inst/shinyApp/ui.R
In GweeXianYao/metaboliteR: What the Package Does (Title Case) TODO!!
library(shiny)
library(shinyBS)
library(shinythemes)
library(shinyjs)
library(metaboliteR)
library(Rcpp)
shinyUI(ui = tagList(
# Color error output red
tags$head(
tags$style(HTML("
.shiny-output-error-validation {
color: red;
}
"))),
# Padding for fixed top menu bar
tags$style(type="text/css", "body {padding-top: 70px;}"),
useShinyjs(),
withMathJax(),
navbarPage(id = "menu_bar", position = "fixed-top",
theme = shinytheme("lumen"), # <--- To use a theme, uncomment this
strong("metaboliteR"), # Main title name
# HOME --------------------------------------------------------------------
tabPanel("Home",
column(12, align="center",
# img(src='logo.png', align = "right", height=120, width=100),
tags$img(src = "metaboliteR-banner.png",height=160,width=800), # placeholder for banner
# h2(strong("Welcome to the Shiny App for Analysing Metabolomic Data Using R")),
# Buttons box
column(12, align="center",
# PPCA method button
column(6, align = "right",
# "PPCA",
br(),
tags$button(
id = "PPCA_method_button",
class = "btn action-button",
tags$img(src = "PPCA-logo.png",
height = "200px",
width = "400px"),
style = "background-color:white"
),
br(),
# "this method is good"
),
# MPPCA method button
column(6, align = "left",
# "MPPCA",
br(),
tags$button(
id = "MPPCA_method_button",
class = "btn action-button",
tags$img(src = "MPPCA-logo.png",
height = "200px",
width = "400px"),
style = "background-color:white"
),
br(),
# "this method is good"
),
# Insert vertical spacing between buttons
column(12),
# DPPCA method button
column(6, align = "right",
# "DPPCA",
br(),
tags$button(
id = "DPPCA_method_button",
class = "btn action-button",
tags$img(src = "DPPCA-logo.png",
height = "200px",
width = "400px"),
style = "background-color:white"
),
br(),
# "this method is good"
),
# Sample Size Estimation button
column(6, align = "left",
# "Sample Size Estimation",
br(),
tags$button(
id = "Sample_Size_Estimation_button",
class = "btn action-button",
tags$img(src = "Sample_Size_Estimation-logo.png",
height = "200px",
width = "400px"),
style = "background-color:white"
),
br(),
# "this method is good"
),
),
column(12, align="center",
br(),
h4(strong("To get started, click on the method you require.")),
)
)
),
# ANALYTICS ---------------------------------------------------------------
navbarMenu("Analytics",
# ppca --------------------------------------------------------------------
tabPanel("PPCA/PPCCA",
conditionalPanel(condition="input.PPCA_plot_tabs!='PPCA_steps'",
# PPCA sidebar controls
sidebarPanel(width = 3,
# tags$style("#PPCA_tooltip+.popover{width:170px;}"),
# bsPopover("PPCA_tooltip",
# title = "Probabilistic Principal Component Analysis",
# content = "",
# trigger = "hover", placement = "right"),
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_data'",
textOutput("arg"),
# PPCA data inputs
fileInput("PPCA_input_file", h4("File input:", bsButton("PPCA_data_tooltip", label = "",
icon = icon("question"), size = "extra-small")),
multiple = F, accept = c("text/csv", "text/comma-separated-values, text/plain", ".csv"),
placeholder = "Enter Metabolomic Data Here"),
bsPopover("PPCA_data_tooltip", title="Metabolomic Data File",
content="Should consist of at least the Spectral Data from NMR/MS. If there are Covariates/Labels, it should be in the first few columns. This File Input only accepts .txt and .csv",
trigger = "hover"),
radioButtons(inputId = 'PPCA_data_choice', label = NULL,
choiceNames = c("Use Your Own Data", "Use Example Data"),
choiceValues = c(TRUE, FALSE),
selected = TRUE, inline = TRUE),
# tags$hr(),
h4(helpText("Is there a header in the data?")),
checkboxInput(inputId = 'PPCA_header', label = 'Tick for Yes', value = TRUE),
radioButtons(inputId = 'PPCA_sep', label = h4(helpText("How is the data separated?")),
inline = TRUE, choices = c(Comma=',',Semicolon=';',Tab='\t', Space=''), selected = ','),
sliderInput("PPCA_cov_slider", h4("Covariates Columns:"), 0, 10, c(0,0)),
helpText("When covariates are selected, they will appear in the 'Covariates Data' tab"),
sliderInput("PPCA_label_slider", h4("Group Labels Column:"), 0, 10, 0),
helpText("When group labels are selected, they will appear in the 'Group Labels' tab"),
hidden(textInput("PPCA_ignore_column", h4("Columns Ignored for Spectral Data:"), "", placeholder="Column number/names (e.g: 2,3,5 or 9.98,9.94,9.86)")),
h4(helpText("Enter Desired Scaling:", bsButton("PPCA_scale_type_tooltip", label = "",
icon = icon("question"), size = "extra-small"))),
bsPopover("PPCA_scale_type_tooltip", title="Types of Scaling",
content="Choosing any scale will pre-process the data by the given formula. Further details on Guide Tab.",
trigger = "hover"),
radioButtons(inputId = 'PPCA_scale', label = NULL,
choiceNames = c("None", 'PQN Scale',
"Auto Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}\\)",
"Pareto Scale \\(\\space\\) \\(\\frac{x-\\mu}{\\sqrt{\\sigma}}\\)",
"Range Scale \\(\\space\\) \\(\\frac{x-\\mu}{x_{max} - x_{min}}\\)",
"Vast Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}(\\frac{\\mu}{\\sigma}\\))"),
choiceValues = c('none', 'PQN', 'autoscale', 'paretoscale', 'rangescale', 'vastscale'),
selected = 'none'),
div(helpText("*μ is the column mean value while σ is the standard deviation"), style = "font-size:80%"),
tags$hr(),
actionButton("PPCA_parameter_button", "Go to Model Specification",
width = "100%", class = "btn-default"),
),
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_specification'",
actionButton("PPCA_data_button", "Go to PPCA Data Input",
width = "100%", class = "btn-default"),
h3(helpText('Model Specifications:')),
sliderInput("PPCA_PC_slider", h4("Range of Principal Components:"),
min = 1, max = 10, value = c(1, 4)),
sliderInput("PPCA_bootstrap_n_slider", h4("Numbers of Bootstrap:"), 2, 100, 5),
bsPopover("PPCA_bootstrap_n_slider", title="",
content="Higher Bootstrap, Better Uncertainty but Longer Wait Time", trigger = "hover"),
h4("Do you want to include", span("Covariates?", style="color:blue")),
disabled(checkboxInput(inputId = 'PPCA_covariates_check', label = span('Tick for Yes', style="color:blue", title = "Please make sure covariates data are selected in Data tabs to enable this selection"), value = FALSE)),
radioButtons("PPCA_choose_q", label = h4("Output Optimize Model?"),
choiceNames = c("Use Optimal PC", "Use Maximum PC"),
choiceValues = c(TRUE, FALSE)),
div(style="display:inline-block",
numericInput("PPCA_epsilon", label = "Covergence Criterion:", value=0.01, min=1e-5, max=1e2, ),
),
div(style="display:inline-block",
"\\(\\space\\space\\)",
),
div(style="display:inline-block",
numericInput("PPCA_max_iter", label = "Max Number of Iteration:", value=1e3, min=1, max=1e6),
),
tags$h6("Click button to run the model using desired specifications:"),
div(style="display:inline-block",
disabled(actionButton("PPCA_submit_para_btn", "Submit Model Specifications", class = "btn-primary")),
),
div(style="display:inline-block",
strong(textOutput("PPCA_optimal_q")),
),
p("Please input data first", id="PPCA_data_check_for_submit", style="color:red"),
tags$hr(),
),
conditionalPanel(condition="input.PPCA_plot_tabs!='PPCA_specification' & input.PPCA_plot_tabs!='PPCA_data' & input.PPCA_plot_tabs!='PPCA_steps'",
# tags$br(),
actionButton("PPCA_return_param", "Return to Model Specification",
width = "100%", class = "btn-default"),
h3(helpText('Graph Controls:'))
),
# Score & Loading
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_score_plot' || input.PPCA_plot_tabs=='PPCA_loading_plot'",
sliderInput("PPCA_x_PC", h4("X-axis Principal Component:"), min=1, max=10, value=4, step=1, ticks=F),
sliderInput("PPCA_y_PC", h4("Y-axis Principal Component:"), min=1, max=10, value=4, step=1, ticks=F),
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_score_plot'",
sliderInput("PPCA_post_int", h4("Posterior Interval:"), min=0.8, max=0.9999, value=0.95, ticks=F),
disabled(downloadButton("dl_ppca_score_btn", span("Download", title="Download a Table of All Score Estimates."))),
),
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_loading_plot'",
disabled(downloadButton("dl_ppca_loadings_btn", span("Download", title="Download a Table of All Loadings Estimates."))),
)
),
# Loadings Analysis & Influence plot
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_loading_analysis_plot' || input.PPCA_plot_tabs=='PPCA_influence_plot'",
sliderInput("PPCA_spectral_plot_PC", h4("Principal Component for Plot:"), min=1, max=10, value=1, step=1, ticks=F),
sliderInput("PPCA_conf_int", h4("Confidence Interval:"), min=0.8, max=0.9999, value=0.95, ticks=F),
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_loading_analysis_plot'",
sliderInput("PPCA_n_load_analysis", h5("Numbers of Significant Bin for Loading Analysis Plot"), min=1, max=20, value=5, step=1, ticks=F),
disabled(downloadButton("dl_ppca_loadings_analysis_btn", span("Download", title="Download a table of data used to produced the graph."))),
# downloadButton("dl_significant_btn1", span("Download", title="Download a Table of All Significant Spectral Bins Names"))
)
),
),
),
mainPanel(width = 9, id = "PPCA_main_layout",
# Style for influence tab, PPCA tabs
tags$style(HTML("
.tabbable > .nav > li > a[data-value='PPCA_influence_plot'] {color:blue}
.tabbable > .nav > li > a[data-value='PPCA_spectral'] {background-color:lightyellow}
.tabbable > .nav > li > a[data-value='PPCA_covariates'] {background-color:lightyellow; color:blue}
.tabbable > .nav > li > a[data-value='PPCA_labels'] {background-color:lightyellow}
.tabbable > .nav > li[class=active] > a {background-color:grey; color:white}
#PPCA_data_tabs li:nth-child(4) {float:right}
#PPCA_data_tabs li > a[data-value='PPCA_original_data_table']{background-color:lightgrey}
")),
tabsetPanel(id = "PPCA_plot_tabs", selected = "PPCA_steps",
tabPanel("Quick Start", value = "PPCA_steps",
column(width = 6, offset = 0.5,
br(),
h1(strong(tags$u("Usage Guide:"))),
h3(
tags$div(
tags$ol(
tags$li("Enter your data in the 'PPCA Data' tab"),
br(),
tags$li("Specify the model parameters in the 'Model Specification' tab"),
br(),
tags$li("In the 'Model Specification' tab, click the 'SUBMIT MODEL SPECIFICATIONS' button for the model to run."),
br(),
tags$li("Once the model finishes running, plots will appear under the different plot tabs beside the 'Model Specification' tab."),
br(),
actionButton("PPCA_data_QS", "PPCA Data", class = "btn-default"),
actionButton("PPCA_model_spec_QS", "Model Specification", class = "btn-default"),
)
)
),
br(),
),
column(width = 6, offset = 0.5,
br(),
h4(tags$u("Note: PPCA demo below, click play to animate")),
sliderInput("PPCA_gif_slider", label=NULL, min = 0, max=218, value=0, ticks = FALSE,
animate = animationOptions(interval = 300)),
hidden(imageOutput("PPCA_gif_demo")),
br(),
br(),
)
),
tabPanel("PPCA Data", value = "PPCA_data",
br(),
tabsetPanel(id = "PPCA_data_tabs",
tabPanel(span("Spectral Data", title="Data from NMR or Mass Spectroscopy (MS). Please make sure: rows are samples/observations, columns are spectral bins"),
value = 'PPCA_spectral',
strong(h3(textOutput("PPCA_spectral_file_name"))),
dataTableOutput("PPCA_spectral_data_table"),
),
tabPanel(span("Covariates Data", title="Sample attributes (eg: weight, gender, regions, etc)"),
value = 'PPCA_covariates',
# h3(textOutput("PPCA_spectral_file_name")),
dataTableOutput("PPCA_covariates_data_table")
),
