputId = "select_sample_based_on_string_fluorescence_dr_parameter_plot",
label = "Select sample based on string (separated by ;)"
),
textInput(inputId = "exclude_sample_based_on_strings_fluorescence_dr_parameter_plot",
label = "Exclude sample based on strings (separated by ;)"
),
checkboxInput(inputId = 'normalize_to_reference_fluorescence_dr_parameter_plot',
label = 'normalize to reference',
value = FALSE),
h3("Customize plot appearance"),
# Conditional Panel
conditionalPanel(condition = "input.normalize_to_reference_fluorescence_dr_parameter_plot",
# reactive selection
selectInput(inputId = 'reference_condition_fluorescence_dr_parameter_plot',
label = 'Reference condition',
choices = ""
)
),
sliderInput(inputId = "basesize_fluorescence_dr_parameter_plot",
label = "Base font size",
min = 10,
max = 35,
value = 23,
step = 0.5),
sliderInput(inputId = "label.size_fluorescence_dr_parameter_plot",
label = "Label font size",
min = 5,
max = 35,
value = 20,
step = 0.5)
),
mainPanel(
plotOutput("fluorescence_dr_parameter_plot",
width = "100%", height = "800px"),
h3(strong("Export plot")),
fluidRow(
column(width = 4,
numericInput(inputId = "width_download_fluorescence_dr_parameter_plot",
label = "Width (in inches)",
value = 7)
), # column
column(width = 4,
numericInput(inputId = "height_download_fluorescence_dr_parameter_plot",
label = "Height (in inches)",
value = 6)
), # column
column(width = 4,
numericInput(inputId = "dpi_download_fluorescence_dr_parameter_plot",
label = "DPI",
value = 300)
), # column
column(width = 5,
downloadButton('download_fluorescence_dr_parameter_plot',"Download Plot"),
radioButtons("format_download_fluorescence_dr_parameter_plot",
label = NULL,
choices = c("PNG" = ".png",
"PDF" = ".pdf"),
selected = ".png",
inline = TRUE)
), # column
) # fluidRow
) # mainPanel
), # tabPanel Fluorescence_Parameter_Plots
) # tabsetPanel(type = "tabs",
), # tabPanel(title = "Fluorescence Plots"
), # navbarMenu("Visualize"
#____REPORT____####
tabPanel(span("Report", title = "Generate a PDF or HTML report summarizing the results."),
value = "tabPanel_Report", icon=icon("file-contract"),
tabsetPanel(type = "tabs", id = "tabsetPanel_Report",
##____Growth report___####
tabPanel(title = "Growth", value = "tabPanel_report_growth",
sidebarPanel(width = 6,
selectInput(inputId = 'report_filetype_growth',
label = 'Choose file type',
choices = c('PDF' = 'pdf', 'HTML' = 'html')),
conditionalPanel(condition = "input.report_filetype_growth == 'pdf'",
fluidRow(
column(12,
div(
downloadButton(outputId = 'download_report_growth_pdf',
label = "Render Report",
icon = icon("file-pdf"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
),
conditionalPanel(condition = "input.report_filetype_growth == 'html'",
fluidRow(
column(12,
div(
downloadButton(outputId = 'download_report_growth_html',
label = "Render Report",
icon = icon("file-code"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
),
) # sidebarPanel
), # tabPanel(title = "Growth", value = "tabs_export_data_growth",
tabPanel(title = "Fluorescence", value = "tabPanel_report_fluorescence",
sidebarPanel(width = 6,
selectInput(inputId = 'report_filetype_fluorescence',
label = 'Choose file type',
choices = c('PDF' = 'pdf', 'HTML' = 'html')),
conditionalPanel(condition = "input.report_filetype_fluorescence == 'pdf'",
fluidRow(
column(12,
div(
downloadButton(outputId = 'download_report_fluorescence_pdf',
label = "Render Report",
icon = icon("file-pdf"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
),
conditionalPanel(condition = "input.report_filetype_fluorescence == 'html'",
fluidRow(
column(12,
div(
downloadButton(outputId = 'download_report_fluorescence_html',
label = "Render Report",
icon = icon("file-code"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
),
) # sidebarPanel
) # tabPanel(title = "fluorescence", value = "tabs_export_data_fluorescence",
), # tabsetPanel(type = "tabs", id = "tabs_report",
checkboxInput(inputId = "report_issues",
label = "Creating reports does not work",
value = FALSE),
conditionalPanel(condition = "input.report_issues",
actionButton(inputId = "run_report_fix",
label = "Run Report Troubleshooting"),
bsPopover(id = "run_report_fix", title = HTML("<em>Run Report Troubleshooting</em>"), content = HTML("This process will take several minutes!<br><br>Reinstalls TinyTeX, updates tlmgr and ensures that Pandoc is recognized correctly.")),
)
), # tabPanel("Report", value = "tabPanel_Report", icon=icon("file-contract"),
#___Export RData___####
tabPanel(span("Data Export", title = "Export all computation results as RData file."),
icon = icon("download"),
value = "tabPanel_Export_RData",
tabsetPanel(type = "tabs", id = "tabsetPanel_Export_Data",
##____Growth results export____####
tabPanel(title = "Growth", value = "tabPanel_export_data_growth",
sidebarPanel(width = 3,
fluidRow(
column(12,
div(
downloadButton(outputId = 'export_RData_growth',
label = "Export RData file",
icon = icon("file-export"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
)
),
## Fluorescence results export____####
tabPanel(title = "Fluorescence", value = "tabPanel_export_data_fluorescence",
sidebarPanel(width = 3,
fluidRow(
column(12,
div(
downloadButton(outputId = 'export_RData_fluorescence',
label = "Export RData file",
icon = icon("file-export"),
style="padding:5px; font-size:120%"),
style="float:right")
)
)
)
),
)
),
#___Import RData___####
tabPanel(span("Data Import", title = "Import and RData file with results from a previous QurvE analysis."),
icon = icon("upload"),
value = "tabPanel_Import_RData",
tabsetPanel(type = "tabs", id = "tabsetPanel_Import_Data",
##____Growth results export____####
tabPanel(title = "Growth", value = "tabPanel_import_data_growth",
sidebarPanel(width = 3,
fluidRow(
column(12,
fileInput(inputId = 'import_RData_growth',
label = 'Choose growth RData file',
accept = c('.rdata')
),
conditionalPanel(
condition = 'output.RData_growth_uploaded',
div(
actionButton(inputId = "read_RData_growth",
label = "Read data",
icon=icon("upload"),
style="padding:5px; font-size:120%"),
style="float:right")
),
)
)
)
),
## Fluorescence results export____####
tabPanel(title = "Fluorescence", value = "tabPanel_import_data_fluorescence",
sidebarPanel(width = 3,
fluidRow(
column(12,
fileInput(inputId = 'import_RData_fluorescence',
label = 'Choose fluorescence RData file',
accept = c('.rdata')
),
conditionalPanel(
condition = 'output.RData_fluorescence_uploaded',
div(
actionButton(inputId = "read_RData_fluorescence",
label = "Read data",
icon=icon("upload"),
style="padding:5px; font-size:120%"),
style="float:right")
),
)
)
)
),
)
),
#____ABOUT US____####
tabPanel("About Us",
mainPanel(
h2("Creators"),
'Nicolas Wirth', tags$a(icon("twitter"), href="https://twitter.com/The_NiWi"), br(),
'Jonathan Funk', tags$a(icon("twitter"), href="https://twitter.com/JonathanFunk12"),
h2("Bug reports"),
uiOutput("bug_report"),
h2("Cite QurvE"),
"Wirth, N.T., Funk, J., Donati, S. et al. QurvE: user-friendly software for the analysis of biological growth and fluorescence data. Nat Protoc (2023). https://doi.org/10.1038/s41596-023-00850-7"
# h2("Publications"),
# ''
)
),
) # navbarPage
) # div(
) # hidden(
) # tagList(
)
#____SERVER____####
server <- function(input, output, session){
# Disable navbar menus before running computations
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Export_RData]")
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Report]")
shinyjs::disable(selector = "#navbar li a[data-value=navbarMenu_Visualize]")
shinyjs::disable(selector = "#navbar li a[data-value=navbarMenu_Computation]")
shinyjs::disable(selector = "#navbar li a[data-value=navbarMenu_Results]")
shinyjs::disable(selector = "#navbar li a[data-value=navbarMenu_Validate]")
# Disable menus based on the type of computations run
observe({
if(is.null(results$growth)){
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Results_Growth]")
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Validate_Growth]")
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Visualize_Growth]")
} else {
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Results_Growth]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Validate_Growth]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Visualize_Growth]")
}
if(is.null(results$fluorescence)){
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Results_Fluorescence]")
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Validate_Fluorescence]")
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Visualize_Fluorescence]")
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Visualize_GrowthandFluorescence]")
} else {
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Results_Fluorescence]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Validate_Fluorescence]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Visualize_Fluorescence]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Visualize_GrowthandFluorescence]")
}
if(!is.null(results$custom_data)){
grodata <- results$custom_data
} else if(!is.null(results$parsed_data)){
grodata <- results$parsed_data
} else {
grodata <- NULL
}
if(!is.null(grodata)){
if(length(grodata$fluorescence) < 2){
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Computation_Fluorescence]")
} else {
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Computation_Fluorescence]")
}
if(length(grodata$growth) < 2){
shinyjs::disable(selector = "#navbar li a[data-value=tabPanel_Computation_Growth]")
} else {
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Computation_Growth]")
}
}
})
# sidePanel_width <- reactive({
# if(as.numeric(input$dimension[1]) < 1000) return(TRUE)
# else return(6)
# })
results <- reactiveValues()
### Function help modals ####
output$gcFitLinear_tooltip <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/growth.gcFitLinear.Rd", out = paste0(tempfile("docs_gcFitLinear"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_growth.gcFitLinear,{
showModal(QurvE:::help_modal(size = "l", idcss = "gcFitLinear",
htmlOutput("gcFitLinear_tooltip"), easyClose = T )
)
})
observeEvent(input$tooltip_growth.gcFitLinear_validate,{
showModal(QurvE:::help_modal(size = "l", idcss = "gcFitLinear",
htmlOutput("gcFitLinear_tooltip"), easyClose = T )
)
})
output$rdm.data_tooltip <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/rdm.data.Rd", out = paste0(tempfile("docs_rdm.data"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_rdm.data,{
showModal(QurvE:::help_modal(size = "l", idcss = "rdm.data",
htmlOutput("rdm.data_tooltip"), easyClose = T )
)
})
output$gcFitSpline_tooltip <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/growth.gcFitSpline.Rd", out = paste0(tempfile("docs_gcFitSpline"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_growth.gcFitSpline,{
showModal(QurvE:::help_modal(size = "l", idcss = "gcFitSpline",
htmlOutput("gcFitSpline_tooltip"), easyClose = T )
)
})
observeEvent(input$tooltip_growth.gcFitSpline_validate,{
showModal(QurvE:::help_modal(size = "l", idcss = "gcFitSpline",
htmlOutput("gcFitSpline_tooltip"), easyClose = T )
)
})
output$gcFitModel_tooltip <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/growth.gcFitModel.Rd", out = paste0(tempfile("docs_gcFitModel"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_growth.gcFitModel,{
showModal(QurvE:::help_modal(size = "l", idcss = "gcFitModel",
htmlOutput("gcFitModel_tooltip"), easyClose = T )
)
})
observeEvent(input$tooltip_growth.gcFitModel_validate,{
showModal(QurvE:::help_modal(size = "l", idcss = "gcFitModel",
htmlOutput("gcFitModel_tooltip"), easyClose = T )
)
})
output$tooltip_growth_workflow <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/growth.workflow.Rd", out = paste0(tempfile("docs_growth_workflow"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_growth_workflow,{
showModal(QurvE:::help_modal(size = "l", idcss = "growthworkflow",
htmlOutput("tooltip_growth_workflow"), easyClose = T )
)
})
output$tooltip_growth_dr <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/growth.drFit.Rd", out = paste0(tempfile("docs_growth_drFit"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_growth_dr,{
showModal(QurvE:::help_modal(size = "l", idcss = "growthdr",
htmlOutput("tooltip_growth_dr"), easyClose = T )
)
})
output$flFitLinear_tooltip <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/flFitLinear.Rd", out = paste0(tempfile("docs_flFitLinear"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_flFitLinear,{
showModal(QurvE:::help_modal(size = "l", idcss = "flFitLinear",
htmlOutput("flFitLinear_tooltip"), easyClose = T )
)
})
observeEvent(input$tooltip_flFitLinear_validate,{
showModal(QurvE:::help_modal(size = "l", idcss = "flFitLinear",
htmlOutput("flFitLinear_tooltip"), easyClose = T )
)
})
output$flFitSpline_tooltip <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/flFitSpline.Rd", out = paste0(tempfile("docs_flFitSpline"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_flFitSpline,{
showModal(QurvE:::help_modal(size = "l", idcss = "flFitSpline",
htmlOutput("flFitSpline_tooltip"), easyClose = T )
)
})
observeEvent(input$tooltip_flFitSpline_validate,{
showModal(QurvE:::help_modal(size = "l", idcss = "flFitSpline",
htmlOutput("flFitSpline_tooltip"), easyClose = T )
)
})
output$tooltip_fl_workflow <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/fl.workflow.Rd", out = paste0(tempfile("docs_fl_workflow"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
output$tooltip_fl_dr <- renderText({
temp <- tools::Rd2HTML("../shiny_manuals/fl.drFit.Rd", out = paste0(tempfile("docs_fl_dr"), ".txt"))
content <- readLines(temp)
file.remove(temp)
content
})
observeEvent(input$tooltip_fl_workflow,{
showModal(QurvE:::help_modal(size = "l", idcss = "flworkflow",
htmlOutput("tooltip_fl_workflow"), easyClose = T )
)
})
observeEvent(input$tooltip_fl_dr,{
showModal(QurvE:::help_modal(size = "l", idcss = "fldr",
htmlOutput("tooltip_fl_dr"), easyClose = T )
)
})
#____Read data____####
# Test if fluorescence data is contained in custom/parsed object
output$fluorescence_present <- reactive({
if(!is.null(results$custom_data)){
grodata <- results$custom_data
} else if(!is.null(results$parsed_data)){
grodata <- results$parsed_data
} else return(FALSE)
if(length(grodata$fluorescence) > 1 || length(grodata$fluorescence2) > 1){
return(TRUE)
} else return(FALSE)
})
outputOptions(output, 'fluorescence_present', suspendWhenHidden=FALSE)
# Test if more than one two concentrations are being tested
output$more_than_two_conc <- reactive({
if(!is.null(results$custom_data)){
grodata <- results$custom_data
} else if(!is.null(results$parsed_data)){
grodata <- results$parsed_data
} else if(!is.null(results$growth)){
grodata <- results$growth
} else if(!is.null(results$fluorescence)){
grodata <- results$fluorescence
}
else return(FALSE)
if(length(unique(grodata$expdesign$concentration)) > 2 )
return(TRUE)
else
return(FALSE)
})
outputOptions(output, 'more_than_two_conc', suspendWhenHidden=FALSE)
##___Custom____####
hide("Custom_Data_Tables")
hide("norm_type_custom")
### Test if custom_file_growth was loaded
output$growthfileUploaded <- reactive({
if(is.null(input$custom_file_growth)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'growthfileUploaded', suspendWhenHidden=FALSE)
### Test if custom_file_fluorescence was loaded
output$fluorescencefileUploaded <- reactive({
if(is.null(input$custom_file_fluorescence)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'fluorescencefileUploaded', suspendWhenHidden=FALSE)
## Test if custom_file_fluorescence was loaded
output$fluorescence2fileUploaded <- reactive({
if(is.null(input$custom_file_fluorescence2)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'fluorescence2fileUploaded', suspendWhenHidden=FALSE)
observe({
if(!is.null(input$custom_file_fluorescence) && (!is.null(input$custom_file_growth) | !is.null(input$custom_file_fluorescence2))){
show("norm_type_custom")
} else {
hide("norm_type_custom")
return(NULL)
}
norm.types <- c()
if(!is.null(input$custom_file_growth)){
norm.types <- c(norm.types, "growth")
}
if(!is.null(input$custom_file_fluorescence2)){
norm.types <- c(norm.types, "fluorescence 2")
}
if(length(norm.types) > 0){
show("norm_type_custom")
} else {
hide("norm_type_custom")
}
updateSelectInput(inputId = "norm_type_custom",
choices = norm.types
)
})
# Read data upon click on [Read data]
observeEvent(input$read_custom,{
showModal(modalDialog("Reading data input...", footer=NULL))
growth.file <- input$custom_file_growth
fl.file <- input$custom_file_fluorescence
fl2.file <- input$custom_file_fluorescence2
if(is.null(growth.file) && is.null(fl.file) ) return(NULL)
if(input$convert_time_equation_custom == "" || is.na(input$convert_time_equation_custom)) convert.time <- NULL
else convert.time <- input$convert_time_equation_custom
fl.normtype <- ifelse(input$load_fl2_data_custom, "fl2", "growth")
## Read data
try(
results$custom_data <- read_data(data.growth = growth.file$datapath,
data.fl = fl.file$datapath,
data.fl2 = fl2.file$datapath,
data.format = "col",
sheet.growth = input$custom_growth_sheets,
sheet.fl = input$custom_fluorescence_sheets,
sheet.fl2 = input$custom_fluorescence2_sheets,
csvsep = input$separator_custom_growth,
dec = input$decimal_separator_custom_growth,
csvsep.fl = input$separator_custom_fluorescence,
dec.fl = input$decimal_separator_custom_fluorescence,
csvsep.fl2 = input$separator_custom_fluorescence2,
dec.fl2 = input$decimal_separator_custom_fluorescence2,
subtract.blank = input$subtract_blank_custom,
convert.time = convert.time,
calib.growth = ifelse(input$calibration_growth_custom, input$calibration_equation_growth_custom, ""),
calib.fl = ifelse(input$calibration_fluorescence_custom, input$calibration_equation_fluorescence_custom, ""),
calib.fl2 = ifelse(input$calibration_fluorescence2_custom, input$calibration_equation_fluorescence2_custom, ""),
fl.normtype = fl.normtype
)
)
if(length(results$custom_data)<2){
showModal(modalDialog("Data could not be extracted from the provided file. Did you correctly format your custom data?", easyClose = T, footer=NULL))
} else {
hide("data_instruction")
show("Custom_Data_Tables")
hide("Parsed_Data_Tables")
if("growth" %in% names(results$custom_data) && length(results$custom_data$growth)>1){
# show [Run Computation] button in Computation-Growth
show("run_growth")
}
# if("growth" %in% names(results$custom_data) && length(results$custom_data$fluorescence2)>1){
# showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence2")
# }
# Remove eventually pre-loaded parsed data
results$parsed_data <- NULL
hide("parsed_reads_growth")
hide("parsed_reads_fluorescence")
hide("parsed_reads_fluorescence2")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_norm_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_norm_fluorescence")
# hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence2")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Computation]")
removeModal()
}
})
### Render custom growth table
# growth_data_custom <- reactive({
# inFile <- input$custom_file_growth
#
# if(is.null(inFile))
# return(NULL)
#
# filename <- inFile$datapath
# dec <- input$decimal_separator_custom_growth
# csvsep <- input$separator_custom_growth
# if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "csv") {
# dat <-
# utils::read.csv(
# filename,
# dec = dec,
# sep = csvsep,
# header = FALSE,
# stringsAsFactors = FALSE,
# fill = T,
# na.strings = "",
# quote = "",
# comment.char = "",
# check.names = F
# )
# } else if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "xls" |
# stringr::str_replace(filename, ".{1,}\\.", "") == "xlsx") {
# showModal(modalDialog("Reading data file...", footer=NULL))
# try(
# dat <- data.frame(suppressMessages(readxl::read_excel(filename, col_names = FALSE, sheet = input$custom_growth_sheets, progress = T)))
# )
# removeModal()
# } else if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "tsv") {
# dat <-
# utils::read.csv(
# filename,
# dec = dec,
# sep = "\t",
# header = FALSE,
# stringsAsFactors = FALSE,
# fill = T,
# na.strings = "",
# quote = "",
# comment.char = "",
# check.names = F
# )
# } else if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "txt") {
# dat <-
# utils::read.table(
# filename,
# dec = dec,
# sep = "\t",
# header = FALSE,
# stringsAsFactors = FALSE,
# fill = T,
# na.strings = "",
# quote = "",
# comment.char = "",
# check.names = F
# )
# }
# if(exists("dat")){
# dat[-(1:3),] <- apply(dat[-(1:3),], 2, as.numeric) %>% apply(., 2, round, digits = 2)
#
# colnames(dat)[1] <- "Time"
# dat[1,1] <- ""
# return(dat)
# } else return(NULL)
# })
# output$growth_data_rendered <- DT::renderDT({
# dat <- growth_data_custom()
# DT::datatable(dat,
# options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
# escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(dat)-3)))
# })
### Render experimental design table
custom_data_table_expdesign <- reactive({
if(is.null(growth_data_custom_processed()) && is.null(custom_table_fluorescence_processed())) return(NULL)
else if(!is.null(growth_data_custom_processed())){
dat <- growth_data_custom_processed()
}
else if(!is.null(custom_table_fluorescence_processed())){
dat <- custom_table_fluorescence_processed()
}
dat.mat <- t(dat)
label <- unlist(lapply(1:nrow(dat.mat), function(x) paste(dat.mat[x,1], dat.mat[x,2], dat.mat[x,3], sep = " | ")))
condition <- dat.mat[, 1]
replicate <- dat.mat[, 2]
concentration <- dat.mat[, 3]
expdesign <- data.frame(label, condition, replicate, concentration, check.names = FALSE)
expdesign[-1, ]
})
output$custom_data_table_expdesign <- DT::renderDT({
expdesign <- custom_data_table_expdesign()
DT::datatable(expdesign,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
observe({
# if(exists("growth_data_custom") && !is.null(growth_data_custom()) && is.null(growth_data_custom_processed()) ){
# showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_growth")
# } else {
# hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_growth")
# }
# if(exists("custom_table_fluorescence") && !is.null(custom_table_fluorescence()) && is.null(custom_table_fluorescence_processed()) ){
# showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence", select = TRUE)
# } else {
# hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence")
# }
if(exists("custom_data_table_expdesign") && !is.null(custom_data_table_expdesign())){
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_expdesign")
} else {
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_expdesign")
}
if(exists("growth_data_custom_processed") && !is.null(growth_data_custom_processed())){
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_growth_processed", select = TRUE)
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_growth", select = FALSE)
} else {
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_growth_processed")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_growth")
}
if(exists("custom_table_fluorescence_processed") && !is.null(custom_table_fluorescence_processed()) ){
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence_processed", select = TRUE)
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_fluorescence", select = FALSE)
} else {
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence_processed")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_fluorescence")
}
if(exists("custom_table_norm_fluorescence_processed") && !is.null(custom_table_norm_fluorescence_processed()) ){
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_norm_fluorescence_processed", select = TRUE)
showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_norm_fluorescence", select = FALSE)
} else {
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_norm_fluorescence_processed")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_norm_fluorescence")
}
if(exists("custom_table_fluorescence_processed") && !is.null(custom_table_fluorescence_processed()) &&
exists("growth_data_custom_processed") && !is.null(growth_data_custom_processed()) &&
all(results$custom_data$growth == results$custom_data$fluorescence, na.rm = T)){
showModal(modalDialog("growth and Fluorescence data are identical. Did you assign the correct files and/or sheets?", easyClose = T))
}
})
### Render custom fluorescence table
custom_table_fluorescence <- reactive({
inFile <- input$custom_file_fluorescence
if(is.null(inFile))
return(NULL)
filename <- inFile$datapath
dec <- input$decimal_separator_custom_fluorescence
csvsep <- input$separator_custom_fluorescence
if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "csv") {
f1 <-
utils::read.csv(
filename,
dec = dec,
sep = csvsep,
header = FALSE,
stringsAsFactors = FALSE,
fill = T,
na.strings = "",
quote = "",
comment.char = "",
check.names = F
)
} else if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "xls" |
stringr::str_replace(filename, ".{1,}\\.", "") == "xlsx") {
showModal(
modalDialog(
HTML("<strong>Reading data file...</strong><br><br><i>Note: Reading .xls/.xlsx files can take up to several minutes. Consider saving as .csv/.tsv/.txt before running QurvE.</i>"),
footer=NULL))
try(
f1 <- data.frame(suppressMessages(readxl::read_excel(filename, col_names = FALSE, sheet = input$custom_fluorescence_sheets, progress = T)))
)
removeModal()
} else if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "tsv") {
f1 <-
utils::read.csv(
filename,
dec = dec,
sep = "\t",
header = FALSE,
stringsAsFactors = FALSE,
fill = T,
na.strings = "",
quote = "",
comment.char = "",
check.names = F
)
} else if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "txt") {
f1 <-
utils::read.table(
filename,
dec = dec,
sep = "\t",
header = FALSE,
stringsAsFactors = FALSE,
fill = T,
na.strings = "",
quote = "",
comment.char = "",
check.names = F
)
}
if(exists("f1")){
f1[-(1:3),] <- apply(apply(f1[-(1:3),], 2, as.numeric), 2, round, digits = 2)
colnames(f1)[1] <- "Time"
f1[1,1] <- ""
return(f1)
} else return(NULL)
})
output$custom_table_fluorescence <- DT::renderDT({
f1 <- custom_table_fluorescence()
DT::datatable(f1,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(f1)-3)))
})
# Render processed growth table
growth_data_custom_processed <- reactive({
if(is.null(results$custom_data) || length(results$custom_data$growth) < 2) return(NULL)
table_growth <- t(results$custom_data$growth)
table_growth[-(1:3), ] <- apply(apply(table_growth[-(1:3), ], 2, as.numeric), 2, round, digits = 3)
rownames(table_growth)[-(1:3)] <- ""
table_growth <- cbind(data.frame("Time" = c("","","", round(as.numeric(results$custom_data$time[1,]), digits = 2))),
table_growth)
table_growth
})
output$growth_data_custom_processed <- DT::renderDT({
table_growth <- growth_data_custom_processed()
DT::datatable(table_growth,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(table_growth)-3)))
})
# render processed fluorescence table
custom_table_fluorescence_processed <- reactive({
if(is.null(results$custom_data) || length(results$custom_data$fluorescence)<2) return(NULL)
table_fl <- t(results$custom_data$fluorescence)
table_fl[-(1:3), ] <- apply(apply(table_fl[-(1:3), ], 2, as.numeric), 2, round, digits = 1)
rownames(table_fl)[-(1:3)] <- ""
table_fl <- cbind(data.frame("Time" = c("","","", round(as.numeric(results$custom_data$time[1,]), digits = 2))),
table_fl)
table_fl
})
output$custom_table_fluorescence_processed <- DT::renderDT({
table_fl <- custom_table_fluorescence_processed()
DT::datatable(table_fl,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(table_fl)-3)))
})
# render processed normalized fluorescence table
custom_table_norm_fluorescence_processed <- reactive({
if(is.null(results$custom_data) || length(results$custom_data$norm.fluorescence)<2) return(NULL)
table_fl <- t(results$custom_data$norm.fluorescence)
table_fl[-(1:3), ] <- apply(apply(table_fl[-(1:3), ], 2, as.numeric), 2, round, digits = 1)
rownames(table_fl)[-(1:3)] <- ""
table_fl <- cbind(data.frame("Time" = c("","","", round(as.numeric(results$custom_data$time[1,]), digits = 2))),
table_fl)
table_fl
})
output$custom_table_norm_fluorescence_processed <- DT::renderDT({
table_fl <- custom_table_norm_fluorescence_processed()
DT::datatable(table_fl,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(table_fl)-3)))
})
custom_raw_growth_plot <- reactive({
if(is.null(results$custom_data) || length(results$custom_data$growth) < 2) return(NULL)
try(
suppressWarnings(
plot.grodata(x = results$custom_data,
data.type = "growth",
IDs = NULL
)
)
)
})
output$custom_raw_growth_plot <- renderPlot({
custom_raw_growth_plot()
})
custom_raw_fluorescence_plot <- reactive({
if(is.null(results$custom_data) || length(results$custom_data$fluorescence) < 2) return(NULL)
try(
suppressWarnings(
plot.grodata(x = results$custom_data,
data.type = "fl",
IDs = NULL
)
)
)
})
output$custom_raw_fluorescence_plot <- renderPlot({
custom_raw_fluorescence_plot()
})
custom_raw_norm_fluorescence_plot <- reactive({
if(is.null(results$custom_data) || length(results$custom_data$norm.fluorescence) < 2) return(NULL)
try(
suppressWarnings(
plot.grodata(x = results$custom_data,
data.type = "norm.fl",
IDs = NULL
)
)
)
})
output$custom_raw_norm_fluorescence_plot <- renderPlot({
custom_raw_norm_fluorescence_plot()
})
output$custom_growth_format <- reactive({
if(is.null(input$custom_file_growth)) return(NULL)
filename <- input$custom_file_growth$name
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
format
})
outputOptions(output, 'custom_growth_format', suspendWhenHidden=FALSE)
output$custom_fluorescence_format <- reactive({
if(is.null(input$custom_file_fluorescence)) return(NULL)
filename <- input$custom_file_fluorescence$name
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
format
})
outputOptions(output, 'custom_fluorescence_format', suspendWhenHidden=FALSE)
output$custom_fluorescence2_format <- reactive({
if(is.null(input$custom_file_fluorescence2)) return(NULL)
filename <- input$custom_file_fluorescence2$name
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
format
})
outputOptions(output, 'custom_fluorescence2_format', suspendWhenHidden=FALSE)
growth_excel_sheets <- reactive({
filename <- input$custom_file_growth$datapath
if(is.null(input$custom_file_growth)) return("")
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
if(format != "xlsx" && format != "xls") return("")
sheets <- readxl::excel_sheets(input$custom_file_growth$datapath)
sheets
})
fluorescence_excel_sheets <- reactive({
filename <- input$custom_file_fluorescence$datapath
