inst/HiTMaP_GUI/ui.R
In MASHUOA/HiTMaP: A collection of tools for imaging MS data processing
#
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
ui <- function(request) {
if (!require(shiny)) install.packages("shiny")
library(shiny)
if (!require(shinyjs)) install.packages("shinyjs")
library(shinyjs)
if (!require(future)) install.packages("future")
library(future)
library(shinythemes)
library(HiTMaP)
if (!require(data.table)) install.packages("data.table")
library(data.table)
library(magrittr)
library(shinyFiles)
#source("projectdir/UIproject.R")
#source("Pre-processing/UIPre-processing.R")
if (!require(DT)) install.packages("DT")
library(DT)
library(stringr)
DEBUG_CONSOLE_OUTPUT <- "debugConsoleOutput"
NUM_ASYNC_TASKS_RUNNING <- "NUM_ASYNC_TASKS_RUNNING"
ASYNC_DATA <- "ASYNC_DATA"
FAKE_DATA_INFORMATION <- "FAKE_DATA_INFORMATION"
folder_file_DTDT<-"folder_file_DTDT"
HiTMaP:::Peptide_modification(retrive_ID=NULL,update_unimod=F)
enzymes_option<-names(HiTMaP::Cleavage_rules_fun())
unimod.modification.df<-merge(unimod.df$modifications,unimod.df$specificity,by.x=c("record_id"),by.y=c("mod_key"),all.x=T)
unimod.modification.df=unimod.modification.df[unimod.modification.df$hidden==0,]
unimod.df$positions_new<-unimod.df$positions
unimod.df$positions_new$position_ext<-str_replace(unimod.df$positions_new$position,"Anywhere|Any N-term|Any C-term","Peptide")
unimod.df$positions_new$position_ext<-str_replace(unimod.df$positions_new$position_ext,"Protein N-term|Protein C-term","Protein")
unimod.modification.df<-merge(unimod.modification.df,unimod.df$positions_new,by.x="position_key",by.y="record_id")
unimod.modification.df<-unimod.modification.df[unimod.modification.df$ex_code_name!="",]
unimod.modification.df<-unimod.modification.df[order(as.numeric(unimod.modification.df$record_id)),]
mod_option<-paste0(unimod.modification.df$record_id,": ",unimod.modification.df$code_name," @ ",unimod.modification.df$position_ext,":",unimod.modification.df$position_key," @ ", unimod.modification.df$one_letter)
adducts_option<-HiTMaP:::Build_adduct_list()$Name
fluidPage(
theme = shinythemes::shinytheme("lumen"),
#shinythemes::themeSelector(),
# tag to have textAreaInput of width 100%
tags$style(HTML("
.shiny-input-container:not(.shiny-input-container-inline) {
width: 100%;}
.hiddenLink {visibility: hidden;}"
)),
useShinyjs()
,
img(src = "tm.bmp", width = 360),
#h2(" MALDI-MSI Proteomics annotation"),
br(),
fluidRow(
column(2,br(),
shinyDirButton("Projectdir", "Choose or create Project", "Create or choose a Project folder",icon = icon("folder-open"), class = "btn-primary"),br()
),
column(2,br(),
downloadButton(outputId = "projectSavebtn", label = "Save project data")
),
column(2,br(),
actionButton("reset", "Reset setting")
),
column(2,br(),
bookmarkButton(label = "Bookmark setting",title = "Bookmark this project's setting and use the URL for later use.", id = "project_bookmark",icon = icon("save")),br()
),
column(1,br(),
h5("Project selected:"),br()),
column(3,br(),h5(textOutput("Projectdir"))),br()),
tabsetPanel(
id = "maintabset", type = "tabs",
tabPanel("Project", wellPanel(
#UIproject()
{
sidebarLayout(
sidebarPanel(
# img(src = "tm.bmp", height =72 , width = 260), br(),h5("MALDI-MSI Proteomics annotation"),br(),
# shinyDirButton("Projectdir", "Chose or creat Project", "Creat and choose a Project folder"),
# br(),h4("Project"),
# verbatimTextOutput("Projectdir"),
# br(),
# verbatimTextOutput("imzmlibd"),
# br(),
fileInput("imzmlibd", "Choose imzml file(s)",