tabPanel(span("Group Labels", title="This data is used for colours in plots (eg: Treatment group vs Control group)"),
value = 'PPCA_labels',
# h3(textOutput("PPCA_spectral_file_name")),
dataTableOutput("PPCA_labels_data_table")
),
tabPanel("Original Data",
value = "PPCA_original_data_table",
br(),
column(12,
column(3, "Covariates Highlighted Color", style = "background-color:lightblue; color:blue"),
column(3, "Group Labels Highlighted Color", style = "background-color:orange;"),
# column(3, "Columns Ignored Highlighted Color", style = "background-color:grey;color:white"),
),
uiOutput("PPCA_original_data_table_UI")
# dataTableOutput("PPCA_original_data_table"),
# tags$style(type="text/css", paste0("#DPPCA_original_data_table td:nth-child(", 3, ") {text-align:center;background-color:green;color: white;text-align:center}"))
)
)
),
tabPanel("Model Specification", value = "PPCA_specification",
br(),
bsCollapse(id = "PPCA_desc", open = "PPCA/PPCCA Parameters Description",
bsCollapsePanel("PPCA/PPCCA Parameters Description",
includeHTML("PPCA_desc_pd.html"),
style = "primary"),
bsCollapsePanel("PPCA/PPCCA Conceptual Explanation",
includeHTML("PPCA_desc_ce.html"),
style = "info"),
bsCollapsePanel("PPCA/PPCCA Technical Details",
includeHTML("PPCA_desc_td.html"),
style = "info")
)
),
tabPanel("Score Plots", value = "PPCA_score_plot",
plotOutput("PPCA_plot_score"),
),
tabPanel("Loadings Plots", value = "PPCA_loading_plot",
plotOutput("PPCA_plot_loadings"),
),
tabPanel("Loadings Analysis Plot", value = "PPCA_loading_analysis_plot",
plotOutput("PPCA_plot_loadings_analysis"),
),
tabPanel("Loadings Influence Plot", value = "PPCA_influence_plot",
plotOutput("PPCA_influence_report"),
),
tabPanel("Model Selection Plot", value = "PPCA_bic_plot",
plotOutput("PPCA_plot_bic"),
),
tabPanel("Algorithm Covergence Plot", value = "PPCA_ll_plot",
plotOutput("PPCA_plot_ll_conv"),
)
)
)
),
# mppca -------------------------------------------------------------------
tabPanel("MPPCA",
conditionalPanel(condition="input.MPPCA_plot_tabs!='MPPCA_steps'",
sidebarPanel(width=3,
# tags$div("MPPCA",
# title="Mixed PPCA that deals with multiple groups"),
# value = "MPPCA_tab",
conditionalPanel(condition="input.MPPCA_plot_tabs=='MPPCA_data'",
# MPPCA data inputs
fileInput("MPPCA_input_file", h4("File input:", bsButton("MPPCA_data_tooltip", label = "",
icon = icon("question"), size = "extra-small")),
multiple = F, accept = c("text/csv", "text/comma-separated-values, text/plain", ".csv"),
placeholder = "Enter Metabolomic Data Here"),
bsPopover("MPPCA_data_tooltip", title="Metabolomic Data File",
content="Should consist of at least the Spectral Data from NMR/MS. If there are Labels, it should be in the first few columns. This File Input only accepts .txt and .csv",
trigger = "hover"),
radioButtons(inputId = 'MPPCA_data_choice', label = NULL,
choiceNames = c("Use Your Own Data", "Use Example Data"),
choiceValues = c(TRUE, FALSE),
selected = TRUE, inline = TRUE),
# tags$hr(),
h4(helpText("Is there a header in the data?")),
checkboxInput(inputId = 'MPPCA_header', label = 'Tick for Yes', value = TRUE),
radioButtons(inputId = 'MPPCA_sep', label = h4(helpText("How is the data separated?")),
inline = TRUE, choices = c(Comma=',',Semicolon=';',Tab='\t', Space=''), selected = ','),
sliderInput("MPPCA_cov_slider", h4("Covariates Columns:"), 0, 10, c(0,0)),
# helpText("When covariates are selected, they will appear in the 'Covariates Data' tab"),
sliderInput("MPPCA_label_slider", h4("Group Labels Column:"), 0, 10, 0),
helpText("When group labels are selected, they will appear in the 'Group Labels' tab"),
hidden(textInput("MPPCA_ignore_column", h4("Columns Ignored for Spectral Data:"), "", placeholder="Column number/names (e.g: 2,3,5 or 9.98,9.94,9.86)")),
h4(helpText("Enter Desired Scaling:", bsButton("MPPCA_scale_type_tooltip", label = "",
icon = icon("question"), size = "extra-small"))),
bsPopover("MPPCA_scale_type_tooltip", title="Types of Scaling",
content="Choosing any scale will pre-process the data by the given formula. Further details on Guide Tab.",
trigger = "hover"),
radioButtons(inputId = 'MPPCA_scale', label = NULL,
choiceNames = c("None", 'PQN Scale',
"Auto Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}\\)",
"Pareto Scale \\(\\space\\) \\(\\frac{x-\\mu}{\\sqrt{\\sigma}}\\)",
"Range Scale \\(\\space\\) \\(\\frac{x-\\mu}{x_{max} - x_{min}}\\)",
"Vast Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}(\\frac{\\mu}{\\sigma}\\))"),
choiceValues = c('none', 'PQN', 'autoscale', 'paretoscale', 'rangescale', 'vastscale'),
selected = 'none'),
div(helpText("*μ is the column mean value while σ is the standard deviation"), style = "font-size:80%"),
tags$hr(),
actionButton("MPPCA_parameter_button", "Go to Model Specification",
width = "100%", class = "btn-default"),
),
conditionalPanel(condition="input.MPPCA_plot_tabs=='MPPCA_specification'",
actionButton("MPPCA_data_button", "Go to Data Input",
width = "100%", class = "btn-default"),
h3(helpText('Model Parameters:')),
sliderInput("MPPCA_group_slider", h4("Range of Groups Estimated:"),
min = 1, max = 10, value = c(1,4)),
sliderInput("MPPCA_PC_slider", h4("Range of Principal Components:"),
min = 1, max = 10, value = c(1, 4)),
sliderInput("MPPCA_bootstrap_n_slider", h4("Numbers of Bootstrap:"), 2, 100, 5),
bsTooltip("MPPCA_bootstrap_n_slider", title="Higher Bootstrap, Better Uncertainty but Longer Wait Time", trigger = "hover"),
# h4("Include Covariates?"),
# disabled(checkboxInput(inputId = 'covariates_mix_check', label = span('Tick for Yes', title = "Please make sure covariates data are selected in Data tabs to enable this selection"), value = FALSE)),
# radioButtons("choose_q", label = h4("Output Optimize Model?"), choices = c(Use_Optimal_PC=TRUE, Use_Maximum_PC=FALSE)),
div(style="display:inline-block",
numericInput("MPPCA_epsilon", label = "Covergence Criterion:", value=0.01, min=1e-5, max=1e2),
),
# div(style="display:inline-block",
# "\\(\\space\\space\\)",
# ),
# div(style="display:inline-block",
# numericInput("max_iter_mix", label = "Max Number of Iteration:", value=1e3, min=1, max=1e6),
# ),
tags$h6("Click button to run the model using desired parameters:"),
div(style="display:inline-block",
disabled(actionButton("MPPCA_submit_para_btn", "Submit Parameters", class = "btn-primary")),
),
div(style="display:inline-block",
strong(textOutput("MPPCA_optimal_q")),
),
div(style="display:inline-block",
strong(textOutput("MPPCA_optimal_g")),
),
p("Please input data first", id="MPPCA_data_check_for_submit", style="color:red"),
tags$hr(),
),
# Graph controls
conditionalPanel(condition="input.MPPCA_plot_tabs!='MPPCA_specification' & input.MPPCA_plot_tabs!='MPPCA_data' & input.MPPCA_plot_tabs!='MPPCA_steps'",
# tags$br(),
actionButton("MPPCA_return_param", "Return to Model Specification",
width = "100%", class = "btn-default"),
h3(helpText('Graph Controls:'))
),
# MPPCA Score
conditionalPanel(condition="input.MPPCA_plot_tabs=='MPPCA_score_plot'",
sliderInput("MPPCA_x_PC", h4("X-axis Principal Component:"), min=1, max=10, value=4, step=1, ticks=F),
sliderInput("MPPCA_y_PC", h4("Y-axis Principal Component:"), min=1, max=10, value=4, step=1, ticks=F),
sliderInput("MPPCA_post_int", h4("Posterior Interval:"), min=0.8, max=0.9999, value=0.95, ticks=F),
disabled(downloadButton("dl_mppca_score_btn", span("Download", title="Download a Table of All Score Estimates."))),
),
# MPPCA Loadings & Loading Analysis plot
conditionalPanel(condition="input.MPPCA_plot_tabs=='MPPCA_loading_plot' || input.MPPCA_plot_tabs=='MPPCA_loading_analysis_plot'",
sliderInput("MPPCA_group", h4("Group Selected:"), min=1, max=10, value=4, step=1),
conditionalPanel(condition="input.MPPCA_plot_tabs=='MPPCA_loading_analysis_plot'",
sliderInput("MPPCA_analysis_plot_PC", h4("Principal Component for Plot:"), min=1, max=10, value=1, step=1, ticks=F),
sliderInput("MPPCA_conf_int", h4("Confidence Interval:"), min=0.8, max=0.9999, value=0.95, ticks=F),
sliderInput("MPPCA_n_load_analysis", h5("Numbers of Significant Bin for Loading Analysis Plot"), min=1, max=20, value=5, step=1, ticks=F),
disabled(downloadButton("dl_mppca_loadings_analysis_btn", span("Download", title="Download a table of data used to produced the graph."))),
),
conditionalPanel(condition="input.MPPCA_plot_tabs=='MPPCA_loading_plot'",
disabled(downloadButton("dl_mppca_loadings_btn", span("Download", title="Download a Table of All Loadings Estimates."))),
)
),
),
),
# MPCCA plots
mainPanel(width = 9, id = "MPPCA_main_layout",
# Style for MPPCA tabs
tags$style(HTML("
.tabbable > .nav > li > a[data-value='MPPCA_spectral'] {background-color:lightyellow}
.tabbable > .nav > li > a[data-value='MPPCA_covariates'] {background-color:lightyellow; color:blue}
.tabbable > .nav > li > a[data-value='MPPCA_labels'] {background-color:lightyellow}
.tabbable > .nav > li[class=active] > a {background-color:grey; color:white}
#MPPCA_data_tabs li:nth-child(3) {float:right}
#MPPCA_data_tabs li > a[data-value='MPPCA_original_data_table']{background-color:lightgrey}
")),
tabsetPanel(id = "MPPCA_plot_tabs", selected = "MPPCA_steps",
tabPanel("Quick Start", value = "MPPCA_steps",
column(width = 6, offset = 0.5,
br(),
h1(strong(tags$u("Usage Guide:"))),
h3(
tags$div(
tags$ol(
tags$li("Enter your data in the 'MPPCA Data' tab"),
br(),
tags$li("Specify the model parameters in the 'Model Specification' tab"),
br(),
tags$li("In the 'Model Specification' tab, click the 'SUBMIT MODEL SPECIFICATIONS' button for the model to run."),
br(),
tags$li("Once the model finishes running, plots will appear under the different plot tabs beside the 'Model Specification' tab."),
br(),
actionButton("MPPCA_data_QS", "MPPCA Data", class = "btn-default"),
actionButton("MPPCA_model_spec_QS", "Model Specification", class = "btn-default"),
)
)
),
br(),
),
column(width = 6, offset = 0.5,
br(),
h4(tags$u("Note: MPPCA demo below, click play to animate")),
sliderInput("MPPCA_gif_slider", label=NULL, min = 0, max=218, value=0, ticks = FALSE,
animate = animationOptions(interval = 300)),
hidden(imageOutput("MPPCA_gif_demo")),
br(),
br(),
)
),
tabPanel("MPPCA Data", value = "MPPCA_data",
br(),
tabsetPanel(id = "MPPCA_data_tabs",
tabPanel(span("Spectral Data", title="Data from NMR or Mass Spectroscopy (MS)"),
value = 'MPPCA_spectral',
strong(h3(textOutput("MPPCA_spectral_file_name"))),
dataTableOutput("MPPCA_spectral_data_table"),
),
# tabPanel(span("Covariates Data", title="Sample attributes (eg: weight, gender, regions, etc)"),
# value = 'MPPCA_covariates',
# dataTableOutput("MPPCA_covariates_data_table")
# ),
tabPanel(span("Group Labels", title="This data is used for colours in plots (eg: Treatment group vs Control group)"),
value = 'MPPCA_labels',
dataTableOutput("MPPCA_labels_data_table")
),
tabPanel("Original Data",
value = "MPPCA_original_data_table",
br(),
column(12,
# column(3, "Covariates Highlighted Color", style = "background-color:lightblue; color:blue"),
column(3, "Group Labels Highlighted Color", style = "background-color:orange;"),
# column(3, "Columns Ignored Highlighted Color", style = "background-color:grey;color:white"),
),
uiOutput("MPPCA_original_data_table_UI")
# dataTableOutput("PPCA_original_data_table"),
# tags$style(type="text/css", paste0("#DPPCA_original_data_table td:nth-child(", 3, ") {text-align:center;background-color:green;color: white;text-align:center}"))
)
)
),
tabPanel("Model Specification", value = "MPPCA_specification",
br(),
bsCollapse(id = "MPPCA_desc", open = "MPPCA Parameters Description",