if(is.null(input$custom_file_fluorescence)) return("")
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
if(format != "xlsx" && format != "xls") return("")
sheets <- readxl::excel_sheets(input$custom_file_fluorescence$datapath)
sheets
})
fluorescence2_excel_sheets <- reactive({
filename <- input$custom_file_fluorescence2$datapath
if(is.null(input$custom_file_fluorescence2)) return("")
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
if(format != "xlsx" && format != "xls") return("")
sheets <- readxl::excel_sheets(input$custom_file_fluorescence2$datapath)
sheets
})
observe({
updateSelectInput(inputId = "custom_growth_sheets",
choices = growth_excel_sheets()
)})
observe({
updateSelectInput(inputId = "custom_fluorescence_sheets",
choices = fluorescence_excel_sheets()
)})
observe({
updateSelectInput(inputId = "custom_fluorescence2_sheets",
choices = fluorescence2_excel_sheets()
)})
observeEvent(input$random_data_growth, {
# display a modal dialog with a header, textinput and action buttons
showModal(
modalDialog(
tags$h2('Please enter parameters to simulate growth curves'),
textInput('d.random.growth', 'Number of samples', placeholder = "35"),
textInput('y0.random.growth', 'Start growth', placeholder = "0.05"),
textInput('tmax.random.growth', 'Maximum time value', placeholder = "24"),
textInput('mu.random.growth', 'Maximum growth rate', placeholder = "0.6"),
textInput('lambda.random.growth', 'Minimum lag time', placeholder = "5"),
textInput('A.random.growth', 'Maximum growth', placeholder = "3"),
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.random.data.growth', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_rdm.data",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
) # footer
) # modalDialog
) # showModal
})
# Generate random dataset
observeEvent(input$submit.random.data.growth, {
d <- ifelse(input$d.random.growth == "", 35, as.numeric(input$d.random.growth))
y0 <- ifelse(input$y0.random.growth == "", 0.05, as.numeric(input$y0.random.growth))
tmax <- ifelse(input$tmax.random.growth == "", 24, as.numeric(input$tmax.random.growth))
mu <- ifelse(input$mu.random.growth == "", 0.6, as.numeric(input$mu.random.growth))
lambda <- ifelse(input$lambda.random.growth == "", 5, as.numeric(input$lambda.random.growth))
A <- ifelse(input$A.random.growth == "", 3, as.numeric(input$A.random.growth))
results$custom_data <- rdm.data(d, y0 = y0, tmax = tmax, mu = mu, lambda = lambda, A = A, label = "Test")
hide("data_instruction")
show("Custom_Data_Tables")
hide("Parsed_Data_Tables")
if("growth" %in% names(results$custom_data) && length(results$custom_data$growth)>1){
# show [Run Computation] button in Computation-Growth
show("run_growth")
}
# Remove eventually pre-loaded parsed data
results$parsed_data <- NULL
hide("parsed_reads_growth")
hide("parsed_reads_fluorescence")
hide("parsed_reads_fluorescence2")
hide("norm_type_parse")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_norm_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_norm_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Computation]")
results$df_random_data <- data.frame("time" = c("time", "","", results$custom_data$time[1,]),
"data" = t(results$custom_data$data))
colnames(results$df_random_data ) <- results$df_random_data [1,]
write.csv(results$df_random_data[-1,], file = paste0(tempdir(), "/random_data.csv"))
results$random_data_growth <- paste0(tempdir(), "/random_data.csv")
removeModal()
})
observe({
growth.file <- results$random_data_growth
if(is.null(growth.file)) return(NULL)
## Read data
try(
results$custom_data <- read_data(results$df_random_data,
# data.fluoro2 = fl2.file$datapath,
data.format = "col"
)
)
if(length(results$custom_data)<2){
showModal(modalDialog("Data could not be extracted from the provided file. Did you correctly format your custom data?", easyClose = T, footer=NULL))
} else {
hide("data_instruction")
show("Custom_Data_Tables")
hide("Parsed_Data_Tables")
if("growth" %in% names(results$custom_data) && length(results$custom_data$growth)>1){
# show [Run Computation] button in Computation-Growth
show("run_growth")
}
# if("growth" %in% names(results$custom_data) && length(results$custom_data$fluorescence2)>1){
# showTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence2")
# }
# Remove eventually pre-loaded parsed data
results$parsed_data <- NULL
hide("parsed_reads_growth")
hide("parsed_reads_fluorescence")
hide("parsed_reads_norm_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_norm_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_norm_fluorescence")
# hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence2")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Computation]")
hide("random_data_growth")
removeModal()
}
})
##__Parse data____####
### Hide elements to guide user
hide("Parsed_Data_Tables")
hide("parsed_reads_growth")
hide("parsed_reads_fluorescence")
hide("parsed_reads_fluorescence2")
hide("parse_data")
hide("norm_type_parse")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence")
# hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence2")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
observe({
if(exists("parsed_data_table_expdesign") && !is.null(parsed_data_table_expdesign())){
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
} else {
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
}
if(exists("parsed_data_table_growth") && !is.null(parsed_data_table_growth())){
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth", select = TRUE)
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth", select = FALSE)
} else {
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth")
}
if(exists("parsed_data_table_fluorescence") && !is.null(parsed_data_table_fluorescence()) ){
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence", select = TRUE)
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence", select = FALSE)
} else {
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence")
}
if(exists("parsed_data_table_norm_fluorescence") && !is.null(parsed_data_table_norm_fluorescence()) ){
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_norm_fluorescence", select = TRUE)
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_norm_fluorescence", select = FALSE)
} else {
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_norm_fluorescence")
hideTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_norm_fluorescence")
}
})
# Update placeholder for time conversion based on selected software
observe({
if("Gen5" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = "y = x * 24")
}
if("Chi.Bio" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = "y = x / 3600")
}
if("GrowthProfiler" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = "y = x / 60")
}
if("Tecan" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = "y = x / 3600")
}
if("Biolector" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = NULL, placeholder = "")
}
if("VictorNivo" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = "y = x / 3600")
}
if("VictorX3" %in% input$platereader_software){
updateTextInput(session, "convert_time_equation_plate_reader", value = "y = x / 60")
}
})
### Test if parse_file was loaded
output$parsefileUploaded <- reactive({
if(is.null(input$parse_file)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'parsefileUploaded', suspendWhenHidden=FALSE)
parse_excel_sheets <- reactive({
filename <- input$parse_file$datapath
if(is.null(input$parse_file)) return("")
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
if(format != "xlsx" && format != "xls") return("")
sheets <- readxl::excel_sheets(input$parse_file$datapath)
sheets
})
map_excel_sheets <- reactive({
filename <- input$map_file$datapath
if(is.null(input$map_file)) return("")
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
if(format != "xlsx" && format != "xls") return("")
sheets <- readxl::excel_sheets(input$map_file$datapath)
sheets
})
### Test if map_file was loaded
output$mapfileUploaded <- reactive({
if(is.null(input$map_file)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'mapfileUploaded', suspendWhenHidden=FALSE)
### get file formats for parse_file and map_file
output$parse_file_format <- reactive({
if(is.null(input$parse_file)) return(NULL)
filename <- input$parse_file$name
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
format
})
outputOptions(output, 'parse_file_format', suspendWhenHidden=FALSE)
output$map_file_format <- reactive({
if(is.null(input$map_file)) return(NULL)
filename <- input$map_file$name
format <- stringr::str_replace_all(filename, ".{1,}\\.", "")
format
})
outputOptions(output, 'map_file_format', suspendWhenHidden=FALSE)
### Extract reads from data file
selected_inputs_parsed_reads <- reactive({
inFile <- input$parse_file
if(is.null(inFile))
return(NULL)
filename <- inFile$datapath
if (stringr::str_replace_all(filename, ".{1,}\\.", "") == "xls" |
stringr::str_replace(filename, ".{1,}\\.", "") == "xlsx"){
showModal(modalDialog(HTML("<strong>Reading data file...</strong><br><br><i>Note: Reading .xls/.xlsx files can take up to several minutes. Consider saving as .csv/.tsv/.txt before running QurvE.</i>"), footer=NULL))
}else {
showModal(modalDialog("Reading data file...", footer=NULL))
}
if("Gen5" %in% input$platereader_software){
try(reads <- QurvE:::parse_properties_Gen5Gen6(file=filename,
csvsep = input$separator_parse,
dec = input$decimal_separator_parse,
sheet = ifelse(input$parse_data_sheets == "Sheet1", 1, input$parse_data_sheets) ),
silent = FALSE
)
}
if("Chi.Bio" %in% input$platereader_software){
try(reads <- QurvE:::parse_properties_chibio(file=filename,
csvsep = input$separator_parse,
dec = input$decimal_separator_parse,
sheet = ifelse(input$parse_data_sheets == "Sheet1", 1, input$parse_data_sheets) ),
silent = FALSE
)
}
if("Tecan" %in% input$platereader_software){
try(reads <- QurvE:::parse_properties_tecan(file=filename,
csvsep = input$separator_parse,
dec = input$decimal_separator_parse,
sheet = ifelse(input$parse_data_sheets == "Sheet1", 1, input$parse_data_sheets) ),
silent = FALSE
)
}
if("Biolector" %in% input$platereader_software){
try(reads <- QurvE:::parse_properties_biolector(file=filename,
csvsep = input$separator_parse,
dec = input$decimal_separator_parse,
sheet = ifelse(input$parse_data_sheets == "Sheet1", 1, input$parse_data_sheets) ),
silent = FALSE
)
}
if("VictorNivo" %in% input$platereader_software){
try(reads <- QurvE:::parse_properties_victornivo(file=filename,
csvsep = input$separator_parse,
dec = input$decimal_separator_parse,
sheet = ifelse(input$parse_data_sheets == "Sheet1", 1, input$parse_data_sheets) ),
silent = FALSE
)
}
if("VictorX3" %in% input$platereader_software){
try(reads <- QurvE:::parse_properties_victorx3(file=filename,
csvsep = input$separator_parse,
dec = input$decimal_separator_parse,
sheet = ifelse(input$parse_data_sheets == "Sheet1", 1, input$parse_data_sheets) ),
silent = FALSE
)
}
if(exists("reads") && length(reads) > 0){
show("parsed_reads_growth")
show("parsed_reads_fluorescence")
if(length(reads)>1){
show("parsed_reads_fluorescence2")
} else {
hide("parsed_reads_fluorescence2")
}
show("parse_data")
removeModal()
reads <- c(reads, "Ignore")
} else if(!("GrowthProfiler" %in% input$platereader_software) &&
!is.null(input$platereader_software)){
if(stringr::str_replace_all(filename, ".{1,}\\.", "") == "xlsx")
showModal(modalDialog("No read data could be extracted from the provided data file. Did you choose the correct software and sheet?", easyClose = T, footer=NULL))
if(stringr::str_replace_all(filename, ".{1,}\\.", "") == "csv")
showModal(modalDialog("No read data could be extracted from the provided data file. Did you choose the correct software and separator?", easyClose = T, footer=NULL))
else
showModal(modalDialog("No read data could be extracted from the provided data file. Did you choose the correct software?", easyClose = T, footer=NULL))
} else {
show("parse_data")
removeModal()
}
})
observe({
reads <- selected_inputs_parsed_reads()
if(length(reads) < 2)
return(NULL)
if(length(reads) > 1 && input$parsed_reads_fluorescence != "Ignore"){
show("parsed_reads_fluorescence2")
show("norm_type_parse")
} else {
hide("parsed_reads_fluorescence2")
hide("norm_type_parse")
}
norm.types <- c()
if(input$parsed_reads_growth != "Ignore"){
norm.types <- c(norm.types, "growth")
}
if(input$parsed_reads_fluorescence2 != "Ignore"){
norm.types <- c(norm.types, "fluorescence 2")
}
if(length(norm.types) > 0){
show("norm_type_parse")
} else {
hide("norm_type_parse")
}
updateSelectInput(inputId = "norm_type_parse",
choices = norm.types
)
})
observe({
updateSelectInput(inputId = "parsed_reads_growth",
choices = selected_inputs_parsed_reads()
)
})
observe({
updateSelectInput(inputId = "parsed_reads_fluorescence",
choices = selected_inputs_parsed_reads()
)
})
observe({
updateSelectInput(inputId = "parsed_reads_fluorescence2",
choices = selected_inputs_parsed_reads()
)
})
observeEvent(input$mapping_included_in_parse,{
if(input$mapping_included_in_parse) hide("map_file")
if(!input$mapping_included_in_parse) show("map_file")
}, ignoreInit = TRUE)
#### Parse data and extract read tabs
observeEvent(input$parse_data,{
if(input$norm_type_parse == "fluorescence 2"){
fl.normtype <- "fl2"
} else {
fl.normtype <- "growth"
}
showModal(modalDialog("Parsing data input...", footer=NULL))
if(input$convert_time_equation_plate_reader == "" || is.na(input$convert_time_equation_plate_reader)) convert.time <- NULL
else convert.time <- input$convert_time_equation_plate_reader
if(input$mapping_included_in_parse){
try(
results$parsed_data <- QurvE:::parse_data_shiny(
data.file = input$parse_file$datapath,
map.file = input$parse_file$datapath,
software = input$platereader_software,
convert.time = if(input$convert_time_values_plate_reader){
convert.time
} else {
NULL
},
sheet.data = input$parse_data_sheets,
sheet.map = input$map_data_sheets,
csvsep.data = input$separator_parse,
dec.data = input$decimal_separator_parse,
csvsep.map = input$separator_map,
dec.map = input$decimal_separator_map,
subtract.blank = input$subtract_blank_plate_reader,
growth.nm = input$parsed_reads_growth,
fl.nm = ifelse(
input$parsed_reads_fluorescence == input$parsed_reads_growth,
NA,
input$parsed_reads_fluorescence
),
calib.growth = ifelse(input$calibration_growth_plate_reader, input$calibration_equation_growth_plate_reader, ""),
calib.fl = ifelse(input$calibration_fluorescence_plate_reader, input$calibration_equation_fluorescence_plate_reader, ""),
calib.fl2 = ifelse(input$calibration_fluorescence2_plate_reader, input$calibration_equation_fluorescence2_plate_reader, ""),
fl2.nm = ifelse(
input$parsed_reads_fluorescence2 == input$parsed_reads_growth |
input$parsed_reads_fluorescence2 == input$parsed_reads_fluorescence,
NA,
input$parsed_reads_fluorescence2
),
fl.normtype = fl.normtype
)
)
} else {
if(is.null(input$map_file$datapath)){
showModal(
modalDialog(
HTML(
"<strong>Parsing data input...</strong><br><br>No mapping file was provided. The samples will be identified based on their well position (A1, A2, A3, etc.). Grouping options will not be available if you run any further analysis with QurvE."
),
easyClose = FALSE,
footer=NULL
)
)
}
try(
results$parsed_data <- QurvE:::parse_data_shiny(
data.file = input$parse_file$datapath,
map.file = input$map_file$datapath,
software = input$platereader_software,
convert.time = if(input$convert_time_values_plate_reader){
convert.time
} else {
NULL
},
sheet.data = input$parse_data_sheets,
sheet.map = input$map_data_sheets,
csvsep.data = input$separator_parse,
dec.data = input$decimal_separator_parse,
csvsep.map = input$separator_map,
dec.map = input$decimal_separator_map,
subtract.blank = input$subtract_blank_plate_reader,
growth.nm = input$parsed_reads_growth,
fl.nm = ifelse(
input$parsed_reads_fluorescence == input$parsed_reads_growth,
NA,
input$parsed_reads_fluorescence
),
calib.growth = ifelse(input$calibration_growth_plate_reader, input$calibration_equation_growth_plate_reader, ""),
calib.fl = ifelse(input$calibration_fluorescence_plate_reader, input$calibration_equation_fluorescence_plate_reader, ""),
calib.fl2 = ifelse(input$calibration_fluorescence2_plate_reader, input$calibration_equation_fluorescence2_plate_reader, ""),
fl2.nm = ifelse(
input$parsed_reads_fluorescence2 == input$parsed_reads_growth |
input$parsed_reads_fluorescence2 == input$parsed_reads_fluorescence,
NA,
input$parsed_reads_fluorescence2
),
fl.normtype = fl.normtype
)
)
}
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_expdesign")
hide("mapping_layout")
hide("Custom_Data_Tables")
show("Parsed_Data_Tables")
if("growth" %in% names(results$parsed_data) && length(results$parsed_data$growth)>1){
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_growth")
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_growth")
# show [Run Computation] button in Computation-Growth
show("run_growth")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Computation]")
}
if("fluorescence" %in% names(results$parsed_data) && length(results$parsed_data$fluorescence)>1){
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_tables_fluorescence")
showTab(inputId = "tabsetPanel_parsed_tables", target = "tabPanel_parsed_plots_fluorescence")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Computation]")
}
# Remove eventually pre-loaded custom data
results$custom_data <- NULL
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_growth_processed")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_growth")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence_processed")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_fluorescence")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_norm_fluorescence_processed")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_plots_norm_fluorescence")
# hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_fluorescence2")
hideTab(inputId = "tabsetPanel_custom_tables", target = "tabPanel_custom_tables_expdesign")
removeModal()
})
#### Generate parsed tables to display in [DATA] tab
parsed_data_table_growth <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$growth) < 2) return(NULL)
table_growth <- t(results$parsed_data$growth)
if(dim(table_growth)[2]<2){
table_growth[-(1:3), ] <- round(as.numeric(table_growth[-(1:3), ]), digits = 3)
}else{
table_growth[-(1:3), ] <- apply(apply(table_growth[-(1:3), ], 2, as.numeric), 2, round, digits = 3)
}
rownames(table_growth)[-(1:3)] <- ""
table_growth <- cbind(data.frame("Time" = c("","","", round(as.numeric(results$parsed_data$time[1,]), digits = 2))),
table_growth)
table_growth
})
output$parsed_data_table_growth <- DT::renderDT({
DT::datatable(parsed_data_table_growth(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(parsed_data_table_growth())-3)))
})
parsed_data_table_fluorescence <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$fluorescence)<2) return(NULL)
table_fl <- t(results$parsed_data$fluorescence)
table_fl[-(1:3), ] <- apply(apply(table_fl[-(1:3), ], 2, as.numeric), 2, round, digits = 1)
rownames(table_fl)[-(1:3)] <- ""
table_fl <- cbind(data.frame("Time" = c("","","", round(as.numeric(results$parsed_data$time[1,]), digits = 2))),
table_fl)
table_fl
})
output$parsed_data_table_fluorescence <- DT::renderDT({
DT::datatable(parsed_data_table_fluorescence(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(parsed_data_table_fluorescence())-3)))
})
# render processed normalized fluorescence table
parsed_data_table_norm_fluorescence <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$norm.fluorescence)<2) return(NULL)
table_fl <- t(results$parsed_data$norm.fluorescence)
table_fl[-(1:3), ] <- apply(apply(table_fl[-(1:3), ], 2, as.numeric), 2, round, digits = 1)
rownames(table_fl)[-(1:3)] <- ""
table_fl <- cbind(data.frame("Time" = c("","","", round(as.numeric(results$parsed_data$time[1,]), digits = 2))),
table_fl)
table_fl
})
output$parsed_data_table_norm_fluorescence <- DT::renderDT({
DT::datatable(parsed_data_table_norm_fluorescence(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = c("Condition", "Replicate", "Concentration", rep("", nrow(parsed_data_table_norm_fluorescence())-3)))
})
parsed_data_table_expdesign <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$expdesign)<2) return(NULL)
expdesign <- results$parsed_data$expdesign
expdesign
})
output$parsed_data_table_expdesign <- DT::renderDT({
expdesign <- parsed_data_table_expdesign()
DT::datatable(expdesign,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE, rownames = T)
})
parsed_raw_growth_plot <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$growth) < 2) return(NULL)
try(
suppressWarnings(
plot.grodata(x = results$parsed_data,
data.type = "growth",
IDs = NULL
)
)
)
})
output$parsed_raw_growth_plot <- renderPlot({
parsed_raw_growth_plot()
})
parsed_raw_fluorescence_plot <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$fluorescence) < 2) return(NULL)
try(
suppressWarnings(
plot.grodata(x = results$parsed_data,
data.type = "fl",
IDs = NULL
)
)
)
})
output$parsed_raw_fluorescence_plot <- renderPlot({
parsed_raw_fluorescence_plot()
})
observe({
updateSelectInput(inputId = "parse_data_sheets",
choices = parse_excel_sheets()
)})
observe({
if(input$mapping_included_in_parse){
updateSelectInput(inputId = "map_data_sheets",
choices = parse_excel_sheets()
)
} else {
updateSelectInput(inputId = "map_data_sheets",
choices = map_excel_sheets()
)
}
})
parsed_raw_norm_fluorescence_plot <- reactive({
if(is.null(results$parsed_data) || length(results$parsed_data$norm.fluorescence) < 2) return(NULL)
try(
suppressWarnings(
plot.grodata(x = results$parsed_data,
data.type = "norm.fl",
IDs = NULL
)
)
)
})
output$parsed_raw_norm_fluorescence_plot <- renderPlot({
parsed_raw_norm_fluorescence_plot()
})
##__Table_download____####
output$download_custom_tables_growth_processed <- downloadHandler(
filename = function() {
paste("custom_growth_data", ".csv", sep="")
},
content = function(file) {
table <- growth_data_custom_processed()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_custom_tables_fluorescence_processed <- downloadHandler(
filename = function() {
paste("custom_fluorescence_data", ".csv", sep="")
},
content = function(file) {
table <- custom_table_fluorescence_processed()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_custom_tables_norm_fluorescence_processed <- downloadHandler(
filename = function() {
paste("custom_norm_fluorescence_data", ".csv", sep="")
},
content = function(file) {
table <- custom_table_norm_fluorescence_processed()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_custom_tables_expdesign <- downloadHandler(
filename = function() {
paste("custom_expdesign", ".csv", sep="")
},
content = function(file) {
table <- custom_data_table_expdesign()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_parsed_tables_growth <- downloadHandler(
filename = function() {
paste("parsed_growth_data", ".csv", sep="")
},
content = function(file) {
table <- parsed_data_table_growth()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_parsed_tables_fluorescence <- downloadHandler(
filename = function() {
paste("parsed_fluorescence_data", ".csv", sep="")
},
content = function(file) {
table <- parsed_data_table_fluorescence()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_parsed_data_table_norm_fluorescence <- downloadHandler(
filename = function() {
paste("parsed_norm_fluorescence_data", ".csv", sep="")
},
content = function(file) {
table <- parsed_data_table_norm_fluorescence()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_parsed_tables_expdesign <- downloadHandler(
filename = function() {
paste("parsed_expdesign", ".csv", sep="")
},
content = function(file) {
if(is.null(results$parsed_data) || length(results$parsed_data$expdesign)<2) return(NULL)
table <- results$parsed_data$expdesign
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
# Computation ####
##____Growth____#####
hide("run_growth")
selected_inputs_response_parameter_growth <- reactive({
select_options <- c()
if(input$linear_regression_growth) select_options <- c(select_options, 'mu.linfit', 'lambda.linfit', 'dY.linfit',
'A.linfit')
if(input$nonparametric_fit_growth) select_options <- c(select_options, 'mu.spline', 'lambda.spline',
'A.spline', 'dY.spline', 'integral.spline')
if(input$parametric_fit_growth) select_options <- c(select_options, 'mu.model', 'lambda.model', 'A.model', 'integral.model')
select_options
})
observe({
updateSelectInput(session,
inputId = "response_parameter_growth",
choices = selected_inputs_response_parameter_growth()
)
})
observeEvent(input$run_growth,{
## Read data
# Choose data input
if(!is.null(results$custom_data)){
grodata <- results$custom_data
} else if(!is.null(results$parsed_data)){
grodata <- results$parsed_data
} else return(NULL)
if (is.null(input$number_of_bootstrappings_growth) || is.na(input$number_of_bootstrappings_growth) || input$number_of_bootstrappings_growth == "NULL" || input$number_of_bootstrappings_growth == "") {
nboot.gc <- 0.55
} else {
nboot.gc <- as.numeric(input$number_of_bootstrappings_growth)
}
if (is.null(input$smoothing_factor_nonparametric_growth) || is.na(input$smoothing_factor_nonparametric_growth) || input$smoothing_factor_nonparametric_growth == "NULL" || input$smoothing_factor_nonparametric_growth == "") {
smooth.gc <- 0.55
} else {
smooth.gc <- as.numeric(input$smoothing_factor_nonparametric_growth)
}
if (is.null(input$smoothing_factor_growth_dr) || is.na(input$smoothing_factor_growth_dr) || input$smoothing_factor_growth_dr == "NULL" || input$smoothing_factor_growth_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_growth_dr)
}
if (is.null(input$number_of_bootstrappings_dr_growth) || is.na(input$number_of_bootstrappings_dr_growth) || input$number_of_bootstrappings_dr_growth == "NULL" || input$number_of_bootstrappings_dr_growth == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_growth)
}
if (is.null(input$R2_threshold_growth) || is.na(input$R2_threshold_growth) || input$R2_threshold_growth == "NULL" || input$R2_threshold_growth == "") {
lin.R2 <- 0.95
} else {
lin.R2 <- as.numeric(input$R2_threshold_growth)
}
if (is.null(input$RSD_threshold_growth) || is.na(input$RSD_threshold_growth) || input$RSD_threshold_growth == "NULL" || input$RSD_threshold_growth == "") {
lin.RSD <- 0.1
} else {
lin.RSD <- as.numeric(input$RSD_threshold_growth)
}
if (is.null(input$dY_threshold_growth) || is.na(input$dY_threshold_growth) || input$dY_threshold_growth == "NULL" || input$dY_threshold_growth == "") {
lin.dY <- 0.05
} else {
lin.dY <- as.numeric(input$dY_threshold_growth)
}
if (is.null(input$minimum_growth_growth) || is.na(input$minimum_growth_growth) || input$minimum_growth_growth == "NULL" || input$minimum_growth_growth == "") {
min.growth <- 0
} else {
min.growth <- as.numeric(input$minimum_growth_growth)
}
if (is.null(input$maximum_growth_growth) || is.na(input$maximum_growth_growth) || input$maximum_growth_growth == "NULL" || input$maximum_growth_growth == "") {
max.growth <- NA
} else {
max.growth <- as.numeric(input$maximum_growth_growth)
}
if (is.null(input$t0_growth) || is.na(input$t0_growth) || input$t0_growth == "NULL" || input$t0_growth == "") {
t0 <- 0
} else {
t0 <- as.numeric(input$t0_growth)
}
if (is.null(input$tmax_growth) || is.na(input$tmax_growth) || input$tmax_growth == "NULL" || input$tmax_growth == "") {
tmax <- NA
} else {
tmax <- as.numeric(input$tmax_growth)
}
if (is.null(input$growth_threshold_growth) || is.na(input$growth_threshold_growth) || input$growth_threshold_growth == "NULL" || input$growth_threshold_growth == "") {
growth.thresh <- 1.5
} else {
growth.thresh <- as.numeric(input$growth_threshold_growth)
}
fit.opt <- c()
if(input$linear_regression_growth){
fit.opt <- c(fit.opt,
'l')
}
if(input$parametric_fit_growth){
fit.opt <- c(fit.opt,
'm')
# combine selected models into vector
models <- c()
if(input$logistic_growth == TRUE) models <- c(models, "logistic")
if(input$richards_growth == TRUE) models <- c(models, "richards")
if(input$gompertz_growth == TRUE) models <- c(models, "gompertz")
if(input$extended_gompertz_growth == TRUE) models <- c(models, "gompertz.exp")
if(input$huang_growth == TRUE) models <- c(models, "huang")
if(input$baranyi_growth == TRUE) models <- c(models, "baranyi")
} else {
models <- c("logistic")
}
if(input$nonparametric_fit_growth){
fit.opt <- c(fit.opt,
's')
}
showModal(modalDialog("Running computations...", footer=NULL))
# Run growth workflow
shiny::withProgress(message = "Computations completed",
try(
results$growth <-
suppressWarnings(
QurvE::growth.workflow(grodata = grodata,
ec50 = input$perform_ec50_growth,
fit.opt = fit.opt,
t0 = t0,
tmax = tmax,
min.growth = min.growth,
max.growth = max.growth,
log.x.gc = input$log_transform_time_growth,
log.y.lin = input$log_transform_data_linear_growth,
log.y.model = input$log_transform_data_parametric_growth,
log.y.spline = input$log_transform_data_nonparametric_growth,
biphasic = input$biphasic_growth,
lin.h = input$custom_sliding_window_size_value_growth,
lin.R2 = lin.R2,
lin.RSD = lin.RSD,
lin.dY = lin.dY,
interactive = FALSE,
nboot.gc = nboot.gc,
smooth.gc = smooth.gc,
model.type = models,
growth.thresh = growth.thresh,
dr.method = input$dr_method_growth,
dr.parameter = input$response_parameter_growth,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_growth,
log.y.dr = input$log_transform_response_growth,
nboot.dr = nboot.dr,
suppress.messages = T,
report = NULL,
shiny = TRUE
)
)
)
)
if(!is.null("results$growth")){
# ENABLE DISABLED PANELS AFTER RUNNING COMPUTATION
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Export_RData]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Report]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Visualize]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Results]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Validate]")
removeModal()
}
if(exists("results$growth") && !is.null("results$growth"))
showModal(modalDialog(geterrmessage()) )
})
##____Fluorescence____#####
# Create vector of x_types based on presence of data types
output$normalized_fl_present <- reactive({
if(!is.null(results$custom_data)){
grodata <- results$custom_data
} else if(!is.null(results$parsed_data)){
grodata <- results$parsed_data
if(input$parsed_reads_growth == input$parsed_reads_fluorescence)
showModal(modalDialog("Identical values for growth and fluorescence data. Did you choose the correct data identifiers?"))