multiple = T,
accept = c(".imzml",
".ibd")),
fileInput("database", "Choose Fasta database(s)",
multiple = T,
accept = c(".fasta")),
fileInput("config", "Choose configuration file(s)",
multiple = FALSE,
accept = c("text/csv/xlsx",
"text/comma-separated-values,text/plain",
".csv",".xlsx")),
fileInput("allfiles", "Choose and upload all file(s)",
multiple = T)
),
mainPanel(
#downloadButton(outputId = "projectSavebtn", label = "Save project data"),
#h5("Download ID/Summary folder"),br(),
h3("Project stats"),
DT::dataTableOutput(folder_file_DTDT)
))}
),icon = icon("briefcase")),
tabPanel("Pre-processing", wellPanel(
#UIPre-processing()
{
fluidRow(
column(3,
#img(src = "tm.bmp", height =72 , width = 260), br(),h5("MALDI-MSI Proteomics annotation"),br(),
# verbatimTextOutput("imzmlibd"),
# br(),
selectizeInput(
'imzml_file_Pre_processing', 'Select IMS file(s)', choices = NULL,
multiple = TRUE, options = list(maxItems = 1000)
),br(),
actionButton(inputId="Pre_processing_run", icon=NULL, label="Start Pre-proccessing (optional)"),
br(),
selectizeInput(
'rotate_pre', 'Select a rotation configuration file (optional)', choices = NULL,
multiple = F, options = list(maxItems = 1000)
),
sliderInput("mzrange_pre", label = ("Set m/z Range"), min = 0,
max = 4000, value = c(500, 4000)),
sliderInput("tolerance_pre", label = ("Set tolerance in ppm"), min = 0,
max = 50, value = 5),
checkboxInput("force_Pre_process_pre", label = "Force pre-process data", value = FALSE),
checkboxInput("use_Pre_processRDS_pre", label = "Use pre-processed RDS", value = TRUE),
selectInput("normalize_pre", label = ("Normalize method"),
choices = list("TIC" = "tic", "RMS" = "rms", "Reference" = "reference", "Disable"="Disable"),
selected = "rms"),
selectInput("smoothSignal_pre", label = ("Smooth Signal method"),
choices = list("Gaussian" = "gaussian", "Moving average" = "ma", "Savitzky-Golay" = "sgolay", "Disable"="Disable"),
selected = "Disable"),
selectInput("peakpick_pre", label = ("Peak picking method"),
choices = list("Median absolute deviation" = "mad", "Simple" = "simple", "Adaptive" = "adaptive", "Disable"="Disable"),
selected = "adaptive"),
selectInput("reduceBaseline_pre", label = ("Reduce Baseline method"),
choices = list("Local minimum" = "locmin", "Median" = "median", "Disable"="Disable"),
selected = "Disable"),
sliderInput("peakAlign_pre", label = ("Set peak alignment tolerance (ppm)"), min = 1,
max = 100, value = 8)
),
column(3,
# selectizeInput(
# 'RDA_imzml_file_stats', 'Select file(s) for statistical analysis', choices = NULL,
# multiple = T, options = list(maxItems = 1000)
# ),br(),
actionButton(inputId="segmentation_run", icon=NULL, label="Start Image segmentation", class = "btn-primary"),
br(),br(),
actionButton(inputId="PCA_run", icon=NULL, label="Start PCA analysis (optional)"),
br(),
selectInput("Smooth_range_pre", label = ("Denoising level"),
choices = list("None" = 0, "Weak R1" = 1, "Medium R2" = 2, "Strong R3" = 3),
selected = 1),
sliderInput("Segmentation_per_file_pre", label = ("Set initial number of segmentation"), min = 1,
max = 20, value = 6),
sliderInput("Segmentation_variance_coverage_pre", label = ("Set variance coverage"), min = 0,
max = 1, value = 0.8),
selectInput("Segmentation_pre", label = ("Segmentation method"),
choices = list("Spatial kMeans" = "spatialKMeans", "Spatial Shrunken Centroids" = "spatialShrunkenCentroids", "none" = "none","def_file"="def_file"),
selected = 1),
selectInput("Segmentation_ncomp_pre", label = ("Segmentation nComp"),