bsCollapsePanel("MPPCA Parameters Description",
includeHTML("MPPCA_desc_pd.html"),
style = "primary"),
bsCollapsePanel("MPPCA Conceptual Explanation",
includeHTML("MPPCA_desc_ce.html"),
style = "info"),
bsCollapsePanel("MPPCA Technical Details",
includeHTML("MPPCA_desc_td.html"),
style = "info")
)
),
tabPanel("Score Plot", value = "MPPCA_score_plot",
plotOutput("MPPCA_plot_score")
),
tabPanel("Loading Plot", value = "MPPCA_loading_plot",
plotOutput("MPPCA_plot_loadings")
),
tabPanel("Loading Analysis Plot", value = "MPPCA_loading_analysis_plot",
plotOutput("MPPCA_plot_loadings_analysis")
),
# tabPanel("Contingency Table", value = "contin_MPPCA_table",
# "If Group Labels are not given, error will appear as:",
# tags$br(),
# "Error: all arguments must have the same length",
# tags$br(),
# "Fix it by selecting Group Labels Column in 'Data' tab.",
# verbatimTextOutput("MPPCA_contin_table")
# ),
tabPanel("BIC Plot", value = "MPPCA_BIC_plot",
plotOutput("MPPCA_plot_BIC")
)
)
)
),
# dppca -------------------------------------------------------------------
tabPanel("DPPCA",
conditionalPanel(condition="input.DPPCA_plot_tabs!='DPPCA_steps'",
# DPPCA sidebar controls
sidebarPanel(width = 3,
# tags$style("#DPPCA_tooltip+.popover{width:170px;}"),
# bsPopover("DPPCA_tooltip",
# title = "Dynamic Probabilistic Principal Component Analysis",
# content = "",
# trigger = "hover", placement = "right"),
conditionalPanel(condition="input.DPPCA_plot_tabs=='DPPCA_data'",
# DPPCA data inputs
fileInput("DPPCA_input_file", h4("File input:", bsButton("DPPCA_data_tooltip", label = "",
icon = icon("question"), size = "extra-small")),
multiple = F, accept = c("text/csv", "text/comma-separated-values, text/plain", ".csv"),
placeholder = "Enter Metabolomic Data Here"),
bsPopover("DPPCA_data_tooltip", title="Metabolomic Data File",
content="Should consist of at least the Spectral Data from NMR/MS and a column specifying the different time point. If there are Labels, it should be in the first few columns. This File Input only accepts .txt and .csv",
trigger = "hover"),
radioButtons(inputId = 'DPPCA_data_choice', label = NULL,
choiceNames = c("Use Your Own Data", "Use Example Data"),
choiceValues = c(TRUE, FALSE),
selected = TRUE, inline = TRUE),
# tags$hr(),
h4(helpText("Is there a header in the data?")),
checkboxInput(inputId = 'DPPCA_header', label = 'Tick for Yes', value = TRUE),
radioButtons(inputId = 'DPPCA_sep', label = h4(helpText("How is the data separated?")),
inline = TRUE, choices = c(Comma=',',Semicolon=';',Tab='\t', Space=''), selected = ','),
# sliderInput("DPPCA_subjectID_slider", h4("Subject ID Column:"), 0, 10, 0),
sliderInput("DPPCA_time_slider", h4("Time Column:"), 0, 10, 3),
helpText("Specify Time Column to enable time selection below"),
selectInput( "DPPCA_time_selected", label = "Time Point Selected for Data Shown", choices = NULL),
sliderInput("DPPCA_label_slider", h4("Group Labels Column:"), 0, 10, 4),
helpText("When group labels are selected, they will appear in the 'Group Labels' tab"),
textInput("DPPCA_ignore_column", h4("Columns Ignored for Spectral Data:"),
value = "1,2",
placeholder="Column number/names (e.g: 2,3,5 or 9.98,9.94,9.86)"),
h4(helpText("Enter Desired Scaling:", bsButton("DPPCA_scale_type_tooltip", label = "",
icon = icon("question"), size = "extra-small"))),
bsPopover("DPPCA_scale_type_tooltip", title="Types of Scaling",
content="Choosing any scale will pre-process the data by the given formula. Further details on Guide Tab.",
trigger = "hover"),
radioButtons(inputId = 'DPPCA_scale', label = NULL,
choiceNames = c("None", 'PQN Scale',
"Auto Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}\\)",
"Pareto Scale \\(\\space\\) \\(\\frac{x-\\mu}{\\sqrt{\\sigma}}\\)",
"Range Scale \\(\\space\\) \\(\\frac{x-\\mu}{x_{max} - x_{min}}\\)",
"Vast Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}(\\frac{\\mu}{\\sigma}\\))"),
choiceValues = c('none', 'PQN', 'autoscale', 'paretoscale', 'rangescale', 'vastscale'),
selected = 'none'),
div(helpText("*μ is the column mean value while σ is the standard deviation"), style = "font-size:80%"),
tags$hr(),
actionButton("DPPCA_parameter_button", "Go to Model Settings",
width = "100%", class = "btn-default"),
),
conditionalPanel(condition="input.DPPCA_plot_tabs=='DPPCA_specification'",
actionButton("DPPCA_data_button", "Go to Data Input",
width = "100%", class = "btn-default"),
br(),
br(),
tabsetPanel(id = "DPPCA_sidetab",
tabPanel("Model Parameters", value = "DPPCA_model_parameters",
# h3(helpText('Model Parameters:')),
sliderInput("DPPCA_PC_slider", h4("Number of Principal Components:"),
min = 1, max = 10, value = 2,),
radioButtons(inputId = 'DPPCA_priors_choice', label = h4("Model Priors Settings:"),
choices = c("Default", 'Custom'), inline = TRUE),
helpText("Clicking Default will reset the values in Model Priors!"),
sliderInput("DPPCA_chain_slider", h4("Length of Chain:"),
min = 100, max = 3000, value = 500, step=100),
sliderInput("DPPCA_burn_slider", h4("Length of Burn-in Period:"),
min = 1, max = 1000, value = 50, step=10),
sliderInput("DPPCA_thin_slider", h4("Number of Thinning on Chain:"),
min = 1, max = 500, value = 10, step=10),
# sliderInput("DPPCA_post_burn_slider", h4("Length of Further Burn-in:"),
# min = 100, max = 1000, value = 100, step=100),
# helpText("Further burn-in applied to chains of loadings and scores to calculate posteriors"),
# sliderInput("DPPCA_post_thin_slider", h4("Number of Further Thinning:"),
# min = 100, max = 1000, value = 100, step=100),
# helpText("Further thinning applied to chains of loadings and scores to calculate posteriors"),
tags$hr(),
tags$h6("Click button to run the model using desired parameters:"),
div(style="display:inline-block",
disabled(actionButton("DPPCA_submit_para_btn", "Submit Parameters", class = "btn-primary")),
),
# div(style="display:inline-block",
# strong(textOutput("MPPCA_optimal_q")),
# ),
# div(style="display:inline-block",
# strong(textOutput("MPPCA_optimal_g")),
# ),
p("Please input data first", id="DPPCA_data_check_for_submit", style="color:red"),
tags$hr(),
),
tabPanel("Model Priors", value = "DPPCA_model_priors",
tags$table(
tags$tr(width = "100%",
tags$td(width = "60%", div("prior_alpha")),
tags$td(width = "40%", textInput(inputId = "prior_alpha", value = 6, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", tags$div("prior_beta")),
tags$td(width = "40%", textInput(inputId = "prior_beta", value = 0.5, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", div("prior_sigma2_nu")),
tags$td(width = "40%", textInput(inputId = "prior_sigma2_nu", value = 10, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", tags$div("prior_mu_phi")),
tags$td(width = "40%", textInput(inputId = "prior_mu_phi", value = 0.75, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", div("prior_sigma2_phi")),
tags$td(width = "40%", textInput(inputId = "prior_sigma2_phi", value = 0.1, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", tags$div("prior_alpha_V")),
tags$td(width = "40%", textInput(inputId = "prior_alpha_V", value = 6, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", div("prior_beta_V")),
tags$td(width = "40%", textInput(inputId = "prior_beta_V", value = 0.5, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", tags$div("prior_sigma2_mu")),
tags$td(width = "40%", textInput(inputId = "prior_sigma2_mu", value = 10, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", div("prior_sigma2_PHI")),
tags$td(width = "40%", textInput(inputId = "prior_sigma2_PHI", value = 0.1, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", tags$div("prior_mu_PHI")),
tags$td(width = "40%", textInput(inputId = "prior_mu_PHI", value = 0.75, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", tags$div("prior_omega_inv")),
tags$td(width = "40%", textInput(inputId = "prior_omega_inv", value = 1, label = NULL)))
)
)
)
),
conditionalPanel(condition="input.DPPCA_plot_tabs!='DPPCA_data' & input.DPPCA_plot_tabs!='DPPCA_specification' & input.DPPCA_plot_tabs!='DPPCA_LMM_modelling' & input.DPPCA_plot_tabs!='DPPCA_steps'",
actionButton("DPPCA_return_param", "Return to Model Specification",
width = "100%", class = "btn-default"),
h3(helpText('Graph Controls:')),
h3(tags$div(style='color:orange',"As DPPCA takes time to run, this is a pre-run results for plots", id="Pre-run_results_warning")),
radioButtons(inputId = 'DPPCA_result_choice', label = NULL,
choiceNames = c("Use Output Result", "Use Example Result"),
choiceValues = c(TRUE, FALSE),
selected = TRUE, inline = TRUE),
),
conditionalPanel(condition="input.DPPCA_plot_tabs=='DPPCA_loading_analysis_plot'",
sliderInput("DPPCA_spectral_plot_PC", h4("Principal Component for Plot:"), min=1, max=2, value=1, step=1, ticks=F),
sliderInput("DPPCA_cred_int", h4("Credible Interval:"), min=0.8, max=0.9999, value=0.95, ticks=F),
sliderInput("DPPCA_load_analysis_time", h4("Time for Plot:"), min=1, max=8, value=1, step=1, ticks=F),
sliderInput("DPPCA_n_load_analysis", h5("Numbers of Significant Bin for Loading Analysis Plot"), min=1, max=20, value=5, step=1, ticks=F),
# disabled(downloadButton("dl_dppca_loadings_analysis_btn", span("Download", title="Download a table of data used to produced the graph."))),
),
conditionalPanel(condition="input.DPPCA_plot_tabs=='DPPCA_LMM_modelling'",
h3(helpText('Linear Mixed Model:')),
h3(tags$div(style='color:orange',"As DPPCA takes time to run, this is a pre-run results for plots", id="Pre-run_results_warning2")),
sliderInput("DPPCA_LMM_cred_int", h4("Significance level (alpha value):"), min=0.0001, max=0.2, value=0.05, ticks=F),
tags$hr(),
tags$h6("Click button to run the LMM model:"),
div(style="display:inline-block",
actionButton("DPPCA_submit_LMM_btn", "Submit Parameters", class = "btn-primary"),
),
),
),
),
mainPanel(width = 9, id = "DPPCA_main_layout",
# Style for DPPCA tabs
tags$style(HTML("
.tabbable > .nav > li > a[data-value='DPPCA_original'] {background-color:lightyellow}
.tabbable > .nav > li > a[data-value='DPPCA_spectral'] {background-color:lightyellow}
.tabbable > .nav > li > a[data-value='DPPCA_labels'] {background-color:lightyellow}
.tabbable > .nav > li[class=active] > a {background-color:grey; color:white}
#DPPCA_data_tabs li:nth-child(3) {float:right}
#DPPCA_data_tabs li > a[data-value='DPPCA_original_data_table']{background-color:lightgrey}
")),
tabsetPanel(id = "DPPCA_plot_tabs", selected = "DPPCA_steps",
tabPanel("Quick Start", value = "DPPCA_steps",
column(width = 6, offset = 0.5,
br(),
h1(strong(tags$u("Usage Guide:"))),
h3(
tags$div(
tags$ol(
tags$li("Enter your data in the 'DPPCA Data' tab"),
br(),
tags$li("Specify the model parameters in the 'Model Specification' tab"),
br(),
tags$li("In the 'Model Specification' tab, click the 'SUBMIT MODEL SPECIFICATIONS' button for the model to run."),
br(),
tags$li("Once the model finishes running, plots will appear under the different plot tabs beside the 'Model Specification' tab."),
br(),
actionButton("DPPCA_data_QS", "DPPCA Data", class = "btn-default"),
actionButton("DPPCA_model_spec_QS", "Model Specification", class = "btn-default"),
)
)
),
br(),
),
column(width = 6, offset = 0.5,
br(),
# h4(tags$u("Note: DPPCA demo below, click play to animate")),
# sliderInput("DPPCA_gif_slider", label=NULL, min = 0, max=218, value=0, ticks = FALSE,
# animate = animationOptions(interval = 300)),
# hidden(imageOutput("DPPCA_gif_demo")),
br(),
br(),
)
),
tabPanel("DPPCA Data", value = "DPPCA_data",