} else return(FALSE)
if(length(grodata$norm.fluorescence) > 1){
return(TRUE)
} else {
return(FALSE)
}
})
outputOptions(output, 'normalized_fl_present', suspendWhenHidden=FALSE)
selected_inputs_fluorescence_x_types <- reactive({
if(is.null(results$custom_data) && is.null(results$parsed_data)) return(NULL)
if(!is.null(results$custom_data)) data <- results$custom_data
if(!is.null(results$parsed_data)) data <- results$parsed_data
x_types <- c()
if(length(data$growth) > 1) x_types <- c(x_types, 'growth')
if(length(data$time) > 1) x_types <- c(x_types, 'time')
x_types
})
observe({
updateSelectInput(session,
inputId = "data_type_x_fluorescence",
choices = selected_inputs_fluorescence_x_types()
)
})
selected_inputs_response_parameter_fluorescence <- reactive({
select_options <- c()
if(input$linear_regression_fluorescence) select_options <- c(select_options, 'max_slope.linfit', 'lambda.linfit', 'dY.linfit',
'A.linfit')
if(input$nonparametric_fit_fluorescence) select_options <- c(select_options, 'max_slope.spline', 'lambda.spline',
'A.spline', 'dY.spline', 'integral.spline')
select_options
})
observe({
updateSelectInput(session,
inputId = "response_parameter_fluorescence",
choices = selected_inputs_response_parameter_fluorescence()
)
})
observeEvent(input$run_fluorescence,{
# Choose data input
if(!is.null(results$custom_data)){
grodata <- results$custom_data
} else if(!is.null(results$parsed_data)){
grodata <- results$parsed_data
} else return(NULL)
if (is.null(input$smoothing_factor_nonparametric_fluorescence) || is.na(input$smoothing_factor_nonparametric_fluorescence) || input$smoothing_factor_nonparametric_fluorescence == "NULL" || input$smoothing_factor_nonparametric_fluorescence == "") {
smooth.fl = 0.75
} else {
smooth.fl <- as.numeric(input$smoothing_factor_nonparametric_fluorescence)
}
if (is.null(input$smoothing_factor_fluorescence_dr) || is.na(input$smoothing_factor_fluorescence_dr) || input$smoothing_factor_fluorescence_dr == "NULL" || input$smoothing_factor_fluorescence_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_fluorescence_dr)
}
if (is.null(input$number_of_bootstrappings_fluorescence) || is.na(input$number_of_bootstrappings_fluorescence) || input$number_of_bootstrappings_fluorescence == "NULL" || input$number_of_bootstrappings_fluorescence == "") {
nboot.fl <- 0
} else {
nboot.fl <- as.numeric(input$number_of_bootstrappings_fluorescence)
}
if (is.null(input$number_of_bootstrappings_dr_fluorescence) || is.na(input$number_of_bootstrappings_dr_fluorescence) || input$number_of_bootstrappings_dr_fluorescence == "NULL" || input$number_of_bootstrappings_dr_fluorescence == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_fluorescence)
}
if (is.null(input$R2_threshold_fluorescence) || is.na(input$R2_threshold_fluorescence) || input$R2_threshold_fluorescence == "NULL" || input$R2_threshold_fluorescence == "") {
lin.R2 <- 0.95
} else {
lin.R2 <- as.numeric(input$R2_threshold_fluorescence)
}
if (is.null(input$RSD_threshold_fluorescence) || is.na(input$RSD_threshold_fluorescence) || input$RSD_threshold_fluorescence == "NULL" || input$RSD_threshold_fluorescence == "") {
lin.RSD <- 0.1
} else {
lin.RSD <- as.numeric(input$RSD_threshold_fluorescence)
}
if (is.null(input$dY_threshold_fluorescence) || is.na(input$dY_threshold_fluorescence) || input$dY_threshold_fluorescence == "NULL" || input$dY_threshold_fluorescence == "") {
lin.dY <- 0.05
} else {
lin.dY <- as.numeric(input$dY_threshold_fluorescence)
}
if (is.null(input$growth_threshold_in_percent_fluorescence) || is.na(input$growth_threshold_in_percent_fluorescence) || input$growth_threshold_in_percent_fluorescence == "NULL" || input$growth_threshold_in_percent_fluorescence == "") {
growth.thresh <- 1.5
} else {
growth.thresh <- as.numeric(input$growth_threshold_in_percent_fluorescence)
}
if (is.null(input$t0_fluorescence) || is.na(input$t0_fluorescence) || input$t0_fluorescence == "NULL" || input$t0_fluorescence == "") {
t0 <- 0
} else {
t0 <- as.numeric(input$t0_fluorescence)
}
if (is.null(input$minimum_growth_fluorescence) || is.na(input$minimum_growth_fluorescence) || input$minimum_growth_fluorescence == "NULL" || input$minimum_growth_fluorescence == "") {
min.growth <- 0
} else {
min.growth <- as.numeric(input$minimum_growth_fluorescence)
}
if (is.null(input$maximum_growth_fluorescence) || is.na(input$maximum_growth_fluorescence) || input$maximum_growth_fluorescence == "NULL" || input$maximum_growth_fluorescence == "") {
max.growth <- NA
} else {
max.growth <- as.numeric(input$maximum_growth_fluorescence)
}
if (is.null(input$tmax_fluorescence) || is.na(input$tmax_fluorescence) || input$tmax_fluorescence == "NULL" || input$tmax_fluorescence == "") {
tmax <- NA
} else {
tmax <- as.numeric(input$tmax_fluorescence)
}
fit.opt <- c()
if(input$linear_regression_fluorescence){
fit.opt <- c(fit.opt,
'l')
}
if(input$nonparametric_fit_fluorescence){
fit.opt <- c(fit.opt,
's')
}
# removeModal()
showModal(modalDialog("Running computations...", footer=NULL))
# Run fluorescence workflow
try(
shiny::withProgress(message = "Computations completed",
results$fluorescence <-
suppressWarnings(
QurvE::fl.workflow(grodata = grodata,
ec50 = input$perform_ec50_fluorescence,
fit.opt = fit.opt,
x_type = input$data_type_x_fluorescence,
norm_fl = input$normalize_fluorescence,
t0 = t0,
min.growth = min.growth,
log.x.lin = input$log_transform_x_linear_fluorescence,
log.x.spline = input$log_transform_x_nonparametric_fluorescence,
log.y.lin = input$log_transform_data_linear_fluorescence,
log.y.spline = input$log_transform_data_nonparametric_fluorescence,
lin.h = as.numeric(input$custom_sliding_window_size_value_fluorescence),
lin.R2 = lin.R2,
lin.RSD = lin.RSD,
lin.dY = lin.dY,
biphasic = input$biphasic_fluorescence,
interactive = FALSE,
dr.parameter = input$response_parameter_fluorescence,
dr.method = input$dr_method_fluorescence,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_fluorescence,
log.y.dr = input$log_transform_response_fluorescence,
nboot.dr = nboot.dr,
nboot.fl = nboot.fl,
smooth.fl = smooth.fl,
growth.thresh = growth.thresh,
suppress.messages = T,
neg.nan.act = FALSE,
clean.bootstrap = TRUE,
report = NULL,
out.dir = NULL,
out.nm = NULL,
export.fig = FALSE,
shiny = TRUE,
tmax = tmax,
max.growth = max.growth
)
)
)
)
if(!is.null("results$fluorescence")){
## ENABLE DISABLED PANELS AFTER RUNNING COMPUTATION
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Export_RData]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Report]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Visualize]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Results]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Validate]")
removeModal()
}
if(exists("results$fluorescence") && !is.null("results$fluorescence"))
showModal(modalDialog(geterrmessage()) )
})
# Results ####
## Growth ####
observe({
if(!is.null(results$growth)){
if(!("s" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Spline")
hideTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Spline_bt")
} else{
showTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Spline")
}
if(!("l" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Linear")
} else {
showTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Linear")
}
if(!("m" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Model")
} else {
showTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Model")
}
if(!("s" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt) || results$growth$control$nboot.gc <= 1){
hideTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Spline_bt")
} else {
showTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_Spline_bt")
}
if(length(results$growth$drFit) >1 && length(results$growth$drFit$drTable) > 1){
showTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_DR")
} else {
hideTab(inputId = "tabsetPanel_Results_Growth", target = "tabPanel_Results_Growth_DR")
}
}
})
### Linear ####
table_growth_linear <- reactive({
res.table.gc <- results$growth$gcFit$gcTable
table_linear <- data.frame("Sample|Replicate|Conc." = paste(res.table.gc$TestId, res.table.gc$AddId, res.table.gc$concentration, sep = "|"),
"µ<sub>max</sub>" = ifelse(res.table.gc$mu.linfit==0 | is.na(res.table.gc$mu.linfit), "", ifelse(is.na(res.table.gc$mu2.linfit), round(as.numeric(res.table.gc$mu.linfit), 3), paste0("<strong>", round(as.numeric(res.table.gc$mu.linfit), 3), "</strong>", " (", round(as.numeric(res.table.gc$mu2.linfit), 3), ")"))),
"t<sub>D</sub>" = ifelse(res.table.gc$mu.linfit==0 | is.na(res.table.gc$mu.linfit), "", ifelse(is.na(res.table.gc$mu2.linfit), round(log(2)/as.numeric(res.table.gc$mu.linfit), 2), paste0("<strong>", round(log(2)/as.numeric(res.table.gc$mu.linfit), 2), "</strong>", " (", round(log(2)/as.numeric(res.table.gc$mu2.linfit), 2), ")"))),
"λ" = round(as.numeric(res.table.gc$lambda.linfit), 2),
"ΔY" = round(as.numeric(res.table.gc$dY.linfit), 3),
"y<sub>max</sub>" = round(as.numeric(res.table.gc$A.linfit), 3),
"t<sub>start</sub><br>(µ<sub>max</sub>)" = ifelse(is.na(res.table.gc$mu2.linfit), round(as.numeric(res.table.gc$tmu.start.linfit), 2), paste0("<strong>", round(as.numeric(res.table.gc$tmu.start.linfit), 2), "</strong>", " (", round(as.numeric(res.table.gc$tmu2.start.linfit), 2), ")")),
"t<sub>end</sub><br>(µ<sub>max</sub>)" = ifelse(is.na(res.table.gc$mu2.linfit), round(as.numeric(res.table.gc$tmu.end.linfit), 2), paste0("<strong>", round(as.numeric(res.table.gc$tmu.end.linfit), 2), "</strong>", " (", round(as.numeric(res.table.gc$tmu2.end.linfit), 2), ")")),
"R<sup>2</sup><br>(linear fit)" = ifelse(is.na(res.table.gc$mu2.linfit), round(as.numeric(res.table.gc$r2mu.linfit), 3), paste0("<strong>", round(as.numeric(res.table.gc$r2mu.linfit), 3), "</strong>", " (", round(as.numeric(res.table.gc$r2mu2.linfit), 3), ")")),
stringsAsFactors = FALSE, check.names = F)
table_linear
})
output$results_table_growth_linear <- DT::renderDT({
DT::datatable(table_growth_linear(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_growth_linear_group <- reactive({
gcTable <- results$growth$gcFit$gcTable
table <- QurvE:::table_group_growth_linear(gcTable)
colnames(table) <- c("Sample|Conc.", "\u03bc<sub>max</sub>", "t<sub>D</sub>", "\u03bb",
"\u2206Y", "y<sub>max</sub>", "t<sub>start</sub><br>(\u03bc<sub>max</sub>)",
"t<sub>end</sub><br>(\u03bc<sub>max</sub>)")
table
})
output$results_table_growth_linear_group <- DT::renderDT({
DT::datatable(table_growth_linear_group(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
### Spline ####
table_growth_spline <- reactive({
res.table.gc <- results$growth$gcFit$gcTable
table_spline <- data.frame("Sample|Replicate|Conc." = paste(res.table.gc$TestId, res.table.gc$AddId, res.table.gc$concentration, sep = "|"),
"µ<sub>max</sub>" = ifelse(res.table.gc$mu.spline==0 | is.na(res.table.gc$mu.spline), "", ifelse(is.na(res.table.gc$mu2.spline), round(as.numeric(res.table.gc$mu.spline), 3), paste0("<strong>", round(as.numeric(res.table.gc$mu.spline), 3), "</strong>", " (", round(as.numeric(res.table.gc$mu2.spline), 3), ")"))),
"t<sub>D</sub>" = ifelse(res.table.gc$mu.spline==0 | is.na(res.table.gc$mu.spline), "", ifelse(is.na(res.table.gc$mu2.spline), round(log(2)/as.numeric(res.table.gc$mu.spline), 2), paste0("<strong>", round(log(2)/as.numeric(res.table.gc$mu.spline), 2), "</strong>", " (", round(log(2)/as.numeric(res.table.gc$mu2.spline), 2), ")"))),
"λ" = round(as.numeric(res.table.gc$lambda.spline), 2),
"y<sub>max</sub>" = round(as.numeric(res.table.gc$A.spline), 3),
"ΔY" = round(as.numeric(res.table.gc$dY.spline), 3),
"t<sub>max</sub>" = ifelse(is.na(res.table.gc$mu2.spline), round(as.numeric(res.table.gc$tmax.spline), 2), paste0("<strong>", round(as.numeric(res.table.gc$tmax.spline), 2), "</strong>", " (", round(as.numeric(res.table.gc$tmax2.spline), 2), ")")),
"smooth.<br>fac" = res.table.gc$smooth.spline, check.names = F)
table_spline
})
output$results_table_growth_spline <- DT::renderDT({
DT::datatable(table_growth_spline(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_growth_spline_group <- reactive({
gcTable <- results$growth$gcFit$gcTable
table <- QurvE:::table_group_growth_spline(gcTable)
colnames(table) <- c("Sample|Conc.", "\u03bc<sub>max</sub>", "t<sub>D</sub>", "\u03bb",
"\u2206Y", "y<sub>max</sub>", "t(\u03bc<sub>max</sub>)")
table
})
output$results_table_growth_spline_group <- DT::renderDT({
DT::datatable(table_growth_spline_group(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
### Spline BT ####
table_growth_spline_bt <- reactive({
res.table.gc <- results$growth$gcFit$gcTable
table_spline <- data.frame("Sample|Replicate|Conc." = paste(res.table.gc$TestId, res.table.gc$AddId, res.table.gc$concentration, sep = "|"),
"µ<sub>max</sub>" = ifelse(res.table.gc$mu.bt==0 | is.na(res.table.gc$mu.bt),
"",
paste0(round(as.numeric(res.table.gc$mu.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.gc$stdmu.bt), 3)
)
),
"t<sub>D</sub>" = ifelse(res.table.gc$mu.bt==0 | is.na(res.table.gc$mu.bt),
"",
paste0(round(log(2)/as.numeric(res.table.gc$mu.bt), 3) ,
" \u00B1 ",
round(log(2)/as.numeric(res.table.gc$stdmu.bt), 3)
)
),
"λ" = ifelse(res.table.gc$lambda.bt==0 | is.na(res.table.gc$lambda.bt),
"",
paste0(round(as.numeric(res.table.gc$lambda.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.gc$stdlambda.bt ), 3)
)
),
"y<sub>max</sub>" = ifelse(res.table.gc$lambda.bt==0 | is.na(res.table.gc$lambda.bt),
"",
paste0(round(as.numeric(res.table.gc$lambda.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.gc$stdlambda.bt ), 3)
)
),
"ΔY" = ifelse(res.table.gc$dY.bt==0 | is.na(res.table.gc$dY.bt),
"",
paste0(round(as.numeric(res.table.gc$dY.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.gc$stddY.bt ), 3)
)
),
"smooth.<br>fac" = res.table.gc$smooth.spline, check.names = F)
table_spline
})
output$results_table_growth_spline_bt <- DT::renderDT({
DT::datatable(table_growth_spline_bt(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
### Model ####
table_growth_model <- reactive({
try({
res.table.gc <- results$growth$gcFit$gcTable
table_model <- data.frame("Sample|Replicate|Conc." = paste(res.table.gc$TestId, res.table.gc$AddId, res.table.gc$concentration, sep = "|"),
"Model" = res.table.gc$used.model,
"µ<sub>max</sub>" = ifelse(res.table.gc$mu.model==0 | is.na(res.table.gc$mu.model), "", paste(round(as.numeric(res.table.gc$mu.model), 3),"\u00B1", round(as.numeric(res.table.gc$stdmu.model),3))),
"t<sub>D</sub>" = paste(ifelse(res.table.gc$mu.model==0 | is.na(res.table.gc$mu.model), "", paste(round(log(2)/as.numeric(res.table.gc$mu.model), 2), "\u00B1", round(sqrt(((-log(2)*as.numeric(res.table.gc$stdmu.model))/(as.numeric(res.table.gc$mu.model))^2)^2), 2)))),
"λ" = ifelse(res.table.gc$lambda.model==0 | is.na(res.table.gc$lambda.model), "", paste(round(as.numeric(res.table.gc$lambda.model), 2), "\u00B1", round(as.numeric(res.table.gc$stdlambda.model),3))),
"A" = ifelse(res.table.gc$A.model==0 | is.na(res.table.gc$A.model), "", paste(round(as.numeric(res.table.gc$A.model), 3), "\u00B1", round(as.numeric(res.table.gc$stdA.model),3))),
stringsAsFactors = FALSE, check.names = F)
if(!is.null(res.table.gc)){
if ( "richards" %in% res.table.gc$used.model ){
table_model <- suppressWarnings(cbind(table_model, data.frame("ν" = round(as.numeric(res.table.gc$parameter_nu.model), 3), stringsAsFactors = FALSE, check.names = F)))
}
if ( "gompertz" %in% res.table.gc$used.model ){
table_model <- suppressWarnings(cbind(table_model, data.frame("α" = round(as.numeric(res.table.gc$parameter_alpha.model), 3), stringsAsFactors = FALSE, check.names = F)))
}
if ( "gompertz.exp" %in% res.table.gc$used.model ){
table_model <- suppressWarnings(cbind(table_model, data.frame("t<sub>shift</sub>" = round(as.numeric(res.table.gc$parameter_t_shift.model), 3), stringsAsFactors = FALSE, check.names = F)))
}
if ("baranyi" %in% res.table.gc$used.model || "huang" %in% res.table.gc$used.model ){
table_model <- suppressWarnings(cbind(table_model, data.frame("y0" = round(as.numeric(res.table.gc$parameter_y0.model), 3), stringsAsFactors = FALSE, check.names = F)))
}
}
table_model
})
})
output$results_table_growth_model <- DT::renderDT({
table_model <- table_growth_model()
DT::datatable(table_model,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_growth_model_group <- reactive({
gcTable <- results$growth$gcFit$gcTable
table <- QurvE:::table_group_growth_model(gcTable)
colnames(table) <- c("Sample|Conc.", "\u03bc<sub>max</sub>", "t<sub>D</sub>", "\u03bb",
"y<sub>max</sub>", "\u2206Y")
table
})
output$results_table_growth_model_group <- DT::renderDT({
DT::datatable(table_growth_model_group(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
### DR Spline ####
table_growth_dr_spline <- reactive({
if(results$growth$drFit$control$dr.method == "spline"){
try({
res.table.dr <- results$growth$drFit$drTable
table_dr <- data.frame("Test" = res.table.dr$Test,
"EC50" = round(as.numeric(res.table.dr[["EC50"]]), 3),
"Response(EC50)" = round(as.numeric(res.table.dr[["yEC50"]]), 3),
"Test parameter" = res.table.dr[["test"]],
stringsAsFactors = FALSE, check.names = F)
if(!is.null(res.table.dr)){
if(results$growth$control$log.x.dr){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("EC50 (original)" = round(as.numeric(res.table.dr[["EC50.orig"]]), 3),
stringsAsFactors = FALSE, check.names = F)))
}
if(results$growth$control$log.y.dr){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("Response(EC50) (original)" = round(as.numeric(res.table.dr[["yEC50.orig"]]), 3),
stringsAsFactors = FALSE, check.names = F)))
}
if ( results$growth$control$nboot.dr > 2 ){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("Mean EC50 (bootstrap)" = paste(round(as.numeric(res.table.dr[["drboot.meanEC50"]]), 3), round(as.numeric(res.table.dr[["drboot.sdEC50"]]), 3), sep = " \u00B1 "),
"Response(EC50) (bootstrap)" = paste(round(as.numeric(res.table.dr[["drboot.meanEC50y"]]), 3), round(as.numeric(res.table.dr[["drboot.sdEC50y"]]), 3), sep = " \u00B1 "),
stringsAsFactors = FALSE, check.names = F)))
}
}
table_dr
})
}
else{
models <- invisible(drc:::getMeanFunctions(display = F))
model.names <- c(unlist(lapply(1:length(models), function(x) models[[x]][1])), "weibull2x", "gammadr", "multi2")
model.descr <- c(unlist(lapply(1:length(models), function(x) models[[x]][2])), "four-parameter Weibull model", "Gamma dose-response model", "Multistage dose-response model with quadratic terms")
try({
res.table.dr <- results$growth$drFit$drTable
table_dr <- data.frame("Test" = res.table.dr$Test,
"EC50 \u00B1 SE" = paste0(round(as.numeric(res.table.dr[["EC50.Estimate"]]), 3), " \u00B1 ", round(as.numeric(res.table.dr[["EC50.Std..Error"]]), 3)),
"Response(EC50)" = round(as.numeric(res.table.dr[["yEC50"]]), 3),
"Test parameter" = res.table.dr[["test"]],
"Model" = paste0(res.table.dr[["model"]], ": ", model.descr[match(res.table.dr[["model"]], model.names)]),
stringsAsFactors = FALSE, check.names = F)
table_dr
})
}
})
output$results_table_growth_dr_spline <- DT::renderDT({
table_dr <- table_growth_dr_spline()
DT::datatable(table_dr,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "All")) ),
escape = FALSE)
})
###____Table Download____####
output$download_table_growth_linear <- downloadHandler(
filename = function() {
paste("growth_results_linear_fits", ".csv", sep="")
},
content = function(file) {
table <- table_growth_linear()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_linear_group <- downloadHandler(
filename = function() {
paste("growth_results_linear_fits_grouped", ".csv", sep="")
},
content = function(file) {
table <- table_growth_linear_group()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_spline <- downloadHandler(
filename = function() {
paste("growth_results_spline_fits", ".csv", sep="")
},
content = function(file) {
table <- table_growth_spline()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_spline_group <- downloadHandler(
filename = function() {
paste("growth_results_spline_fits_grouped", ".csv", sep="")
},
content = function(file) {
table <- table_growth_spline_group()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_spline_bt <- downloadHandler(
filename = function() {
paste("growth_results_spline_bootstrappings", ".csv", sep="")
},
content = function(file) {
table <- table_growth_spline_bt()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_model <- downloadHandler(
filename = function() {
paste("growth_results_model_fits", ".csv", sep="")
},
content = function(file) {
table <- table_growth_model()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_model_group <- downloadHandler(
filename = function() {
paste("growth_results_model_fits_grouped", ".csv", sep="")
},
content = function(file) {
table <- table_growth_model_group()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_growth_dr <- downloadHandler(
filename = function() {
paste("growth_results_dose-response_analysis", ".csv", sep="")
},
content = function(file) {
table <- table_growth_dr_spline()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
## Fluorescence ####
observe({
if(!is.null(results$fluorescence)){
if(!("s" %in% results$fluorescence$control$fit.opt || "a" %in% results$fluorescence$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_Spline")
hideTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Growth_Fluorescence_bt")
} else {
showTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_Spline")
}
if(!("l" %in% results$fluorescence$control$fit.opt || "a" %in% results$fluorescence$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_Linear")
} else {
showTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_Linear")
}
if(!("s" %in% results$fluorescence$control$fit.opt || "a" %in% results$fluorescence$control$fit.opt) || results$fluorescence$control$nboot.fl <=1){
hideTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_Spline_bt")
} else {
showTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_Spline_bt")
}
if(length(results$fluorescence$drFit) >1 && length(results$fluorescence$drFit$drTable) > 1 && results$fluorescence$drFit$control$dr.method == "spline"){
showTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_DR_Spline")
} else {
hideTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_DR_Spline")
}
if(length(results$fluorescence$drFit) >1 && length(results$fluorescence$drFit$drTable) > 1 && results$fluorescence$drFit$control$dr.method == "model"){
showTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_DR_Model")
} else {
hideTab(inputId = "tabsetPanel_Results_Fluorescence", target = "tabPanel_Results_Fluorescence_DR_Model")
}
}
})
table_fluorescence_linear <- reactive({
res.table.fl <- results$fluorescence$flFit$flTable
table_linear <- data.frame("Sample|Replicate|Conc." = paste(res.table.fl$TestId, res.table.fl$AddId, res.table.fl$concentration, sep = "|"),
"slope<sub>max</sub>" = ifelse(res.table.fl$max_slope.linfit==0 | is.na(res.table.fl$max_slope.linfit), "", ifelse(is.na(res.table.fl$max_slope2.linfit), round(as.numeric(res.table.fl$max_slope.linfit), 3), paste0("<strong>", round(as.numeric(res.table.fl$max_slope.linfit), 3), "</strong>", " (", round(as.numeric(res.table.fl$max_slope2.linfit), 3), ")"))),
"t<sub>D</sub>" = ifelse(res.table.fl$max_slope.linfit==0 | is.na(res.table.fl$max_slope.linfit), "", ifelse(is.na(res.table.fl$max_slope2.linfit), round(log(2)/as.numeric(res.table.fl$max_slope.linfit), 2), paste0("<strong>", round(log(2)/as.numeric(res.table.fl$max_slope.linfit), 2), "</strong>", " (", round(log(2)/as.numeric(res.table.fl$max_slope2.linfit), 2), ")"))),
"λ" = round(as.numeric(res.table.fl$lambda.linfit), 2),
"ΔY" = round(as.numeric(res.table.fl$dY.linfit), 3),
"y<sub>max</sub>" = round(as.numeric(res.table.fl$A.linfit), 3),
"x<sub>start</sub><br>(slope<sub>max</sub>)" = ifelse((is.na(res.table.fl$max_slope2.linfit)), round(as.numeric(res.table.fl$x.mu.start.linfit), 2), paste0("<strong>", round(as.numeric(res.table.fl$x.mu.start.linfit), 2), "</strong>", " (", round(as.numeric(res.table.fl$x.mu2.start.linfit), 2), ")")),
"x<sub>end</sub><br>(slope<sub>max</sub>)" = ifelse((is.na(res.table.fl$max_slope2.linfit)), round(as.numeric(res.table.fl$x.mu.end.linfit), 2), paste0("<strong>", round(as.numeric(res.table.fl$x.mu.end.linfit), 2), "</strong>", " (", round(as.numeric(res.table.fl$x.mu2.end.linfit), 2), ")")),
"R<sup>2</sup><br>(linear fit)" = ifelse((is.na(res.table.fl$max_slope2.linfit)), round(as.numeric(res.table.fl$r2mu.linfit), 3), paste0("<strong>", round(as.numeric(res.table.fl$r2mu.linfit), 3), "</strong>", " (", round(as.numeric(res.table.fl$r2mu.linfit), 3), ")")),
stringsAsFactors = FALSE, check.names = F)
table_linear
})
output$results_table_fluorescence_linear <- DT::renderDT({
DT::datatable(table_fluorescence_linear(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_fluorescence_linear_group <- reactive({
flTable <- results$fluorescence$flFit$flTable
table <- QurvE:::table_group_fluorescence_linear(flTable)
colnames(table) <- c("Sample|Conc.", "slope<sub>max</sub>", "\u03bb",
"\u0394Y", "y<sub>max</sub>", "x<sub>start</sub><br>(\u03bc<sub>max</sub>)",
"x<sub>end</sub><br>(\u03bc<sub>max</sub>)")
table
})
output$results_table_fluorescence_linear_group <- DT::renderDT({
DT::datatable(table_fluorescence_linear_group(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_fluorescence_spline <- reactive({
res.table.fl <- results$fluorescence$flFit$flTable
table_spline <- data.frame("Sample|Replicate|Conc." = paste(res.table.fl$TestId, res.table.fl$AddId, res.table.fl$concentration, sep = "|"),
"slope<sub>max</sub>" = ifelse(res.table.fl$max_slope.spline==0 | is.na(res.table.fl$max_slope.spline), "", ifelse(is.na(res.table.fl$max_slope2.spline), round(as.numeric(res.table.fl$max_slope.spline), 3), paste0("<strong>", round(as.numeric(res.table.fl$max_slope.spline), 3), "</strong>", " (", round(as.numeric(res.table.fl$max_slope2.spline), 3), ")"))),
"λ" = round(as.numeric(res.table.fl$lambda.spline), 2),
"y<sub>max</sub>" = round(as.numeric(res.table.fl$A.spline), 3),
"ΔY" = round(as.numeric(res.table.fl$dY.spline), 3),
"x<sub>max</sub>" = ifelse(is.na(res.table.fl$max_slope2.spline), round(as.numeric(res.table.fl$x.max.spline), 2), paste0("<strong>", round(as.numeric(res.table.fl$x.max.spline), 2), "</strong>", " (", round(as.numeric(res.table.fl$x.max2.spline), 2), ")")),
"smooth.<br>fac" = res.table.fl$smooth.spline, check.names = F)
table_spline
})
output$results_table_fluorescence_spline <- DT::renderDT({
DT::datatable(table_fluorescence_spline(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_fluorescence_spline_group <- reactive({
flTable <- results$fluorescence$flFit$flTable
QurvE:::table_group_fluorescence_spline(flTable)
colnames(table) <- c("Sample|Conc.", "slope<sub>max</sub>", "\u03bb",
"\u0394Y", "y<sub>max</sub>", "x(slope<sub>max</sub>)")
table
})
output$results_table_fluorescence_spline_group <- DT::renderDT({
DT::datatable(table_fluorescence_spline_group(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_fluorescence_spline_bt <- reactive({
res.table.fl <- results$fluorescence$flFit$flTable
table_spline <- data.frame("Sample|Replicate|Conc." = paste(res.table.fl$TestId, res.table.fl$AddId, res.table.fl$concentration, sep = "|"),
"µ<sub>max</sub>" = ifelse(res.table.fl$max_slope.bt==0 | is.na(res.table.fl$max_slope.bt),
"",
paste0(round(as.numeric(res.table.fl$max_slope.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.fl$stdmax_slope.bt), 3)
)
),
"t<sub>D</sub>" = ifelse(res.table.fl$max_slope.bt==0 | is.na(res.table.fl$max_slope.bt),
"",
paste0(round(log(2)/as.numeric(res.table.fl$max_slope.bt), 3) ,
" \u00B1 ",
round(log(2)/as.numeric(res.table.fl$stdmax_slope.bt), 3)
)
),
"λ" = ifelse(res.table.fl$lambda.bt==0 | is.na(res.table.fl$lambda.bt),
"",
paste0(round(as.numeric(res.table.fl$lambda.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.fl$stdlambda.bt ), 3)
)
),
"y<sub>max</sub>" = ifelse(res.table.fl$lambda.bt==0 | is.na(res.table.fl$lambda.bt),
"",
paste0(round(as.numeric(res.table.fl$lambda.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.fl$stdlambda.bt ), 3)
)
),
"ΔY" = ifelse(res.table.fl$dY.bt==0 | is.na(res.table.fl$dY.bt),
"",
paste0(round(as.numeric(res.table.fl$dY.bt), 3) ,
" \u00B1 ",
round(as.numeric(res.table.fl$stddY.bt ), 3)
)
),
"smooth.<br>fac" = res.table.fl$smooth.spline, check.names = F)
table_spline
})
output$results_table_fluorescence_spline_bt <- DT::renderDT({
DT::datatable(table_fluorescence_spline_bt(),
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "50", "All")) ),
escape = FALSE)
})
table_fluorescence_dr_spline <- reactive({
try({
res.table.dr <- results$fluorescence$drFit$drTable
table_dr <- data.frame("Test" = res.table.dr$Test,
"EC50" = round(as.numeric(res.table.dr[["EC50"]]), 3),
"Response(EC50)" = round(as.numeric(res.table.dr[["yEC50"]]), 3),
"Test parameter" = res.table.dr[["test"]],
stringsAsFactors = FALSE, check.names = F)
if(!is.null(res.table.dr)){
if(results$fluorescence$control$log.x.dr){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("EC50 (original)" = round(as.numeric(res.table.dr[["EC50.orig"]]), 3),
stringsAsFactors = FALSE, check.names = F)))
}
if(results$fluorescence$control$log.y.dr){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("Response(EC50) (original)" = round(as.numeric(res.table.dr[["yEC50.orig"]]), 3),
stringsAsFactors = FALSE, check.names = F)))
}
if ( results$fluorescence$control$nboot.dr > 2 ){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("Mean EC50 (bootstrap)" = paste(round(as.numeric(res.table.dr[["drboot.meanEC50"]]), 3), round(as.numeric(res.table.dr[["drboot.sdEC50"]]), 3), sep = " \u00B1 "),
"Response(EC50) (bootstrap)" = paste(round(as.numeric(res.table.dr[["drboot.meanEC50y"]]), 3), round(as.numeric(res.table.dr[["drboot.sdEC50y"]]), 3), sep = " \u00B1 "),
stringsAsFactors = FALSE, check.names = F)))
}
}
table_dr
})
})
output$results_table_fluorescence_dr_spline <- DT::renderDT({
table_dr <- table_fluorescence_dr_spline()
DT::datatable(table_dr,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "All")) ),
escape = FALSE)
})
table_fluorescence_dr_model <- reactive({
try({
res.table.dr <- results$fluorescence$drFit$drTable
table_dr <- data.frame("Test" = res.table.dr$Test,
"Leakiness (min. FL)" = round(as.numeric(res.table.dr[["y.min"]]), 3),
"Sensitivity (K, EC50)" = round(as.numeric(res.table.dr[["K"]]), 3),
"Fold change (fc)" = round(as.numeric(res.table.dr[["fc"]]), 3),
"Cooperativity (n)" = round(as.numeric(res.table.dr[["n"]]), 3),
"Response(EC50)" = round(as.numeric(res.table.dr[["yEC50"]]), 3),
"Maximum FL" = round(as.numeric(res.table.dr[["y.max"]]), 3),
stringsAsFactors = FALSE, check.names = F)
if(!is.null(res.table.dr)){
if(results$fluorescence$control$log.x.dr){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("K (original)" = round(as.numeric(res.table.dr[["K.orig"]]), 3),
stringsAsFactors = FALSE, check.names = F)))
}
if(results$fluorescence$control$log.y.dr){
table_dr <- suppressWarnings(cbind(table_dr,
data.frame("Response(EC50) (original)" = round(as.numeric(res.table.dr[["yEC50.orig"]]), 3),
stringsAsFactors = FALSE, check.names = F)))
}
}
table_dr
})
})
output$results_table_fluorescence_dr_model <- DT::renderDT({
table_dr_model <- table_fluorescence_dr_model()
DT::datatable(table_dr_model,
options = list(pageLength = 25, info = FALSE, lengthMenu = list(c(15, 25, 50, -1), c("15","25", "All")) ),
escape = FALSE)
})
###____Table Download____####
output$download_table_fluorescence_linear <- downloadHandler(
filename = function() {
paste("fluorescence_results_linear_fits", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_linear()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_fluorescence_linear_group <- downloadHandler(
filename = function() {
paste("fluorescence_results_linear_fits_grouped", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_linear_group()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_fluorescence_spline <- downloadHandler(
filename = function() {
paste("fluorescence_results_spline_fits", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_spline()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_fluorescence_spline_group <- downloadHandler(
filename = function() {
paste("fluorescence_results_spline_fits_grouped", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_spline_group()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_fluorescence_spline_bt <- downloadHandler(
filename = function() {
paste("fluorescence_results_spline_bootstrappings", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_spline_bt()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_fluorescence_dr_spline <- downloadHandler(
filename = function() {
paste("fluorescence_results_dose-response_analysis_spline", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_dr_spline()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
output$download_table_fluorescence_dr_model <- downloadHandler(
filename = function() {
paste("fluorescence_results_dose-response_analysis_model", ".csv", sep="")
},
content = function(file) {
table <- table_fluorescence_dr_model()
colnames(table) <- gsub("<sub>", "_", gsub("</sub>|<sup>|</sup>", "", gsub("<br>", " ", colnames(table))))
QurvE:::write.csv.utf8.BOM(table, file)
}
)
# Validate ####
## Growth #####
observe({
if(!is.null(results$growth)){
if(!("s" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Spline")
} else {
showTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Spline")
}
if(!("l" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Linear")
} else {
showTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Linear")
}
if(!("m" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Model")
} else {
showTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Model")
}
if(!("s" %in% results$growth$control$fit.opt || "a" %in% results$growth$control$fit.opt) || results$growth$control$nboot.gc <=1){
hideTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Spline_bt")
} else {
showTab(inputId = "tabsetPanel_Validate_Growth", target = "tabPanel_Validate_Growth_Spline_bt")
}
}
})
#### Hide [Restore Fit] buttons when starting the app
hide("restore_growth_linear"); hide("restore_growth_spline"); hide("restore_growth_model")
hide("restore_dr_growth"); hide("restore_dr_growth2"); hide("restore_dr_growth3");
hide("restore_dr_fluorescence"); hide("restore_dr_fluorescence2"); hide("restore_dr_fluorescence3")
# hide("restore_dr_spline_individual_fluorescence")
#### Hide [Restore Fit] buttons whenever a sample is changed
observeEvent(input$sample_validate_growth_linear, {
hide("restore_growth_linear")
})
observeEvent(input$sample_validate_growth_spline, {
hide("restore_growth_spline")
})
observeEvent(input$sample_validate_growth_model, {
hide("restore_growth_model")
})
#------- Initialize the Memory to store settings
selected_vals_validate_growth <- reactiveValues(sample_validate_growth_linear = 1,
sample_validate_growth_spline = 1,
sample_validate_growth_model = 1,
sample_validate_growth_spline_bt = 1)
#------ Whenever any of the inputs are changed, it only modifies the memory
observe({
req(input$sample_validate_growth_linear)
selected_vals_validate_growth$sample_validate_growth_linear <- input$sample_validate_growth_linear
})
observe({
req(input$sample_validate_growth_spline)
selected_vals_validate_growth$sample_validate_growth_spline <- input$sample_validate_growth_spline
})
observe({
req(input$sample_validate_growth_model)
selected_vals_validate_growth$sample_validate_growth_model <- input$sample_validate_growth_model
})
observe({
req(input$sample_validate_growth_spline_bt)
selected_vals_validate_growth$sample_validate_growth_spline_bt <- input$sample_validate_growth_spline_bt
})
### Linear Fits ####
selected_inputs_validate_growth_linear_sample <- reactive({
results <- results$growth
if(is.null(results)) return("")
if("l" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
select_samples <- names(results$gcFit$gcFittedLinear)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_growth_linear",
choices = selected_inputs_validate_growth_linear_sample(),
selected = selected_vals_validate_growth$sample_validate_growth_linear
)})
logy_validate_growth_plot_linear <- reactive({
if(input$logy_validate_growth_plot_linear) return("y")
else return("")
})
validate_growth_plot_linear <- reactive({
results <- results$growth
# Define x- and y-axis limits
if(any(input$y_range_min_validate_growth_plot_linear == "",
input$y_range_max_validate_growth_plot_linear == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_growth_plot_linear),
as.numeric(input$y_range_max_validate_growth_plot_linear))
}
if(any(input$y_range_min_derivative_validate_growth_plot_linear == "",
input$y_range_max_derivative_validate_growth_plot_linear == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_growth_plot_linear),
as.numeric(input$y_range_max_derivative_validate_growth_plot_linear))
}
if(any(input$x_range_min_validate_growth_plot_linear == "",
input$x_range_max_validate_growth_plot_linear == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_growth_plot_linear),
as.numeric(input$x_range_max_validate_growth_plot_linear))
}
if (length(results$gcFit$gcFittedLinear[[ifelse(
selected_vals_validate_growth$sample_validate_growth_linear == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_linear
) ||
selected_vals_validate_growth$sample_validate_growth_linear == "" ,
1,
selected_vals_validate_growth$sample_validate_growth_linear
)]]) > 1) {
if(input$diagnostics_validate_growth_plot_linear){
try(
suppressWarnings(
plot.gcFitLinear(results$gcFit$gcFittedLinear[[ifelse(
selected_vals_validate_growth$sample_validate_growth_linear == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_linear
),
1,
selected_vals_validate_growth$sample_validate_growth_linear
)]],
which = "fit_diagnostics",
pch = input$shape_type_validate_growth_plot_linear,
log = logy_validate_growth_plot_linear(),
cex.point = input$shape_size_validate_growth_plot_linear,
cex.lab = input$axis_size_validate_growth_plot_linear,
cex.axis = input$lab_size_validate_growth_plot_linear,
lwd = input$line_width_validate_growth_plot_linear,
color = input$color_validate_growth_plot_linear
)
)
)
} else {
try(
suppressWarnings(
plot.gcFitLinear(
results$gcFit$gcFittedLinear[[ifelse(
selected_vals_validate_growth$sample_validate_growth_linear == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_linear
),
1,
selected_vals_validate_growth$sample_validate_growth_linear
)]],
pch = input$shape_type_validate_growth_plot_linear,
log = logy_validate_growth_plot_linear(),
cex.point = input$shape_size_validate_growth_plot_linear,
cex.lab = input$axis_size_validate_growth_plot_linear,
cex.axis = input$lab_size_validate_growth_plot_linear,
lwd = input$line_width_validate_growth_plot_linear,
y.lim = y.lim,
x.lim = x.lim,
color = input$color_validate_growth_plot_linear
)
)
)
}
}
}
)
output$validate_growth_plot_linear <- renderPlot({
validate_growth_plot_linear()
})
lin.rerun.param <- reactiveValues()
observeEvent(input$rerun_growth_linear, {
# display a modal dialog with a header, textinput and action buttons
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