choices = list("auto-detect" = "auto-detect", "3" = 3, "6" = 6, "9" = 9),
selected = 1),
selectizeInput(
'Segmentation_def_pre', 'Select one Segmentation label file', choices = NULL,
multiple = F, options = list(maxItems = 1000)
)
),
column(6,
h3("Segmentation Result"),
br(),
selectizeInput(
'ID_folders_stats', 'Select ID folder for result visualize', choices = NULL,
multiple = T, options = list(maxItems = 1)
),
selectizeInput(
'Stats_img', 'Select Statistical result', choices = NULL,
multiple = T, options = list(maxItems = 1)
),
br(),
tagList(
actionButton(("zoom_m"), "", icon = icon("magnifying-glass-minus"), width = "40px",
style = "border-radius: 25px; padding: 0px;"),
actionButton(("zoom_p"), "", icon = icon("magnifying-glass-plus"), width = "40px",
style = "border-radius: 25px; padding: 0px;")
),
imageOutput("Stats_img_output"),
#uiOutput("Stats_img_output"),
br()
))
}
),icon = icon("think-peaks")),
tabPanel("Proteomics annotation", wellPanel(
{
fluidRow(
column(4,
actionButton(inputId="Proteomics_run", icon=NULL, label="Run Proteomics pipeline", class = "btn-primary"),br(),br(),
h3("Candidates and FDR"),
br(),
#verbatimTextOutput("imzmlibd"),
selectizeInput(
'Fastadatabase', 'Select database', choices = NULL,
multiple = TRUE, options = list(maxItems = 1)
),
# selectInput("mode", label = ("Mode"),
# choices = list("Proteomics" = "Proteomics", "Metabolomics" = "Metabolomics"),
# selected = 1),
selectInput(
'Digestion_site', 'Enzyme(s)', choices = enzymes_option, selected= "trypsin",
multiple = TRUE
),
textInput("Digestion_site_2", label = ("Optional digestion rule(s)"), value = ""),
sliderInput("missedCleavages", label = ("Missed Cleavages Range"), min = 0,
max = 5, value = c(0, 1)),
selectInput(
'adducts', 'Adduct(s)', choices =adducts_option, selected= "M+H",
multiple=TRUE, selectize=TRUE
),
selectizeInput(
'Modifications_fix', 'Fixed modification(s)', choices = mod_option,
multiple = TRUE, options = list(maxItems = 10)
),
selectizeInput(
'Modifications_var', 'Variable modification(s)', choices = mod_option,
multiple = TRUE, options = list(maxItems = 10)
),
textInput("Substitute_AA_AA", label = ("Substituted amino acid"), value = ""),
textInput("Substitute_AA_mol", label = ("Formula for Substitution"), value = ""),
checkboxInput("Substitute_AA_mol_wwater",label = "Formula without dehydration",value = T),
checkboxInput("use_previous_candidates", label = "Load previously generated candidate", value = TRUE),
checkboxInput("output_candidatelist", label = "Output generated candidate", value = TRUE),
checkboxInput("Database_stats", label = "Output generated candidate", value = FALSE),
br()
),
column(4,
#img(src = "tm.bmp", height =72 , width = 260), br(),h5("MALDI-MSI Proteomics annotation"),br(),
# verbatimTextOutput("imzmlibd"),
# br(),
selectizeInput(
'imzml_file', 'Select multiple file for annotation', choices = NULL,
multiple = TRUE, options = list(maxItems = 1000)
),
h3("Pre-processing"),br(),
selectizeInput(
'rotate', 'Select a rotation configuration file (optional)', choices = NULL,
multiple = F, options = list(maxItems = 1000)
),
sliderInput("mzrange", label = ("Set m/z Range"), min = 0,
max = 4000, value = c(500, 4000)),
sliderInput("tolerance", label = ("Set tolerance in ppm"), min = 0,
max = 50, value = 5),
checkboxInput("force_Pre_process", label = "Force pre-process data", value = FALSE),
checkboxInput("use_Pre_processRDS", label = "Use pre-processed RDS", value = TRUE),