br(),
tabsetPanel(id = "DPPCA_data_tabs",
# tabPanel(span("Original Data", title="Data from NMR or Mass Spectroscopy (MS)"),
# value = 'DPPCA_original',
# strong(h3(textOutput("DPPCA_spectral_file_name"))),
# dataTableOutput("DPPCA_original_data_table"),
# ),
tabPanel("Spectral Data",
value = 'DPPCA_spectral',
strong(h3(textOutput("DPPCA_spectral_file_name"))),
h4(tags$div(style='color:green', textOutput("DPPCA_time_point_showing"))),
dataTableOutput("DPPCA_spectral_data_table")
),
tabPanel(span("Group Labels", title="This data is used for colours in plots (eg: Treatment group vs Control group)"),
value = 'DPPCA_labels',
dataTableOutput("DPPCA_labels_data_table")
),
tabPanel("Original Data",
value = "DPPCA_original_data_table",
br(),
column(12,
column(2, "Time Highlighted Color", style = "background-color:lightgreen;"),
column(3, "Group Labels Highlighted Color", style = "background-color:orange;"),
column(3, "Columns Ignored Highlighted Color", style = "background-color:grey;color:white"),
),
uiOutput("DPPCA_original_data_table_UI"),
# dataTableOutput("DPPCA_original_data_table"),
# tags$style(type="text/css", paste0("#DPPCA_original_data_table td:nth-child(", 3, ") {text-align:center;background-color:green;color: white;text-align:center}"))
)
)
),
tabPanel("Model Specification", value = "DPPCA_specification",
column(12, align="center",
h2(strong("---Placeholder for DPPCA Explanation---"))
)
),
tabPanel("Chain Convergence", value = "DPPCA_chain_conver",
br(),
actionButton("DPPCA_convergence_next_btn", "Next Random Sample Chain for Score/Loadings",
style="float:right", class = "btn-default"),
plotOutput("DPPCA_plot_load_score_chain"),
br(),
br(),
plotOutput("DPPCA_plot_persistence_chain")
),
tabPanel("Time Influence", value = "DPPCA_time_inf",
plotOutput("DPPCA_plot_persistence_histogram"),
br(),
verbatimTextOutput("DPPCA_plot_persistence_summary")
),
tabPanel("Loading Analysis Plot", value = "DPPCA_loading_analysis_plot",
plotOutput("DPPCA_plot_top_loadings"),
),
tabPanel("LMM Modelling", value = "DPPCA_LMM_modelling",
plotOutput("DPPCA_plot_LMM_fit"),
br(),
verbatimTextOutput("DPPCA_LMM_summary")
)
)
)
)
),
# Sample Size Estimation --------------------------------------------------
navbarMenu("Sample Size Estimation",
# for ppca ----------------------------------------------------------------
tabPanel("For PPCA/PPCCA",
conditionalPanel(condition="input.sample_size_PPCA_tab!='sample_size_PPCA_steps'",
sidebarPanel(width = 3,
conditionalPanel(condition="input.sample_size_PPCA_tab=='sample_size_PPCA_pilot_data'",
fileInput("sample_size_PPCA_pilot_data", h4("File input:", bsButton("sample_size_PPCA_pilot_data_tooltip", label = "",
icon = icon("question"), size = "extra-small")),
multiple = F, accept = c("text/csv", "text/comma-separated-values, text/plain", ".csv"),
placeholder = "Enter Metabolomic Data Here"),
bsPopover("sample_size_PPCA_pilot_data_tooltip", title="Metabolomic Data File",
content="Should consist of at least the Spectral Data from NMR/MS. If there are Labels, it should be in the first few columns. This File Input only accepts .txt and .csv",
trigger = "hover"),
radioButtons(inputId = 'sample_size_PPCA_data_choice', label = NULL,
choiceNames = c("Use Your Own Data", "Use Example Data"),
choiceValues = c(TRUE, FALSE),
selected = TRUE, inline = TRUE),
# tags$hr(),
h4(helpText("Is there a header in the data?")),
checkboxInput(inputId = 'sample_size_PPCA_header', label = 'Tick for Yes', value = TRUE),
radioButtons(inputId = 'sample_size_PPCA_sep', label = h4(helpText("How is the data separated?")),
inline = TRUE, choices = c(Comma=',',Semicolon=';',Tab='\t', Space=''), selected = ','),
sliderInput("sample_size_PPCA_cov_slider", h4("Covariates Columns:"), 0, 10, c(0,0)),
helpText("When covariates are selected, they will appear in the 'Covariates Data' tab"),
# sliderInput("sample_size_PPCA_label_slider", h4("Group Labels Column:"), 0, 10, 0),
# helpText("When group labels are selected, they will appear in the 'Group Labels' tab"),
textInput("sample_size_PPCA_ignore_column", h4("Columns Ignored for Spectral Data:"), "", placeholder="Column number/names (e.g: 2,3,5 or 9.98,9.94,9.86)"),
h4(helpText("Enter Desired Scaling:", bsButton("scale_type_sample_PPCA_tooltip", label = "",
icon = icon("question"), size = "extra-small"))),
bsPopover("scale_type_sample_PPCA_tooltip", title="Types of Scaling",
content="Choosing any scale will pre-process the data by the given formula. Further details on Guide Tab.",
trigger = "hover"),
radioButtons(inputId = 'sample_size_PPCA_scale', label = NULL,
choiceNames = c("None", 'PQN Scale',
"Auto Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}\\)",
"Pareto Scale \\(\\space\\) \\(\\frac{x-\\mu}{\\sqrt{\\sigma}}\\)",
"Range Scale \\(\\space\\) \\(\\frac{x-\\mu}{x_{max} - x_{min}}\\)",
"Vast Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}(\\frac{\\mu}{\\sigma}\\))"),
choiceValues = c('none', 'PQN', 'autoscale', 'paretoscale', 'rangescale', 'vastscale'),
selected = 'none'),
div(helpText("*μ is the column mean value while σ is the standard deviation"), style = "font-size:80%"),
hr(),
actionButton("sample_size_PPCA_parameter_button", "Go to Model Settings",
width = "100%", class = "btn-default"),
),
conditionalPanel(condition="input.sample_size_PPCA_tab=='sample_size_PPCA_specification'",
actionButton("sample_size_PPCA_pilot_data_button", "Go to Pilot Data",
width = "100%", class = "btn-default"),
h3(helpText("Sample Size Estimation Parameters:")),
hr(),
h4("Do you want to include Pilot Data?"),
checkboxInput(inputId = 'sample_size_PPCA_pilot_check', label = 'Tick for Yes', value = FALSE),
h4("Do you want to include", span("Covariates?", style="color:blue")),
checkboxInput(inputId = 'sample_size_PPCA_covariates_check', label = span('Tick for Yes', style="color:blue", title = "Please make sure covariates data are selected in Data tabs to enable this selection"), value = FALSE),
# Input: Spectral Bins
sliderInput("sample_size_PPCA_n_bins", h4("Number of Spectral Bins:"),
min = 50, max = 400, value = 100),
# Input: Proportion of significant bins (m)
sliderInput("sample_size_PPCA_prop_sig_bins", h4("Proportion of Significant Bins (m):"),
min = 0.1, max = 1, value = 0.5, step = 0.05),
# Input: Target FDR
sliderInput("sample_size_PPCA_target_fdr", h4("Target FDR:"),
min = 0.01, max = 0.2, step = 0.01, value = 0.05),
h4(helpText(tags$u("To investigate the impact of 'm' on FDR please choose:"))),
# Input: Sample size per group n1 and n2
sliderInput("sample_size_PPCA_n1", h4("Group 1 Sample Size:"),
min = 1, max = 100, value = 5, step = 1),
sliderInput("sample_size_PPCA_n2", h4("Group 2 Sample Size:"),
min = 1, max = 100, value = 5, step = 1),
actionButton("sample_size_PPCA_submit_button", "Calculate Sample Size", class = "btn-primary"),
hidden(p("Pilot Data Selected but not Given", id="sample_size_PPCA_data_check_for_submit", style="color:red")),
),
conditionalPanel(condition="input.sample_size_PPCA_tab!='sample_size_PPCA_pilot_data' & input.sample_size_PPCA_tab!='sample_size_PPCA_specification' & input.sample_size_PPCA_tab!='sample_size_PPCA_steps'",
actionButton("sample_size_PPCA_return_param_button", "Return to Estimation Parameters",
width = "100%", class = "btn-default"),
h4("Parameters Used:"),
tableOutput("estimation_parameter_PPCA_report"),
),
),
),
mainPanel(width = 9, id = "sample_size_PPCA_main_layout",
# Style for data tabs
tags$style(HTML("
.tabbable > .nav > li > a[data-value='sample_size_PPCA_spectral'] {background-color:lightyellow}
.tabbable > .nav > li > a[data-value='sample_size_PPCA_covariates'] {background-color:lightyellow; color:blue}
# .tabbable > .nav > li > a[data-value='PPCA_labels'] {background-color:lightyellow}
.tabbable > .nav > li[class=active] > a {background-color:grey; color:white}
")),
tabsetPanel(id="sample_size_PPCA_tab", selected = "sample_size_PPCA_steps",
tabPanel("Quick Start", value = "sample_size_PPCA_steps",
column(width = 6, offset = 0.5,
br(),
h1(strong(tags$u("Usage Guide:"))),
h3(
tags$div(
tags$ol(
h4(helpText("(optional) If you have pilot data, you may enter them in the 'Pilot Data' tab")),
tags$li("Specify the model parameters in the 'Model Specification' tab"),
br(),
tags$li("In the 'Model Specification' tab, click the 'Calculate Sample Size' button for the model to run."),
br(),
tags$li("Once the model finishes running, plots will appear under the different plot tabs beside the 'Model Specification' tab."),
br(),
actionButton("sample_size_PPCA_data_QS", "Pilot Data", class = "btn-default"),
actionButton("sample_size_PPCA_model_spec_QS", "Model Specification", class = "btn-default"),
)
)
),
br(),
),
column(width = 6, offset = 0.5,
br(),
# h4(tags$u("Note: Sample Size Estimation demo below, click play to animate")),
# sliderInput("sample_size_PPCA_gif_slider", label=NULL, min = 0, max=218, value=0, ticks = FALSE,
# animate = animationOptions(interval = 300)),
# hidden(imageOutput("sample_size_PPCA_gif_demo")),
br(),
br(),
)
),
tabPanel("Pilot Data", value = "sample_size_PPCA_pilot_data",
br(),
tabsetPanel(id = "sample_size_PPCA_data_tabs",
tabPanel(span("Spectral Data", title="Data from NMR or Mass Spectroscopy (MS)"),
value = 'sample_size_PPCA_spectral',
strong(h3(textOutput("sample_size_PPCA_spectral_file_name"))),
dataTableOutput("sample_size_PPCA_spectral_data_table"),
),
tabPanel(span("Covariates Data", title="Sample attributes (eg: weight, gender, regions, etc)"),
value = 'sample_size_PPCA_covariates',
# h3(textOutput("PPCA_spectral_file_name")),
dataTableOutput("sample_size_PPCA_covariates_data_table")
)
# tabPanel(span("Group Labels", title="This data is used for colours in plots (eg: Treatment group vs Control group)"),
# value = 'sample_size_PPCA_labels',
# # h3(textOutput("PPCA_spectral_file_name")),
# dataTableOutput("PPCA_labels_data_table")
# )
)
),
tabPanel("Model Specification", value = "sample_size_PPCA_specification",
br(),
column(12, align="center",
h2(strong("---Placeholder for Sample Size Explanation---"))
)
# bsCollapse(id = "sample_size_PPCA_desc", open = "Sample Size Estimation using PPCA Usage Guide",
# bsCollapsePanel("Sample Size Estimation using PPCA Usage Guide",
#
# includeHTML("Sample_size_PPCA_desc_ug.html"),
# style = "primary")
# bsCollapsePanel("Sample Size Estimation using PPCA Conceptual Explanation",
#
# includeHTML("Sample_size_PPCA_desc_ce.html"),
# style = "info"),
# bsCollapsePanel("Sample Size Estimation using PPCA Technical Details",
#
# includeHTML("Sample_size_PPCA_desc_td.html"),
# style = "info")
# )
),
tabPanel("Sample Size Estimation", value = "sample_size_PPCA_estimation",
plotOutput("sample_size_PPCA_estimate")
),
tabPanel("Varying the Significant Bins",
plotOutput("sample_size_PPCA_significant_bins")
)
)
)
)
),
# GUIDES ------------------------------------------------------------------
# tabPanel("Guides", "This panel is intentionally left blank",
# mainPanel(width = 9,
# tabsetPanel(
# tabPanel("Tab 3", "This panel is intentionally left blank",
# textInput("txt", "Text input:", "general"),
# tags$h5("Default actionButton:"),
# actionButton("action", "Search"),
#
# tags$h5("actionButton with CSS class:"),
# actionButton("action2", "Action button", class = "btn-primary")
# )
# ),
# verbatimTextOutput("debug")
# ),
# ),
# ABOUT US ----------------------------------------------------------------
tabPanel("About Us",
"-placeholder-"
)
)
))