checkboxInput('log.y.lin.rerun', 'log-transform growth values', value = results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$log.y.lin),
textInput('t0.lin.rerun', 'Minimum time (t0)', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$t0)),
textInput('tmax.lin.rerun', 'Maximum time (tmax)', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$tmax)),
textInput('min.growth.lin.rerun', 'Minimum growth measurement', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$min.growth)),
textInput('max.growth.lin.rerun', 'Maximum growth measurement', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$max.growth)),
textAreaInput('quota.rerun', 'Quota', placeholder = HTML(paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$quota,
"\n",
"include regression windows with slope = ", expression(µ[max]), " * quota into the final linear fit."))),
textInput('lin.h.rerun', 'Sliding window size (h)', placeholder = paste0("previously: ",
ifelse(!is.null(results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$lin.h),
results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$lin.h,
"NULL"))),
textInput('lin.R2.rerun', 'R2 threshold', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$lin.R2)),
textInput('lin.RSD.rerun', 'RSD threshold for slope', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control$lin.RSD)),
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.linear.growth', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_growth.gcFitLinear_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
})
# Re-run selected linear fit with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.linear.growth, {
if(!is.null(results$growth$gcFit)){
showModal(modalDialog("Fitting sample data...", footer = NULL))
# store previous fit in memory
selected_vals_validate_growth$restore_growth_linear <- results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]
# Re-run fit and store in results object
actwell <- results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$raw.data
acttime <- results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$raw.time
control <- results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$control
control_new <- control
gcID <- results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]]$gcID
lin.h.new <- ifelse(!is.na(as.numeric(input$lin.h.rerun)), as.numeric(input$lin.h.rerun), as.numeric(control$lin.h))
if(!is.na(lin.h.new)) control_new$lin.h <- lin.h.new
control_new$lin.R2 <- ifelse(!is.na(as.numeric(input$lin.R2.rerun)), as.numeric(input$lin.R2.rerun), control$lin.R2)
control_new$lin.RSD <- ifelse(!is.na(as.numeric(input$lin.RSD.rerun)), as.numeric(input$lin.RSD.rerun), control$lin.RSD)
control_new$t0 <- ifelse(!is.na(as.numeric(input$t0.lin.rerun)), as.numeric(input$t0.lin.rerun), control$t0)
control_new$tmax <- ifelse(!is.na(as.numeric(input$tmax.lin.rerun)), as.numeric(input$tmax.lin.rerun), control$tmax)
control_new$log.y.lin <- input$log.y.lin.rerun
min.growth.lin.new <- ifelse(!is.na(as.numeric(input$min.growth.lin.rerun)), as.numeric(input$min.growth.lin.rerun), control$min.growth)
if(is.numeric(min.growth.lin.new)){
if(!is.na(min.growth.lin.new) && all(as.vector(actwell) < min.growth.lin.new)){
message(paste0("Start growth values need to be greater than 'min.growth'.\nThe minimum start value in your dataset is: ",
min(as.vector(actwell)),". 'min.growth' was not adjusted."), call. = FALSE)
} else if(!is.na(min.growth.lin.new)){
control_new$min.growth <- min.growth.lin.new
}
}
control_new$max.growth <- ifelse(!is.na(as.numeric(input$max.growth.lin.rerun)), as.numeric(input$max.growth.lin.rerun), control$max.growth)
quota_new <- ifelse(!is.na(as.numeric(input$quota.rerun)), as.numeric(input$quota.rerun), 0.95)
try(
results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]] <-
growth.gcFitLinear(acttime, actwell,
gcID = gcID,
control = control_new,
quota = quota_new)
)
# Update gcTable with new results
res.table.gc <- results$growth$gcFit$gcTable
fit.summary <- QurvE:::summary.gcFitLinear(results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]])
sample.ndx <- ifelse(is.numeric(selected_vals_validate_growth$sample_validate_growth_linear),
selected_vals_validate_growth$sample_validate_growth_linear,
match(selected_vals_validate_growth$sample_validate_growth_linear, names(results$growth$gcFit$gcFittedLinear)))
results$growth$gcFit$gcTable[sample.ndx, colnames(res.table.gc) %in% colnames(fit.summary)] <- fit.summary
# Re-run drFit if respective fit type (linear/spline/model) was chosen as dr.parameter
dr.parameter.fit.method <- gsub(".+\\.", "", results$growth$control$dr.parameter)
if(length(results$growth$drFit) > 1 && dr.parameter.fit.method == "linfit"){
# Store previous drFit in memory
selected_vals_validate_growth$restore_growth_drFit <- results$growth$drFit
# Re-run drFit
if (is.null(input$smoothing_factor_growth_dr) || is.na(input$smoothing_factor_growth_dr) || input$smoothing_factor_growth_dr == "NULL" || input$smoothing_factor_growth_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_growth_dr)
}
if (is.null(input$number_of_bootstrappings_dr_growth) || is.na(input$number_of_bootstrappings_dr_growth) || input$number_of_bootstrappings_dr_growth == "NULL" || input$number_of_bootstrappings_dr_growth == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_growth)
}
showModal(modalDialog("Re-calculating dose-response analysis...", footer = NULL))
try(
results$growth$drFit <-
suppressWarnings(
growth.drFit(gcTable = results$growth$gcFit$gcTable,
control = growth.control(dr.method = input$dr_method_growth,
dr.parameter = results$growth$control$dr.parameter,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_growth,
log.y.dr = input$log_transform_response_growth,
nboot.dr = nboot.dr
)
)
)
)
}
# Show [Restore fit] button
show("restore_growth_linear")
}
removeModal()
})
# Restore previous linear fit upon click on [Restore Fit]
observeEvent(input$restore_growth_linear, {
# restore previous fit from memory
results$growth$gcFit$gcFittedLinear[[selected_vals_validate_growth$sample_validate_growth_linear]] <- selected_vals_validate_growth$restore_growth_linear
dr.parameter.fit.method <- gsub(".+\\.", "", results$growth$control$dr.parameter)
if(length(results$growth$drFit) > 1 && dr.parameter.fit.method == "linfit"){
results$growth$drFit <- selected_vals_validate_growth$restore_growth_drFit
}
hide("restore_growth_linear")
})
output$download_growth_validate_linear <- downloadHandler(
filename = function() {
paste("linear_fit_", gsub(" \\| ", "_", selected_vals_validate_growth$sample_validate_growth_linear), input$format_download_growth_validate_linear, sep="")
},
content = function(file) {
if(input$format_download_growth_validate_linear == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_growth_validate_linear,
height = input$height_download_growth_validate_linear)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_growth_validate_linear,
height = input$height_download_growth_validate_linear)
}
} else {
grDevices::png(file = file,
width = input$width_download_growth_validate_linear,
height = input$height_download_growth_validate_linear,
units = "in",
res = input$dpi_download_growth_validate_linear)
}
if(input$logy_validate_growth_plot_linear) log <- "y"
else log <- ""
results <- results$growth
# Define x- and y-axis limits
if(any(input$y_range_min_validate_growth_plot_linear == "",
input$y_range_max_validate_growth_plot_linear == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_growth_plot_linear),
as.numeric(input$y_range_max_validate_growth_plot_linear))
}
if(any(input$y_range_min_derivative_validate_growth_plot_linear == "",
input$y_range_max_derivative_validate_growth_plot_linear == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_growth_plot_linear),
as.numeric(input$y_range_max_derivative_validate_growth_plot_linear))
}
if(any(input$x_range_min_validate_growth_plot_linear == "",
input$x_range_max_validate_growth_plot_linear == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_growth_plot_linear),
as.numeric(input$x_range_max_validate_growth_plot_linear))
}
if (length(results$gcFit$gcFittedLinear[[ifelse(
selected_vals_validate_growth$sample_validate_growth_linear == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_linear
),
1,
selected_vals_validate_growth$sample_validate_growth_linear
)]]) > 1) {
suppressWarnings(
plot.gcFitLinear(results$gcFit$gcFittedLinear[[ifelse(selected_vals_validate_growth$sample_validate_growth_linear == "1" || is.null(selected_vals_validate_growth$sample_validate_growth_linear), 1, selected_vals_validate_growth$sample_validate_growth_linear)]],
pch = input$shape_type_validate_growth_plot_linear,
log = log,
cex.point = input$shape_size_validate_growth_plot_linear,
cex.lab = input$axis_size_validate_growth_plot_linear,
cex.axis = input$lab_size_validate_growth_plot_linear,
lwd = input$line_width_validate_growth_plot_linear,
y.lim = y.lim,
x.lim = x.lim
)
)
if(input$diagnostics_validate_growth_plot_linear){
suppressWarnings(
plot.gcFitLinear(results$gcFit$gcFittedLinear[[ifelse(
selected_vals_validate_growth$sample_validate_growth_linear == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_linear
),
1,
selected_vals_validate_growth$sample_validate_growth_linear
)]],
which = "fit_diagnostics",
pch = input$shape_type_validate_growth_plot_linear,
log = log,
cex.point = input$shape_size_validate_growth_plot_linear,
cex.lab = input$axis_size_validate_growth_plot_linear,
cex.axis = input$lab_size_validate_growth_plot_linear,
lwd = input$line_width_validate_growth_plot_linear
)
)
}
}
dev.off()
},
contentType = ifelse(input$format_download_growth_validate_linear == ".pdf", "image/pdf", "image/png")
)
### Spline Fits ####
selected_inputs_validate_growth_spline_sample <- reactive({
results <- results$growth
if(is.null(results)) return("")
if("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
select_samples <- names(results$gcFit$gcFittedSplines)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_growth_spline",
choices = selected_inputs_validate_growth_spline_sample(),
selected = selected_vals_validate_growth$sample_validate_growth_spline
)
})
output$validate_growth_plot_spline <- renderPlot({
results <- results$growth
if(length(results$gcFit$gcFittedSplines[[ifelse(
selected_vals_validate_growth$sample_validate_growth_spline == "1" ||
selected_vals_validate_growth$sample_validate_growth_spline == "" ||
is.null(selected_vals_validate_growth$sample_validate_growth_spline),
1,
selected_vals_validate_growth$sample_validate_growth_spline
)]]) > 1) {
showModal(modalDialog("Creating plot...", footer = NULL))
try(
suppressWarnings(
plot.gcFitSpline(
results$gcFit$gcFittedSplines[[ifelse(
selected_vals_validate_growth$sample_validate_growth_spline == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_spline
),
1,
selected_vals_validate_growth$sample_validate_growth_spline
)]],
log.y = input$logy_validate_growth_plot_spline,
x.lim = c(
input$x_range_min_validate_growth_plot_spline,
input$x_range_max_validate_growth_plot_spline
),
y.lim = c(
input$y_range_min_validate_growth_plot_spline,
input$y_range_max_validate_growth_plot_spline
),
y.lim.deriv = c(
input$y_range_min_derivative_validate_growth_plot_spline,
input$y_range_max_derivative_validate_growth_plot_spline
),
lwd = input$line_width_validate_growth_plot_spline,
cex.point = input$shape_size_validate_growth_plot_spline,
basesize = input$base_size_validate_growth_plot_spline,
n.ybreaks = input$nbreaks_validate_growth_plot_spline,
pch = input$shape_type_validate_growth_plot_spline,
deriv = input$plot_derivative_validate_growth_plot_spline,
colSpline = input$color_validate_growth_plot_spline
)
)
)
removeModal()
}
})
spline.rerun.param <- reactiveValues()
observeEvent(input$rerun_growth_spline, {
# display a modal dialog with a header, textinput and action buttons
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
textInput('t0.spline.rerun', 'Minimum time (t0)', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$control$t0)),
textInput('tmax.spline.rerun', 'Maximum time (tmax)', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$control$tmax)),
textInput('min.growth.spline.rerun', 'Minimum growth measurement', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$control$min.growth)),
textInput('max.growth.spline.rerun', 'Maximum growth measurement', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$control$max.growth)),
textInput('smooth.gc.rerun', 'Smoothing factor', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$control$smooth.gc)),
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.spline.growth', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_growth.gcFitSpline_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
})
# Re-run selected spline fit with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.spline.growth, {
if(!is.null(results$growth$gcFit)){
# store previous fit in memory
selected_vals_validate_growth$restore_growth_spline <- results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]
# Re-run fit and store in results object
actwell <- results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$data.in
acttime <- results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$time.in
control <- results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$control
control_new <- control
gcID <- results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]]$gcID
control_new$smooth.gc <- ifelse(!is.na(as.numeric(input$smooth.gc.rerun)), as.numeric(input$smooth.gc.rerun), control$smooth.gc)
control_new$t0 <- ifelse(!is.na(as.numeric(input$t0.spline.rerun)), as.numeric(input$t0.spline.rerun), control$t0)
control_new$tmax <- ifelse(!is.na(as.numeric(input$tmax.spline.rerun)), as.numeric(input$tmax.spline.rerun), control$tmax)
min.growth.spline.new <- ifelse(!is.na(as.numeric(input$min.growth.spline.rerun)), as.numeric(input$min.growth.spline.rerun), control$min.growth)
if(is.numeric(min.growth.spline.new)){
if(!is.na(min.growth.spline.new) && all(as.vector(actwell) < min.growth.spline.new)){
message(paste0("Start growth values need to be greater than 'min.growth'.\nThe minimum start value in your dataset is: ",
min(as.vector(actwell)),". 'min.growth' was not adjusted."), call. = FALSE)
} else if(!is.na(min.growth.spline.new)){
control_new$min.growth <- min.growth.spline.new
}
}
control_new$max.growth <- ifelse(!is.na(as.numeric(input$max.growth.spline.rerun)), as.numeric(input$max.growth.spline.rerun), control$max.growth)
showModal(modalDialog("Fitting sample data...", footer = NULL))
try(
results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]] <-
growth.gcFitSpline(acttime, actwell,
gcID = gcID,
control = control_new)
)
# Update gcTable with new results
res.table.gc <- results$growth$gcFit$gcTable
fit.summary <- QurvE:::summary.gcFitSpline(results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]])
sample.ndx <- ifelse(is.numeric(selected_vals_validate_growth$sample_validate_growth_spline),
selected_vals_validate_growth$sample_validate_growth_spline,
match(selected_vals_validate_growth$sample_validate_growth_spline, names(results$growth$gcFit$gcFittedSplines)))
results$growth$gcFit$gcTable[sample.ndx, colnames(res.table.gc) %in% colnames(fit.summary)] <- fit.summary
# Re-run drFit if respective fit type (linear/spline/model) was chosen as dr.parameter
dr.parameter.fit.method <- gsub(".+\\.", "", results$growth$control$dr.parameter)
if(length(results$growth$drFit) > 1 && dr.parameter.fit.method == "spline"){
# Store previous drFit in memory
selected_vals_validate_growth$restore_growth_drFit <- results$growth$drFit
# Re-run drFit
if (is.null(input$smoothing_factor_growth_dr) || is.na(input$smoothing_factor_growth_dr) || input$smoothing_factor_growth_dr == "NULL" || input$smoothing_factor_growth_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_growth_dr)
}
if (is.null(input$number_of_bootstrappings_dr_growth) || is.na(input$number_of_bootstrappings_dr_growth) || input$number_of_bootstrappings_dr_growth == "NULL" || input$number_of_bootstrappings_dr_growth == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_growth)
}
showModal(modalDialog("Re-calculating dose-response analysis...", footer = NULL))
try(
results$growth$drFit <-
suppressWarnings(
growth.drFit(gcTable = results$growth$gcFit$gcTable,
control = growth.control(dr.method = input$dr_method_growth,
dr.parameter = results$growth$control$dr.parameter,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_growth,
log.y.dr = input$log_transform_response_growth,
nboot.dr = nboot.dr
)
)
)
)
}
# Show [Restore fit] button
show("restore_growth_spline")
}
removeModal()
})
# Restore previous spline fit upon click on [Restore Fit]
observeEvent(input$restore_growth_spline, {
# restore previous fit from memory
results$growth$gcFit$gcFittedSplines[[selected_vals_validate_growth$sample_validate_growth_spline]] <- selected_vals_validate_growth$restore_growth_spline
hide("restore_growth_spline")
dr.parameter.fit.method <- gsub(".+\\.", "", results$growth$control$dr.parameter)
if(length(results$growth$drFit) > 1 && dr.parameter.fit.method == "spline"){
results$growth$drFit <- selected_vals_validate_growth$restore_growth_drFit
}
})
output$download_growth_validate_spline <- downloadHandler(
filename = function() {
paste("spline_fit_", gsub(" \\| ", "_", selected_vals_validate_growth$sample_validate_growth_spline), input$format_download_growth_validate_spline, sep="")
},
content = function(file) {
if(input$format_download_growth_validate_spline == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_growth_validate_spline,
height = input$height_download_growth_validate_spline,
dpi = input$dpi_download_growth_validate_spline,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_growth_validate_spline,
height = input$height_download_growth_validate_spline,
dpi = input$dpi_download_growth_validate_spline,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_growth_validate_spline,
height = input$height_download_growth_validate_spline,
dpi = input$dpi_download_growth_validate_spline,
device = png)
}
},
contentType = ifelse(input$format_download_growth_validate_spline == ".pdf", "image/pdf", "image/png")
)
output$download_growth_validate_spline_values <- downloadHandler(
filename = function() {
paste("values_spline_fit_", gsub(" \\| ", "_", selected_vals_validate_growth$sample_validate_growth_spline), ".csv", sep="")
},
content = function(file) {
results <- results$growth
if(length(results$gcFit$gcFittedSplines[[ifelse(
selected_vals_validate_growth$sample_validate_growth_spline == "1" ||
selected_vals_validate_growth$sample_validate_growth_spline == "" ||
is.null(selected_vals_validate_growth$sample_validate_growth_spline),
1,
selected_vals_validate_growth$sample_validate_growth_spline
)]]) > 1) {
try(p <-
suppressWarnings(
plot.gcFitSpline(
results$gcFit$gcFittedSplines[[ifelse(
selected_vals_validate_growth$sample_validate_growth_spline == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_spline
),
1,
selected_vals_validate_growth$sample_validate_growth_spline
)]],
export = FALSE,
plot = FALSE,
log.y = FALSE,
deriv = F
)
))
}
table <- data.frame("x" = as.numeric(p$data$fit.time), "y" = as.numeric(p$data$fit.data))
QurvE:::write.csv.utf8.BOM(table, file)
},
contentType = ".csv"
)
output$download_growth_validate_spline_deriv_values <- downloadHandler(
filename = function() {
paste("values_spline_fit_derivative_", gsub(" \\| ", "_", selected_vals_validate_growth$sample_validate_growth_spline), ".csv", sep="")
},
content = function(file) {
results <- results$growth
if (length(results$gcFit$gcFittedSplines[[ifelse(
selected_vals_validate_growth$sample_validate_growth_spline == "1" ||
selected_vals_validate_growth$sample_validate_growth_spline == "" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_spline
),
1,
selected_vals_validate_growth$sample_validate_growth_spline
)]]) > 1) {
table <- as.data.frame(results$gcFit$gcFittedSplines[[ifelse(
selected_vals_validate_growth$sample_validate_growth_spline == "1" ||
is.null(
selected_vals_validate_growth$sample_validate_growth_spline
),
1,
selected_vals_validate_growth$sample_validate_growth_spline
)]]$spline.deriv1)
}
QurvE:::write.csv.utf8.BOM(table, file)
},
contentType = ".csv"
)
### Model Fits ####
selected_inputs_validate_growth_model_sample <- reactive({
results <- results$growth
if(is.null(results)) return("")
if("m" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
select_samples <- names(results$gcFit$gcFittedModel)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_growth_model",
choices = selected_inputs_validate_growth_model_sample(),
selected = selected_vals_validate_growth$sample_validate_growth_model
)})
output$validate_growth_plot_model <- renderPlot({
results <- results$growth
if(length(results$gcFit$gcFittedModels[[ifelse(selected_vals_validate_growth$sample_validate_growth_model == "1"||
selected_vals_validate_growth$sample_validate_growth_model == "" ||
is.null(selected_vals_validate_growth$sample_validate_growth_model), 1, selected_vals_validate_growth$sample_validate_growth_model)]]) > 1){
showModal(modalDialog("Creating plot...", footer=NULL))
try(
suppressWarnings(
plot.gcFitModel(results$gcFit$gcFittedModels[[ifelse(selected_vals_validate_growth$sample_validate_growth_model == "1" || is.null(selected_vals_validate_growth$sample_validate_growth_model), 1, selected_vals_validate_growth$sample_validate_growth_model)]],
colModel = input$color_validate_growth_plot_model,
pch = input$shape_type_validate_growth_plot_model,
basesize = input$base_size_validate_growth_plot_model,
cex.point = input$shape_size_validate_growth_plot_model,
lwd = input$line_width_validate_growth_plot_model,
n.ybreaks = input$nbreaks_validate_growth_plot_model,
eq.size = input$eqsize_validate_growth_plot_model,
export = FALSE
)
)
)
removeModal()
}
})
model.rerun.param <- reactiveValues()
observeEvent(input$rerun_growth_model, {
# display a modal dialog with a header, textinput and action buttons
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
textInput('t0.model.rerun', 'Minimum time (t0)', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$t0)),
textInput('min.growth.model.rerun', 'Minimum growth measurement', placeholder = paste0("previously: ", results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$min.growth)),
wellPanel(
h4(strong('Models:')),
style='padding: 1; padding-top: 0; padding-bottom: 0',
checkboxInput(inputId = 'logistic_growth_rerun',
label = 'logistic',
value = ("logistic" %in% results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$model.type)),
checkboxInput(inputId = 'richards_growth_rerun',
label = 'Richards',
value = ("richards" %in% results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$model.type)),
checkboxInput(inputId = 'gompertz_growth_rerun',
label = 'Gompertz',
value = ("gompertz" %in% results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$model.type)),
checkboxInput(inputId = 'extended_gompertz_growth_rerun',
label = 'extended Gompertz',
value = ("gompertz.exp" %in% results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$model.type)),
checkboxInput(inputId = 'huang_growth_rerun',
label = 'Huang',
value = ("huang" %in% results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$model.type)),
checkboxInput(inputId = 'baranyi_growth_rerun',
label = 'Baranyi and Roberts',
value = ("baranyi" %in% results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control$model.type))
),
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.model', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_growth.gcFitModel_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
})
# Re-run selected model fit with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.model, {
if(!is.null(results$growth$gcFit)){
# store previous fit in memory
selected_vals_validate_growth$restore_growth_model <- results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]
# Re-run fit and store in results object
actwell <- results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$data.in
acttime <- results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$time.in
control <- results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$control
control_new <- control
gcID <- results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$gcID
control_new$t0 <- ifelse(!is.na(as.numeric(input$t0.model.rerun)), as.numeric(input$t0.model.rerun), control$t0)
min.growth.model.new <- ifelse(!is.na(as.numeric(input$min.growth.model.rerun)), as.numeric(input$min.growth.model.rerun), control$min.growth)
if(is.numeric(min.growth.model.new)){
if(!is.na(min.growth.model.new) && all(as.vector(actwell) < min.growth.model.new)){
message(paste0("Start growth values need to be greater than 'min.growth'.\nThe minimum start value in your dataset is: ",
min(as.vector(actwell)),". 'min.growth' was not adjusted."), call. = FALSE)
} else if(!is.na(min.growth.model.new)){
control_new$min.growth <- min.growth.model.new
}
}
# combine selected models into vector
models <- c()
if(input$logistic_growth_rerun == TRUE) models <- c(models, "logistic")
if(input$richards_growth_rerun == TRUE) models <- c(models, "richards")
if(input$gompertz_growth_rerun == TRUE) models <- c(models, "gompertz")
if(input$extended_gompertz_growth_rerun == TRUE) models <- c(models, "gompertz.exp")
if(input$huang_growth_rerun == TRUE) models <- c(models, "huang")
if(input$baranyi_growth_rerun == TRUE) models <- c(models, "baranyi")
control_new$model.type <- models
showModal(modalDialog("Fitting sample data...", footer = NULL))
try(results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]] <-
growth.gcFitModel(acttime, actwell,
gcID = gcID,
control = control_new))
# Update gcTable with new results
res.table.gc <- results$growth$gcFit$gcTable
fit.summary <- QurvE:::summary.gcFitModel(results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]])
sample.ndx <- ifelse(is.numeric(selected_vals_validate_growth$sample_validate_growth_model),
selected_vals_validate_growth$sample_validate_growth_model,
match(selected_vals_validate_growth$sample_validate_growth_model, names(results$growth$gcFit$gcFittedModels)))
results$growth$gcFit$gcTable[sample.ndx, colnames(res.table.gc) %in% colnames(fit.summary)] <- fit.summary
results$growth$gcFit$gcTable[sample.ndx, "used.model"] <- results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]]$model
# Re-run drFit if respective fit type (linear/spline/model) was chosen as dr.parameter
dr.parameter.fit.method <- gsub(".+\\.", "", results$growth$control$dr.parameter)
if(length(results$growth$drFit) > 1 && dr.parameter.fit.method == "model"){
# Store previous drFit in memory
selected_vals_validate_growth$restore_growth_drFit <- results$growth$drFit
# Re-run drFit
if (is.null(input$smoothing_factor_growth_dr) || is.na(input$smoothing_factor_growth_dr) || input$smoothing_factor_growth_dr == "NULL" || input$smoothing_factor_growth_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_growth_dr)
}
if (is.null(input$number_of_bootstrappings_dr_growth) || is.na(input$number_of_bootstrappings_dr_growth) || input$number_of_bootstrappings_dr_growth == "NULL" || input$number_of_bootstrappings_dr_growth == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_growth)
}
showModal(modalDialog("Re-calculating dose-response analysis...", footer = NULL))
try(
results$growth$drFit <-
suppressWarnings(
growth.drFit(gcTable = results$growth$gcFit$gcTable,
control = growth.control(dr.method = input$dr_method_growth,
dr.parameter = results$growth$control$dr.parameter,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_growth,
log.y.dr = input$log_transform_response_growth,
nboot.dr = nboot.dr
)
)
)
)
}
# Show [Restore fit] button
show("restore_growth_model")
}
removeModal()
})
# Restore previous model fit upon click on [Restore Fit]
observeEvent(input$restore_growth_model, {
# store previous fit from memory
results$growth$gcFit$gcFittedModels[[selected_vals_validate_growth$sample_validate_growth_model]] <- selected_vals_validate_growth$restore_growth_model
dr.parameter.fit.method <- gsub(".+\\.", "", results$growth$control$dr.parameter)
if(length(results$growth$drFit) > 1 && dr.parameter.fit.method == "model"){
results$growth$drFit <- selected_vals_validate_growth$restore_growth_drFit
}
hide("restore_growth_model")
})
output$download_growth_validate_model <- downloadHandler(
filename = function() {
paste("model_fit_", gsub(" \\| ", "_", selected_vals_validate_growth$sample_validate_growth_model), input$format_download_growth_validate_model, sep="")
},
content = function(file) {
if(input$format_download_growth_validate_model == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_growth_validate_model,
height = input$height_download_growth_validate_model,
dpi = input$dpi_download_growth_validate_model,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_growth_validate_model,
height = input$height_download_growth_validate_model,
dpi = input$dpi_download_growth_validate_model,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_growth_validate_model,
height = input$height_download_growth_validate_model,
dpi = input$dpi_download_growth_validate_model,
device = png)
}
},
contentType = ifelse(input$format_download_growth_validate_model == ".pdf", "image/pdf", "image/png")
)
### Spline Fits BT ####
# observe({
# if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1){
# showTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse")
# } else {
# hideTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse")
# }
# })
selected_inputs_sample_validate_growth_spline_bt <- reactive({
results <- results$growth
if(is.null(results)) return("")
if(("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt) && results$control$nboot.gc > 1){
select_samples <- names(results$gcFit$gcBootSplines)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_growth_spline_bt",
choices = selected_inputs_sample_validate_growth_spline_bt(),
selected = selected_vals_validate_growth$sample_validate_growth_spline_bt
)})
validate_growth_plot_spline_bt <- reactive({
results <- results$growth
# Define x- and y-axis limits
if(any(input$y_range_min_validate_growth_spline_bt == "",
input$y_range_max_validate_growth_spline_bt == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_growth_spline_bt),
as.numeric(input$y_range_max_validate_growth_spline_bt))
}
if(any(input$y_range_min_derivative_validate_growth_spline_bt == "",
input$y_range_max_derivative_validate_growth_spline_bt == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_growth_spline_bt),
as.numeric(input$y_range_max_derivative_validate_growth_spline_bt))
}
if(any(input$x_range_min_validate_growth_spline_bt == "",
input$x_range_max_validate_growth_spline_bt == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_growth_spline_bt),
as.numeric(input$x_range_max_validate_growth_spline_bt))
}
if(length(results$gcFit$gcBootSplines[[ifelse(selected_vals_validate_growth$sample_validate_growth_spline_bt == "1"||
selected_vals_validate_growth$sample_validate_growth_spline_bt == ""||
is.null(selected_vals_validate_growth$sample_validate_growth_spline_bt), 1, selected_vals_validate_growth$sample_validate_growth_spline_bt)]]) > 1){
try(
plot.gcBootSpline(results$gcFit$gcBootSplines[[ifelse(selected_vals_validate_growth$sample_validate_growth_spline_bt == "1"||
selected_vals_validate_growth$sample_validate_growth_spline_bt == ""||
is.null(selected_vals_validate_growth$sample_validate_growth_spline_bt), 1, selected_vals_validate_growth$sample_validate_growth_spline_bt)]],
pch = input$shape_type_validate_growth_spline_bt,
cex.point = input$shape_size_validate_growth_spline_bt,
cex.lab = input$axis_size_validate_growth_spline_bt,
cex.axis = input$lab_size_validate_growth_spline_bt,
lwd = input$line_width_validate_growth_spline_bt,
y.lim = y.lim,
x.lim = x.lim,
y.lim.deriv = ylim.deriv,
deriv = input$plot_derivative_growth_spline_bt,
combine = TRUE,
colSpline = input$color_validate_growth_plot_spline_bt
)
)
}
})
output$validate_growth_plot_spline_bt <- renderPlot({
validate_growth_plot_spline_bt()
})
output$download_growth_validate_spline_bt <- downloadHandler(
filename = function() {
paste("spline_fit_bootstrap_", gsub(" \\| ", "_", input$sample_validate_growth_spline_bt), input$format_download_growth_validate_spline_bt, sep="")
},
content = function(file) {
if(input$format_download_growth_validate_spline_bt == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_growth_validate_spline_bt,
height = input$height_download_growth_validate_spline_bt)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_growth_validate_spline_bt,
height = input$height_download_growth_validate_spline_bt)
}
} else {
grDevices::png(file = file,
width = input$width_download_growth_validate_spline_bt,
height = input$height_download_growth_validate_spline_bt,
units = "in",
res = input$dpi_download_growth_validate_spline_bt)
}
# Generate plot
results <- results$growth$gcFit$gcBootSplines[[ifelse(selected_vals_validate_growth$sample_validate_growth_spline_bt == "1" ||
is.null(selected_vals_validate_growth$sample_validate_growth_spline_bt),
1,
selected_vals_validate_growth$sample_validate_growth_spline_bt)]]
## Define x- and y-axis limits
if(any(input$y_range_min_validate_growth_spline_bt == "",
input$y_range_max_validate_growth_spline_bt == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_growth_spline_bt),
as.numeric(input$y_range_max_validate_growth_spline_bt))
}
if(any(input$y_range_min_derivative_validate_growth_spline_bt == "",
input$y_range_max_derivative_validate_growth_spline_bt == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_growth_spline_bt),
as.numeric(input$y_range_max_derivative_validate_growth_spline_bt))
}
if(any(input$x_range_min_validate_growth_spline_bt == "",
input$x_range_max_validate_growth_spline_bt == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_growth_spline_bt),
as.numeric(input$x_range_max_validate_growth_spline_bt))
}
plot.gcBootSpline(results,
pch = input$shape_type_validate_growth_spline_bt,
cex.point = input$shape_size_validate_growth_spline_bt,
cex.lab = input$axis_size_validate_growth_spline_bt,
cex.axis = input$lab_size_validate_growth_spline_bt,
lwd = input$line_width_validate_growth_spline_bt,
y.lim = y.lim,
x.lim = x.lim,
y.lim.deriv = ylim.deriv,
deriv = input$plot_derivative_growth_spline_bt,
combine = TRUE
)
dev.off()
},
contentType = ifelse(input$format_download_growth_validate_spline_bt == ".pdf", "image/pdf", "image/png")
)
## Fluorescence #####
observe({
if(!is.null(results$fluorescence)){
if(!("s" %in% results$fluorescence$control$fit.opt || "a" %in% results$fluorescence$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Validate_Fluorescence", target = "tabPanel_Validate_Fluorescence_Spline")
} else {
showTab(inputId = "tabsetPanel_Validate_Fluorescence", target = "tabPanel_Validate_Fluorescence_Spline")
}
if(!("l" %in% results$fluorescence$control$fit.opt || "a" %in% results$fluorescence$control$fit.opt)){
hideTab(inputId = "tabsetPanel_Validate_Fluorescence", target = "tabPanel_Validate_Fluorescence_Linear")
} else {
showTab(inputId = "tabsetPanel_Validate_Fluorescence", target = "tabPanel_Validate_Fluorescence_Linear")
}
if(!("s" %in% results$fluorescence$control$fit.opt || "a" %in% results$fluorescence$control$fit.opt) || results$fluorescence$control$nboot.fl <=1){
hideTab(inputId = "tabsetPanel_Validate_Fluorescence", target = "tabPanel_Validate_Fluorescence_Spline_bt")
} else {
showTab(inputId = "tabsetPanel_Validate_Fluorescence", target = "tabPanel_Validate_Fluorescence_Spline_bt")
}
}
})
#### Hide [Restore Fit] buttons when starting the app
hide("restore_fluorescence_linear"); hide("restore_fluorescence_spline")
#### Hide [Restore Fit] buttons whenever a sample is changed
observeEvent(input$sample_validate_fluorescence_linear, {
hide("restore_fluorescence_linear")
})
observeEvent(input$sample_validate_fluorescence_spline, {
hide("restore_fluorescence_spline")
})
#------- Initialize the Memory to store settings
selected_vals_validate_fluorescence <- reactiveValues(sample_validate_fluorescence_linear = 1,
sample_validate_fluorescence_spline = 1,
sample_validate_fluorescence_spline_bt = 1
)
#------ Whenever any of the inputs are changed, it only modifies the memory
observe({
req(input$sample_validate_fluorescence_linear)
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear <- input$sample_validate_fluorescence_linear
})
observe({
req(input$sample_validate_fluorescence_spline)
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline <- input$sample_validate_fluorescence_spline
})
observe({
req(input$sample_validate_fluorescence_spline_bt)
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt <- input$sample_validate_fluorescence_spline_bt
})
### Linear Fits ####
selected_inputs_validate_fluorescence_linear_sample <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
if("l" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
select_samples <- names(results$flFit$flFittedLinear)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_fluorescence_linear",
choices = selected_inputs_validate_fluorescence_linear_sample(),
selected = selected_vals_validate_fluorescence$sample_validate_fluorescence_linear
)})
logy_validate_fluorescence_plot_linear <- reactive({
if(input$logy_validate_fluorescence_plot_linear) return("y")
else return("")
})
# Render plot
output$validate_fluorescence_plot_linear <- renderPlot({
results <- results$fluorescence
# Define x- and y-axis limits
if(any(input$y_range_min_validate_fluorescence_plot_linear == "",
input$y_range_max_validate_fluorescence_plot_linear == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_fluorescence_plot_linear),
as.numeric(input$y_range_max_validate_fluorescence_plot_linear))
}
if(any(input$y_range_min_derivative_validate_fluorescence_plot_linear == "",
input$y_range_max_derivative_validate_fluorescence_plot_linear == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_fluorescence_plot_linear),
as.numeric(input$y_range_max_derivative_validate_fluorescence_plot_linear))
}
if(any(input$x_range_min_validate_fluorescence_plot_linear == "",
input$x_range_max_validate_fluorescence_plot_linear == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_fluorescence_plot_linear),
as.numeric(input$x_range_max_validate_fluorescence_plot_linear))
}
if(length(results$flFit$flFittedLinear[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1"||
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear), 1, selected_vals_validate_fluorescence$sample_validate_fluorescence_linear)]]) > 1){
try(
suppressWarnings(
plot.flFitLinear(results$flFit$flFittedLinear[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1" || is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear), 1, selected_vals_validate_fluorescence$sample_validate_fluorescence_linear)]],
log = logy_validate_fluorescence_plot_linear(),
pch = input$shape_type_validate_fluorescence_plot_linear,
cex.point = input$shape_size_validate_fluorescence_plot_linear,
cex.lab = input$axis_size_validate_fluorescence_plot_linear,
cex.axis = input$lab_size_validate_fluorescence_plot_linear,
lwd = input$line_width_validate_fluorescence_plot_linear,