selectInput("normalize", label = ("Normalize method"),
choices = list("TIC" = "tic", "RMS" = "rms", "Reference" = "reference"),
selected = "rms"),
selectInput("smoothSignal", label = ("Smooth Signal method"),
choices = list("Gaussian" = "gaussian", "Moving average" = "ma", "Savitzky-Golay" = "sgolay"),
selected = "sgolay"),
selectInput("peakpick", label = ("Peak picking method"),
choices = list("Median absolute deviation" = "mad", "Simple" = "simple", "Adaptive" = "adaptive"),
selected = "adaptive"),
selectInput("reduceBaseline", label = ("Reduce Baseline method"),
choices = list("Local minimum" = "locmin", "Median" = "median"),
selected = "locmin"),
sliderInput("peakAlign", label = ("Set peak alignment tolerance (ppm)"), min = 1,
max = 100, value = 8),
h3("Segmentation"),br(),
selectInput("Smooth_range", label = ("Denoising level"),
choices = list("None" = 0, "Weak R1" = 1, "Medium R2" = 2, "Strong R3" = 3),
selected = 1),
sliderInput("Segmentation_per_file", label = ("Set initial number of segmentation"), min = 1,
max = 20, value = 6),
sliderInput("Segmentation_variance_coverage", label = ("Set variance coverage"), min = 0,
max = 1, value = 0.8),
selectInput("Segmentation", label = ("Segmentation method"),
choices = list("Spatial kMeans" = "spatialKMeans", "Spatial Shrunken Centroids" = "spatialShrunkenCentroids", "none" = "none","def_file"="def_file"),
selected = "spatialKMeans"),
selectInput("Segmentation_ncomp", label = ("Segmentation nComp"),
choices = list("auto-detect" = "auto-detect", "3" = 3, "6" = 6, "9" = 9),
selected = "auto-detect"),
selectizeInput(
'Segmentation_def', 'Select one Segmentation label file', choices = NULL,
multiple = F, options = list(maxItems = 1000)
)
# "missedCleavages","Substitute_AA","Decoy_search","Decoy_mode","Decoy_adducts",
# "use_previous_candidates","IMS_analysis","FDR_cutoff","peptide_ID_filter","plot_matching_score",
# "Protein_feature_summary","Peptide_feature_summary","Region_feature_summary","parallel"
#
),
column(4,
br(),h3("IMS analysis"),br(),
sliderInput("mz_tolerance_ppm", label = ("Precusor tolerance (ppm)"), min = 1,
max = 100, value = 5),
sliderInput("threshold", label = ("Set relative signal threshold"), min = 0,
max = 0.2, value = 0.005),
sliderInput("FDR_cutoff", label = ("False discovery rate cut-off"), min = 0,
max = 1, value = 0.05),
sliderInput("peptide_ID_filter", label = ("Minimum peptide number for Prtoein ID"), min = 1,
max = 10, value = 2),
checkboxInput("Decoy_search", label = "Decoy search", value = TRUE),
selectInput("Decoy_mode", label = ("Decoy Mode"),
choices = list("Isotope" = "isotope", "Elements" = "elements", "Adducts"="adducts"),
selected = 1),
selectizeInput("Decoy_adducts", label = ("Decoy adducts"),
choices = c("M+ACN+H","M+IsoProp+H","M+DMSO+H","M+Co","M+Ag","M+Cu","M+He","M+Ne","M+Ar","M+Kr","M+Xe","M+Rn"),
multiple = TRUE, options = list(maxItems = 10)),
checkboxInput("IMS_analysis", label = "IMS analysis", value = TRUE),
checkboxInput("PMFsearch", label = "PMF analysis", value = TRUE),
checkboxInput("Protein_feature_summary", label = "Protein feature summary", value = TRUE),
checkboxInput("Peptide_feature_summary", label = "Peptide feature summary list", value = TRUE),
br()
))
}
),icon = icon("fingerprint")),
tabPanel("Cluster image rendering", wellPanel(
{
sidebarLayout(
sidebarPanel(
# img(src = "tm.bmp", height =72 , width = 260), br(),h5("MALDI-MSI Proteomics annotation"),br(),
# shinyDirButton("Projectdir", "Chose or creat Project", "Creat and choose a Project folder"),
# br(),h4("Project"),
# verbatimTextOutput("Projectdir"),
# br(),
# verbatimTextOutput("imzmlibd"),
# br(),
selectizeInput(