GweeXianYao/metaboliteR documentation built on Jan. 21, 2020, 7:18 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library(shiny)
library(shinyBS)
library(shinythemes)
library(shinyjs)
library(metaboliteR)
library(Rcpp)
shinyUI(ui = tagList(
# Color error output red
tags$head(
tags$style(HTML("
.shiny-output-error-validation {
color: red;
}
"))),
# Padding for fixed top menu bar
tags$style(type="text/css", "body {padding-top: 70px;}"),
useShinyjs(),
withMathJax(),
navbarPage(id = "menu_bar", position = "fixed-top",
theme = shinytheme("lumen"), # <--- To use a theme, uncomment this
strong("metaboliteR"), # Main title name
# HOME --------------------------------------------------------------------
tabPanel("Home",
column(12, align="center",
# img(src='logo.png', align = "right", height=120, width=100),
tags$img(src = "metaboliteR-banner.png",height=160,width=800), # placeholder for banner
# h2(strong("Welcome to the Shiny App for Analysing Metabolomic Data Using R")),
# Buttons box
column(12, align="center",
# PPCA method button
column(6, align = "right",
# "PPCA",
br(),
tags$button(
id = "PPCA_method_button",
class = "btn action-button",
tags$img(src = "PPCA-logo.png",
height = "200px",
width = "400px"),
style = "background-color:white"
),
br(),
# "this method is good"
),
# MPPCA method button
column(6, align = "left",
# "MPPCA",
br(),
tags$button(
id = "MPPCA_method_button",
class = "btn action-button",
tags$img(src = "MPPCA-logo.png",
height = "200px",
width = "400px"),
style = "background-color:white"
),
br(),
# "this method is good"
),
# Insert vertical spacing between buttons
column(12),
# DPPCA method button
column(6, align = "right",
# "DPPCA",
br(),
tags$button(
id = "DPPCA_method_button",
class = "btn action-button",
tags$img(src = "DPPCA-logo.png",
height = "200px",
width = "400px"),
style = "background-color:white"
),
br(),
# "this method is good"
),
# Sample Size Estimation button
column(6, align = "left",
# "Sample Size Estimation",
br(),
tags$button(
id = "Sample_Size_Estimation_button",
class = "btn action-button",
tags$img(src = "Sample_Size_Estimation-logo.png",
height = "200px",
width = "400px"),
style = "background-color:white"
),
br(),
# "this method is good"
),
),
column(12, align="center",
br(),
h4(strong("To get started, click on the method you require.")),
)
)
),
# ANALYTICS ---------------------------------------------------------------
navbarMenu("Analytics",
# ppca --------------------------------------------------------------------
tabPanel("PPCA/PPCCA",
conditionalPanel(condition="input.PPCA_plot_tabs!='PPCA_steps'",
# PPCA sidebar controls
sidebarPanel(width = 3,
# tags$style("#PPCA_tooltip+.popover{width:170px;}"),
# bsPopover("PPCA_tooltip",
# title = "Probabilistic Principal Component Analysis",
# content = "",
# trigger = "hover", placement = "right"),
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_data'",
textOutput("arg"),
# PPCA data inputs
fileInput("PPCA_input_file", h4("File input:", bsButton("PPCA_data_tooltip", label = "",
icon = icon("question"), size = "extra-small")),
multiple = F, accept = c("text/csv", "text/comma-separated-values, text/plain", ".csv"),
placeholder = "Enter Metabolomic Data Here"),
bsPopover("PPCA_data_tooltip", title="Metabolomic Data File",
content="Should consist of at least the Spectral Data from NMR/MS. If there are Covariates/Labels, it should be in the first few columns. This File Input only accepts .txt and .csv",
trigger = "hover"),
radioButtons(inputId = 'PPCA_data_choice', label = NULL,
choiceNames = c("Use Your Own Data", "Use Example Data"),
choiceValues = c(TRUE, FALSE),
selected = TRUE, inline = TRUE),
# tags$hr(),
h4(helpText("Is there a header in the data?")),
checkboxInput(inputId = 'PPCA_header', label = 'Tick for Yes', value = TRUE),
radioButtons(inputId = 'PPCA_sep', label = h4(helpText("How is the data separated?")),
inline = TRUE, choices = c(Comma=',',Semicolon=';',Tab='\t', Space=''), selected = ','),
sliderInput("PPCA_cov_slider", h4("Covariates Columns:"), 0, 10, c(0,0)),
helpText("When covariates are selected, they will appear in the 'Covariates Data' tab"),
sliderInput("PPCA_label_slider", h4("Group Labels Column:"), 0, 10, 0),
helpText("When group labels are selected, they will appear in the 'Group Labels' tab"),
hidden(textInput("PPCA_ignore_column", h4("Columns Ignored for Spectral Data:"), "", placeholder="Column number/names (e.g: 2,3,5 or 9.98,9.94,9.86)")),
h4(helpText("Enter Desired Scaling:", bsButton("PPCA_scale_type_tooltip", label = "",
icon = icon("question"), size = "extra-small"))),
bsPopover("PPCA_scale_type_tooltip", title="Types of Scaling",
content="Choosing any scale will pre-process the data by the given formula. Further details on Guide Tab.",
trigger = "hover"),
radioButtons(inputId = 'PPCA_scale', label = NULL,
choiceNames = c("None", 'PQN Scale',
"Auto Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}\\)",
"Pareto Scale \\(\\space\\) \\(\\frac{x-\\mu}{\\sqrt{\\sigma}}\\)",
"Range Scale \\(\\space\\) \\(\\frac{x-\\mu}{x_{max} - x_{min}}\\)",
"Vast Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}(\\frac{\\mu}{\\sigma}\\))"),
choiceValues = c('none', 'PQN', 'autoscale', 'paretoscale', 'rangescale', 'vastscale'),
selected = 'none'),
div(helpText("*μ is the column mean value while σ is the standard deviation"), style = "font-size:80%"),
tags$hr(),
actionButton("PPCA_parameter_button", "Go to Model Specification",
width = "100%", class = "btn-default"),
),
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_specification'",
actionButton("PPCA_data_button", "Go to PPCA Data Input",
width = "100%", class = "btn-default"),
h3(helpText('Model Specifications:')),
sliderInput("PPCA_PC_slider", h4("Range of Principal Components:"),
min = 1, max = 10, value = c(1, 4)),
sliderInput("PPCA_bootstrap_n_slider", h4("Numbers of Bootstrap:"), 2, 100, 5),
bsPopover("PPCA_bootstrap_n_slider", title="",
content="Higher Bootstrap, Better Uncertainty but Longer Wait Time", trigger = "hover"),
h4("Do you want to include", span("Covariates?", style="color:blue")),
disabled(checkboxInput(inputId = 'PPCA_covariates_check', label = span('Tick for Yes', style="color:blue", title = "Please make sure covariates data are selected in Data tabs to enable this selection"), value = FALSE)),
radioButtons("PPCA_choose_q", label = h4("Output Optimize Model?"),
choiceNames = c("Use Optimal PC", "Use Maximum PC"),
choiceValues = c(TRUE, FALSE)),
div(style="display:inline-block",
numericInput("PPCA_epsilon", label = "Covergence Criterion:", value=0.01, min=1e-5, max=1e2, ),
),
div(style="display:inline-block",
"\\(\\space\\space\\)",
),
div(style="display:inline-block",
numericInput("PPCA_max_iter", label = "Max Number of Iteration:", value=1e3, min=1, max=1e6),
),
tags$h6("Click button to run the model using desired specifications:"),
div(style="display:inline-block",
disabled(actionButton("PPCA_submit_para_btn", "Submit Model Specifications", class = "btn-primary")),
),
div(style="display:inline-block",
strong(textOutput("PPCA_optimal_q")),
),
p("Please input data first", id="PPCA_data_check_for_submit", style="color:red"),
tags$hr(),
),
conditionalPanel(condition="input.PPCA_plot_tabs!='PPCA_specification' & input.PPCA_plot_tabs!='PPCA_data' & input.PPCA_plot_tabs!='PPCA_steps'",
# tags$br(),
actionButton("PPCA_return_param", "Return to Model Specification",
width = "100%", class = "btn-default"),
h3(helpText('Graph Controls:'))
),
# Score & Loading
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_score_plot' || input.PPCA_plot_tabs=='PPCA_loading_plot'",
sliderInput("PPCA_x_PC", h4("X-axis Principal Component:"), min=1, max=10, value=4, step=1, ticks=F),
sliderInput("PPCA_y_PC", h4("Y-axis Principal Component:"), min=1, max=10, value=4, step=1, ticks=F),
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_score_plot'",
sliderInput("PPCA_post_int", h4("Posterior Interval:"), min=0.8, max=0.9999, value=0.95, ticks=F),
disabled(downloadButton("dl_ppca_score_btn", span("Download", title="Download a Table of All Score Estimates."))),
),
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_loading_plot'",
disabled(downloadButton("dl_ppca_loadings_btn", span("Download", title="Download a Table of All Loadings Estimates."))),
)
),
# Loadings Analysis & Influence plot
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_loading_analysis_plot' || input.PPCA_plot_tabs=='PPCA_influence_plot'",
sliderInput("PPCA_spectral_plot_PC", h4("Principal Component for Plot:"), min=1, max=10, value=1, step=1, ticks=F),
sliderInput("PPCA_conf_int", h4("Confidence Interval:"), min=0.8, max=0.9999, value=0.95, ticks=F),
conditionalPanel(condition="input.PPCA_plot_tabs=='PPCA_loading_analysis_plot'",
sliderInput("PPCA_n_load_analysis", h5("Numbers of Significant Bin for Loading Analysis Plot"), min=1, max=20, value=5, step=1, ticks=F),
disabled(downloadButton("dl_ppca_loadings_analysis_btn", span("Download", title="Download a table of data used to produced the graph."))),
# downloadButton("dl_significant_btn1", span("Download", title="Download a Table of All Significant Spectral Bins Names"))
)
),
),
),
mainPanel(width = 9, id = "PPCA_main_layout",
# Style for influence tab, PPCA tabs
tags$style(HTML("
.tabbable > .nav > li > a[data-value='PPCA_influence_plot'] {color:blue}
.tabbable > .nav > li > a[data-value='PPCA_spectral'] {background-color:lightyellow}
.tabbable > .nav > li > a[data-value='PPCA_covariates'] {background-color:lightyellow; color:blue}
.tabbable > .nav > li > a[data-value='PPCA_labels'] {background-color:lightyellow}
.tabbable > .nav > li[class=active] > a {background-color:grey; color:white}
#PPCA_data_tabs li:nth-child(4) {float:right}
#PPCA_data_tabs li > a[data-value='PPCA_original_data_table']{background-color:lightgrey}
")),
tabsetPanel(id = "PPCA_plot_tabs", selected = "PPCA_steps",
tabPanel("Quick Start", value = "PPCA_steps",
column(width = 6, offset = 0.5,
br(),
h1(strong(tags$u("Usage Guide:"))),
h3(
tags$div(
tags$ol(
tags$li("Enter your data in the 'PPCA Data' tab"),
br(),
tags$li("Specify the model parameters in the 'Model Specification' tab"),
br(),
tags$li("In the 'Model Specification' tab, click the 'SUBMIT MODEL SPECIFICATIONS' button for the model to run."),
br(),
tags$li("Once the model finishes running, plots will appear under the different plot tabs beside the 'Model Specification' tab."),
br(),
actionButton("PPCA_data_QS", "PPCA Data", class = "btn-default"),
actionButton("PPCA_model_spec_QS", "Model Specification", class = "btn-default"),
)
)
),
br(),
),
column(width = 6, offset = 0.5,
br(),
h4(tags$u("Note: PPCA demo below, click play to animate")),
sliderInput("PPCA_gif_slider", label=NULL, min = 0, max=218, value=0, ticks = FALSE,
animate = animationOptions(interval = 300)),
hidden(imageOutput("PPCA_gif_demo")),
br(),
br(),
)
),
tabPanel("PPCA Data", value = "PPCA_data",
br(),
tabsetPanel(id = "PPCA_data_tabs",
tabPanel(span("Spectral Data", title="Data from NMR or Mass Spectroscopy (MS). Please make sure: rows are samples/observations, columns are spectral bins"),
value = 'PPCA_spectral',
strong(h3(textOutput("PPCA_spectral_file_name"))),
dataTableOutput("PPCA_spectral_data_table"),
),
tabPanel(span("Covariates Data", title="Sample attributes (eg: weight, gender, regions, etc)"),
value = 'PPCA_covariates',
# h3(textOutput("PPCA_spectral_file_name")),
dataTableOutput("PPCA_covariates_data_table")
),