y.lim = y.lim,
x.lim = x.lim,
color = input$color_validate_fluorescence_plot_linear
# ADD FURTHER INPUT (see Notion)
)
)
)
if(input$diagnostics_validate_fluorescence_plot_linear){
try(
suppressWarnings(
plot.flFitLinear(results$flFit$flFittedLinear[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear),
1,
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear)]],
which = "fit_diagnostics",
log = logy_validate_fluorescence_plot_linear(),
pch = input$shape_type_validate_fluorescence_plot_linear,
cex.point = input$shape_size_validate_fluorescence_plot_linear,
cex.lab = input$axis_size_validate_fluorescence_plot_linear,
cex.axis = input$lab_size_validate_fluorescence_plot_linear,
lwd = input$line_width_validate_fluorescence_plot_linear,
y.lim = y.lim,
x.lim = x.lim,
color = input$color_validate_fluorescence_plot_linear
# ADD FURTHER INPUT (see Notion)
)
)
)
}
}
})
# lin.rerun.param <- reactiveValues()
observeEvent(input$rerun_fluorescence_linear, {
# display a modal dialog with a header, textinput and action buttons
if(results$fluorescence$flFit$flFittedLinear[[ifelse(
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1" ||
is.null(
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear
),
1,
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear
)]]$control$x_type == "time"){
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
textInput('t0.lin.rerun.fluorescence', 'Minimum time (t0)', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$t0)),
textInput('tmax.lin.rerun.fluorescence', 'Maximum time (tmax)', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$tmax)),
textAreaInput('quota.rerun.fluorescence', 'Quota', placeholder = HTML(paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$quota,
"\n",
"include regression windows with slope = ", expression(µ[max]), " * quota into the final linear fit."))),
textInput('lin.h.rerun.fluorescence', 'Sliding window size (h)', placeholder = paste0("previously: ",
ifelse(!is.null(results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.h),
results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.h,
"NULL"))),
textInput('lin.R2.rerun.fluorescence', 'R2 threshold', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.R2)),
textInput('lin.RSD.rerun.fluorescence', 'RSD threshold for slope', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.RSD)),
footer=tagList(
fluidRow(
column(12,
div(
actionButton(inputId = "submit.rerun.linear.fluorescence",
label = "Submit"),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_flFitLinear_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
} else {
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
textInput('min.growth.lin.rerun.fluorescence', 'Minimum growth measurement', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$min.growth)),
textInput('max.growth.lin.rerun.fluorescence', 'Maximum growth measurement', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$max.growth)),
textAreaInput('quota.rerun.fluorescence', 'Quota', placeholder = HTML(paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$quota,
"\n",
"include regression windows with slope = ", expression(µ[max]), " * quota into the final linear fit."))),
textInput('lin.h.rerun.fluorescence', 'Sliding window size (h)', placeholder = paste0("previously: ",
ifelse(!is.null(results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.h),
results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.h,
"NULL"))),
textInput('lin.R2.rerun.fluorescence', 'R2 threshold', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.R2)),
textInput('lin.RSD.rerun.fluorescence', 'RSD threshold for slope', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control$lin.RSD)),
footer=tagList(
fluidRow(
column(12,
div(
actionButton(inputId = "submit.rerun.linear.fluorescence",
label = "Submit"),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_flFitLinear_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
}
})
# Re-run selected linear fit with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.linear.fluorescence, {
if(!is.null(results$fluorescence$flFit)){
showModal(modalDialog("Fitting sample data...", footer = NULL))
# store previous fit in memory
selected_vals_validate_fluorescence$restore_fluorescence_linear <- results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]
# Re-run fit and store in results object
actwell <- results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$raw.fl
acttime <- results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$raw.x
control <- results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$control
control_new <- control
ID <- results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]]$ID
lin.h.new <- ifelse(!is.na(as.numeric(input$lin.h.rerun.fluorescence)), as.numeric(input$lin.h.rerun.fluorescence), control$lin.h)
if(!is.na(lin.h.new)) control_new$lin.h <- lin.h.new
control_new$lin.R2 <- ifelse(!is.na(as.numeric(input$lin.R2.rerun.fluorescence)), as.numeric(input$lin.R2.rerun.fluorescence), control$lin.R2)
control_new$lin.RSD <- ifelse(!is.na(as.numeric(input$lin.RSD.rerun.fluorescence)), as.numeric(input$lin.RSD.rerun.fluorescence), control$lin.RSD)
control_new$t0 <- ifelse(!is.na(input$t0.lin.rerun.fluorescence) && !is.null(input$t0.lin.rerun.fluorescence) && input$t0.lin.rerun.fluorescence != "",
as.numeric(input$t0.lin.rerun.fluorescence),
control$t0)
control_new$tmax <- ifelse(!is.na(input$tmax.lin.rerun.fluorescence) && !is.null(input$tmax.lin.rerun.fluorescence) && input$tmax.lin.rerun.fluorescence != "",
as.numeric(input$tmax.lin.rerun.fluorescence),
control$tmax)
min.growth.lin.new <- ifelse(!is.na(input$min.growth.lin.rerun.fluorescence) && !is.null(input$min.growth.lin.rerun.fluorescence) && input$min.growth.lin.rerun.fluorescence != "",
as.numeric(input$min.growth.lin.rerun.fluorescence),
control$min.growth)
if(is.numeric(min.growth.lin.new)){
if(!is.na(min.growth.lin.new) && all(as.vector(actwell) < min.growth.lin.new)){
showModal(
modalDialog(paste0("Start growth values need to be greater than 'min.growth'.\nThe minimum start value in your dataset is: ",
min(as.vector(actwell)),". 'min.growth' was not adjusted."), easyClose = T)
)
} else if(!is.na(min.growth.lin.new)){
control_new$min.growth <- min.growth.lin.new
}
}
control_new$max.growth <- ifelse(!is.na(as.numeric(input$max.growth.lin.rerun.fluorescence)), as.numeric(input$max.growth.lin.rerun.fluorescence), control$max.growth)
quota_new <- ifelse(!is.na(as.numeric(input$quota.rerun.fluorescence)), as.numeric(input$quota.rerun.fluorescence), 0.95)
if(control$x_type == "time"){
try(
results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]] <-
flFitLinear(time = acttime, fl_data = actwell,
ID = ID,
control = control_new,
quota = quota_new)
)
} else {
try(
results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]] <-
flFitLinear(growth = acttime, fl_data = actwell,
ID = ID,
control = control_new,
quota = quota_new)
)
}
# Update gcTable with new results
res.table.fl <- results$fluorescence$flFit$flTable
fit.summary <- QurvE:::summary.flFitLinear(results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]])
sample.ndx <- ifelse(is.numeric(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear),
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear,
match(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear, names(results$fluorescence$flFit$flFittedLinear)))
results$fluorescence$flFit$flTable[sample.ndx, colnames(res.table.fl) %in% colnames(fit.summary)] <- fit.summary
# Re-run drFit if respective fit type (linear/spline) was chosen as dr.parameter
dr.parameter.fit.method <- gsub(".+\\.", "", results$fluorescence$control$dr.parameter)
if(length(results$fluorescence$drFit) > 1 && dr.parameter.fit.method == "linfit"){
# Store previous drFit in memory
selected_vals_validate_fluorescence$restore_fluorescence_drFit <- results$fluorescence$drFit
# Re-run drFit
if (is.null(input$smoothing_factor_fluorescence_dr) || is.na(input$smoothing_factor_fluorescence_dr) || input$smoothing_factor_fluorescence_dr == "NULL" || input$smoothing_factor_fluorescence_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_fluorescence_dr)
}
if (is.null(input$number_of_bootstrappings_dr_fluorescence) || is.na(input$number_of_bootstrappings_dr_fluorescence) || input$number_of_bootstrappings_dr_fluorescence == "NULL" || input$number_of_bootstrappings_dr_fluorescence == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_fluorescence)
}
showModal(modalDialog("Re-calculating dose-response analysis...", footer = NULL))
try(
results$fluorescence$drFit <-
suppressWarnings(
fl.drFit(flTable = results$fluorescence$flFit$flTable,
control = fl.control(dr.method = input$dr_method_fluorescence,
dr.parameter = results$fluorescence$control$dr.parameter,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_fluorescence,
log.y.dr = input$log_transform_response_fluorescence,
nboot.dr = nboot.dr
)
)
)
)
}
# Show [Restore fit] button
show("restore_fluorescence_linear")
}
removeModal()
})
# Restore previous linear fit upon click on [Restore Fit]
observeEvent(input$restore_fluorescence_linear, {
# store previous fit from memory
results$fluorescence$flFit$flFittedLinear[[selected_vals_validate_fluorescence$sample_validate_fluorescence_linear]] <- selected_vals_validate_fluorescence$restore_fluorescence_linear
dr.parameter.fit.method <- gsub(".+\\.", "", results$fluorescence$control$dr.parameter)
if(length(results$fluorescence$drFit) > 1 && dr.parameter.fit.method == "linfit"){
results$fluorescence$drFit <- selected_vals_validate_fluorescence$restore_fluorescence_drFit
}
hide("restore_fluorescence_linear")
})
output$download_fluorescence_validate_linear <- downloadHandler(
filename = function() {
paste("linear_fit_", gsub(" \\| ", "_", selected_vals_validate_fluorescence$sample_validate_fluorescence_linear), input$format_download_fluorescence_validate_linear, sep="")
},
content = function(file) {
if(input$format_download_fluorescence_validate_linear == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_fluorescence_validate_linear,
height = input$height_download_fluorescence_validate_linear)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_fluorescence_validate_linear,
height = input$height_download_fluorescence_validate_linear)
}
} else {
grDevices::png(file = file,
width = input$width_download_fluorescence_validate_linear,
height = input$height_download_fluorescence_validate_linear,
units = "in",
res = input$dpi_download_fluorescence_validate_linear)
}
if(input$logy_validate_fluorescence_plot_linear) log <- "y"
else log <- ""
results <- results$fluorescence
# Define x- and y-axis limits
if(any(input$y_range_min_validate_fluorescence_plot_linear == "",
input$y_range_max_validate_fluorescence_plot_linear == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_fluorescence_plot_linear),
as.numeric(input$y_range_max_validate_fluorescence_plot_linear))
}
if(any(input$y_range_min_derivative_validate_fluorescence_plot_linear == "",
input$y_range_max_derivative_validate_fluorescence_plot_linear == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_fluorescence_plot_linear),
as.numeric(input$y_range_max_derivative_validate_fluorescence_plot_linear))
}
if(any(input$x_range_min_validate_fluorescence_plot_linear == "",
input$x_range_max_validate_fluorescence_plot_linear == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_fluorescence_plot_linear),
as.numeric(input$x_range_max_validate_fluorescence_plot_linear))
}
if(length(results$flFit$flFittedLinear[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1" ||
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear),
1,
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear)]]) > 1){
suppressWarnings(
plot.flFitLinear(results$flFit$flFittedLinear[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1" || is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear), 1, selected_vals_validate_fluorescence$sample_validate_fluorescence_linear)]],
log = log,
pch = input$shape_type_validate_fluorescence_plot_linear,
cex.point = input$shape_size_validate_fluorescence_plot_linear,
cex.lab = input$axis_size_validate_fluorescence_plot_linear,
cex.axis = input$lab_size_validate_fluorescence_plot_linear,
lwd = input$line_width_validate_fluorescence_plot_linear,
y.lim = y.lim,
x.lim = x.lim
# ADD FURTHER INPUT (see Notion)
)
)
if(input$diagnostics_validate_fluorescence_plot_linear){
suppressWarnings(
plot.flFitLinear(results$flFit$flFittedLinear[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == "1"||
selected_vals_validate_fluorescence$sample_validate_fluorescence_linear == ""||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_linear), 1, selected_vals_validate_fluorescence$sample_validate_fluorescence_linear)]],
which = "fit_diagnostics",
log = log,
pch = input$shape_type_validate_fluorescence_plot_linear,
cex.point = input$shape_size_validate_fluorescence_plot_linear,
cex.lab = input$axis_size_validate_fluorescence_plot_linear,
cex.axis = input$lab_size_validate_fluorescence_plot_linear,
lwd = input$line_width_validate_fluorescence_plot_linear,
y.lim = y.lim,
x.lim = x.lim
# ADD FURTHER INPUT (see Notion)
)
)
}
}
dev.off()
},
contentType = ifelse(input$format_download_fluorescence_validate_linear == ".pdf", "image/pdf", "image/png")
)
### Spline Fits ####
selected_inputs_validate_fluorescence_spline_sample <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
if("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
select_samples <- names(results$flFit$flFittedSplines)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_fluorescence_spline",
choices = selected_inputs_validate_fluorescence_spline_sample(),
selected = selected_vals_validate_fluorescence$sample_validate_fluorescence_spline
)})
output$validate_fluorescence_plot_spline <- renderPlot({
results <- results$fluorescence
if(length(results$flFit$flFittedSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "1"||
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
1,
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline)]]) > 1){
showModal(modalDialog("Creating plot...", footer=NULL))
try(
suppressWarnings(
plot.flFitSpline(results$flFit$flFittedSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "1" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
1,
input$sample_validate_fluorescence_spline)]],
log.y = input$logy_validate_fluorescence_plot_spline,
x.lim = c(input$x_range_min_validate_fluorescence_plot_spline, input$x_range_max_validate_fluorescence_plot_spline),
y.lim = c(input$y_range_min_validate_fluorescence_plot_spline,input$y_range_max_validate_fluorescence_plot_spline),
y.lim.deriv = c(input$y_range_min_derivative_validate_fluorescence_plot_spline, input$y_range_max_derivative_validate_fluorescence_plot_spline),
lwd = input$line_width_validate_fluorescence_plot_spline,
basesize = input$base_size_validate_fluorescence_plot_spline,
cex.point = input$shape_size_validate_fluorescence_plot_spline,
n.ybreaks = input$nbreaks__validate_fluorescence_plot_spline,
deriv = input$plot_derivative_validate_fluorescence_plot_spline,
pch = input$shape_type_validate_fluorescence_plot_spline,
colSpline = input$color_validate_fluorescence_plot_spline
)
)
)
removeModal()
}
})
spline.rerun.param.fluorescence <- reactiveValues()
observeEvent(input$rerun_fluorescence_spline, {
# display a modal dialog with a header, textinput and action buttons
if (results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$x_type == "time") {
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
textInput('t0.spline.rerun.fluorescence', 'Minimum time (t0)', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$t0)),
textInput('tmax.spline.rerun.fluorescence', 'Maximum time (tmax)', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$tmax)),
textInput('smooth.fl.rerun.fluorescence', 'Smoothing factor', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$smooth.fl)),
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.spline.fluorescence', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_flFitSpline_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
} else {
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
textInput('min.growth.spline.rerun.fluorescence', 'Minimum growth measurement', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$min.growth)),
textInput('max.growth.spline.rerun.fluorescence', 'Maximum growth measurement', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$max.growth)),
textInput('smooth.fl.rerun.fluorescence', 'Smoothing factor', placeholder = paste0("previously: ", results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control$smooth.fl)),
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.spline.fluorescence', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right"),
div(
actionButton(inputId = "tooltip_flFitSpline_validate",
label = "",
icon=icon("question"),
style="padding:2px; font-size:100%"),
style="float:left")
)
)
)
)
)
}
})
# Re-run selected spline fit with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.spline.fluorescence, {
if(!is.null(results$fluorescence$flFit)){
showModal(modalDialog("Fitting sample data...", footer = NULL))
# store previous fit in memory
selected_vals_validate_fluorescence$restore_fluorescence_spline <- results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]
# Re-run fit and store in results object
actwell <- results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$fl.in
acttime <- results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$x.in
control <- results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$control
control_new <- control
ID <- results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]]$ID
control_new$smooth.fl <- ifelse(!is.na(as.numeric(input$smooth.fl.rerun.fluorescence)), as.numeric(input$smooth.fl.rerun.fluorescence), control$smooth.fl)
control_new$t0 <- ifelse(!is.na(input$t0.spline.rerun.fluorescence) && !is.null(input$t0.spline.rerun.fluorescence) && input$t0.spline.rerun.fluorescence != "",
as.numeric(input$t0.spline.rerun.fluorescence),
control$t0)
control_new$tmax <- ifelse(!is.na(input$tmax.spline.rerun.fluorescence) && !is.null(input$tmax.spline.rerun.fluorescence) && input$tmax.spline.rerun.fluorescence != "",
as.numeric(input$tmax.spline.rerun.fluorescence),
control$tmax)
min.growth.spline.new <- ifelse(!is.na(as.numeric(input$min.growth.spline.rerun.fluorescence)) && !is.null(input$min.growth.spline.rerun.fluorescence),
as.numeric(input$min.growth.spline.rerun.fluorescence),
control$min.growth)
control_new$max.growth <- ifelse(!is.na(as.numeric(input$max.growth.spline.rerun.fluorescence)) && !is.null(input$max.growth.spline.rerun.fluorescence),
as.numeric(input$max.growth.spline.rerun.fluorescence),
control$max.growth)
if(is.numeric(min.growth.spline.new)){
if(!is.na(min.growth.spline.new) && all(as.vector(actwell) < min.growth.spline.new)){
message(paste0("Start growth values need to be greater than 'min.growth'.\nThe minimum start value in your dataset is: ",
min(as.vector(actwell)),". 'min.growth' was not adjusted."), call. = FALSE)
} else if(!is.na(min.growth.spline.new)){
control_new$min.growth <- min.growth.spline.new
}
}
if(control$x_type == "time"){
try(
results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]] <-
flFitSpline(time = acttime, fl_data = actwell,
ID = ID,
control = control_new)
)
} else {
try(
results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]] <-
flFitSpline(growth = acttime, fl_data = actwell,
ID = ID,
control = control_new)
)
}
# Update gcTable with new results
res.table.fl <- results$fluorescence$flFit$flTable
fit.summary <- QurvE:::summary.flFitSpline(results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]])
sample.ndx <- ifelse(is.numeric(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline,
match(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline, names(results$fluorescence$flFit$flFittedSplines)))
results$fluorescence$flFit$flTable[sample.ndx, colnames(res.table.fl) %in% colnames(fit.summary)] <- fit.summary
# Re-run drFit if respective fit type (linear/spline) was chosen as dr.parameter
dr.parameter.fit.method <- gsub(".+\\.", "", results$fluorescence$control$dr.parameter)
if(length(results$fluorescence$drFit) > 1 && dr.parameter.fit.method == "spline"){
# Store previous drFit in memory
selected_vals_validate_fluorescence$restore_fluorescence_drFit <- results$fluorescence$drFit
# Re-run drFit
if (is.null(input$smoothing_factor_fluorescence_dr) || is.na(input$smoothing_factor_fluorescence_dr) || input$smoothing_factor_fluorescence_dr == "NULL" || input$smoothing_factor_fluorescence_dr == "") {
smooth.dr = NULL
} else {
smooth.dr <- as.numeric(input$smoothing_factor_fluorescence_dr)
}
if (is.null(input$number_of_bootstrappings_dr_fluorescence) || is.na(input$number_of_bootstrappings_dr_fluorescence) || input$number_of_bootstrappings_dr_fluorescence == "NULL" || input$number_of_bootstrappings_dr_fluorescence == "") {
nboot.dr <- 0
} else {
nboot.dr <- as.numeric(input$number_of_bootstrappings_dr_fluorescence)
}
showModal(modalDialog("Re-calculating dose-response analysis...", footer = NULL))
try(
results$fluorescence$drFit <-
suppressWarnings(
fl.drFit(flTable = results$fluorescence$flFit$flTable,
control = fl.control(dr.method = input$dr_method_fluorescence,
dr.parameter = results$fluorescence$control$dr.parameter,
smooth.dr = smooth.dr,
log.x.dr = input$log_transform_concentration_fluorescence,
log.y.dr = input$log_transform_response_fluorescence,
nboot.dr = nboot.dr
)
)
)
)
}
# Show [Restore fit] button
show("restore_fluorescence_spline")
}
removeModal()
})
# Restore previous spline fit upon click on [Restore Fit]
observeEvent(input$restore_fluorescence_spline, {
# store previous fit from memory
results$fluorescence$flFit$flFittedSplines[[selected_vals_validate_fluorescence$sample_validate_fluorescence_spline]] <- selected_vals_validate_fluorescence$restore_fluorescence_spline
dr.parameter.fit.method <- gsub(".+\\.", "", results$fluorescence$control$dr.parameter)
if(length(results$fluorescence$drFit) > 1 && dr.parameter.fit.method == "spline"){
results$fluorescence$drFit <- selected_vals_validate_fluorescence$restore_fluorescence_drFit
}
hide("restore_fluorescence_spline")
})
output$download_fluorescence_validate_spline <- downloadHandler(
filename = function() {
paste("spline_fit_", gsub(" \\| ", "_", selected_vals_validate_fluorescence$sample_validate_fluorescence_spline), input$format_download_fluorescence_validate_spline, sep="")
},
content = function(file) {
if(input$format_download_fluorescence_validate_spline == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_fluorescence_validate_spline,
height = input$height_download_fluorescence_validate_spline,
dpi = input$dpi_download_fluorescence_validate_spline,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_fluorescence_validate_spline,
height = input$height_download_fluorescence_validate_spline,
dpi = input$dpi_download_fluorescence_validate_spline,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_fluorescence_validate_spline,
height = input$height_download_fluorescence_validate_spline,
dpi = input$dpi_download_fluorescence_validate_spline,
device = png)
}
},
contentType = ifelse(input$format_download_fluorescence_validate_spline == ".pdf", "image/pdf", "image/png")
)
output$download_fluorescence_validate_spline_values <- downloadHandler(
filename = function() {
paste("values_spline_fit_", gsub(" \\| ", "_", selected_vals_validate_fluorescence$sample_validate_fluorescence_spline), ".csv", sep="")
},
content = function(file) {
results <- results$fluorescence
if(length(results$flFit$flFittedSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "1" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
1,
input$sample_validate_fluorescence_spline)]]) > 1) {
try(p <-
suppressWarnings(
plot.flFitSpline(
results$flFit$flFittedSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "1" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
1,
input$sample_validate_fluorescence_spline)]],
export = FALSE,
plot = FALSE,
log.y = FALSE,
deriv = F
)
))
}
table <- data.frame("x" = as.numeric(p$data$fit.x), "y" = as.numeric(p$data$fit.fl))
QurvE:::write.csv.utf8.BOM(table, file)
},
contentType = ".csv"
)
output$download_fluorescence_validate_spline_deriv_values <- downloadHandler(
filename = function() {
paste("values_spline_fit_derivative_", gsub(" \\| ", "_", selected_vals_validate_fluorescence$sample_validate_fluorescence_spline), ".csv", sep="")
},
content = function(file) {
results <- results$fluorescence
if (length(results$flFit$flFittedSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "1" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
1,
input$sample_validate_fluorescence_spline)]]) > 1) {
table <- as.data.frame(results$flFit$flFittedSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline == "1" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline),
1,
input$sample_validate_fluorescence_spline)]]$spline.deriv1)
}
QurvE:::write.csv.utf8.BOM(table, file)
},
contentType = ".csv"
)
### Spline Fits BT ####
selected_inputs_sample_validate_fluorescence_spline_bt <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
if(("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt) && results$control$nboot.fl > 1){
select_samples <- names(results$flFit$flBootSplines)
} else {
return("")
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "sample_validate_fluorescence_spline_bt",
choices = selected_inputs_sample_validate_fluorescence_spline_bt(),
selected = selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt
)})
validate_fluorescence_plot_spline_bt <- reactive({
results <- results$fluorescence$flFit$flBootSplines[[ifelse(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt == "1"||
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt == "" ||
is.null(selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt),
1,
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt)]]
# Define x- and y-axis limits
if(any(input$y_range_min_validate_fluorescence_spline_bt == "",
input$y_range_max_validate_fluorescence_spline_bt == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_fluorescence_spline_bt),
as.numeric(input$y_range_max_validate_fluorescence_spline_bt))
}
if(any(input$y_range_min_derivative_validate_fluorescence_spline_bt == "",
input$y_range_max_derivative_validate_fluorescence_spline_bt == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_fluorescence_spline_bt),
as.numeric(input$y_range_max_derivative_validate_fluorescence_spline_bt))
}
if(any(input$x_range_min_validate_fluorescence_spline_bt == "",
input$x_range_max_validate_fluorescence_spline_bt == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_fluorescence_spline_bt),
as.numeric(input$x_range_max_validate_fluorescence_spline_bt))
}
try(
plot.flBootSpline(results,
pch = input$shape_type_validate_fluorescence_spline_bt,
cex.point = input$shape_size_validate_fluorescence_spline_bt,
cex.lab = input$axis_size_validate_fluorescence_spline_bt,
cex.axis = input$lab_size_validate_fluorescence_spline_bt,
lwd = input$line_width_validate_fluorescence_spline_bt,
y.lim = y.lim,
x.lim = x.lim,
y.lim.deriv = ylim.deriv,
deriv = input$plot_derivative_fluorescence_spline_bt,
combine = T,
colSpline = input$color_validate_fluorescence_spline_bt
)
)
})
output$validate_fluorescence_plot_spline_bt <- renderPlot({
validate_fluorescence_plot_spline_bt()
})
output$download_fluorescence_validate_spline_bt <- downloadHandler(
filename = function() {
paste("spline_fit_bootstrap_", gsub(" \\| ", "_", input$sample_validate_fluorescence_spline_bt), input$format_download_fluorescence_validate_spline_bt, sep="")
},
content = function(file) {
if(input$format_download_fluorescence_validate_spline_bt == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_fluorescence_validate_spline_bt,
height = input$height_download_fluorescence_validate_spline_bt)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_fluorescence_validate_spline_bt,
height = input$height_download_fluorescence_validate_spline_bt)
}
} else {
grDevices::png(file = file,
width = input$width_download_fluorescence_validate_spline_bt,
height = input$height_download_fluorescence_validate_spline_bt,
units = "in",
res = input$dpi_download_fluorescence_validate_spline_bt)
}
# Generate plot
results <-
results$fluorescence$flFit$flBootSplines[[ifelse(
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt == "1" ||
is.null(
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt
),
1,
selected_vals_validate_fluorescence$sample_validate_fluorescence_spline_bt
)]]
# Define x- and y-axis limits
if(any(input$y_range_min_validate_fluorescence_spline_bt == "",
input$y_range_max_validate_fluorescence_spline_bt == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_validate_fluorescence_spline_bt),
as.numeric(input$y_range_max_validate_fluorescence_spline_bt))
}
if(any(input$y_range_min_derivative_validate_fluorescence_spline_bt == "",
input$y_range_max_derivative_validate_fluorescence_spline_bt == "")){
ylim.deriv <- NULL
} else {
ylim.deriv <- c(as.numeric(input$y_range_min_derivative_validate_fluorescence_spline_bt),
as.numeric(input$y_range_max_derivative_validate_fluorescence_spline_bt))
}
if(any(input$x_range_min_validate_fluorescence_spline_bt == "",
input$x_range_max_validate_fluorescence_spline_bt == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_validate_fluorescence_spline_bt),
as.numeric(input$x_range_max_validate_fluorescence_spline_bt))
}
plot.flBootSpline(results,
pch = input$shape_type_validate_fluorescence_spline_bt,
cex.point = input$shape_size_validate_fluorescence_spline_bt,
cex.lab = input$axis_size_validate_fluorescence_spline_bt,
cex.axis = input$lab_size_validate_fluorescence_spline_bt,
lwd = input$line_width_validate_fluorescence_spline_bt,
y.lim = y.lim,
x.lim = x.lim,
y.lim.deriv = ylim.deriv,
deriv = input$plot_derivative_fluorescence_spline_bt
)
dev.off()
},
contentType = ifelse(input$format_download_fluorescence_validate_spline_bt == ".pdf", "image/pdf", "image/png")
)
# Visualize ####
## Growth Plots: #####
### Group Plots ####
selected_inputs_visualize_growth_group <- reactive({
results <- results$growth
if(is.null(results)) return("")
if(input$plot_group_averages_growth_group_plot){
select_samples <- results$expdesign$condition
} else {
select_samples <- results$expdesign$label
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_growth_group",
choices = selected_inputs_visualize_growth_group()
)
})
observe({
updateSelectInput(session,
inputId = "groups_visualize_growth_group",
choices = selected_inputs_visualize_growth_group()
)
})
growth_group_plot <- reactive({
results <- results$growth
if(is.null(input$custom_colors_group_plot) || is.na(input$custom_colors_group_plot) || input$custom_colors_group_plot == ""){
cols <- NULL
} else {
cols <- toupper(unlist(str_split(input$custom_colors_group_plot, ", |; |,|;")))
}
if(input$select_string_visualize_growth_group){
suppressWarnings(
plot.grofit(results,
data.type = input$data_type_growth_group_plot,
IDs = NULL,
names = input$select_samples_based_on_string_growth_group_plot,
conc = input$select_samples_based_on_concentration_growth_group_plot,
exclude.nm = input$exclude_samples_based_on_string_growth_group_plot,
exclude.conc = input$exclude_samples_based_on_concentration_growth_group_plot,
mean = input$plot_group_averages_growth_group_plot,
deriv = input$plot_derivative_growth_group_plot,
log.y = input$log_transform_y_axis_growth_group_plot,
x.lim = c(input$x_range_min_growth_group_plot, input$x_range_max_growth_group_plot),
y.lim = c(input$y_range_min_growth_group_plot,input$y_range_max_growth_group_plot),
y.lim.deriv = c(input$y_range_min_derivative_growth_group_plot, input$y_range_max_derivative_growth_group_plot),
y.title = input$y_axis_title_growth_group_plot,
x.title = input$x_axis_title_growth_group_plot,
y.title.deriv = input$y_axis_title_derivative_growth_group_plot,
n.ybreaks = input$nbreaks_growth_group_plot,
lwd = input$line_width_growth_group_plot,
basesize = input$base_size_growth_group_plot,
legend.position = input$legend_position_group_plot,
legend.ncol = input$legend_ncol_group_plot,
color_groups = input$color_groups_group_plot,
group_pals = input$color_palettes_group_plot,
colors = cols
)
)
}
else{
suppressWarnings(
plot.grofit(results,
data.type = input$data_type_growth_group_plot,
IDs = if(input$plot_group_averages_growth_group_plot){
input$groups_visualize_growth_group
}else{
input$samples_visualize_growth_group
},
names = NULL,
conc = input$select_samples_based_on_concentration_growth_group_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_samples_based_on_concentration_growth_group_plot,
mean = input$plot_group_averages_growth_group_plot,
deriv = input$plot_derivative_growth_group_plot,
log.y = input$log_transform_y_axis_growth_group_plot,
x.lim = c(input$x_range_min_growth_group_plot, input$x_range_max_growth_group_plot),
y.lim = c(input$y_range_min_growth_group_plot,input$y_range_max_growth_group_plot),
y.lim.deriv = c(input$y_range_min_derivative_growth_group_plot, input$y_range_max_derivative_growth_group_plot),
y.title = input$y_axis_title_growth_group_plot,
x.title = input$x_axis_title_growth_group_plot,
y.title.deriv = input$y_axis_title_derivative_growth_group_plot,
n.ybreaks = input$nbreaks_growth_group_plot,
lwd = input$line_width_growth_group_plot,
basesize = input$base_size_growth_group_plot,
legend.position = input$legend_position_group_plot,
color_groups = input$color_groups_group_plot,
group_pals = input$color_palettes_group_plot,
legend.ncol = input$legend_ncol_group_plot,
colors = cols
)
)
}
})
output$growth_group_plot <- renderPlot({
growth_group_plot()
})
observe({
if(input$plot_derivative_growth_group_plot && input$data_type_growth_group_plot == 'spline') h <- 9
else h <- 6
updateSelectInput(inputId = "height_download_growth_group_plot",
selected = h
)
})
output$download_growth_group_plot <- downloadHandler(
filename = function() {
paste("growth_group_plot", input$format_download_growth_group_plot, sep="")
},
content = function(file) {
if(input$format_download_growth_group_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_growth_group_plot,
height = input$height_download_growth_group_plot,
dpi = input$dpi_download_growth_group_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_growth_group_plot,
height = input$height_download_growth_group_plot,
dpi = input$dpi_download_growth_group_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_growth_group_plot,
height = input$height_download_growth_group_plot,
dpi = input$dpi_download_growth_group_plot,
device = png)
}
},
contentType = ifelse(input$format_download_growth_group_plot == ".pdf", "image/pdf", "image/png")
)
### DR Plots Spline ####
observe({
if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1 && results$growth$drFit$control$dr.method == "spline"){
showTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse_Spline")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse_Spline")
}
})
output$more_than_one_drfit_spline <- reactive({
if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drFittedSplines) > 1) return(TRUE)
else return(FALSE)
})
outputOptions(output, 'more_than_one_drfit_spline', suspendWhenHidden=FALSE)
dose_response_growth_plot_combined <- reactive({
results <- results$growth$drFit
plot.drFit(results,
combine = TRUE,
pch = input$shape_type_dose_response_growth_plot,
names = input$select_samples_based_on_string_dose_response_growth_plot,
exclude.nm = input$exclude_samples_based_on_string_dose_response_growth_plot,
y.lim = c(as.numeric(input$y_range_min_dose_response_growth_plot), as.numeric(input$y_range_max_dose_response_growth_plot)),
x.lim = c(as.numeric(input$x_range_min_dose_response_growth_plot), as.numeric(input$x_range_max_dose_response_growth_plot)),
y.title = input$y_axis_title_dose_response_growth_plot,
x.title = input$x_axis_title_dose_response_growth_plot,
cex.point = input$shape_size_dose_response_growth_plot,
basesize = input$base_size_dose_response_growth_plot,
lwd = input$line_width_dose_response_growth_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_growth_plot,
log.y = input$log_transform_y_axis_dose_response_growth_plot,
log.x = input$log_transform_x_axis_dose_response_growth_plot
)
})
rerun_dr_growth_buttons <- reactive({
list(input$rerun_dr_growth, input$rerun_dr_growth2, input$rerun_dr_growth3)
})
observeEvent(rerun_dr_growth_buttons(), {
if(input$rerun_dr_growth==0 && input$rerun_dr_growth2==0 && input$rerun_dr_growth3==0){
return()
}
select_options <- c()
if(any("l" %in% results$growth$control$fit.opt)) select_options <- c(select_options, 'mu.linfit', 'lambda.linfit', 'dY.linfit',
'A.linfit')
if(any("s" %in% results$growth$control$fit.opt)) select_options <- c(select_options, 'mu.spline', 'lambda.spline',
'A.spline', 'dY.spline', 'integral.spline')
if(any("m" %in% results$growth$control$fit.opt)) select_options <- c(select_options, 'mu.model', 'lambda.model', 'A.model', 'integral.model')
select_options
# display a modal dialog with a header, textinput and action buttons
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
selectInput(inputId = "dr_method_growth_rerun",
label = "Method",
choices = c("Dose-response models" = "model",
"Response spline fit" = "spline")
),
bsPopover(id = "dr_method_growth_rerun",
title = HTML("<em>dr.method</em>"),
placement = "right",
content = "Fit either a various dose-response models (Ritz et al., 2015) to response-vs.-concentration data and select the best model based on the lowest AIC, or apply a nonparametric (spline) fit."),
selectInput(inputId = "response_parameter_growth_rerun",
label = "Response Parameter",
choices = select_options),
bsPopover(id = "response_parameter_growth_rerun", title = HTML("<em>dr.parameter</em>"), content = "Choose the response parameter to be used for creating a dose response curve.", placement = "top"),
conditionalPanel(
condition = 'input.dr_method_growth_rerun == "spline"',
tags$div(title="Perform a log(x+1) transformation on concentration values.",
checkboxInput(inputId = 'log_transform_concentration_growth_rerun',
label = 'Log transform concentration')
),
tags$div(title="Perform a log(y+1) transformation on response values.",
checkboxInput(inputId = 'log_transform_response_growth_rerun',
label = 'Log transform response')
),
textInput(
inputId = 'smoothing_factor_growth_dr_rerun',
label = 'Smoothing factor dose-response splines',
value = "",
placeholder = "NULL (choose automatically)"
),
bsPopover(id = "smoothing_factor_growth_dr_rerun", title = HTML("<em>smooth.dr</em>"), content = "\\'spar\\' argument in the R function smooth.spline() used to create the dose response curve."),
QurvE:::numberInput(
inputId = 'number_of_bootstrappings_dr_growth_rerun',
label = 'Number of bootstrappings',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "number_of_bootstrappings_dr_growth_rerun", title = HTML("<em>nboot.dr</em>"), content = "Optional: Define the number of bootstrap samples for EC50 estimation. Bootstrapping resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to determine the EC50.")