'imzml_file_Pre_processing', 'Select result file for rendering', choices = NULL,
multiple = TRUE, options = list(maxItems = 1000)
),
h3("Pre-processing"),br(),
sliderInput("mzrange", label = ("Set m/z Range"), min = 0,
max = 4000, value = c(500, 4000)),
selectInput("Smooth_range", label = ("Denoising level"),
choices = list("None" = 0, "Weak R1" = 1, "Medium R2" = 2, "Strong R3" = 3),
selected = 2),
checkboxInput("force_Pre_process", label = "Force pre-process data", value = TRUE),
checkboxInput("use_Pre_processRDS", label = "Use pre-processed RDS", value = TRUE),
selectInput("normalize", label = ("Normalize method"),
choices = list("tic" = "tic", "rms" = "rms", "reference" = "reference"),
selected = 2),
selectInput("smoothSignal", label = ("Smooth Signal method"),
choices = list("gaussian" = "gaussian", "ma" = "ma", "sgolay" = "sgolay"),
selected = 3),
selectInput("reduceBaseline", label = ("Reduce Baseline method"),
choices = list("locmin" = "locmin", "median" = "median"),
selected = 1),
sliderInput("peakAlign", label = ("Set peakAlign tolerance (ppm)"), min = 1,
max = 100, value = 8)
),
mainPanel(
h3("Cluster image"),
verbatimTextOutput("input_print")
))}
),icon = icon("layer-group")),
# tabPanel("Server", wellPanel(
# serverCreator()
# )),
# tabPanel("Global", wellPanel(
# globalCreator()
# )),
# tabPanel("Export result", wellPanel(
# downloadButton(outputId = "creatorSavebtn", label = "Save shinyWYSIWYG data"),
# fileInput(inputId = "creatorLoadbtn", label = "", accept = ".RData", buttonLabel = "Load shinyWYSIWYG data"),
# actionButton("input_print_button","input"),
# verbatimTextOutput("rotate_pre")
#
# ),icon = icon("file-export
tabPanel("Target selection", wellPanel(
{
fluidRow(
column(3,
selectizeInput(
'Target_table_file', 'Select Target ID', choices = NULL,
multiple = F, options = list(maxItems = 1000)
),br(),
selectizeInput(
'ID_table_file', 'Select ID input', choices = NULL,
multiple = F, options = list(maxItems = 1000)
),br(),
selectInput("Analysis_pipeline", label = ("Analysis pipeline"),
choices = list("ProteinPilot" = "ProteinPilot", "Fragpipe" = "Fragpipe", "UserTable" = "UserTable"),
selected = "ProteinPilot"),
sliderInput("pep_length", label = ("Set peptide length range"), min = 5,
max = 50, value = c(7, 40)),
sliderInput("Score_cutoff", label = ("Set score cut-off"), min = 0,
max = 100, value = 50),
sliderInput("ppm_cutoff", label = ("Set tolerance in ppm"), min = 0,
max = 100, value = 15),
sliderInput("TopN_Feat", label = ("Set TopN Features to report"), min = 0,
max = 100, value = 5),
checkboxInput("Peptideatlas_mapping", label = "Use Peptideatlas Data", value = TRUE),
),column(3,actionButton(inputId="PRM_processing_run", icon=NULL, label="Start PRM candidate selection"),
br())
)
}
),icon = icon("circle-dot")),
tabPanel("Task manager", wellPanel(
h5(paste0('Available Cores: ', future::availableCores())),
column(3,h5('Running task:')),column(3,actionButton('Refresh_Running_task',"Refresh")),
DT::dataTableOutput(NUM_ASYNC_TASKS_RUNNING),
numericInput("duration", "Duration", value = 5, min = 0),
actionButton("start_proc_future", h5("get data using future")),
h5("Task console (most recent first)"),
DT::dataTableOutput(DEBUG_CONSOLE_OUTPUT),
uiOutput("hidden_downloads")
),icon = icon("stack-overflow"))
)
)
}
MASHUOA/HiTMaP documentation built on May 1, 2024, 8:26 p.m.