tabPanel(span("Group Labels", title="This data is used for colours in plots (eg: Treatment group vs Control group)"),
value = 'PPCA_labels',
# h3(textOutput("PPCA_spectral_file_name")),
dataTableOutput("PPCA_labels_data_table")
),
tabPanel("Original Data",
value = "PPCA_original_data_table",
br(),
column(12,
column(3, "Covariates Highlighted Color", style = "background-color:lightblue; color:blue"),
column(3, "Group Labels Highlighted Color", style = "background-color:orange;"),
# column(3, "Columns Ignored Highlighted Color", style = "background-color:grey;color:white"),
),
uiOutput("PPCA_original_data_table_UI")
# dataTableOutput("PPCA_original_data_table"),
# tags$style(type="text/css", paste0("#DPPCA_original_data_table td:nth-child(", 3, ") {text-align:center;background-color:green;color: white;text-align:center}"))
)
)
),
tabPanel("Model Specification", value = "PPCA_specification",
br(),
bsCollapse(id = "PPCA_desc", open = "PPCA/PPCCA Parameters Description",
bsCollapsePanel("PPCA/PPCCA Parameters Description",
includeHTML("PPCA_desc_pd.html"),
style = "primary"),
bsCollapsePanel("PPCA/PPCCA Conceptual Explanation",
includeHTML("PPCA_desc_ce.html"),
style = "info"),
bsCollapsePanel("PPCA/PPCCA Technical Details",
includeHTML("PPCA_desc_td.html"),
style = "info")
)
),
tabPanel("Score Plots", value = "PPCA_score_plot",
plotOutput("PPCA_plot_score"),
),
tabPanel("Loadings Plots", value = "PPCA_loading_plot",
plotOutput("PPCA_plot_loadings"),
),
tabPanel("Loadings Analysis Plot", value = "PPCA_loading_analysis_plot",
plotOutput("PPCA_plot_loadings_analysis"),
),
tabPanel("Loadings Influence Plot", value = "PPCA_influence_plot",
plotOutput("PPCA_influence_report"),
),
tabPanel("Model Selection Plot", value = "PPCA_bic_plot",
plotOutput("PPCA_plot_bic"),
),
tabPanel("Algorithm Covergence Plot", value = "PPCA_ll_plot",
plotOutput("PPCA_plot_ll_conv"),
)
)
)
),
# mppca -------------------------------------------------------------------
tabPanel("MPPCA",
conditionalPanel(condition="input.MPPCA_plot_tabs!='MPPCA_steps'",
sidebarPanel(width=3,
# tags$div("MPPCA",
# title="Mixed PPCA that deals with multiple groups"),
# value = "MPPCA_tab",
conditionalPanel(condition="input.MPPCA_plot_tabs=='MPPCA_data'",
# MPPCA data inputs
fileInput("MPPCA_input_file", h4("File input:", bsButton("MPPCA_data_tooltip", label = "",
icon = icon("question"), size = "extra-small")),
multiple = F, accept = c("text/csv", "text/comma-separated-values, text/plain", ".csv"),
placeholder = "Enter Metabolomic Data Here"),
bsPopover("MPPCA_data_tooltip", title="Metabolomic Data File",
content="Should consist of at least the Spectral Data from NMR/MS. If there are Labels, it should be in the first few columns. This File Input only accepts .txt and .csv",
trigger = "hover"),
radioButtons(inputId = 'MPPCA_data_choice', label = NULL,
choiceNames = c("Use Your Own Data", "Use Example Data"),
choiceValues = c(TRUE, FALSE),
selected = TRUE, inline = TRUE),
# tags$hr(),
h4(helpText("Is there a header in the data?")),
checkboxInput(inputId = 'MPPCA_header', label = 'Tick for Yes', value = TRUE),
radioButtons(inputId = 'MPPCA_sep', label = h4(helpText("How is the data separated?")),
inline = TRUE, choices = c(Comma=',',Semicolon=';',Tab='\t', Space=''), selected = ','),
sliderInput("MPPCA_cov_slider", h4("Covariates Columns:"), 0, 10, c(0,0)),
# helpText("When covariates are selected, they will appear in the 'Covariates Data' tab"),
sliderInput("MPPCA_label_slider", h4("Group Labels Column:"), 0, 10, 0),
helpText("When group labels are selected, they will appear in the 'Group Labels' tab"),
hidden(textInput("MPPCA_ignore_column", h4("Columns Ignored for Spectral Data:"), "", placeholder="Column number/names (e.g: 2,3,5 or 9.98,9.94,9.86)")),
h4(helpText("Enter Desired Scaling:", bsButton("MPPCA_scale_type_tooltip", label = "",
icon = icon("question"), size = "extra-small"))),
bsPopover("MPPCA_scale_type_tooltip", title="Types of Scaling",
content="Choosing any scale will pre-process the data by the given formula. Further details on Guide Tab.",
trigger = "hover"),
radioButtons(inputId = 'MPPCA_scale', label = NULL,
choiceNames = c("None", 'PQN Scale',
"Auto Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}\\)",
"Pareto Scale \\(\\space\\) \\(\\frac{x-\\mu}{\\sqrt{\\sigma}}\\)",
"Range Scale \\(\\space\\) \\(\\frac{x-\\mu}{x_{max} - x_{min}}\\)",
"Vast Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}(\\frac{\\mu}{\\sigma}\\))"),
choiceValues = c('none', 'PQN', 'autoscale', 'paretoscale', 'rangescale', 'vastscale'),
selected = 'none'),
div(helpText("*μ is the column mean value while σ is the standard deviation"), style = "font-size:80%"),
tags$hr(),
actionButton("MPPCA_parameter_button", "Go to Model Specification",
width = "100%", class = "btn-default"),
),
conditionalPanel(condition="input.MPPCA_plot_tabs=='MPPCA_specification'",
actionButton("MPPCA_data_button", "Go to Data Input",
width = "100%", class = "btn-default"),
h3(helpText('Model Parameters:')),
sliderInput("MPPCA_group_slider", h4("Range of Groups Estimated:"),
min = 1, max = 10, value = c(1,4)),
sliderInput("MPPCA_PC_slider", h4("Range of Principal Components:"),
min = 1, max = 10, value = c(1, 4)),
sliderInput("MPPCA_bootstrap_n_slider", h4("Numbers of Bootstrap:"), 2, 100, 5),
bsTooltip("MPPCA_bootstrap_n_slider", title="Higher Bootstrap, Better Uncertainty but Longer Wait Time", trigger = "hover"),
# h4("Include Covariates?"),
# disabled(checkboxInput(inputId = 'covariates_mix_check', label = span('Tick for Yes', title = "Please make sure covariates data are selected in Data tabs to enable this selection"), value = FALSE)),
# radioButtons("choose_q", label = h4("Output Optimize Model?"), choices = c(Use_Optimal_PC=TRUE, Use_Maximum_PC=FALSE)),
div(style="display:inline-block",
numericInput("MPPCA_epsilon", label = "Covergence Criterion:", value=0.01, min=1e-5, max=1e2),
),
# div(style="display:inline-block",
# "\\(\\space\\space\\)",
# ),
# div(style="display:inline-block",
# numericInput("max_iter_mix", label = "Max Number of Iteration:", value=1e3, min=1, max=1e6),
# ),
tags$h6("Click button to run the model using desired parameters:"),
div(style="display:inline-block",
disabled(actionButton("MPPCA_submit_para_btn", "Submit Parameters", class = "btn-primary")),
),
div(style="display:inline-block",
strong(textOutput("MPPCA_optimal_q")),
),
div(style="display:inline-block",
strong(textOutput("MPPCA_optimal_g")),
),
p("Please input data first", id="MPPCA_data_check_for_submit", style="color:red"),
tags$hr(),
),
# Graph controls
conditionalPanel(condition="input.MPPCA_plot_tabs!='MPPCA_specification' & input.MPPCA_plot_tabs!='MPPCA_data' & input.MPPCA_plot_tabs!='MPPCA_steps'",
# tags$br(),
actionButton("MPPCA_return_param", "Return to Model Specification",
width = "100%", class = "btn-default"),
h3(helpText('Graph Controls:'))
),
# MPPCA Score
conditionalPanel(condition="input.MPPCA_plot_tabs=='MPPCA_score_plot'",
sliderInput("MPPCA_x_PC", h4("X-axis Principal Component:"), min=1, max=10, value=4, step=1, ticks=F),
sliderInput("MPPCA_y_PC", h4("Y-axis Principal Component:"), min=1, max=10, value=4, step=1, ticks=F),
sliderInput("MPPCA_post_int", h4("Posterior Interval:"), min=0.8, max=0.9999, value=0.95, ticks=F),
disabled(downloadButton("dl_mppca_score_btn", span("Download", title="Download a Table of All Score Estimates."))),
),
# MPPCA Loadings & Loading Analysis plot
conditionalPanel(condition="input.MPPCA_plot_tabs=='MPPCA_loading_plot' || input.MPPCA_plot_tabs=='MPPCA_loading_analysis_plot'",
sliderInput("MPPCA_group", h4("Group Selected:"), min=1, max=10, value=4, step=1),
conditionalPanel(condition="input.MPPCA_plot_tabs=='MPPCA_loading_analysis_plot'",
sliderInput("MPPCA_analysis_plot_PC", h4("Principal Component for Plot:"), min=1, max=10, value=1, step=1, ticks=F),
sliderInput("MPPCA_conf_int", h4("Confidence Interval:"), min=0.8, max=0.9999, value=0.95, ticks=F),
sliderInput("MPPCA_n_load_analysis", h5("Numbers of Significant Bin for Loading Analysis Plot"), min=1, max=20, value=5, step=1, ticks=F),
disabled(downloadButton("dl_mppca_loadings_analysis_btn", span("Download", title="Download a table of data used to produced the graph."))),
),
conditionalPanel(condition="input.MPPCA_plot_tabs=='MPPCA_loading_plot'",
disabled(downloadButton("dl_mppca_loadings_btn", span("Download", title="Download a Table of All Loadings Estimates."))),
)
),
),
),
# MPCCA plots
mainPanel(width = 9, id = "MPPCA_main_layout",
# Style for MPPCA tabs
tags$style(HTML("
.tabbable > .nav > li > a[data-value='MPPCA_spectral'] {background-color:lightyellow}
.tabbable > .nav > li > a[data-value='MPPCA_covariates'] {background-color:lightyellow; color:blue}
.tabbable > .nav > li > a[data-value='MPPCA_labels'] {background-color:lightyellow}
.tabbable > .nav > li[class=active] > a {background-color:grey; color:white}
#MPPCA_data_tabs li:nth-child(3) {float:right}
#MPPCA_data_tabs li > a[data-value='MPPCA_original_data_table']{background-color:lightgrey}
")),
tabsetPanel(id = "MPPCA_plot_tabs", selected = "MPPCA_steps",
tabPanel("Quick Start", value = "MPPCA_steps",
column(width = 6, offset = 0.5,
br(),
h1(strong(tags$u("Usage Guide:"))),
h3(
tags$div(
tags$ol(
tags$li("Enter your data in the 'MPPCA Data' tab"),
br(),
tags$li("Specify the model parameters in the 'Model Specification' tab"),
br(),
tags$li("In the 'Model Specification' tab, click the 'SUBMIT MODEL SPECIFICATIONS' button for the model to run."),
br(),
tags$li("Once the model finishes running, plots will appear under the different plot tabs beside the 'Model Specification' tab."),
br(),
actionButton("MPPCA_data_QS", "MPPCA Data", class = "btn-default"),
actionButton("MPPCA_model_spec_QS", "Model Specification", class = "btn-default"),
)
)
),
br(),
),
column(width = 6, offset = 0.5,
br(),
h4(tags$u("Note: MPPCA demo below, click play to animate")),
sliderInput("MPPCA_gif_slider", label=NULL, min = 0, max=218, value=0, ticks = FALSE,
animate = animationOptions(interval = 300)),
hidden(imageOutput("MPPCA_gif_demo")),
br(),
br(),
)
),
tabPanel("MPPCA Data", value = "MPPCA_data",
br(),
tabsetPanel(id = "MPPCA_data_tabs",
tabPanel(span("Spectral Data", title="Data from NMR or Mass Spectroscopy (MS)"),
value = 'MPPCA_spectral',
strong(h3(textOutput("MPPCA_spectral_file_name"))),
dataTableOutput("MPPCA_spectral_data_table"),
),
# tabPanel(span("Covariates Data", title="Sample attributes (eg: weight, gender, regions, etc)"),
# value = 'MPPCA_covariates',
# dataTableOutput("MPPCA_covariates_data_table")
# ),
tabPanel(span("Group Labels", title="This data is used for colours in plots (eg: Treatment group vs Control group)"),
value = 'MPPCA_labels',
dataTableOutput("MPPCA_labels_data_table")
),
tabPanel("Original Data",
value = "MPPCA_original_data_table",
br(),
column(12,
# column(3, "Covariates Highlighted Color", style = "background-color:lightblue; color:blue"),
column(3, "Group Labels Highlighted Color", style = "background-color:orange;"),
# column(3, "Columns Ignored Highlighted Color", style = "background-color:grey;color:white"),
),
uiOutput("MPPCA_original_data_table_UI")
# dataTableOutput("PPCA_original_data_table"),
# tags$style(type="text/css", paste0("#DPPCA_original_data_table td:nth-child(", 3, ") {text-align:center;background-color:green;color: white;text-align:center}"))
)
)
),
tabPanel("Model Specification", value = "MPPCA_specification",
br(),
bsCollapse(id = "MPPCA_desc", open = "MPPCA Parameters Description",