), #conditionalPanel(condition = 'input.dr_method_growth_rerun == "spline"')
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.dr.growth', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right")
)
)
)
)
)
})
# Re-run dose-response analysis with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.dr.growth, {
if(!is.null(results$growth$drFit)){
showModal(modalDialog("Performing dose-reponse analysis...", footer = NULL))
# store previous fit in memory
selected_vals_validate_growth$restore_dr_growth <- results$growth$drFit
# Re-run fit and store in results object
gcTable <- results$growth$gcFit$gcTable
control <- results$growth$drFit$control
control_new <- results$growth$control <- control
control_new$dr.method <- input$dr_method_growth_rerun
control_new$dr.parameter <- input$response_parameter_growth_rerun
control_new$smooth.dr <- as.numeric(input$smoothing_factor_growth_dr_rerun)
if(control_new$smooth.dr == "" || is.na(control_new$smooth.dr))
control_new$smooth.dr <- NULL
control_new$nboot.dr <- input$number_of_bootstrappings_dr_growth_rerun
control_new$log.x.dr <- input$log_transform_concentration_growth_rerun
control_new$log.y.dr <- input$log_transform_response_growth_rerun
try(
results$growth$drFit <-
growth.drFit(gcTable, control = control_new)
)
# Show [Restore fit] button
show("restore_dr_growth")
show("restore_dr_growth2")
show("restore_dr_growth3")
}
removeModal()
})
restore_dr_growth_buttons <- reactive({
list(input$restore_dr_growth, input$restore_dr_growth2, input$restore_dr_growth3)
})
# Restore previous linear fit upon click on [Restore Fit]
observeEvent(restore_dr_growth_buttons(), {
if(input$restore_dr_growth==0 && input$restore_dr_growth2==0 && input$restore_dr_growth3==0){
return()
}
# store previous fit from memory
results$growth$drFit <- selected_vals_validate_growth$restore_dr_growth
hide("restore_dr_growth")
hide("restore_dr_growth2")
hide("restore_dr_growth3")
})
output$dose_response_growth_plot_combined <- renderPlot({
dose_response_growth_plot_combined()
})
output$download_dose_response_growth_plot_combined <- downloadHandler(
filename = function() {
paste("dose_response_growth_combined", input$format_download_dose_response_growth_plot_combined, sep="")
},
content = function(file) {
if(input$format_download_dose_response_growth_plot_combined == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_dose_response_growth_plot_combined,
height = input$height_download_dose_response_growth_plot_combined,
dpi = input$dpi_download_dose_response_growth_plot_combined,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_dose_response_growth_plot_combined,
height = input$height_download_dose_response_growth_plot_combined,
dpi = input$dpi_download_dose_response_growth_plot_combined,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_dose_response_growth_plot_combined,
height = input$height_download_dose_response_growth_plot_combined,
dpi = input$dpi_download_dose_response_growth_plot_combined,
device = png)
}
},
contentType = ifelse(input$format_download_dose_response_growth_plot_combined == ".pdf", "image/pdf", "image/png")
)
### DR Plots Model ####
observe({
if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1 && results$growth$drFit$control$dr.method == "model"){
showTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse_Model")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse_Model")
}
})
output$more_than_one_drfit_model <- reactive({
if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drFittedModels) > 1) return(TRUE)
else return(FALSE)
})
outputOptions(output, 'more_than_one_drfit_model', suspendWhenHidden=FALSE)
select_inputs_individual_plots_dose_response_growth_plot_model <- reactive({
if (length(results$growth$drFit)>1) names(results$growth$drFit$drFittedModels)
else return("")
})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_growth_plot_model",
choices = select_inputs_individual_plots_dose_response_growth_plot_model())
})
select_inputs_show_breaks_dose_response_growth_plot_model <- reactive({
if(!input$log_transform_x_axis_dose_response_growth_plot_model)
return(FALSE)
else
return(TRUE)
})
observe({
updateCheckboxInput(inputId = "show_break_dose_response_growth_plot_model",
value = select_inputs_show_breaks_dose_response_growth_plot_model())
})
dose_response_growth_plot_model <- reactive({
# Define log-transformation of axes
if(input$log_transform_x_axis_dose_response_growth_plot_model){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(!any(input$y_range_min_dose_response_growth_plot_model == "",
input$y_range_max_dose_response_growth_plot_model == "") &&
!any(is.null(input$y_range_min_dose_response_growth_plot_model),
is.null(input$y_range_max_dose_response_growth_plot_model))){
y.lim <- c(as.numeric(input$y_range_min_dose_response_growth_plot_model),
as.numeric(input$y_range_max_dose_response_growth_plot_model))
}
if(!any(input$x_range_min_dose_response_growth_plot_model == "",
input$x_range_max_dose_response_growth_plot_model == "") &&
!any(is.null(input$x_range_min_dose_response_growth_plot_model),
is.null(input$x_range_max_dose_response_growth_plot_model))){
x.lim <- c(as.numeric(input$x_range_min_dose_response_growth_plot_model),
as.numeric(input$x_range_max_dose_response_growth_plot_model))
}
results <- results$growth$drFit$drFittedModels[[ifelse(input$individual_plots_dose_response_growth_plot_model == "1" || is.null(input$individual_plots_dose_response_growth_plot_model), 1, input$individual_plots_dose_response_growth_plot_model)]]
try(
plot.drFitModel(results,
pch = input$shape_type_dose_response_growth_plot_model,
cex.point = input$shape_size_dose_response_growth_plot_model,
cex.lab = input$axis_size_dose_response_growth_plot_model,
cex.axis = input$lab_size_dose_response_growth_plot_model,
log = log,
lwd = input$line_width_dose_response_growth_plot_model,
ec50line = input$show_ec50_indicator_lines_dose_response_growth_plot_model,
y.lim = if(!exists("y.lim")){
substitute()
} else {
y.lim
},
x.lim = if(!exists("x.lim")){
substitute()
} else {
x.lim
},
broken = input$show_break_dose_response_growth_plot_model,
n.xbreaks = input$nbreaks_x_growth_dose_response_plot_model,
n.ybreaks = input$nbreaks_y_growth_dose_response_plot_model,
xlab = input$x_axis_title_dose_response_growth_plot_model,
ylab = input$y_axis_title_dose_response_growth_plot_model,
bp = ifelse(is.na(input$bp_dose_response_growth_plot_model)||(input$bp_dose_response_growth_plot_model == ""), rlang::missing_arg(), input$bp_dose_response_growth_plot_model)
)
)
})
output$dose_response_growth_plot_model <- renderPlot({
dose_response_growth_plot_model()
})
output$download_dose_response_growth_plot_model <- downloadHandler(
filename = function() {
paste("dose_response_growth_model", input$format_download_dose_response_growth_plot_model, sep="")
},
content = function(file) {
if(input$format_download_dose_response_growth_plot_individual == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_dose_response_growth_plot_model,
height = input$height_download_dose_response_growth_plot_model)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_dose_response_growth_plot_model,
height = input$height_download_dose_response_growth_plot_model)
}
} else {
grDevices::png(file = file,
width = input$width_download_dose_response_growth_plot_model,
height = input$height_download_dose_response_growth_plot_model,
units = "in",
res = input$dpi_download_dose_response_growth_plot_model)
}
# Define log-transformation of axes
if(input$log_transform_x_axis_dose_response_growth_plot_model){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(!any(input$y_range_min_dose_response_growth_plot_model == "",
input$y_range_max_dose_response_growth_plot_model == "") &&
!any(is.null(input$y_range_min_dose_response_growth_plot_model),
is.null(input$y_range_max_dose_response_growth_plot_model))){
y.lim <- c(as.numeric(input$y_range_min_dose_response_growth_plot_model),
as.numeric(input$y_range_max_dose_response_growth_plot_model))
}
if(!any(input$x_range_min_dose_response_growth_plot_model == "",
input$x_range_max_dose_response_growth_plot_model == "") &&
!any(is.null(input$x_range_min_dose_response_growth_plot_model),
is.null(input$x_range_max_dose_response_growth_plot_model))){
x.lim <- c(as.numeric(input$x_range_min_dose_response_growth_plot_model),
as.numeric(input$x_range_max_dose_response_growth_plot_model))
}
results <- results$growth$drFit$drFittedModels[[ifelse(input$individual_plots_dose_response_growth_plot_model == "1" || is.null(input$individual_plots_dose_response_growth_plot_model), 1, input$individual_plots_dose_response_growth_plot_model)]]
try(
plot.drFitModel(results,
pch = input$shape_type_dose_response_growth_plot_model,
cex.point = input$shape_size_dose_response_growth_plot_model,
cex.lab = input$axis_size_dose_response_growth_plot_model,
cex.axis = input$lab_size_dose_response_growth_plot_model,
log = log,
lwd = input$line_width_dose_response_growth_plot_model,
ec50line = input$show_ec50_indicator_lines_dose_response_growth_plot_model,
y.lim = ifelse(!exists("y.lim"), substitute(), y.lim),
x.lim = ifelse(!exists("x.lim"), substitute(), x.lim),
broken = input$show_break_dose_response_growth_plot_model,
n.xbreaks = input$nbreaks_x_growth_dose_response_plot_model,
n.ybreaks = input$nbreaks_y_growth_dose_response_plot_model,
xlab = input$x_axis_title_dose_response_growth_plot_model,
ylab = input$y_axis_title_dose_response_growth_plot_model,
bp = ifelse(is.na(input$bp_dose_response_growth_plot_model)||(input$bp_dose_response_growth_plot_model == ""), rlang::missing_arg(), input$bp_dose_response_growth_plot_model)
)
)
dev.off()
},
contentType = ifelse(input$format_download_dose_response_growth_plot_model == ".pdf", "image/pdf", "image/png")
)
### DR Plots Spline Individual ####
dose_response_growth_plot_individual <- reactive({
results <- results$growth$drFit$drFittedSplines[[ifelse(input$individual_plots_dose_response_growth_plot == "1" || is.null(input$individual_plots_dose_response_growth_plot), 1, input$individual_plots_dose_response_growth_plot)]]
# Define log-transformation of axes
if(input$log_transform_y_axis_dose_response_growth_plot &&
input$log_transform_x_axis_dose_response_growth_plot){
log <- "xy"
} else if(input$log_transform_y_axis_dose_response_growth_plot){
log <- "y"
} else if(input$log_transform_x_axis_dose_response_growth_plot){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(any(input$y_range_min_dose_response_growth_plot == "",
input$y_range_max_dose_response_growth_plot == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_dose_response_growth_plot),
as.numeric(input$y_range_max_dose_response_growth_plot))
}
if(any(input$x_range_min_dose_response_growth_plot == "",
input$x_range_max_dose_response_growth_plot == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_dose_response_growth_plot),
as.numeric(input$x_range_max_dose_response_growth_plot))
}
plot.drFitSpline(results,
combine = FALSE,
pch = input$shape_type_dose_response_growth_plot,
cex.point = input$shape_size_dose_response_growth_plot,
cex.lab = input$axis_size_dose_response_growth_plot,
cex.axis = input$lab_size_dose_response_growth_plot,
y.title = input$y_axis_title_dose_response_growth_plot,
x.title = input$x_axis_title_dose_response_growth_plot,
log = log,
lwd = input$line_width_dose_response_growth_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_growth_plot,
y.lim = y.lim,
x.lim = x.lim
)
})
output$dose_response_growth_plot_individual <- renderPlot({
dose_response_growth_plot_individual()
})
output$download_dose_response_growth_plot_individual <- downloadHandler(
filename = function() {
paste("dose_response_growth_", gsub(" \\| ", "_", input$individual_plots_dose_response_growth_plot), input$format_download_dose_response_growth_plot_individual, sep="")
},
content = function(file) {
if(input$format_download_dose_response_growth_plot_individual == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_dose_response_growth_plot_individual,
height = input$height_download_dose_response_growth_plot_individual)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_dose_response_growth_plot_individual,
height = input$height_download_dose_response_growth_plot_individual)
}
} else {
grDevices::png(file = file,
width = input$width_download_dose_response_growth_plot_individual,
height = input$height_download_dose_response_growth_plot_individual,
units = "in",
res = input$dpi_download_dose_response_growth_plot_individual)
}
results <- results$growth$drFit$drFittedSplines[[ifelse(input$individual_plots_dose_response_growth_plot == "1" || is.null(input$individual_plots_dose_response_growth_plot), 1, input$individual_plots_dose_response_growth_plot)]]
# Define log-transformation of axes
if(input$log_transform_y_axis_dose_response_growth_plot &&
input$log_transform_x_axis_dose_response_growth_plot){
log <- "xy"
} else if(input$log_transform_y_axis_dose_response_growth_plot){
log <- "y"
} else if(input$log_transform_x_axis_dose_response_growth_plot){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(any(input$y_range_min_dose_response_growth_plot == "",
input$y_range_max_dose_response_growth_plot == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_dose_response_growth_plot),
as.numeric(input$y_range_max_dose_response_growth_plot))
}
if(any(input$x_range_min_dose_response_growth_plot == "",
input$x_range_max_dose_response_growth_plot == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_dose_response_growth_plot),
as.numeric(input$x_range_max_dose_response_growth_plot))
}
plot.drFitSpline(results,
combine = FALSE,
pch = input$shape_type_dose_response_growth_plot,
cex.point = input$shape_size_dose_response_growth_plot,
cex.lab = input$axis_size_dose_response_growth_plot,
cex.axis = input$lab_size_dose_response_growth_plot,
y.title = input$y_axis_title_dose_response_growth_plot,
x.title = input$x_axis_title_dose_response_growth_plot,
log = log,
lwd = input$line_width_dose_response_growth_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_growth_plot,
y.lim = y.lim,
x.lim = x.lim
)
dev.off()
},
contentType = ifelse(input$format_download_dose_response_growth_plot_individual == ".pdf", "image/pdf", "image/png")
)
### DR Plots (Bootstrap) ####
observe({
if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1 && results$growth$control$nboot.dr > 1){
showTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse_Spline_bt")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponse_Spline_bt")
}
})
dose_response_growth_plot_individual_bt <- reactive({
results <- results$growth$drFit$drBootSplines[[ifelse(input$individual_plots_dose_response_growth_plot_bt == "1" || is.null(input$individual_plots_dose_response_growth_plot_bt), 1, input$individual_plots_dose_response_growth_plot_bt)]]
plot.drBootSpline(results,
pch = input$shape_type_dose_response_growth_plot_bt,
cex.point = input$shape_size_dose_response_growth_plot_bt,
cex.lab = input$axis_size_dose_response_growth_plot_bt,
cex.axis = input$lab_size_dose_response_growth_plot_bt,
lwd = input$line_width_dose_response_growth_plot_bt,
combine = TRUE
)
})
output$dose_response_growth_plot_individual_bt <- renderPlot({
dose_response_growth_plot_individual_bt()
})
output$download_dose_response_growth_plot_individual_bt <- downloadHandler(
filename = function() {
paste("dose_response_boot_growth_", gsub(" \\| ", "_", input$individual_plots_dose_response_growth_plot_bt), input$format_download_dose_response_growth_plot_individual_bt, sep="")
},
content = function(file) {
if(input$format_download_dose_response_growth_plot_individual_bt == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_dose_response_growth_plot_individual_bt,
height = input$height_download_dose_response_growth_plot_individual_bt)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_dose_response_growth_plot_individual_bt,
height = input$height_download_dose_response_growth_plot_individual_bt)
}
} else {
grDevices::png(file = file,
width = input$width_download_dose_response_growth_plot_individual_bt,
height = input$height_download_dose_response_growth_plot_individual_bt,
units = "in",
res = input$dpi_download_dose_response_growth_plot_individual_bt)
}
results <- results$growth$drFit$drBootSplines[[input$individual_plots_dose_response_growth_plot_bt]]
plot.drBootSpline(results,
pch = input$shape_type_dose_response_growth_plot_bt,
cex.point = input$shape_size_dose_response_growth_plot_bt,
cex.lab = input$axis_size_dose_response_growth_plot_bt,
cex.axis = input$lab_size_dose_response_growth_plot_bt,
lwd = input$line_width_dose_response_growth_plot_bt,
combine = TRUE
)
dev.off()
},
contentType = ifelse(input$format_download_dose_response_growth_plot_individual_bt == ".pdf", "image/pdf", "image/png")
)
### Parameter Plots ####
selected_inputs_visualize_parameter_growth_plot <- reactive({
results <- results$growth
if(is.null(results)) return("")
select_samples <- results$expdesign$condition
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_parameter_growth_plot",
choices = selected_inputs_visualize_parameter_growth_plot()
)
})
growth_parameter_plot <- reactive({
results <- results$growth
if (input$normalize_to_reference_growth_parameter_plot){
reference.conc <- as.numeric(input$reference_concentration_growth_parameter_plot)
reference.nm <- input$reference_condition_growth_parameter_plot
} else {
reference.conc <- NULL
reference.nm <- NULL
}
if(is.null(input$custom_colors_growth_parameter_plot) || is.na(input$custom_colors_growth_parameter_plot) || input$custom_colors_growth_parameter_plot == ""){
cols <- NULL
} else {
cols <- toupper(unlist(str_split(input$custom_colors_growth_parameter_plot, ", |; |,|;")))
}
if(input$select_string_visualize_parameter_growth_plot){
suppressWarnings(
plot.parameter(results,
param = input$parameter_parameter_growth_plot,
IDs = NULL,
names = input$select_sample_based_on_string_growth_parameter_plot,
conc = input$select_sample_based_on_concentration_growth_parameter_plot,
exclude.nm = input$exclude_sample_based_on_strings_growth_parameter_plot,
exclude.conc = input$exclude_sample_based_on_concentration_growth_parameter_plot,
reference.nm = reference.nm,
reference.conc = reference.conc,
shape.size = input$shape.size_growth_parameter_plot,
basesize = input$basesize_growth_parameter_plot,
label.size = input$label.size_growth_parameter_plot,
legend.position = input$legend_position_growth_parameter_plot,
legend.ncol = input$legend_ncol_growth_parameter_plot,
order_by_conc = input$sort_by_conc_growth_parameter_plot,
colors = cols
)
)
} else {
suppressWarnings(
plot.parameter(results,
param = input$parameter_parameter_growth_plot,
IDs = input$samples_visualize_parameter_growth_plot,
names = NULL,
conc = input$select_sample_based_on_concentration_growth_parameter_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_sample_based_on_concentration_growth_parameter_plot,
reference.nm = reference.nm,
reference.conc = reference.conc,
shape.size = input$shape.size_growth_parameter_plot,
basesize = input$basesize_growth_parameter_plot,
label.size = input$label.size_growth_parameter_plot,
legend.position = input$legend_position_growth_parameter_plot,
legend.ncol = input$legend_ncol_growth_parameter_plot,
order_by_conc = input$sort_by_conc_growth_parameter_plot,
colors = cols
)
)
}
})
output$growth_parameter_plot <- renderPlot({
growth_parameter_plot()
})
output$download_growth_parameter_plot <- downloadHandler(
filename = function() {
paste("growth_parameter_plot", input$format_download_growth_parameter_plot, sep="")
},
content = function(file) {
if(input$format_download_growth_parameter_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_growth_parameter_plot,
height = input$height_download_growth_parameter_plot,
dpi = input$dpi_download_growth_parameter_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_growth_parameter_plot,
height = input$height_download_growth_parameter_plot,
dpi = input$dpi_download_growth_parameter_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_growth_parameter_plot,
height = input$height_download_growth_parameter_plot,
dpi = input$dpi_download_growth_parameter_plot,
device = png)
}
},
contentType = ifelse(input$format_download_growth_parameter_plot == ".pdf", "image/pdf", "image/png")
)
selected_inputs_parameter_growth_parameter_plot <- reactive({
results <- results$growth
gc_parameters <- c()
if("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Growth rate (Spline)' = 'mu.spline',
'Growth rate phase 2 (Spline)' = 'mu.spline',
"Doubling time (Spline)" = "tD.spline",
"Doubling time phase 2 (Spline)" = "tD.spline",
'Lag time (Spline)' = 'lambda.spline',
'Maximum growth measurement (Spline)' = 'A.spline',
'ΔGrowth measurement (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
} else {
gc_parameters <- c(gc_parameters,
'Growth rate (Spline)' = 'mu.spline',
"Doubling time (Spline)" = "tD.spline",
'Lag time (Spline)' = 'lambda.spline',
'Maximum growth measurement (Spline)' = 'A.spline',
'ΔGrowth measurement (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
}
}
if("l" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Growth rate (linear fit)' = 'mu.linfit',
'Growth rate phase 2 (linear fit)' = 'mu.linfit',
"Doubling time (linear fit)" = "tD.linfit",
"Doubling time phase 2 (linear fit)" = "tD.linfit",
'Lag time (linear fit)' = 'lambda.linfit',
'Maximum growth measurement (linear fit)' = 'A.linfit',
'ΔGrowth measurement (linear fit)' = 'dY.linfit')
} else {
gc_parameters <- c(gc_parameters,
'Growth rate (linear fit)' = 'mu.linfit',
"Doubling time (linear fit)" = "tD.linfit",
'Lag time (linear fit)' = 'lambda.linfit',
'Maximum growth measurement (linear fit)' = 'A.linfit',
'ΔGrowth measurement (linear fit)' = 'dY.linfit')
}
}
if("m" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
gc_parameters <- c(gc_parameters,
'Growth rate (model)' = 'mu.model',
'Doubling time (model)' = 'tD.model',
'Lag time (model)' = 'lambda.model',
'Maximum growth measurement (model)' = 'A.model')
}
gc_parameters
})
selected_inputs_reference_condition_growth_parameter_plot <- reactive({
results <- results$growth
results$expdesign$condition
})
select_inputs_reference_concentration_growth_parameter_plot <- reactive({
results <- results$growth
results$expdesign$concentration
})
select_inputs_individual_plots_dose_response_growth_plot <- reactive({
if (length(results$growth$drFit)>1) names(results$growth$drFit$drFittedSplines)
else return("")
})
observe({
updateSelectInput(inputId = "parameter_parameter_growth_plot",
choices = selected_inputs_parameter_growth_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_condition_growth_parameter_plot",
choices = selected_inputs_reference_condition_growth_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_concentration_growth_parameter_plot",
choices = select_inputs_reference_concentration_growth_parameter_plot()
)})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_growth_plot",
choices = select_inputs_individual_plots_dose_response_growth_plot())
})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_growth_plot_bt",
choices = select_inputs_individual_plots_dose_response_growth_plot_bt())
})
### Grid Plot ####
observe({
if(!exists("output$more_than_two_conc") || is.null(output$more_than_two_conc))
return(NULL)
if(!output$more_than_two_conc){
updateSelectInput(session = session,
inputId = "sort_by_conc_growth_grid_plot",
selected = FALSE)
}
})
observe({
if(input$select_string_visualize_growth_grid)
updateSelectInput(session = session,
inputId = "order_matters_visualize_growth_grid",
selected = FALSE)
})
selected_inputs_parameter_growth_grid_plot <- reactive({
results <- results$growth
gc_parameters <- c()
if("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Growth rate (Spline)' = 'mu.spline',
'Growth rate phase 2 (Spline)' = 'mu.spline',
"Doubling time (Spline)" = "tD.spline",
"Doubling time phase 2 (Spline)" = "tD.spline",
'Lag time (Spline)' = 'lambda.spline',
'Maximum growth measurement (Spline)' = 'A.spline',
'ΔGrowth measurement (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
} else {
gc_parameters <- c(gc_parameters,
'Growth rate (Spline)' = 'mu.spline',
"Doubling time (Spline)" = "tD.spline",
'Lag time (Spline)' = 'lambda.spline',
'Maximum growth measurement (Spline)' = 'A.spline',
'ΔGrowth measurement (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
}
}
if("l" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Growth rate (linear fit)' = 'mu.linfit',
'Growth rate phase 2 (linear fit)' = 'mu.linfit',
"Doubling time (linear fit)" = "tD.linfit",
"Doubling time phase 2 (linear fit)" = "tD.linfit",
'Lag time (linear fit)' = 'lambda.linfit',
'Maximum growth measurement (linear fit)' = 'A.linfit',
'ΔGrowth measurement (linear fit)' = 'dY.linfit')
} else {
gc_parameters <- c(gc_parameters,
'Growth rate (linear fit)' = 'mu.linfit',
"Doubling time (linear fit)" = "tD.linfit",
'Lag time (linear fit)' = 'lambda.linfit',
'Maximum growth measurement (linear fit)' = 'A.linfit',
'ΔGrowth measurement (linear fit)' = 'dY.linfit')
}
}
if("m" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
gc_parameters <- c(gc_parameters,
'Growth rate (model)' = 'mu.model',
'Doubling time (model)' = 'tD.model',
'Lag time (model)' = 'lambda.model',
'Maximum growth measurement (model)' = 'A.model')
}
gc_parameters
})
observe({
updateSelectInput(inputId = "parameter_parameter_grid_plot",
choices = selected_inputs_parameter_growth_grid_plot()
)})
selected_inputs_visualize_growth_grid <- reactive({
results <- results$growth
if(is.null(results)) return("")
if(input$plot_group_averages_growth_grid_plot){
select_samples <- results$expdesign$condition
} else {
select_samples <- results$expdesign$label
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_growth_grid",
choices = selected_inputs_visualize_growth_grid()
)
})
observe({
updateSelectInput(session,
inputId = "groups_visualize_growth_grid",
choices = selected_inputs_visualize_growth_grid()
)
})
growth_grid_plot <- reactive({
results <- results$growth
if(input$select_string_visualize_growth_grid){
suppressWarnings(
plot.grid(results,
data.type = input$data_type_growth_grid_plot,
IDs = NULL,
sort_by_ID = FALSE,
names = input$select_samples_based_on_string_growth_grid_plot,
conc = input$select_samples_based_on_concentration_growth_grid_plot,
exclude.nm = input$exclude_samples_based_on_string_growth_grid_plot,
exclude.conc = input$exclude_samples_based_on_concentration_growth_grid_plot,
mean = input$plot_group_averages_growth_grid_plot,
log.y = input$log_transform_y_axis_growth_grid_plot,
x.lim = c(input$x_range_min_growth_grid_plot, input$x_range_max_growth_grid_plot),
y.lim = c(input$y_range_min_growth_grid_plot,input$y_range_max_growth_grid_plot),
y.title = input$y_axis_title_growth_grid_plot,
x.title = input$x_axis_title_growth_grid_plot,
n.ybreaks = input$nbreaks_growth_grid_plot,
lwd = input$line_width_growth_grid_plot,
basesize = input$base_size_growth_grid_plot,
pal = input$color_palettes_grid_plot,
invert.pal = input$invert_color_palette_grid_plot,
sort_by_conc = input$sort_by_conc_growth_grid_plot,
legend.lim = c(input$legend_lim_min_growth_grid_plot, input$legend_lim_max_growth_grid_plot),
nrow = input$nrows_growth_grid_plot,
param = input$parameter_parameter_grid_plot
)
)
}
else{
suppressWarnings(
plot.grid(results,
data.type = input$data_type_growth_grid_plot,
IDs = if(input$plot_group_averages_growth_grid_plot){
input$groups_visualize_growth_grid
}else{
input$samples_visualize_growth_grid
},
sort_by_ID = input$order_matters_visualize_growth_grid,
names = NULL,
conc = input$select_samples_based_on_concentration_growth_grid_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_samples_based_on_concentration_growth_grid_plot,
mean = input$plot_group_averages_growth_grid_plot,
log.y = input$log_transform_y_axis_growth_grid_plot,
x.lim = c(input$x_range_min_growth_grid_plot, input$x_range_max_growth_grid_plot),
y.lim = c(input$y_range_min_growth_grid_plot,input$y_range_max_growth_grid_plot),
y.title = input$y_axis_title_growth_grid_plot,
x.title = input$x_axis_title_growth_grid_plot,
n.ybreaks = input$nbreaks_growth_grid_plot,
lwd = input$line_width_growth_grid_plot,
basesize = input$base_size_growth_grid_plot,
pal = input$color_palettes_grid_plot,
invert.pal = input$invert_color_palette_grid_plot,
sort_by_conc = input$sort_by_conc_growth_grid_plot,
legend.lim = c(input$legend_lim_min_growth_grid_plot, input$legend_lim_max_growth_grid_plot),
nrow = input$nrows_growth_grid_plot,
param = input$parameter_parameter_grid_plot
)
)
}
})
output$growth_grid_plot <- renderPlot({
growth_grid_plot()
})
output$download_growth_grid_plot <- downloadHandler(
filename = function() {
paste("growth_grid_plot", input$format_download_growth_grid_plot, sep="")
},
content = function(file) {
if(input$format_download_growth_grid_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_growth_grid_plot,
height = input$height_download_growth_grid_plot,
dpi = input$dpi_download_growth_grid_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_growth_grid_plot,
height = input$height_download_growth_grid_plot,
dpi = input$dpi_download_growth_grid_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_growth_grid_plot,
height = input$height_download_growth_grid_plot,
dpi = input$dpi_download_growth_grid_plot,
device = png)
}
},
contentType = ifelse(input$format_download_growth_grid_plot == ".pdf", "image/pdf", "image/png")
)
### DR Parameter Plots ####
observe({
if(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1){
showTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponseParameters")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Growth", target = "tabPanel_Visualize_Growth_DoseResponseParameters")
}
})
growth_dr_parameter_plot <- reactive({
results <- results$growth
if (input$normalize_to_reference_growth_dr_parameter_plot){
reference.nm <- input$reference_condition_growth_dr_parameter_plot
} else {
reference.nm <- NULL
}
param <- input$parameter_dr_parameter_growth_plot
suppressWarnings(
plot.dr_parameter(results,
param = param,
names = input$select_sample_based_on_string_growth_dr_parameter_plot,
exclude.nm = input$exclude_sample_based_on_strings_growth_dr_parameter_plot,
reference.nm = reference.nm,
basesize = input$basesize_growth_dr_parameter_plot,
label.size = input$label.size_growth_dr_parameter_plot
)
)
})
output$growth_dr_parameter_plot <- renderPlot({
growth_dr_parameter_plot()