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#
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
ui <- function(request) {
if (!require(shiny)) install.packages("shiny")
library(shiny)
if (!require(shinyjs)) install.packages("shinyjs")
library(shinyjs)
if (!require(future)) install.packages("future")
library(future)
library(shinythemes)
library(HiTMaP)
if (!require(data.table)) install.packages("data.table")
library(data.table)
library(magrittr)
library(shinyFiles)
#source("projectdir/UIproject.R")
#source("Pre-processing/UIPre-processing.R")
if (!require(DT)) install.packages("DT")
library(DT)
library(stringr)
DEBUG_CONSOLE_OUTPUT <- "debugConsoleOutput"
NUM_ASYNC_TASKS_RUNNING <- "NUM_ASYNC_TASKS_RUNNING"
ASYNC_DATA <- "ASYNC_DATA"
FAKE_DATA_INFORMATION <- "FAKE_DATA_INFORMATION"
folder_file_DTDT<-"folder_file_DTDT"
HiTMaP:::Peptide_modification(retrive_ID=NULL,update_unimod=F)
enzymes_option<-names(HiTMaP::Cleavage_rules_fun())
unimod.modification.df<-merge(unimod.df$modifications,unimod.df$specificity,by.x=c("record_id"),by.y=c("mod_key"),all.x=T)
unimod.modification.df=unimod.modification.df[unimod.modification.df$hidden==0,]
unimod.df$positions_new<-unimod.df$positions
unimod.df$positions_new$position_ext<-str_replace(unimod.df$positions_new$position,"Anywhere|Any N-term|Any C-term","Peptide")
unimod.df$positions_new$position_ext<-str_replace(unimod.df$positions_new$position_ext,"Protein N-term|Protein C-term","Protein")
unimod.modification.df<-merge(unimod.modification.df,unimod.df$positions_new,by.x="position_key",by.y="record_id")
unimod.modification.df<-unimod.modification.df[unimod.modification.df$ex_code_name!="",]
unimod.modification.df<-unimod.modification.df[order(as.numeric(unimod.modification.df$record_id)),]
mod_option<-paste0(unimod.modification.df$record_id,": ",unimod.modification.df$code_name," @ ",unimod.modification.df$position_ext,":",unimod.modification.df$position_key," @ ", unimod.modification.df$one_letter)
adducts_option<-HiTMaP:::Build_adduct_list()$Name
fluidPage(
theme = shinythemes::shinytheme("lumen"),
#shinythemes::themeSelector(),
# tag to have textAreaInput of width 100%
tags$style(HTML("
.shiny-input-container:not(.shiny-input-container-inline) {
width: 100%;}
.hiddenLink {visibility: hidden;}"
)),
useShinyjs()
,
img(src = "tm.bmp", width = 360),
#h2(" MALDI-MSI Proteomics annotation"),
br(),
fluidRow(
column(2,br(),
shinyDirButton("Projectdir", "Choose or create Project", "Create or choose a Project folder",icon = icon("folder-open"), class = "btn-primary"),br()
),
column(2,br(),
downloadButton(outputId = "projectSavebtn", label = "Save project data")
),
column(2,br(),
actionButton("reset", "Reset setting")
),
column(2,br(),
bookmarkButton(label = "Bookmark setting",title = "Bookmark this project's setting and use the URL for later use.", id = "project_bookmark",icon = icon("save")),br()
),
column(1,br(),
h5("Project selected:"),br()),
column(3,br(),h5(textOutput("Projectdir"))),br()),
tabsetPanel(
id = "maintabset", type = "tabs",
tabPanel("Project", wellPanel(
#UIproject()
{
sidebarLayout(
sidebarPanel(
# img(src = "tm.bmp", height =72 , width = 260), br(),h5("MALDI-MSI Proteomics annotation"),br(),
# shinyDirButton("Projectdir", "Chose or creat Project", "Creat and choose a Project folder"),
# br(),h4("Project"),
# verbatimTextOutput("Projectdir"),
# br(),
# verbatimTextOutput("imzmlibd"),
# br(),
fileInput("imzmlibd", "Choose imzml file(s)",
multiple = T,
accept = c(".imzml",
".ibd")),
fileInput("database", "Choose Fasta database(s)",
multiple = T,
accept = c(".fasta")),
fileInput("config", "Choose configuration file(s)",
multiple = FALSE,
accept = c("text/csv/xlsx",
"text/comma-separated-values,text/plain",
".csv",".xlsx")),
fileInput("allfiles", "Choose and upload all file(s)",