bsCollapsePanel("MPPCA Parameters Description",
includeHTML("MPPCA_desc_pd.html"),
style = "primary"),
bsCollapsePanel("MPPCA Conceptual Explanation",
includeHTML("MPPCA_desc_ce.html"),
style = "info"),
bsCollapsePanel("MPPCA Technical Details",
includeHTML("MPPCA_desc_td.html"),
style = "info")
)
),
tabPanel("Score Plot", value = "MPPCA_score_plot",
plotOutput("MPPCA_plot_score")
),
tabPanel("Loading Plot", value = "MPPCA_loading_plot",
plotOutput("MPPCA_plot_loadings")
),
tabPanel("Loading Analysis Plot", value = "MPPCA_loading_analysis_plot",
plotOutput("MPPCA_plot_loadings_analysis")
),
# tabPanel("Contingency Table", value = "contin_MPPCA_table",
# "If Group Labels are not given, error will appear as:",
# tags$br(),
# "Error: all arguments must have the same length",
# tags$br(),
# "Fix it by selecting Group Labels Column in 'Data' tab.",
# verbatimTextOutput("MPPCA_contin_table")
# ),
tabPanel("BIC Plot", value = "MPPCA_BIC_plot",
plotOutput("MPPCA_plot_BIC")
)
)
)
),
# dppca -------------------------------------------------------------------
tabPanel("DPPCA",
conditionalPanel(condition="input.DPPCA_plot_tabs!='DPPCA_steps'",
# DPPCA sidebar controls
sidebarPanel(width = 3,
# tags$style("#DPPCA_tooltip+.popover{width:170px;}"),
# bsPopover("DPPCA_tooltip",
# title = "Dynamic Probabilistic Principal Component Analysis",
# content = "",
# trigger = "hover", placement = "right"),
conditionalPanel(condition="input.DPPCA_plot_tabs=='DPPCA_data'",
# DPPCA data inputs
fileInput("DPPCA_input_file", h4("File input:", bsButton("DPPCA_data_tooltip", label = "",
icon = icon("question"), size = "extra-small")),
multiple = F, accept = c("text/csv", "text/comma-separated-values, text/plain", ".csv"),
placeholder = "Enter Metabolomic Data Here"),
bsPopover("DPPCA_data_tooltip", title="Metabolomic Data File",
content="Should consist of at least the Spectral Data from NMR/MS and a column specifying the different time point. If there are Labels, it should be in the first few columns. This File Input only accepts .txt and .csv",
trigger = "hover"),
radioButtons(inputId = 'DPPCA_data_choice', label = NULL,
choiceNames = c("Use Your Own Data", "Use Example Data"),
choiceValues = c(TRUE, FALSE),
selected = TRUE, inline = TRUE),
# tags$hr(),
h4(helpText("Is there a header in the data?")),
checkboxInput(inputId = 'DPPCA_header', label = 'Tick for Yes', value = TRUE),
radioButtons(inputId = 'DPPCA_sep', label = h4(helpText("How is the data separated?")),
inline = TRUE, choices = c(Comma=',',Semicolon=';',Tab='\t', Space=''), selected = ','),
# sliderInput("DPPCA_subjectID_slider", h4("Subject ID Column:"), 0, 10, 0),
sliderInput("DPPCA_time_slider", h4("Time Column:"), 0, 10, 3),
helpText("Specify Time Column to enable time selection below"),
selectInput( "DPPCA_time_selected", label = "Time Point Selected for Data Shown", choices = NULL),
sliderInput("DPPCA_label_slider", h4("Group Labels Column:"), 0, 10, 4),
helpText("When group labels are selected, they will appear in the 'Group Labels' tab"),
textInput("DPPCA_ignore_column", h4("Columns Ignored for Spectral Data:"),
value = "1,2",
placeholder="Column number/names (e.g: 2,3,5 or 9.98,9.94,9.86)"),
h4(helpText("Enter Desired Scaling:", bsButton("DPPCA_scale_type_tooltip", label = "",
icon = icon("question"), size = "extra-small"))),
bsPopover("DPPCA_scale_type_tooltip", title="Types of Scaling",
content="Choosing any scale will pre-process the data by the given formula. Further details on Guide Tab.",
trigger = "hover"),
radioButtons(inputId = 'DPPCA_scale', label = NULL,
choiceNames = c("None", 'PQN Scale',
"Auto Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}\\)",
"Pareto Scale \\(\\space\\) \\(\\frac{x-\\mu}{\\sqrt{\\sigma}}\\)",
"Range Scale \\(\\space\\) \\(\\frac{x-\\mu}{x_{max} - x_{min}}\\)",
"Vast Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}(\\frac{\\mu}{\\sigma}\\))"),
choiceValues = c('none', 'PQN', 'autoscale', 'paretoscale', 'rangescale', 'vastscale'),
selected = 'none'),
div(helpText("*μ is the column mean value while σ is the standard deviation"), style = "font-size:80%"),
tags$hr(),
actionButton("DPPCA_parameter_button", "Go to Model Settings",
width = "100%", class = "btn-default"),
),
conditionalPanel(condition="input.DPPCA_plot_tabs=='DPPCA_specification'",
actionButton("DPPCA_data_button", "Go to Data Input",
width = "100%", class = "btn-default"),
br(),
br(),
tabsetPanel(id = "DPPCA_sidetab",
tabPanel("Model Parameters", value = "DPPCA_model_parameters",
# h3(helpText('Model Parameters:')),
sliderInput("DPPCA_PC_slider", h4("Number of Principal Components:"),
min = 1, max = 10, value = 2,),
radioButtons(inputId = 'DPPCA_priors_choice', label = h4("Model Priors Settings:"),
choices = c("Default", 'Custom'), inline = TRUE),
helpText("Clicking Default will reset the values in Model Priors!"),
sliderInput("DPPCA_chain_slider", h4("Length of Chain:"),
min = 100, max = 3000, value = 500, step=100),
sliderInput("DPPCA_burn_slider", h4("Length of Burn-in Period:"),
min = 1, max = 1000, value = 50, step=10),
sliderInput("DPPCA_thin_slider", h4("Number of Thinning on Chain:"),
min = 1, max = 500, value = 10, step=10),
# sliderInput("DPPCA_post_burn_slider", h4("Length of Further Burn-in:"),
# min = 100, max = 1000, value = 100, step=100),
# helpText("Further burn-in applied to chains of loadings and scores to calculate posteriors"),
# sliderInput("DPPCA_post_thin_slider", h4("Number of Further Thinning:"),
# min = 100, max = 1000, value = 100, step=100),
# helpText("Further thinning applied to chains of loadings and scores to calculate posteriors"),
tags$hr(),
tags$h6("Click button to run the model using desired parameters:"),
div(style="display:inline-block",
disabled(actionButton("DPPCA_submit_para_btn", "Submit Parameters", class = "btn-primary")),
),
# div(style="display:inline-block",
# strong(textOutput("MPPCA_optimal_q")),
# ),
# div(style="display:inline-block",
# strong(textOutput("MPPCA_optimal_g")),
# ),
p("Please input data first", id="DPPCA_data_check_for_submit", style="color:red"),
tags$hr(),
),
tabPanel("Model Priors", value = "DPPCA_model_priors",
tags$table(
tags$tr(width = "100%",
tags$td(width = "60%", div("prior_alpha")),
tags$td(width = "40%", textInput(inputId = "prior_alpha", value = 6, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", tags$div("prior_beta")),
tags$td(width = "40%", textInput(inputId = "prior_beta", value = 0.5, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", div("prior_sigma2_nu")),
tags$td(width = "40%", textInput(inputId = "prior_sigma2_nu", value = 10, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", tags$div("prior_mu_phi")),
tags$td(width = "40%", textInput(inputId = "prior_mu_phi", value = 0.75, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", div("prior_sigma2_phi")),
tags$td(width = "40%", textInput(inputId = "prior_sigma2_phi", value = 0.1, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", tags$div("prior_alpha_V")),
tags$td(width = "40%", textInput(inputId = "prior_alpha_V", value = 6, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", div("prior_beta_V")),
tags$td(width = "40%", textInput(inputId = "prior_beta_V", value = 0.5, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", tags$div("prior_sigma2_mu")),
tags$td(width = "40%", textInput(inputId = "prior_sigma2_mu", value = 10, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", div("prior_sigma2_PHI")),
tags$td(width = "40%", textInput(inputId = "prior_sigma2_PHI", value = 0.1, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", tags$div("prior_mu_PHI")),
tags$td(width = "40%", textInput(inputId = "prior_mu_PHI", value = 0.75, label = NULL))),
tags$tr(width = "100%",
tags$td(width = "60%", tags$div("prior_omega_inv")),
tags$td(width = "40%", textInput(inputId = "prior_omega_inv", value = 1, label = NULL)))
)
)
)
),
conditionalPanel(condition="input.DPPCA_plot_tabs!='DPPCA_data' & input.DPPCA_plot_tabs!='DPPCA_specification' & input.DPPCA_plot_tabs!='DPPCA_LMM_modelling' & input.DPPCA_plot_tabs!='DPPCA_steps'",
actionButton("DPPCA_return_param", "Return to Model Specification",
width = "100%", class = "btn-default"),
h3(helpText('Graph Controls:')),
h3(tags$div(style='color:orange',"As DPPCA takes time to run, this is a pre-run results for plots", id="Pre-run_results_warning")),
radioButtons(inputId = 'DPPCA_result_choice', label = NULL,
choiceNames = c("Use Output Result", "Use Example Result"),
choiceValues = c(TRUE, FALSE),
selected = TRUE, inline = TRUE),
),
conditionalPanel(condition="input.DPPCA_plot_tabs=='DPPCA_loading_analysis_plot'",
sliderInput("DPPCA_spectral_plot_PC", h4("Principal Component for Plot:"), min=1, max=2, value=1, step=1, ticks=F),
sliderInput("DPPCA_cred_int", h4("Credible Interval:"), min=0.8, max=0.9999, value=0.95, ticks=F),
sliderInput("DPPCA_load_analysis_time", h4("Time for Plot:"), min=1, max=8, value=1, step=1, ticks=F),
sliderInput("DPPCA_n_load_analysis", h5("Numbers of Significant Bin for Loading Analysis Plot"), min=1, max=20, value=5, step=1, ticks=F),
# disabled(downloadButton("dl_dppca_loadings_analysis_btn", span("Download", title="Download a table of data used to produced the graph."))),
),
conditionalPanel(condition="input.DPPCA_plot_tabs=='DPPCA_LMM_modelling'",
h3(helpText('Linear Mixed Model:')),
h3(tags$div(style='color:orange',"As DPPCA takes time to run, this is a pre-run results for plots", id="Pre-run_results_warning2")),
sliderInput("DPPCA_LMM_cred_int", h4("Significance level (alpha value):"), min=0.0001, max=0.2, value=0.05, ticks=F),
tags$hr(),
tags$h6("Click button to run the LMM model:"),
div(style="display:inline-block",
actionButton("DPPCA_submit_LMM_btn", "Submit Parameters", class = "btn-primary"),
),
),
),
),
mainPanel(width = 9, id = "DPPCA_main_layout",
# Style for DPPCA tabs
tags$style(HTML("
.tabbable > .nav > li > a[data-value='DPPCA_original'] {background-color:lightyellow}
.tabbable > .nav > li > a[data-value='DPPCA_spectral'] {background-color:lightyellow}
.tabbable > .nav > li > a[data-value='DPPCA_labels'] {background-color:lightyellow}
.tabbable > .nav > li[class=active] > a {background-color:grey; color:white}
#DPPCA_data_tabs li:nth-child(3) {float:right}
#DPPCA_data_tabs li > a[data-value='DPPCA_original_data_table']{background-color:lightgrey}
")),
tabsetPanel(id = "DPPCA_plot_tabs", selected = "DPPCA_steps",
tabPanel("Quick Start", value = "DPPCA_steps",
column(width = 6, offset = 0.5,
br(),
h1(strong(tags$u("Usage Guide:"))),
h3(
tags$div(
tags$ol(
tags$li("Enter your data in the 'DPPCA Data' tab"),
br(),
tags$li("Specify the model parameters in the 'Model Specification' tab"),
br(),
tags$li("In the 'Model Specification' tab, click the 'SUBMIT MODEL SPECIFICATIONS' button for the model to run."),
br(),
tags$li("Once the model finishes running, plots will appear under the different plot tabs beside the 'Model Specification' tab."),
br(),
actionButton("DPPCA_data_QS", "DPPCA Data", class = "btn-default"),
actionButton("DPPCA_model_spec_QS", "Model Specification", class = "btn-default"),
)
)
),
br(),
),
column(width = 6, offset = 0.5,
br(),
# h4(tags$u("Note: DPPCA demo below, click play to animate")),
# sliderInput("DPPCA_gif_slider", label=NULL, min = 0, max=218, value=0, ticks = FALSE,
# animate = animationOptions(interval = 300)),
# hidden(imageOutput("DPPCA_gif_demo")),
br(),
br(),
)
),
tabPanel("DPPCA Data", value = "DPPCA_data",