})
output$download_growth_dr_parameter_plot <- downloadHandler(
filename = function() {
paste("growth_dr_parameter_plot", input$format_download_growth_dr_parameter_plot, sep="")
},
content = function(file) {
if(input$format_download_growth_dr_parameter_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_growth_dr_parameter_plot,
height = input$height_download_growth_dr_parameter_plot,
dpi = input$dpi_download_growth_dr_parameter_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_growth_dr_parameter_plot,
height = input$height_download_growth_dr_parameter_plot,
dpi = input$dpi_download_growth_dr_parameter_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_growth_dr_parameter_plot,
height = input$height_download_growth_dr_parameter_plot,
dpi = input$dpi_download_growth_dr_parameter_plot,
device = png)
}
},
contentType = ifelse(input$format_download_growth_dr_parameter_plot == ".pdf", "image/pdf", "image/png")
)
selected_inputs_parameter_growth_dr_parameter_plot <- reactive({
results <- results$growth
if(!is.null(results)){
if(results$control$dr.method == "spline"){
dr_parameters <- c('Response(EC50)' = 'yEC50','EC50' = 'EC50')
if(!is.null(input$number_of_bootstrappings_dr_growth) && !is.na(input$number_of_bootstrappings_dr_growth) && input$number_of_bootstrappings_dr_growth != "" && as.numeric(input$number_of_bootstrappings_dr_growth) > 1){
dr_parameters <- c(dr_parameters, 'Response(EC50) - Bootstrap' = 'drboot.meanEC50', 'EC50 - Bootstrap' = 'drboot.meanEC50y')
}
if(input$log_transform_response_growth){
dr_parameters <- c(dr_parameters, 'Original response(EC50)' = 'yEC50.orig')
}
if(input$log_transform_concentration_growth){
dr_parameters <- c(dr_parameters, 'Original EC50' = 'EC50.orig')
}
} else {
dr_parameters <- c('Response(EC50)' = 'yEC50','EC50' = 'EC50.Estimate')
}
dr_parameters
}
})
selected_inputs_reference_condition_growth_dr_parameter_plot <- reactive({
results <- results$growth
results$expdesign$condition
})
select_inputs_individual_plots_dose_response_growth_plot_bt <- reactive({
if (length(results$growth$drFit)>1 && results$growth$control$nboot.dr > 1) names(results$growth$drFit$drBootSplines)
else return("")
})
observe({
updateSelectInput(inputId = "parameter_dr_parameter_growth_plot",
choices = selected_inputs_parameter_growth_dr_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_condition_growth_dr_parameter_plot",
choices = selected_inputs_reference_condition_growth_dr_parameter_plot()
)})
## Fluorescence Plots: #####
### Group Plots ####
selected_inputs_visualize_fluorescence_group <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
if(input$plot_group_averages_fluorescence_group_plot){
select_samples <- results$expdesign$condition
} else {
select_samples <- results$expdesign$label
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_fluorescence_group",
choices = selected_inputs_visualize_fluorescence_group()
)
})
observe({
updateSelectInput(session,
inputId = "groups_visualize_fluorescence_group",
choices = selected_inputs_visualize_fluorescence_group()
)
})
observe({
if (length(results$fluorescence$flFit)>1){
if(input$data_type_fluorescence_group_plot == "raw") y_axis <- "Fluorescence"
if(input$data_type_fluorescence_group_plot == "spline" && results$fluorescence$control$norm_fl){
y_axis <- "Normalized fluorescence"
}
if(input$data_type_fluorescence_group_plot == "spline" && !results$fluorescence$control$norm_fl){
y_axis <- "Fluorescence"
}
if(input$data_type_fluorescence_group_plot == "norm.fl") y_axis <- "Normalized fluorescence"
}
else {
y_axis <- ""
}
updateSelectInput(inputId = "y_axis_title_fluorescence_group_plot",
selected = y_axis
)
})
observe({
if (length(results$fluorescence$flFit)>1){
if(input$data_type_fluorescence_group_plot == "norm.fl" || input$data_type_fluorescence_group_plot == "raw" ){ # || data.type == "raw2" || data.type == "norm.fl2"
x_axis <- "Time"
} else if (results$fluorescence$control$x_type == "growth"){
x_axis <- "growth"
} else {
x_axis <- "Time"
}
}
else {
x_axis <- ""
}
updateSelectInput(inputId = "x_axis_title_fluorescence_group_plot",
selected = x_axis
)
})
observe({
if (length(results$fluorescence$flFit)>1){
if(input$data_type_fluorescence_group_plot == "spline" && results$fluorescence$control$x_type == "growth"){
shinyjs::hide(id = "plot_group_averages_fluorescence_group_plot")
} else {
shinyjs::show(id = "plot_group_averages_fluorescence_group_plot")
}
}
})
selected_inputs_fluorescence_group_plot_data_type <- reactive({
results <- results$fluorescence
selection <- c()
if(length(results$data$fluorescence) > 1){
selection <- c(selection, "Raw fluorescence" = "raw")
}
if(length(results$data$norm.fluorescence) > 1){
selection <- c(selection, "Normalized FL" = "norm.fl")
}
if(length(results$data$fluorescence) > 1 && "s" %in% results$control$fit.opt){
selection <- c(selection, "Spline fits FL" = "spline")
}
selection
})
output$fluorescence_group_plot <- renderPlot({
results <- results$fluorescence
if(is.null(input$custom_colors_fluorescence_group_plot) || is.na(input$custom_colors_fluorescence_group_plot) || input$custom_colors_fluorescence_group_plot == ""){
cols <- NULL
} else {
cols <- toupper(unlist(str_split(input$custom_colors_fluorescence_group_plot, ", |; |,|;")))
}
if(input$data_type_fluorescence_group_plot == "spline" && results$control$x_type == "growth"){
plot_mean <- FALSE
} else {
plot_mean <- input$plot_group_averages_fluorescence_group_plot
}
if(input$select_string_visualize_fluorescence_group){
suppressWarnings(
plot.flFitRes(
results,
data.type = input$data_type_fluorescence_group_plot,
IDs = NULL,
names = input$select_samples_based_on_string_fluorescence_group_plot,
conc = input$select_samples_based_on_concentration_fluorescence_group_plot,
exclude.nm = input$exclude_samples_based_on_string_fluorescence_group_plot,
exclude.conc = input$exclude_samples_based_on_concentration_fluorescence_group_plot,
mean = plot_mean,
deriv = input$plot_derivative_fluorescence_group_plot,
log.y = input$log_transform_y_axis_fluorescence_group_plot,
x.lim = c(input$x_range_min_fluorescence_group_plot, input$x_range_max_fluorescence_group_plot),
y.lim = c(input$y_range_min_fluorescence_group_plot,input$y_range_max_fluorescence_group_plot),
y.lim.deriv = c(input$y_range_min_derivative_fluorescence_group_plot, input$y_range_max_derivative_fluorescence_group_plot),
y.title = input$y_axis_title_fluorescence_group_plot,
x.title = input$x_axis_title_fluorescence_group_plot,
y.title.deriv = input$y_axis_title_derivative_fluorescence_group_plot,
lwd = input$line_width_fluorescence_group_plot,
basesize = input$base_size_fluorescence_group_plot,
color_groups = input$color_groups_fluorescence_group_plot,
n.ybreaks = input$nbreaks_fluorescence_group_plot,
legend.position = input$legend_position_fluorescence_group_plot,
legend.ncol = input$legend_ncol_fluorescence_group_plot,
group_pals = input$color_palettes_fluorescence_group_plot,
colors = cols
)
)
} else {
suppressWarnings(
plot.flFitRes(
results,
data.type = input$data_type_fluorescence_group_plot,
IDs = if(input$plot_group_averages_fluorescence_group_plot){
input$groups_visualize_fluorescence_group
}else{
input$samples_visualize_fluorescence_group
},
names = NULL,
conc = input$select_samples_based_on_concentration_fluorescence_group_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_samples_based_on_concentration_fluorescence_group_plot,
mean = plot_mean,
deriv = input$plot_derivative_fluorescence_group_plot,
log.y = input$log_transform_y_axis_fluorescence_group_plot,
x.lim = c(input$x_range_min_fluorescence_group_plot, input$x_range_max_fluorescence_group_plot),
y.lim = c(input$y_range_min_fluorescence_group_plot,input$y_range_max_fluorescence_group_plot),
y.lim.deriv = c(input$y_range_min_derivative_fluorescence_group_plot, input$y_range_max_derivative_fluorescence_group_plot),
y.title = input$y_axis_title_fluorescence_group_plot,
x.title = input$x_axis_title_fluorescence_group_plot,
y.title.deriv = input$y_axis_title_derivative_fluorescence_group_plot,
lwd = input$line_width_fluorescence_group_plot,
basesize = input$base_size_fluorescence_group_plot,
n.ybreaks = input$nbreaks_fluorescence_group_plot,
color_groups = input$color_groups_fluorescence_group_plot,
legend.position = input$legend_position_fluorescence_group_plot,
legend.ncol = input$legend_ncol_fluorescence_group_plot,
colors = cols,
group_pals = input$color_palettes_fluorescence_group_plot
)
)
}
})
observe({
if(input$plot_derivative_fluorescence_group_plot && (input$data_type_fluorescence_group_plot == 'spline' )) h <- 9
else h <- 6
updateSelectInput(inputId = "height_download_fluorescence_group_plot",
selected = h
)
})
output$download_fluorescence_group_plot <- downloadHandler(
filename = function() {
paste("fluorescence_group_plot", input$format_download_fluorescence_group_plot, sep="")
},
content = function(file) {
if(input$format_download_growth_dr_parameter_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_fluorescence_group_plot,
height = input$height_download_fluorescence_group_plot,
dpi = input$dpi_download_fluorescence_group_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_fluorescence_group_plot,
height = input$height_download_fluorescence_group_plot,
dpi = input$dpi_download_fluorescence_group_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_fluorescence_group_plot,
height = input$height_download_fluorescence_group_plot,
dpi = input$dpi_download_fluorescence_group_plot,
device = png)
}
},
contentType = ifelse(input$format_download_fluorescence_group_plot == ".pdf", "image/pdf", "image/png")
)
observe({
updateSelectInput(inputId = "data_type_fluorescence_group_plot",
choices = selected_inputs_fluorescence_group_plot_data_type())
})
### DR Plots Spline ####
# Hide Spline dose-response plot if dr.method != "spline"
observe({
if(length(results$fluorescence$drFit) > 1 &&
length(results$fluorescence$drFit$drTable) > 1 &&
results$fluorescence$drFit$control$dr.method == "spline"){
showTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponse_spline")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponse_spline")
}
})
output$dose_response_plot_fluorescence_combined <- renderPlot({
results <- results$fluorescence$drFit
plot.drFit(results,
combine=TRUE,
names = input$select_samples_based_on_string_dose_response_fluorescence_plot,
exclude.nm = input$exclude_samples_based_on_string_dose_response_fluorescence_plot,
pch = input$shape_type_dose_response_fluorescence_plot,
cex.point = input$shape_size_dose_response_fluorescence_plot,
basesize = input$base_size_dose_response_fluorescence_plot,
lwd = input$line_width_dose_response_fluorescence_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_fluorescence_plot,
y.lim = c(as.numeric(input$y_range_min_dose_response_fluorescence_plot), as.numeric(input$y_range_max_dose_response_fluorescence_plot)),
x.lim = c(as.numeric(input$x_range_min_dose_response_fluorescence_plot), as.numeric(input$x_range_max_dose_response_fluorescence_plot)),
y.title = input$y_axis_title_dose_response_fluorescence_plot,
x.title = input$x_axis_title_dose_response_fluorescence_plot,
log.y = input$log_transform_y_axis_dose_response_fluorescence_plot,
log.x = input$log_transform_x_axis_dose_response_fluorescence_plot
)
})
rerun_dr_fluorescence_buttons <- reactive({
list(input$rerun_dr_fluorescence, input$rerun_dr_fluorescence2, input$rerun_dr_fluorescence3)
})
observeEvent(rerun_dr_fluorescence_buttons(), {
if(input$rerun_dr_fluorescence==0 && input$rerun_dr_fluorescence2==0 && input$rerun_dr_fluorescence3==0){
return()
}
select_options <- c()
if(any("l" %in% results$fluorescence$control$fit.opt)) select_options <- c(select_options, 'max_slope.linfit', 'lambda.linfit', 'dY.linfit',
'A.linfit')
if(any("s" %in% results$fluorescence$control$fit.opt)) select_options <- c(select_options, 'max_slope.spline', 'lambda.spline',
'A.spline', 'dY.spline', 'integral.spline')
# display a modal dialog with a header, textinput and action buttons
showModal(
modalDialog(
tags$h2('Please enter adjusted parameters'),
selectInput(inputId = "dr_method_fluorescence_rerun",
label = "Method",
choices = c("Biosensor response model" = "model",
"Response spline fit" = "spline")
),
bsPopover(id = "dr_method_fluorescence_rerun",
placement = "right",
title = HTML("<em>dr.method</em>"),
content = "Fit either a biosensor response model (Meyer et al., 2019) to response-vs.-concentration data, or apply a nonparametric (spline) fit."
),
selectInput(inputId = "response_parameter_fluorescence_rerun",
label = "Response Parameter",
choices = select_options),
tags$div(title="Perform a log(x+1) transformation on concentration values.",
checkboxInput(inputId = 'log_transform_concentration_fluorescence_rerun',
label = 'Log transform concentration')
),
tags$div(title="Perform a log(y+1) transformation on response values.",
checkboxInput(inputId = 'log_transform_response_fluorescence_rerun',
label = 'Log transform response')
),
bsPopover(id = "response_parameter_fluorescence_rerun", title = HTML("<em>dr.parameter</em>"), content = "Choose the response parameter to be used for creating a dose response curve.", placement = "top"),
conditionalPanel(
condition = 'input.dr_method_fluorescence_rerun == "spline"',
textInput(
inputId = 'smoothing_factor_fluorescence_dr_rerun',
label = 'Smoothing factor dose-response splines',
value = "",
placeholder = "NULL (choose automatically)"
),
bsPopover(id = "smoothing_factor_fluorescence_dr_rerun", title = HTML("<em>smooth.dr</em>"), content = "\\'spar\\' argument in the R function smooth.spline() used to create the dose response curve."),
QurvE:::numberInput(
inputId = 'number_of_bootstrappings_dr_fluorescence_rerun',
label = 'Number of bootstrappings',
value = 0,
min = NA,
max = NA,
placeholder = 0
),
bsPopover(id = "number_of_bootstrappings_dr_fluorescence_rerun", title = HTML("<em>nboot.dr</em>"), content = "Optional: Define the number of bootstrap samples for EC50 estimation. Bootstrapping resamples the values in a dataset with replacement and performs a spline fit for each bootstrap sample to determine the EC50.")
), #conditionalPanel(condition = 'input.dr_method_fluorescence_rerun == "spline"')
footer=tagList(
fluidRow(
column(12,
div(
actionButton('submit.rerun.dr.fluorescence', 'Submit'),
style="float:right"),
div(
modalButton('cancel'),
style="float:right")
)
)
)
)
)
})
# Re-run selected linear fit with user-defined parameters upon click on 'submit'
observeEvent(input$submit.rerun.dr.fluorescence, {
if(!is.null(results$fluorescence$drFit)){
showModal(modalDialog("Performing dose-reponse analysis...", footer = NULL))
# store previous fit in memory
selected_vals_validate_fluorescence$restore_dr_fluorescence <- results$fluorescence$drFit
# Re-run fit and store in results object
flTable <- results$fluorescence$flFit$flTable
control <- results$fluorescence$drFit$control
control_new <- results$fluorescence$control <- control
control_new$dr.method <- input$dr_method_fluorescence_rerun
control_new$dr.parameter <- input$response_parameter_fluorescence_rerun
control_new$smooth.dr <- input$smoothing_factor_fluorescence_dr_rerun
if(control_new$smooth.dr == "" || is.na(control_new$smooth.dr))
control_new$smooth.dr <- NULL
control_new$nboot.dr <- input$number_of_bootstrappings_dr_fluorescence_rerun
control_new$log.x.dr <- input$log_transform_concentration_fluorescence_rerun
control_new$log.y.dr <- input$log_transform_response_fluorescence_rerun
if(control_new$dr.method == "spline"){
try(
results$fluorescence$drFit <-
growth.drFit(flTable, control = control_new)
)
} else {
try(
results$fluorescence$drFit <-
fl.drFit(flTable, control = control_new)
)
}
# Show [Restore fit] button
show("restore_dr_fluorescence")
show("restore_dr_fluorescence2")
show("restore_dr_fluorescence3")
}
removeModal()
})
restore_dr_fluorescence_buttons <- reactive({
list(input$restore_dr_fluorescence, input$restore_dr_fluorescence2, input$restore_dr_fluorescence3)
})
# Restore previous linear fit upon click on [Restore Fit]
observeEvent(restore_dr_fluorescence_buttons(), {
if(input$restore_dr_fluorescence==0 && input$restore_dr_fluorescence2==0 && input$restore_dr_fluorescence3==0){
return()
}
# store previous fit from memory
results$fluorescence$drFit <- selected_vals_validate_fluorescence$restore_dr_fluorescence
hide("restore_dr_fluorescence")
hide("restore_dr_fluorescence2")
hide("restore_dr_fluorescence3")
})
output$download_dose_response_plot_fluorescence_combined <- downloadHandler(
filename = function() {
paste("dose_response_fluorescence_combined", input$format_download_dose_response_plot_fluorescence_combined, sep="")
},
content = function(file) {
if(input$format_download_dose_response_plot_fluorescence_combined == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_dose_response_plot_fluorescence_combined,
height = input$height_download_dose_response_plot_fluorescence_combined,
dpi = input$dpi_download_dose_response_plot_fluorescence_combined,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_dose_response_plot_fluorescence_combined,
height = input$height_download_dose_response_plot_fluorescence_combined,
dpi = input$dpi_download_dose_response_plot_fluorescence_combined,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_dose_response_plot_fluorescence_combined,
height = input$height_download_dose_response_plot_fluorescence_combined,
dpi = input$dpi_download_dose_response_plot_fluorescence_combined,
device = png)
}
},
contentType = ifelse(input$format_download_dose_response_plot_fluorescence_combined == ".pdf", "image/pdf", "image/png")
)
### DR Plots Spline Individual ####
output$dose_response_fluorescence_plot_individual <- renderPlot({
results <- results$fluorescence$drFit$drFittedSplines[[ifelse(input$individual_plots_dose_response_fluorescence_plot == "1" || is.null(input$individual_plots_dose_response_fluorescence_plot), 1, input$individual_plots_dose_response_fluorescence_plot)]]
# Define log-transformation of axes
if(input$log_transform_y_axis_dose_response_fluorescence_plot &&
input$log_transform_x_axis_dose_response_fluorescence_plot){
log <- "xy"
} else if(input$log_transform_y_axis_dose_response_fluorescence_plot){
log <- "y"
} else if(input$log_transform_x_axis_dose_response_fluorescence_plot){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(any(input$y_range_min_dose_response_fluorescence_plot == "",
input$y_range_max_dose_response_fluorescence_plot == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_dose_response_fluorescence_plot),
as.numeric(input$y_range_max_dose_response_fluorescence_plot))
}
if(any(input$x_range_min_dose_response_fluorescence_plot == "",
input$x_range_max_dose_response_fluorescence_plot == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_dose_response_fluorescence_plot),
as.numeric(input$x_range_max_dose_response_fluorescence_plot))
}
plot.drFitSpline(results,
combine=FALSE,
pch = input$shape_type_dose_response_fluorescence_plot,
cex.point = input$shape_size_dose_response_fluorescence_plot,
lwd = input$line_width_dose_response_fluorescence_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_fluorescence_plot,
cex.lab = input$axis_size_dose_response_fluorescence_plot,
cex.axis = input$lab_size_dose_response_fluorescence_plot,
y.title = input$y_axis_title_dose_response_fluorescence_plot,
x.title = input$x_axis_title_dose_response_fluorescence_plot,
log = log,
y.lim = y.lim,
x.lim = x.lim)
})
output$download_dose_response_fluorescence_plot_individual <- downloadHandler(
filename = function() {
paste("dose_response_fluorescence_", gsub(" \\| ", "_", input$individual_plots_dose_response_fluorescence_plot), input$format_download_dose_response_fluorescence_plot_individual, sep="")
},
content = function(file) {
if(input$format_download_dose_response_fluorescence_plot_individual == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_dose_response_fluorescence_plot_individual,
height = input$height_download_dose_response_fluorescence_plot_individual)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_dose_response_fluorescence_plot_individual,
height = input$height_download_dose_response_fluorescence_plot_individual)
}
} else {
grDevices::png(file = file,
width = input$width_download_dose_response_fluorescence_plot_individual,
height = input$height_download_dose_response_fluorescence_plot_individual,
units = "in",
res = input$dpi_download_dose_response_fluorescence_plot_individual)
}
results <- results$fluorescence$drFit$drFittedSplines[[ifelse(input$individual_plots_dose_response_fluorescence_plot == "1" || is.null(input$individual_plots_dose_response_fluorescence_plot), 1, input$individual_plots_dose_response_fluorescence_plot)]]
# Define log-transformation of axes
if(input$log_transform_y_axis_dose_response_fluorescence_plot &&
input$log_transform_x_axis_dose_response_fluorescence_plot){
log <- "xy"
} else if(input$log_transform_y_axis_dose_response_fluorescence_plot){
log <- "y"
} else if(input$log_transform_x_axis_dose_response_fluorescence_plot){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(any(input$y_range_min_dose_response_fluorescence_plot == "",
input$y_range_max_dose_response_fluorescence_plot == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_dose_response_fluorescence_plot),
as.numeric(input$y_range_max_dose_response_fluorescence_plot))
}
if(any(input$x_range_min_dose_response_fluorescence_plot == "",
input$x_range_max_dose_response_fluorescence_plot == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_dose_response_fluorescence_plot),
as.numeric(input$x_range_max_dose_response_fluorescence_plot))
}
plot.drFitSpline(results,
combine=FALSE,
pch = input$shape_type_dose_response_fluorescence_plot,
cex.point = input$shape_size_dose_response_fluorescence_plot,
lwd = input$line_width_dose_response_fluorescence_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_fluorescence_plot,
cex.lab = input$axis_size_dose_response_fluorescence_plot,
cex.axis = input$lab_size_dose_response_fluorescence_plot,
y.title = input$y_axis_title_dose_response_fluorescence_plot,
x.title = input$x_axis_title_dose_response_fluorescence_plot,
log = log,
y.lim = y.lim,
x.lim = x.lim)
dev.off()
},
contentType = ifelse(input$format_download_dose_response_fluorescence_plot_individual == ".pdf", "image/pdf", "image/png")
)
select_inputs_individual_plots_dose_response_fluorescence_plot<- reactive({
if (length(results$fluorescence$drFit)>1) names(results$fluorescence$drFit$drFittedSplines)
else return("")
})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_fluorescence_plot",
choices = select_inputs_individual_plots_dose_response_fluorescence_plot())
})
### DR Plots Model individual ####
# Hide Model dose-response plot if dr.method != "model"
observe({
if(length(results$fluorescence$drFit) > 1 &&
length(results$fluorescence$drFit$drTable) > 1 &&
results$fluorescence$drFit$control$dr.method == "model"){
showTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponse_model")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponse_model")
}
})
output$dose_response_model_fluorescence_plot_individual <- renderPlot({
results <- results$fluorescence$drFit$drFittedModels[[ifelse(input$individual_plots_dose_response_model_fluorescence_plot == "1" || is.null(input$individual_plots_dose_response_model_fluorescence_plot), 1, input$individual_plots_dose_response_model_fluorescence_plot)]]
# Define log-transformation of axes
if(input$log_transform_y_axis_dose_response_model_fluorescence_plot &&
input$log_transform_x_axis_dose_response_model_fluorescence_plot){
log <- "xy"
} else if(input$log_transform_y_axis_dose_response_model_fluorescence_plot){
log <- "y"
} else if(input$log_transform_x_axis_dose_response_model_fluorescence_plot){
log <- "x"
} else {
log <- ""
}
# Define x- and y-axis limits
if(any(input$y_range_min_dose_response_model_fluorescence_plot == "",
input$y_range_max_dose_response_model_fluorescence_plot == "")){
y.lim <- NULL
} else {
y.lim <- c(as.numeric(input$y_range_min_dose_response_model_fluorescence_plot),
as.numeric(input$y_range_max_dose_response_model_fluorescence_plot))
}
if(any(input$x_range_min_dose_response_model_fluorescence_plot == "",
input$x_range_max_dose_response_model_fluorescence_plot == "")){
x.lim <- NULL
} else {
x.lim <- c(as.numeric(input$x_range_min_dose_response_model_fluorescence_plot),
as.numeric(input$x_range_max_dose_response_model_fluorescence_plot))
}
plot.drFitFLModel(results,
pch = input$shape_type_dose_response_model_fluorescence_plot,
cex.point = input$shape_size_dose_response_model_fluorescence_plot,
lwd = input$line_width_dose_response_model_fluorescence_plot,
ec50line = input$show_ec50_indicator_lines_dose_response_model_fluorescence_plot,
log = log,
cex.lab = input$axis_size_dose_response_model_fluorescence_plot,
cex.axis = input$lab_size_dose_response_model_fluorescence_plot,
y.lim = y.lim,
x.lim = x.lim
)
})
output$download_dose_response_model_fluorescence_plot_individual <- downloadHandler(
filename = function() {
paste("biosensor_model_fluorescence_", gsub(" \\| ", "_", input$individual_plots_dose_response_model_fluorescence_plot), input$format_download_dose_response_model_fluorescence_plot_individual, sep="")
},
content = function(file) {
if(input$format_download_dose_response_model_fluorescence_plot_individual == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_dose_response_model_fluorescence_plot_individual,
height = input$height_download_dose_response_model_fluorescence_plot_individual,
dpi = input$dpi_download_dose_response_model_fluorescence_plot_individual,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_dose_response_model_fluorescence_plot_individual,
height = input$height_download_dose_response_model_fluorescence_plot_individual,
dpi = input$dpi_download_dose_response_model_fluorescence_plot_individual,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_dose_response_model_fluorescence_plot_individual,
height = input$height_download_dose_response_model_fluorescence_plot_individual,
dpi = input$dpi_download_dose_response_model_fluorescence_plot_individual,
device = png)
}
},
contentType = ifelse(input$format_download_dose_response_model_fluorescence_plot_individual == ".pdf", "image/pdf", "image/png")
)
select_inputs_individual_plots_dose_response_model_fluorescence_plot<- reactive({
if (length(results$fluorescence$drFit)>1) names(results$fluorescence$drFit$drFittedModels)
else return("")
})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_model_fluorescence_plot",
choices = select_inputs_individual_plots_dose_response_model_fluorescence_plot())
})
### Parameter Plots ####
selected_inputs_visualize_parameter_fluorescence_plot <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
select_samples <- results$expdesign$condition
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_parameter_fluorescence_plot",
choices = selected_inputs_visualize_parameter_fluorescence_plot()
)
})
fluorescence_parameter_plot <- reactive({
results <- results$fluorescence
if (input$normalize_to_reference_fluorescence_parameter_plot){
reference.conc <- as.numeric(input$reference_concentration_fluorescence_parameter_plot)
reference.nm <- input$reference_condition_fluorescence_parameter_plot
} else {
reference.conc <- NULL
reference.nm <- NULL
}
if(is.null(input$custom_colors_fluorescence_parameter_plot) || is.na(input$custom_colors_fluorescence_parameter_plot) || input$custom_colors_fluorescence_parameter_plot == ""){
cols <- NULL
} else {
cols <- toupper(unlist(str_split(input$custom_colors_fluorescence_parameter_plot, ", |; |,|;")))
}
if(input$select_string_visualize_parameter_fluorescence_plot){
suppressWarnings(
plot.parameter(results,
param = input$parameter_fluorescence_parameter_fluorescence_plot,
IDs = NULL,
names = input$select_sample_based_on_string_fluorescence_parameter_plot,
conc = input$select_sample_based_on_concentration_fluorescence_parameter_plot,
exclude.nm = input$exclude_sample_based_on_strings_fluorescence_parameter_plot,
exclude.conc = input$exclude_sample_based_on_concentration_fluorescence_parameter_plot,
reference.nm = reference.nm,
reference.conc = reference.conc,
shape.size = input$shape.size_fluorescence_parameter_plot,
basesize = input$basesize_fluorescence_parameter_plot,
label.size = input$label.size_fluorescence_parameter_plot,
legend.position = input$legend_position_fluorescence_parameter_plot,
legend.ncol = input$legend_ncol_fluorescence_parameter_plot,
order_by_conc = input$sort_by_conc_fluorescence_parameter_plot,
colors = cols
)
)
} else {
suppressWarnings(
plot.parameter(results,
param = input$parameter_fluorescence_parameter_fluorescence_plot,
IDs = input$samples_visualize_parameter_fluorescence_plot,
names = NULL,
conc = input$select_sample_based_on_concentration_fluorescence_parameter_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_sample_based_on_concentration_fluorescence_parameter_plot,
reference.nm = reference.nm,
reference.conc = reference.conc,
shape.size = input$shape.size_fluorescence_parameter_plot,
basesize = input$basesize_fluorescence_parameter_plot,
label.size = input$label.size_fluorescence_parameter_plot,
legend.position = input$legend_position_fluorescence_parameter_plot,
legend.ncol = input$legend_ncol_fluorescence_parameter_plot,
order_by_conc = input$sort_by_conc_fluorescence_parameter_plot,
colors = cols
)
)
}
})
output$fluorescence_parameter_plot <- renderPlot({
fluorescence_parameter_plot()
})
output$download_fluorescence_parameter_plot <- downloadHandler(
filename = function() {
paste("fluorescence_parameter_plot", input$format_download_fluorescence_parameter_plot, sep="")
},
content = function(file) {
if(input$format_download_fluorescence_parameter_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_fluorescence_parameter_plot,
height = input$height_download_fluorescence_parameter_plot,
dpi = input$dpi_download_fluorescence_parameter_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_fluorescence_parameter_plot,
height = input$height_download_fluorescence_parameter_plot,
dpi = input$dpi_download_fluorescence_parameter_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_fluorescence_parameter_plot,
height = input$height_download_fluorescence_parameter_plot,
dpi = input$dpi_download_fluorescence_parameter_plot,
device = png)
}
},
contentType = ifelse(input$format_download_fluorescence_parameter_plot == ".pdf", "image/pdf", "image/png")
)