multiple = T)
),
mainPanel(
#downloadButton(outputId = "projectSavebtn", label = "Save project data"),
#h5("Download ID/Summary folder"),br(),
h3("Project stats"),
DT::dataTableOutput(folder_file_DTDT)
))}
),icon = icon("briefcase")),
tabPanel("Pre-processing", wellPanel(
#UIPre-processing()
{
fluidRow(
column(3,
#img(src = "tm.bmp", height =72 , width = 260), br(),h5("MALDI-MSI Proteomics annotation"),br(),
# verbatimTextOutput("imzmlibd"),
# br(),
selectizeInput(
'imzml_file_Pre_processing', 'Select IMS file(s)', choices = NULL,
multiple = TRUE, options = list(maxItems = 1000)
),br(),
actionButton(inputId="Pre_processing_run", icon=NULL, label="Start Pre-proccessing (optional)"),
br(),
selectizeInput(
'rotate_pre', 'Select a rotation configuration file (optional)', choices = NULL,
multiple = F, options = list(maxItems = 1000)
),
sliderInput("mzrange_pre", label = ("Set m/z Range"), min = 0,
max = 4000, value = c(500, 4000)),
sliderInput("tolerance_pre", label = ("Set tolerance in ppm"), min = 0,
max = 50, value = 5),
checkboxInput("force_Pre_process_pre", label = "Force pre-process data", value = FALSE),
checkboxInput("use_Pre_processRDS_pre", label = "Use pre-processed RDS", value = TRUE),
selectInput("normalize_pre", label = ("Normalize method"),
choices = list("TIC" = "tic", "RMS" = "rms", "Reference" = "reference", "Disable"="Disable"),
selected = "rms"),
selectInput("smoothSignal_pre", label = ("Smooth Signal method"),
choices = list("Gaussian" = "gaussian", "Moving average" = "ma", "Savitzky-Golay" = "sgolay", "Disable"="Disable"),
selected = "Disable"),
selectInput("peakpick_pre", label = ("Peak picking method"),
choices = list("Median absolute deviation" = "mad", "Simple" = "simple", "Adaptive" = "adaptive", "Disable"="Disable"),
selected = "adaptive"),
selectInput("reduceBaseline_pre", label = ("Reduce Baseline method"),
choices = list("Local minimum" = "locmin", "Median" = "median", "Disable"="Disable"),
selected = "Disable"),
sliderInput("peakAlign_pre", label = ("Set peak alignment tolerance (ppm)"), min = 1,
max = 100, value = 8)
),
column(3,
# selectizeInput(
# 'RDA_imzml_file_stats', 'Select file(s) for statistical analysis', choices = NULL,
# multiple = T, options = list(maxItems = 1000)
# ),br(),
actionButton(inputId="segmentation_run", icon=NULL, label="Start Image segmentation", class = "btn-primary"),
br(),br(),
actionButton(inputId="PCA_run", icon=NULL, label="Start PCA analysis (optional)"),
br(),
selectInput("Smooth_range_pre", label = ("Denoising level"),
choices = list("None" = 0, "Weak R1" = 1, "Medium R2" = 2, "Strong R3" = 3),
selected = 1),
sliderInput("Segmentation_per_file_pre", label = ("Set initial number of segmentation"), min = 1,
max = 20, value = 6),
sliderInput("Segmentation_variance_coverage_pre", label = ("Set variance coverage"), min = 0,
max = 1, value = 0.8),
selectInput("Segmentation_pre", label = ("Segmentation method"),
choices = list("Spatial kMeans" = "spatialKMeans", "Spatial Shrunken Centroids" = "spatialShrunkenCentroids", "none" = "none","def_file"="def_file"),
selected = 1),
selectInput("Segmentation_ncomp_pre", label = ("Segmentation nComp"),
choices = list("auto-detect" = "auto-detect", "3" = 3, "6" = 6, "9" = 9),
selected = 1),
selectizeInput(
'Segmentation_def_pre', 'Select one Segmentation label file', choices = NULL,
multiple = F, options = list(maxItems = 1000)
)
),
column(6,
h3("Segmentation Result"),
br(),
selectizeInput(
'ID_folders_stats', 'Select ID folder for result visualize', choices = NULL,
multiple = T, options = list(maxItems = 1)
),
selectizeInput(
'Stats_img', 'Select Statistical result', choices = NULL,
multiple = T, options = list(maxItems = 1)
),
br(),
tagList(
actionButton(("zoom_m"), "", icon = icon("magnifying-glass-minus"), width = "40px",