br(),
tabsetPanel(id = "DPPCA_data_tabs",
# tabPanel(span("Original Data", title="Data from NMR or Mass Spectroscopy (MS)"),
# value = 'DPPCA_original',
# strong(h3(textOutput("DPPCA_spectral_file_name"))),
# dataTableOutput("DPPCA_original_data_table"),
# ),
tabPanel("Spectral Data",
value = 'DPPCA_spectral',
strong(h3(textOutput("DPPCA_spectral_file_name"))),
h4(tags$div(style='color:green', textOutput("DPPCA_time_point_showing"))),
dataTableOutput("DPPCA_spectral_data_table")
),
tabPanel(span("Group Labels", title="This data is used for colours in plots (eg: Treatment group vs Control group)"),
value = 'DPPCA_labels',
dataTableOutput("DPPCA_labels_data_table")
),
tabPanel("Original Data",
value = "DPPCA_original_data_table",
br(),
column(12,
column(2, "Time Highlighted Color", style = "background-color:lightgreen;"),
column(3, "Group Labels Highlighted Color", style = "background-color:orange;"),
column(3, "Columns Ignored Highlighted Color", style = "background-color:grey;color:white"),
),
uiOutput("DPPCA_original_data_table_UI"),
# dataTableOutput("DPPCA_original_data_table"),
# tags$style(type="text/css", paste0("#DPPCA_original_data_table td:nth-child(", 3, ") {text-align:center;background-color:green;color: white;text-align:center}"))
)
)
),
tabPanel("Model Specification", value = "DPPCA_specification",
column(12, align="center",
h2(strong("---Placeholder for DPPCA Explanation---"))
)
),
tabPanel("Chain Convergence", value = "DPPCA_chain_conver",
br(),
actionButton("DPPCA_convergence_next_btn", "Next Random Sample Chain for Score/Loadings",
style="float:right", class = "btn-default"),
plotOutput("DPPCA_plot_load_score_chain"),
br(),
br(),
plotOutput("DPPCA_plot_persistence_chain")
),
tabPanel("Time Influence", value = "DPPCA_time_inf",
plotOutput("DPPCA_plot_persistence_histogram"),
br(),
verbatimTextOutput("DPPCA_plot_persistence_summary")
),
tabPanel("Loading Analysis Plot", value = "DPPCA_loading_analysis_plot",
plotOutput("DPPCA_plot_top_loadings"),
),
tabPanel("LMM Modelling", value = "DPPCA_LMM_modelling",
plotOutput("DPPCA_plot_LMM_fit"),
br(),
verbatimTextOutput("DPPCA_LMM_summary")
)
)
)
)
),
# Sample Size Estimation --------------------------------------------------
navbarMenu("Sample Size Estimation",
# for ppca ----------------------------------------------------------------
tabPanel("For PPCA/PPCCA",
conditionalPanel(condition="input.sample_size_PPCA_tab!='sample_size_PPCA_steps'",
sidebarPanel(width = 3,
conditionalPanel(condition="input.sample_size_PPCA_tab=='sample_size_PPCA_pilot_data'",
fileInput("sample_size_PPCA_pilot_data", h4("File input:", bsButton("sample_size_PPCA_pilot_data_tooltip", label = "",
icon = icon("question"), size = "extra-small")),
multiple = F, accept = c("text/csv", "text/comma-separated-values, text/plain", ".csv"),
placeholder = "Enter Metabolomic Data Here"),
bsPopover("sample_size_PPCA_pilot_data_tooltip", title="Metabolomic Data File",
content="Should consist of at least the Spectral Data from NMR/MS. If there are Labels, it should be in the first few columns. This File Input only accepts .txt and .csv",
trigger = "hover"),
radioButtons(inputId = 'sample_size_PPCA_data_choice', label = NULL,
choiceNames = c("Use Your Own Data", "Use Example Data"),
choiceValues = c(TRUE, FALSE),
selected = TRUE, inline = TRUE),
# tags$hr(),
h4(helpText("Is there a header in the data?")),
checkboxInput(inputId = 'sample_size_PPCA_header', label = 'Tick for Yes', value = TRUE),
radioButtons(inputId = 'sample_size_PPCA_sep', label = h4(helpText("How is the data separated?")),
inline = TRUE, choices = c(Comma=',',Semicolon=';',Tab='\t', Space=''), selected = ','),
sliderInput("sample_size_PPCA_cov_slider", h4("Covariates Columns:"), 0, 10, c(0,0)),
helpText("When covariates are selected, they will appear in the 'Covariates Data' tab"),
# sliderInput("sample_size_PPCA_label_slider", h4("Group Labels Column:"), 0, 10, 0),
# helpText("When group labels are selected, they will appear in the 'Group Labels' tab"),
textInput("sample_size_PPCA_ignore_column", h4("Columns Ignored for Spectral Data:"), "", placeholder="Column number/names (e.g: 2,3,5 or 9.98,9.94,9.86)"),
h4(helpText("Enter Desired Scaling:", bsButton("scale_type_sample_PPCA_tooltip", label = "",
icon = icon("question"), size = "extra-small"))),
bsPopover("scale_type_sample_PPCA_tooltip", title="Types of Scaling",
content="Choosing any scale will pre-process the data by the given formula. Further details on Guide Tab.",
trigger = "hover"),
radioButtons(inputId = 'sample_size_PPCA_scale', label = NULL,
choiceNames = c("None", 'PQN Scale',
"Auto Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}\\)",
"Pareto Scale \\(\\space\\) \\(\\frac{x-\\mu}{\\sqrt{\\sigma}}\\)",
"Range Scale \\(\\space\\) \\(\\frac{x-\\mu}{x_{max} - x_{min}}\\)",
"Vast Scale \\(\\quad\\) \\(\\frac{x-\\mu}{\\sigma}(\\frac{\\mu}{\\sigma}\\))"),
choiceValues = c('none', 'PQN', 'autoscale', 'paretoscale', 'rangescale', 'vastscale'),
selected = 'none'),
div(helpText("*μ is the column mean value while σ is the standard deviation"), style = "font-size:80%"),
hr(),
actionButton("sample_size_PPCA_parameter_button", "Go to Model Settings",
width = "100%", class = "btn-default"),
),
conditionalPanel(condition="input.sample_size_PPCA_tab=='sample_size_PPCA_specification'",
actionButton("sample_size_PPCA_pilot_data_button", "Go to Pilot Data",
width = "100%", class = "btn-default"),
h3(helpText("Sample Size Estimation Parameters:")),
hr(),
h4("Do you want to include Pilot Data?"),
checkboxInput(inputId = 'sample_size_PPCA_pilot_check', label = 'Tick for Yes', value = FALSE),
h4("Do you want to include", span("Covariates?", style="color:blue")),
checkboxInput(inputId = 'sample_size_PPCA_covariates_check', label = span('Tick for Yes', style="color:blue", title = "Please make sure covariates data are selected in Data tabs to enable this selection"), value = FALSE),
# Input: Spectral Bins
sliderInput("sample_size_PPCA_n_bins", h4("Number of Spectral Bins:"),
min = 50, max = 400, value = 100),
# Input: Proportion of significant bins (m)
sliderInput("sample_size_PPCA_prop_sig_bins", h4("Proportion of Significant Bins (m):"),
min = 0.1, max = 1, value = 0.5, step = 0.05),
# Input: Target FDR
sliderInput("sample_size_PPCA_target_fdr", h4("Target FDR:"),
min = 0.01, max = 0.2, step = 0.01, value = 0.05),
h4(helpText(tags$u("To investigate the impact of 'm' on FDR please choose:"))),
# Input: Sample size per group n1 and n2
sliderInput("sample_size_PPCA_n1", h4("Group 1 Sample Size:"),
min = 1, max = 100, value = 5, step = 1),
sliderInput("sample_size_PPCA_n2", h4("Group 2 Sample Size:"),
min = 1, max = 100, value = 5, step = 1),
actionButton("sample_size_PPCA_submit_button", "Calculate Sample Size", class = "btn-primary"),
hidden(p("Pilot Data Selected but not Given", id="sample_size_PPCA_data_check_for_submit", style="color:red")),
),
conditionalPanel(condition="input.sample_size_PPCA_tab!='sample_size_PPCA_pilot_data' & input.sample_size_PPCA_tab!='sample_size_PPCA_specification' & input.sample_size_PPCA_tab!='sample_size_PPCA_steps'",
actionButton("sample_size_PPCA_return_param_button", "Return to Estimation Parameters",
width = "100%", class = "btn-default"),
h4("Parameters Used:"),
tableOutput("estimation_parameter_PPCA_report"),
),
),
),
mainPanel(width = 9, id = "sample_size_PPCA_main_layout",
# Style for data tabs
tags$style(HTML("
.tabbable > .nav > li > a[data-value='sample_size_PPCA_spectral'] {background-color:lightyellow}
.tabbable > .nav > li > a[data-value='sample_size_PPCA_covariates'] {background-color:lightyellow; color:blue}
# .tabbable > .nav > li > a[data-value='PPCA_labels'] {background-color:lightyellow}
.tabbable > .nav > li[class=active] > a {background-color:grey; color:white}
")),
tabsetPanel(id="sample_size_PPCA_tab", selected = "sample_size_PPCA_steps",
tabPanel("Quick Start", value = "sample_size_PPCA_steps",
column(width = 6, offset = 0.5,
br(),
h1(strong(tags$u("Usage Guide:"))),
h3(
tags$div(
tags$ol(
h4(helpText("(optional) If you have pilot data, you may enter them in the 'Pilot Data' tab")),
tags$li("Specify the model parameters in the 'Model Specification' tab"),
br(),
tags$li("In the 'Model Specification' tab, click the 'Calculate Sample Size' button for the model to run."),
br(),
tags$li("Once the model finishes running, plots will appear under the different plot tabs beside the 'Model Specification' tab."),
br(),
actionButton("sample_size_PPCA_data_QS", "Pilot Data", class = "btn-default"),
actionButton("sample_size_PPCA_model_spec_QS", "Model Specification", class = "btn-default"),
)
)
),
br(),
),
column(width = 6, offset = 0.5,
br(),
# h4(tags$u("Note: Sample Size Estimation demo below, click play to animate")),
# sliderInput("sample_size_PPCA_gif_slider", label=NULL, min = 0, max=218, value=0, ticks = FALSE,
# animate = animationOptions(interval = 300)),
# hidden(imageOutput("sample_size_PPCA_gif_demo")),
br(),
br(),
)
),
tabPanel("Pilot Data", value = "sample_size_PPCA_pilot_data",
br(),
tabsetPanel(id = "sample_size_PPCA_data_tabs",
tabPanel(span("Spectral Data", title="Data from NMR or Mass Spectroscopy (MS)"),
value = 'sample_size_PPCA_spectral',
strong(h3(textOutput("sample_size_PPCA_spectral_file_name"))),
dataTableOutput("sample_size_PPCA_spectral_data_table"),
),
tabPanel(span("Covariates Data", title="Sample attributes (eg: weight, gender, regions, etc)"),
value = 'sample_size_PPCA_covariates',
# h3(textOutput("PPCA_spectral_file_name")),
dataTableOutput("sample_size_PPCA_covariates_data_table")
)
# tabPanel(span("Group Labels", title="This data is used for colours in plots (eg: Treatment group vs Control group)"),
# value = 'sample_size_PPCA_labels',
# # h3(textOutput("PPCA_spectral_file_name")),
# dataTableOutput("PPCA_labels_data_table")
# )
)
),
tabPanel("Model Specification", value = "sample_size_PPCA_specification",
br(),
column(12, align="center",
h2(strong("---Placeholder for Sample Size Explanation---"))
)
# bsCollapse(id = "sample_size_PPCA_desc", open = "Sample Size Estimation using PPCA Usage Guide",
# bsCollapsePanel("Sample Size Estimation using PPCA Usage Guide",
#
# includeHTML("Sample_size_PPCA_desc_ug.html"),
# style = "primary")
# bsCollapsePanel("Sample Size Estimation using PPCA Conceptual Explanation",
#
# includeHTML("Sample_size_PPCA_desc_ce.html"),
# style = "info"),
# bsCollapsePanel("Sample Size Estimation using PPCA Technical Details",
#
# includeHTML("Sample_size_PPCA_desc_td.html"),
# style = "info")
# )
),
tabPanel("Sample Size Estimation", value = "sample_size_PPCA_estimation",
plotOutput("sample_size_PPCA_estimate")
),
tabPanel("Varying the Significant Bins",
plotOutput("sample_size_PPCA_significant_bins")
)
)
)
)
),
# GUIDES ------------------------------------------------------------------
# tabPanel("Guides", "This panel is intentionally left blank",
# mainPanel(width = 9,
# tabsetPanel(
# tabPanel("Tab 3", "This panel is intentionally left blank",
# textInput("txt", "Text input:", "general"),
# tags$h5("Default actionButton:"),
# actionButton("action", "Search"),
#
# tags$h5("actionButton with CSS class:"),
# actionButton("action2", "Action button", class = "btn-primary")
# )
# ),
# verbatimTextOutput("debug")
# ),
# ),
# ABOUT US ----------------------------------------------------------------
tabPanel("About Us",
"-placeholder-"
)
)
))
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