selected_inputs_parameter_fluorescence_parameter_plot <- reactive({
results <- results$fluorescence
gc_parameters <- c()
if("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Increase rate (Spline)' = 'max_slope.spline',
'Increase rate phase 2 (Spline)' = 'max_slope.spline',
'Maximum fluorescence (Spline)' = 'A.spline',
'ΔFluorescence (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
} else {
gc_parameters <- c(gc_parameters,
'Increase rate (Spline)' = 'max_slope.spline',
'Maximum fluorescence (Spline)' = 'A.spline',
'ΔFluorescence (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
}
}
if("l" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Increase rate (linear fit)' = 'max_slope.linfit',
'Increase rate phase 2 (linear fit)' = 'max_slope.linfit',
'Maximum fluorescence (linear fit)' = 'A.linfit',
'ΔFluorescence (linear fit)' = 'dY.linfit')
} else {
gc_parameters <- c(gc_parameters,
'Increase rate (linear fit)' = 'max_slope.linfit',
'Maximum fluorescence (linear fit)' = 'A.linfit',
'ΔFluorescence (linear fit)' = 'dY.linfit')
}
}
gc_parameters
})
selected_inputs_reference_condition_fluorescence_parameter_plot <- reactive({
results <- results$fluorescence
results$expdesign$condition
})
select_inputs_reference_concentration_fluorescence_parameter_plot <- reactive({
results <- results$fluorescence
results$expdesign$concentration
})
observe({
updateSelectInput(inputId = "parameter_fluorescence_parameter_fluorescence_plot",
choices = selected_inputs_parameter_fluorescence_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_condition_fluorescence_parameter_plot",
choices = selected_inputs_reference_condition_fluorescence_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_concentration_fluorescence_parameter_plot",
choices = select_inputs_reference_concentration_fluorescence_parameter_plot()
)})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_fluorescence_plot",
choices = select_inputs_individual_plots_dose_response_fluorescence_plot())
})
selected_inputs_reference_condition_fluorescence_parameter_plot <- reactive({
results <- results$fluorescence
results$expdesign$condition
})
select_inputs_reference_concentration_fluorescence_parameter_plot <- reactive({
results <- results$fluorescence
results$expdesign$concentration
})
observe({
updateSelectInput(inputId = "reference_condition_fluorescence_parameter_plot",
choices = selected_inputs_reference_condition_fluorescence_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_concentration_fluorescence_parameter_plot",
choices = select_inputs_reference_concentration_fluorescence_parameter_plot()
)})
### Grid Plot ####
observe({
if(!exists("output$more_than_two_conc") || is.null(output$more_than_two_conc))
return(NULL)
if(!output$more_than_two_conc){
updateSelectInput(session = session,
inputId = "sort_by_conc_fluorescence_grid_plot",
selected = FALSE)
}
})
observe({
if(input$select_string_visualize_fluorescence_grid)
updateSelectInput(session = session,
inputId = "order_matters_visualize_fluorescence_grid",
selected = FALSE)
})
selected_inputs_fluorescence_grid_plot_data_type <- reactive({
results <- results$fluorescence
selection <- c()
if(length(results$data$fluorescence) > 1){
selection <- c(selection, "Raw fluorescence" = "raw")
}
if(length(results$data$norm.fluorescence) > 1){
selection <- c(selection, "Normalized FL" = "norm.fl")
}
if(length(results$data$fluorescence) > 1 && "s" %in% results$control$fit.opt){
selection <- c(selection, "Spline fits FL" = "spline")
}
selection
})
observe({
updateSelectInput(inputId = "data_type_fluorescence_grid_plot",
choices = selected_inputs_fluorescence_grid_plot_data_type())
})
selected_inputs_parameter_fluorescence_grid_plot <- reactive({
results <- results$fluorescence
gc_parameters <- c()
if("s" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Increase rate (Spline)' = 'max_slope.spline',
'Increase rate phase 2 (Spline)' = 'max_slope.spline',
'Maximum fluorescence (Spline)' = 'A.spline',
'ΔFluorescence (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
} else {
gc_parameters <- c(gc_parameters,
'Increase rate (Spline)' = 'max_slope.spline',
'Maximum fluorescence (Spline)' = 'A.spline',
'ΔFluorescence (Spline)' = 'dY.spline',
'Area under the curve (Spline)' = 'integral.spline')
}
}
if("l" %in% results$control$fit.opt || "a" %in% results$control$fit.opt){
if(results$control$biphasic){
gc_parameters <- c(gc_parameters,
'Increase rate (linear fit)' = 'max_slope.linfit',
'Increase rate phase 2 (linear fit)' = 'max_slope.linfit',
'Maximum fluorescence (linear fit)' = 'A.linfit',
'ΔFluorescence (linear fit)' = 'dY.linfit')
} else {
gc_parameters <- c(gc_parameters,
'Increase rate (linear fit)' = 'max_slope.linfit',
'Maximum fluorescence (linear fit)' = 'A.linfit',
'ΔFluorescence (linear fit)' = 'dY.linfit')
}
}
gc_parameters
})
observe({
updateSelectInput(inputId = "parameter_parameter_grid_plot_fluorescence",
choices = selected_inputs_parameter_fluorescence_grid_plot()
)})
selected_inputs_visualize_fluorescence_grid <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
if(input$plot_group_averages_fluorescence_grid_plot){
select_samples <- results$expdesign$condition
} else {
select_samples <- results$expdesign$label
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_fluorescence_grid",
choices = selected_inputs_visualize_fluorescence_grid()
)
})
observe({
updateSelectInput(session,
inputId = "groups_visualize_fluorescence_grid",
choices = selected_inputs_visualize_fluorescence_grid()
)
})
fluorescence_grid_plot <- reactive({
results <- results$fluorescence
if(input$y_axis_title_fluorescence_grid_plot == "")
y_axis_title <- NULL
else
y_axis_title <- input$y_axis_title_fluorescence_grid_plot
if(input$x_axis_title_fluorescence_grid_plot == "")
x_axis_title <- NULL
else
x_axis_title <- input$x_axis_title_fluorescence_grid_plot
if(input$select_string_visualize_fluorescence_grid){
suppressWarnings(
plot.grid(results,
data.type = input$data_type_fluorescence_grid_plot,
IDs = NULL,
sort_by_ID = FALSE,
names = input$select_samples_based_on_string_fluorescence_grid_plot,
conc = input$select_samples_based_on_concentration_fluorescence_grid_plot,
exclude.nm = input$exclude_samples_based_on_string_fluorescence_grid_plot,
exclude.conc = input$exclude_samples_based_on_concentration_fluorescence_grid_plot,
mean = input$plot_group_averages_fluorescence_grid_plot,
log.y = input$log_transform_y_axis_fluorescence_grid_plot,
x.lim = c(input$x_range_min_fluorescence_grid_plot, input$x_range_max_fluorescence_grid_plot),
y.lim = c(input$y_range_min_fluorescence_grid_plot,input$y_range_max_fluorescence_grid_plot),
y.title = y_axis_title,
x.title = x_axis_title,
n.ybreaks = input$nbreaks_fluorescence_grid_plot,
lwd = input$line_width_fluorescence_grid_plot,
basesize = input$base_size_fluorescence_grid_plot,
pal = input$color_palettes_grid_plot_fluorescence,
sort_by_conc = input$sort_by_conc_fluorescence_grid_plot,
invert.pal = input$invert_color_palette_grid_plot_fluorescence,
legend.lim = c(input$legend_lim_min_fluorescence_grid_plot, input$legend_lim_max_fluorescence_grid_plot),
nrow = input$nrows_fluorescence_grid_plot,
param = input$parameter_parameter_grid_plot_fluorescence
)
)
}
else{
suppressWarnings(
plot.grid(results,
data.type = input$data_type_fluorescence_grid_plot,
IDs = if(input$plot_group_averages_fluorescence_grid_plot){
input$groups_visualize_fluorescence_grid
}else{
input$samples_visualize_fluorescence_grid
},
sort_by_ID = input$order_matters_visualize_fluorescence_grid,
names = NULL,
conc = input$select_samples_based_on_concentration_fluorescence_grid_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_samples_based_on_concentration_fluorescence_grid_plot,
mean = input$plot_group_averages_fluorescence_grid_plot,
log.y = input$log_transform_y_axis_fluorescence_grid_plot,
x.lim = c(input$x_range_min_fluorescence_grid_plot, input$x_range_max_fluorescence_grid_plot),
y.lim = c(input$y_range_min_fluorescence_grid_plot,input$y_range_max_fluorescence_grid_plot),
y.title = y_axis_title,
x.title = x_axis_title,
n.ybreaks = input$nbreaks_fluorescence_grid_plot,
lwd = input$line_width_fluorescence_grid_plot,
basesize = input$base_size_fluorescence_grid_plot,
pal = input$color_palettes_grid_plot_fluorescence,
invert.pal = input$invert_color_palette_grid_plot_fluorescence,
sort_by_conc = input$sort_by_conc_fluorescence_grid_plot,
legend.lim = c(input$legend_lim_min_fluorescence_grid_plot, input$legend_lim_max_fluorescence_grid_plot),
nrow = input$nrows_fluorescence_grid_plot,
param = input$parameter_parameter_grid_plot_fluorescence
)
)
}
})
output$fluorescence_grid_plot <- renderPlot({
fluorescence_grid_plot()
})
output$download_fluorescence_grid_plot <- downloadHandler(
filename = function() {
paste("fluorescence_grid_plot", input$format_download_fluorescence_grid_plot, sep="")
},
content = function(file) {
if(input$format_download_fluorescence_grid_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_fluorescence_grid_plot,
height = input$height_download_fluorescence_grid_plot,
dpi = input$dpi_download_fluorescence_grid_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_fluorescence_grid_plot,
height = input$height_download_fluorescence_grid_plot,
dpi = input$dpi_download_fluorescence_grid_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_fluorescence_grid_plot,
height = input$height_download_fluorescence_grid_plot,
dpi = input$dpi_download_fluorescence_grid_plot,
device = png)
}
},
contentType = ifelse(input$format_download_fluorescence_grid_plot == ".pdf", "image/pdf", "image/png")
)
### DR Plots (Bootstrap) ####
observe({
if(length(results$fluorescence$drFit) > 1 && length(results$fluorescence$drFit$drTable) > 1 && results$fluorescence$control$nboot.dr > 1){
showTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponse_bt")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponse_bt")
}
})
dose_response_fluorescence_plot_individual_bt <- reactive({
results <- results$fluorescence$drFit$drBootSplines[[ifelse(input$individual_plots_dose_response_fluorescence_plot_bt == "1" || is.null(input$individual_plots_dose_response_fluorescence_plot_bt), 1, input$individual_plots_dose_response_fluorescence_plot_bt)]]
plot.drBootSpline(results,
pch = input$shape_type_dose_response_fluorescence_plot_bt,
cex.point = input$shape_size_dose_response_fluorescence_plot_bt,
cex.lab = input$axis_size_dose_response_fluorescence_plot_bt,
cex.axis = input$lab_size_dose_response_fluorescence_plot_bt,
lwd = input$line_width_dose_response_fluorescence_plot_bt,
combine = TRUE
)
})
output$dose_response_fluorescence_plot_individual_bt <- renderPlot({
dose_response_fluorescence_plot_individual_bt()
})
output$download_dose_response_fluorescence_plot_individual_bt <- downloadHandler(
filename = function() {
paste("dose_response_boot_fluorescence_", gsub(" \\| ", "_", input$individual_plots_dose_response_fluorescence_plot_bt), input$format_download_dose_response_fluorescence_plot_individual_bt, sep="")
},
content = function(file) {
if(input$format_download_dose_response_fluorescence_plot_individual_bt == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
Cairo::CairoPDF(file = file,
width = input$width_download_dose_response_fluorescence_plot_individual_bt,
height = input$height_download_dose_response_fluorescence_plot_individual_bt)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
grDevices::pdf(file = file,
width = input$width_download_dose_response_fluorescence_plot_individual_bt,
height = input$height_download_dose_response_fluorescence_plot_individual_bt)
}
} else {
grDevices::png(file = file,
width = input$width_download_dose_response_fluorescence_plot_individual_bt,
height = input$height_download_dose_response_fluorescence_plot_individual_bt,
units = "in",
res = input$dpi_download_dose_response_fluorescence_plot_individual_bt)
}
results <- results$fluorescence$drFit$drBootSplines[[input$individual_plots_dose_response_fluorescence_plot_bt]]
plot.drBootSpline(results,
pch = input$shape_type_dose_response_fluorescence_plot_bt,
cex.point = input$shape_size_dose_response_fluorescence_plot_bt,
cex.lab = input$axis_size_dose_response_fluorescence_plot_bt,
cex.axis = input$lab_size_dose_response_fluorescence_plot_bt,
lwd = input$line_width_dose_response_fluorescence_plot_bt,
combine = TRUE
)
dev.off()
},
contentType = ifelse(input$format_download_dose_response_fluorescence_plot_individual_bt == ".pdf", "image/pdf", "image/png")
)
### Dual Plot ####
selected_inputs_visualize_dual_plot <- reactive({
results <- results$fluorescence
if(is.null(results)) return("")
if(input$plot_group_averages_dual_plot){
select_samples <- results$expdesign$condition
} else {
select_samples <- results$expdesign$label
}
select_samples
})
observe({
updateSelectInput(session,
inputId = "samples_visualize_dual_plot",
choices = selected_inputs_visualize_dual_plot()
)
})
observe({
if(length(results$fluorescence$data$growth) > 1 && length(results$fluorescence$data$fluorescence) > 1){
showTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPabel_Visualize_Dual")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPabel_Visualize_Dual")
}
})
dual_plot <- reactive({
results <- results$fluorescence
if(is.null(input$custom_colors_dual_plot) || is.na(input$custom_colors_dual_plot) || input$custom_colors_dual_plot == ""){
cols <- NULL
} else {
cols <- toupper(unlist(str_split(input$custom_colors_dual_plot, ", |; |,|;")))
}
if(input$select_string_visualize_dual_plot){
suppressWarnings(
plot.dual(results,
IDs = NULL,
fluorescence = input$fluorescence_type_dual_plot,
names = input$select_samples_based_on_string_dual_plot,
conc = input$select_samples_based_on_concentration_dual_plot,
exclude.nm = input$exclude_samples_based_on_string_dual_plot,
exclude.conc = input$exclude_samples_based_on_concentration_dual_plot,
mean = input$plot_group_averages_dual_plot,
log.y.growth = input$log_transform_y_axis_growth_dual_plot,
log.y.fl = input$log_transform_y_axis_fluorescence_dual_plot,
x.lim = c(input$x_range_min_dual_plot, input$x_range_max_dual_plot),
y.lim.growth = c(input$y_range_min_growth_dual_plot,input$y_range_max_growth_dual_plot),
y.lim.fl = c(input$y_range_min_fluorescence_dual_plot,input$y_range_max_fluorescence_dual_plot),
y.title.fl = input$y_axis_title_fluorescence_dual_plot,
y.title.growth = input$y_axis_title_growth_dual_plot,
x.title = input$x_axis_title_dual_plot,
n.ybreaks = input$nbreaks_dual_plot,
lwd = input$line_width_dual_plot,
basesize = input$base_size_dual_plot,
legend.position = input$legend_position_dual_plot,
legend.ncol = input$legend_ncol_dual_plot,
color_groups = input$color_groups_dual_plot,
colors = cols,
group_pals = input$color_palettes_dual_plot
)
)
} else {
suppressWarnings(
plot.dual(results,
fluorescence = input$fluorescence_type_dual_plot,
IDs = input$samples_visualize_dual_plot,
names = NULL,
conc = input$select_samples_based_on_concentration_dual_plot,
exclude.nm = NULL,
exclude.conc = input$exclude_samples_based_on_concentration_dual_plot,
mean = input$plot_group_averages_dual_plot,
log.y.growth = input$log_transform_y_axis_growth_dual_plot,
log.y.fl = input$log_transform_y_axis_fluorescence_dual_plot,
x.lim = c(input$x_range_min_dual_plot, input$x_range_max_dual_plot),
y.lim.growth = c(input$y_range_min_growth_dual_plot,input$y_range_max_growth_dual_plot),
y.lim.fl = c(input$y_range_min_fluorescence_dual_plot,input$y_range_max_fluorescence_dual_plot),
y.title.fl = input$y_axis_title_fluorescence_dual_plot,
y.title.growth = input$y_axis_title_growth_dual_plot,
x.title = input$x_axis_title_dual_plot,
n.ybreaks = input$nbreaks_dual_plot,
lwd = input$line_width_dual_plot,
basesize = input$base_size_dual_plot,
color_groups = input$color_groups_dual_plot,
legend.position = input$legend_position_dual_plot,
legend.ncol = input$legend_ncol_dual_plot,
group_pals = input$color_palettes_dual_plot,
colors = cols
)
)
}
})
output$dual_plot <- renderPlot({
dual_plot()
})
output$download_dual_plot <- downloadHandler(
filename = function() {
paste("fluorescence_group_plot", input$format_download_dual_plot, sep="")
},
content = function(file) {
if(input$format_download_dual_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_dual_plot,
height = input$height_download_dual_plot,
dpi = input$dpi_download_dual_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_dual_plot,
height = input$height_download_dual_plot,
dpi = input$dpi_download_dual_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_dual_plot,
height = input$height_download_dual_plot,
dpi = input$dpi_download_dual_plot,
device = png)
}
},
contentType = ifelse(input$format_download_dual_plot == ".pdf", "image/pdf", "image/png")
)
selected_inputs_fluorescence_type_dual_plot <- reactive({
results <- results$fluorescence
selection <- c()
if(length(results$data$fluorescence) > 1){
selection <- c(selection, "Raw fluorescence" = "fl")
}
# if(length(results$data$fluorescence2) > 1){
# selection <- c(selection, "Raw fluorescence 2" = "fl2")
# }
if(length(results$data$norm.fluorescence) > 1){
selection <- c(selection, "Normalized FL" = "norm.fl")
}
# if(length(results$data$norm.fluorescence2) > 1){
# selection <- c(selection, "Normalized FL2" = "norm.fl2")
# }
selection
})
observe({
updateSelectInput(inputId = "fluorescence_type_dual_plot",
choices = selected_inputs_fluorescence_type_dual_plot())
})
### DR Parameter Plots ####
observe({
if(length(results$fluorescence$drFit) > 1 && length(results$fluorescence$drFit$drTable) > 1){
showTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponseParameters")
} else {
hideTab(inputId = "tabsetPanel_Visualize_Fluorescence", target = "tabPanel_Visualize_Fluorescence_DoseResponseParameters")
}
})
fluorescence_dr_parameter_plot <- reactive({
results <- results$fluorescence
if (input$normalize_to_reference_fluorescence_dr_parameter_plot){
reference.nm <- input$reference_condition_fluorescence_dr_parameter_plot
} else {
reference.nm <- NULL
}
plot.dr_parameter(results,
param = input$parameter_dr_parameter_fluorescence_plot,
names = input$select_sample_based_on_string_fluorescence_dr_parameter_plot,
exclude.nm = input$exclude_sample_based_on_strings_fluorescence_dr_parameter_plot,
reference.nm = reference.nm,
basesize = input$basesize_fluorescence_dr_parameter_plot,
label.size = input$label.size_fluorescence_dr_parameter_plot
)
})
output$fluorescence_dr_parameter_plot <- renderPlot({
fluorescence_dr_parameter_plot()
})
output$download_fluorescence_dr_parameter_plot <- downloadHandler(
filename = function() {
paste("fluorescence_dr_parameter_plot", input$format_download_fluorescence_dr_parameter_plot, sep="")
},
content = function(file) {
if(input$format_download_fluorescence_dr_parameter_plot == ".pdf"){
if (requireNamespace("Cairo", quietly = TRUE)) {
ggsave(filename = file, width = input$width_download_fluorescence_dr_parameter_plot,
height = input$height_download_fluorescence_dr_parameter_plot,
dpi = input$dpi_download_fluorescence_dr_parameter_plot,
device = cairo_pdf)
} else {
showModal(modalDialog("Package 'Cairo' must be installed to preserve special characters in the exported PDF image", easyClose = T))
ggsave(filename = file, width = input$width_download_fluorescence_dr_parameter_plot,
height = input$height_download_fluorescence_dr_parameter_plot,
dpi = input$dpi_download_fluorescence_dr_parameter_plot,
device = pdf)
}
} else {
ggsave(filename = file, width = input$width_download_fluorescence_dr_parameter_plot,
height = input$height_download_fluorescence_dr_parameter_plot,
dpi = input$dpi_download_fluorescence_dr_parameter_plot,
device = png)
}
},
contentType = ifelse(input$format_download_fluorescence_dr_parameter_plot == ".pdf", "image/pdf", "image/png")
)
selected_inputs_parameter_fluorescence_dr_parameter_plot <- reactive({
results <- results$fluorescence
if(is.null(results)) return(NULL)
if(results$control$dr.method == "spline"){
dr_parameters <- c('Response(EC50)' = 'yEC50','EC50' = 'EC50')
if(results$control$nboot.dr > 1){
dr_parameters <- c(dr_parameters, 'Response(EC50) - Bootstrap' = 'drboot.meanEC50', 'EC50 - Bootstrap' = 'drboot.meanEC50y')
}
if(input$log_transform_response_fluorescence){
dr_parameters <- c(dr_parameters, 'Original response(EC50)' = 'yEC50.orig')
}
if(input$log_transform_concentration_fluorescence){
dr_parameters <- c(dr_parameters, 'Original EC50' = 'EC50.orig')
}
} else {
dr_parameters <- c('Leakiness' = 'y.min','Sensitivity' = 'K', 'Fold change' = 'fc', 'Maximum response' = 'y.max')
}
dr_parameters
})
selected_inputs_reference_condition_fluorescence_dr_parameter_plot <- reactive({
results <- results$fluorescence
results$expdesign$condition
})
select_inputs_individual_plots_dose_response_fluorescence_plot <- reactive({
if (length(results$fluorescence$drFit)>1) names(results$fluorescence$drFit$drFittedSplines)
else return("")
})
select_inputs_individual_plots_dose_response_fluorescence_plot_bt <- reactive({
if (length(results$fluorescence$drFit)>1 && results$fluorescence$control$nboot.dr > 1) names(results$fluorescence$drFit$drBootSplines)
else return("")
})
observe({
updateSelectInput(inputId = "parameter_dr_parameter_fluorescence_plot",
choices = selected_inputs_parameter_fluorescence_dr_parameter_plot()
)})
observe({
updateSelectInput(inputId = "reference_condition_fluorescence_dr_parameter_plot",
choices = selected_inputs_reference_condition_fluorescence_dr_parameter_plot()
)})
observe({
updateSelectInput(inputId = "individual_plots_dose_response_fluorescence_plot_bt",
choices = select_inputs_individual_plots_dose_response_fluorescence_plot_bt()
)})
# Report ####
volumes <- getVolumes() # this makes the directory at the base of your computer.
observe({
if(!is.null(results$growth)){
showTab(inputId = "tabsetPanel_Report", target = "tabPanel_report_growth")
} else {
hideTab(inputId = "tabsetPanel_Report", target = "tabPanel_report_growth")
}
if(!is.null(results$fluorescence)){
showTab(inputId = "tabsetPanel_Report", target = "tabPanel_report_fluorescence")
} else {
hideTab(inputId = "tabsetPanel_Report", target = "tabPanel_report_fluorescence")
}
})
observeEvent(input$run_report_fix, {
# check if pandoc is recognized
showModal(
modalDialog("Running report troubleshooting...", footer = NULL)
)
pandoc.path <- rmarkdown::find_pandoc()$dir
Sys.setenv(RSTUDIO_PANDOC = pandoc.path)
Sys.setenv(PANDOC_PATH = pandoc.path)
# reinstall tinytex
tinytex::tlmgr_update()
tinytex::reinstall_tinytex()
removeModal()
})
## Report Growth ####
output$download_report_growth_pdf <- downloadHandler(
filename = function() {
paste0("GrowthReport.", input$report_filetype_growth)
},
content = function(file) {
if (!requireNamespace("tinytex", quietly = TRUE)) {
showModal(
modalDialog("Please install package 'tinytex' to render PDF reports.", easyClose = T)
)
} else if(!tinytex::is_tinytex()){
# stop("TinyTex was not found on your system. To render PDF reports, please execute tinytex::install_tinytex().")
showModal(
modalDialog(HTML("TinyTeX is required to render PDF reports but was not found on your system. Installing TinyTeX...<br><br>(This requires and active internet connection and will take several minutes)"), footer = NULL)
)
update.packages(ask = FALSE, checkBuilt = TRUE, repos='http://cran.us.r-project.org')
tinytex::install_tinytex()
tinytex::tlmgr_update()
tinytex::reinstall_tinytex()
removeModal()
try(
suppressWarnings(
suppressMessages(
growth.report(grofit = results$growth,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file),
ec50 = ifelse(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1, TRUE, FALSE),
format = input$report_filetype_growth,
export = FALSE,
mean.grp = "all")
)
)
)
} else {
try(
suppressWarnings(
suppressMessages(
growth.report(grofit = results$growth,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file),
ec50 = ifelse(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1, TRUE, FALSE),
format = input$report_filetype_growth,
export = FALSE,
mean.grp = "all")
)
)
)
}
},
contentType = paste0(".", input$report_filetype_growth)
)
output$download_report_growth_html <- downloadHandler(
filename = function() {
paste0("GrowthReport.", input$report_filetype_growth)
},
content = function(file) {
try(
suppressWarnings(
suppressMessages(
growth.report(grofit = results$growth,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file),
ec50 = ifelse(length(results$growth$drFit) > 1 && length(results$growth$drFit$drTable) > 1, TRUE, FALSE),
format = input$report_filetype_growth,
export = FALSE,
mean.grp = "all")
)
)
)
},
contentType = paste0(".", input$report_filetype_growth)
)
## Report Fluorescence ####
output$download_report_fluorescence_pdf <- downloadHandler(
filename = function() {
paste0("FluorescenceReport.", input$report_filetype_fluorescence)
},
content = function(file) {
if (!requireNamespace("tinytex", quietly = TRUE)) {
showModal(
modalDialog("Please install package 'tinytex' to render PDF reports.", easyClose = T)
)
} else if(!tinytex::is_tinytex()){
# stop("TinyTex was not found on your system. To render PDF reports, please execute tinytex::install_tinytex().")
showModal(
modalDialog(HTML("TinyTeX is required to render PDF reports but was not found on your system. Installing TinyTeX...<br><br>(This requires and active internet connection and will take several minutes)"), footer = NULL)
)
update.packages(ask = FALSE, checkBuilt = TRUE, repos='http://cran.us.r-project.org')
tinytex::install_tinytex()
tinytex::tlmgr_update()
tinytex::reinstall_tinytex()
removeModal()
try(
suppressWarnings(
suppressMessages(
fl.report(flFitRes = results$fluorescence,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file),
ec50 = ifelse(length(results$fluorescence$drFit) > 1 && length(results$fluorescence$drFit$drTable) > 1, TRUE, FALSE),
format = input$report_filetype_fluorescence,
export = FALSE,
mean.grp = "all")
)
)
)
} else {
try(
suppressWarnings(
suppressMessages(
fl.report(flFitRes = results$fluorescence,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file),
ec50 = ifelse(length(results$fluorescence$drFit) > 1 && length(results$fluorescence$drFit$drTable) > 1, TRUE, FALSE),
format = input$report_filetype_fluorescence,
export = FALSE,
mean.grp = "all")
)
)
)
}
},
contentType = paste0(".", input$report_filetype_fluorescence)
)
output$download_report_fluorescence_html <- downloadHandler(
filename = function() {
paste0("FluorescenceReport.", input$report_filetype_fluorescence)
},
content = function(file) {
try(
suppressWarnings(
suppressMessages(
fl.report(flFitRes = results$fluorescence,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file),
ec50 = ifelse(length(results$fluorescence$drFit) > 1 && length(results$fluorescence$drFit$drTable) > 1, TRUE, FALSE),
format = input$report_filetype_fluorescence,
export = FALSE,
mean.grp = "all")
)
)
)
},
contentType = paste0(".", input$report_filetype_fluorescence)
)
# Bug report message ####
github_url <- a("QurvE Github", href="https://github.com/NicWir/QurvE/issues")
output$bug_report <- renderUI({
tagList("Please report bugs and user feedback at:", github_url)
})
# Export RData files ####
observe({
if(!is.null(results$growth)){
showTab(inputId = "tabsetPanel_Export_Data", target = "tabPanel_export_data_growth")
} else {
hideTab(inputId = "tabsetPanel_Export_Data", target = "tabPanel_export_data_growth")
}
if(!is.null(results$fluorescence)){
showTab(inputId = "tabsetPanel_Export_Data", target = "tabPanel_export_data_fluorescence")
} else {
hideTab(inputId = "tabsetPanel_Export_Data", target = "tabPanel_export_data_fluorescence")
}
})
## RData Growth ####
output$export_RData_growth <- downloadHandler(
filename = function() {
paste0("GrowthResults.RData")
},
content = function(file) {
try(
suppressWarnings(
suppressMessages(
QurvE:::export_RData(object = results$growth,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file)
)
)
)
)
},
contentType = paste0(".RData")
)
## RData Fluorescence ####
output$export_RData_fluorescence <- downloadHandler(
filename = function() {
paste0("FluorescenceResults.RData")
},
content = function(file) {
try(
suppressWarnings(
suppressMessages(
QurvE:::export_RData(object = results$fluorescence,
out.dir = gsub(paste0("[\\\\|", .Platform$file.sep, "]file.+$"), "", file),
out.nm = gsub(paste0("^.+[\\\\|", .Platform$file.sep, "]"), "", file)
)
)
)
)
},
contentType = paste0(".RData")
)
# Import RData files
## Growth
output$RData_growth_uploaded <- reactive({
if(is.null(input$import_RData_growth)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'RData_growth_uploaded', suspendWhenHidden=FALSE)
observeEvent(input$read_RData_growth,{
showModal(modalDialog("Reading data file...", footer=NULL))
try(load(input$import_RData_growth$datapath))
try(results$growth <- object)
if(!is.null("results$growth")){
# ENABLE DISABLED PANELS AFTER RUNNING COMPUTATION
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Export_RData]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Report]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Visualize]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Results]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Validate]")
removeModal()
} else showModal(modalDialog(geterrmessage(), footer=NULL, easyClose = T))
})
## Fluorescence
output$RData_fluorescence_uploaded <- reactive({
if(is.null(input$import_RData_fluorescence)) return(FALSE)
else return(TRUE)
})
outputOptions(output, 'RData_fluorescence_uploaded', suspendWhenHidden=FALSE)
observeEvent(input$read_RData_fluorescence,{
showModal(modalDialog("Reading data file...", footer=NULL))
try(load(input$import_RData_fluorescence$datapath))
try(results$fluorescence <- object)
if(!is.null("results$fluorescence")){
## ENABLE DISABLED PANELS AFTER RUNNING COMPUTATION
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Export_RData]")
shinyjs::enable(selector = "#navbar li a[data-value=tabPanel_Report]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Visualize]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Results]")
shinyjs::enable(selector = "#navbar li a[data-value=navbarMenu_Validate]")
removeModal()
} else showModal(modalDialog(geterrmessage(), footer=NULL, easyClose = T))
})
# Show content after initializing ####
load_data()
# Close the app when the session completes ####
if(!interactive()) {
session$onSessionEnded(function() {
stopApp()
q("no")
})
}
}
shinyApp(ui = ui, server = server)
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