style = "border-radius: 25px; padding: 0px;"),
actionButton(("zoom_p"), "", icon = icon("magnifying-glass-plus"), width = "40px",
style = "border-radius: 25px; padding: 0px;")
),
imageOutput("Stats_img_output"),
#uiOutput("Stats_img_output"),
br()
))
}
),icon = icon("think-peaks")),
tabPanel("Proteomics annotation", wellPanel(
{
fluidRow(
column(4,
actionButton(inputId="Proteomics_run", icon=NULL, label="Run Proteomics pipeline", class = "btn-primary"),br(),br(),
h3("Candidates and FDR"),
br(),
#verbatimTextOutput("imzmlibd"),
selectizeInput(
'Fastadatabase', 'Select database', choices = NULL,
multiple = TRUE, options = list(maxItems = 1)
),
# selectInput("mode", label = ("Mode"),
# choices = list("Proteomics" = "Proteomics", "Metabolomics" = "Metabolomics"),
# selected = 1),
selectInput(
'Digestion_site', 'Enzyme(s)', choices = enzymes_option, selected= "trypsin",
multiple = TRUE
),
textInput("Digestion_site_2", label = ("Optional digestion rule(s)"), value = ""),
sliderInput("missedCleavages", label = ("Missed Cleavages Range"), min = 0,
max = 5, value = c(0, 1)),
selectInput(
'adducts', 'Adduct(s)', choices =adducts_option, selected= "M+H",
multiple=TRUE, selectize=TRUE
),
selectizeInput(
'Modifications_fix', 'Fixed modification(s)', choices = mod_option,
multiple = TRUE, options = list(maxItems = 10)
),
selectizeInput(
'Modifications_var', 'Variable modification(s)', choices = mod_option,
multiple = TRUE, options = list(maxItems = 10)
),
textInput("Substitute_AA_AA", label = ("Substituted amino acid"), value = ""),
textInput("Substitute_AA_mol", label = ("Formula for Substitution"), value = ""),
checkboxInput("Substitute_AA_mol_wwater",label = "Formula without dehydration",value = T),
checkboxInput("use_previous_candidates", label = "Load previously generated candidate", value = TRUE),
checkboxInput("output_candidatelist", label = "Output generated candidate", value = TRUE),
checkboxInput("Database_stats", label = "Output generated candidate", value = FALSE),
br()
),
column(4,
#img(src = "tm.bmp", height =72 , width = 260), br(),h5("MALDI-MSI Proteomics annotation"),br(),
# verbatimTextOutput("imzmlibd"),
# br(),
selectizeInput(
'imzml_file', 'Select multiple file for annotation', choices = NULL,
multiple = TRUE, options = list(maxItems = 1000)
),
h3("Pre-processing"),br(),
selectizeInput(
'rotate', 'Select a rotation configuration file (optional)', choices = NULL,
multiple = F, options = list(maxItems = 1000)
),
sliderInput("mzrange", label = ("Set m/z Range"), min = 0,
max = 4000, value = c(500, 4000)),
sliderInput("tolerance", label = ("Set tolerance in ppm"), min = 0,
max = 50, value = 5),
checkboxInput("force_Pre_process", label = "Force pre-process data", value = FALSE),
checkboxInput("use_Pre_processRDS", label = "Use pre-processed RDS", value = TRUE),
selectInput("normalize", label = ("Normalize method"),
choices = list("TIC" = "tic", "RMS" = "rms", "Reference" = "reference"),
selected = "rms"),
selectInput("smoothSignal", label = ("Smooth Signal method"),
choices = list("Gaussian" = "gaussian", "Moving average" = "ma", "Savitzky-Golay" = "sgolay"),
selected = "sgolay"),
selectInput("peakpick", label = ("Peak picking method"),
choices = list("Median absolute deviation" = "mad", "Simple" = "simple", "Adaptive" = "adaptive"),
selected = "adaptive"),
selectInput("reduceBaseline", label = ("Reduce Baseline method"),
choices = list("Local minimum" = "locmin", "Median" = "median"),
selected = "locmin"),
sliderInput("peakAlign", label = ("Set peak alignment tolerance (ppm)"), min = 1,
max = 100, value = 8),
h3("Segmentation"),br(),
selectInput("Smooth_range", label = ("Denoising level"),
choices = list("None" = 0, "Weak R1" = 1, "Medium R2" = 2, "Strong R3" = 3),
selected = 1),
sliderInput("Segmentation_per_file", label = ("Set initial number of segmentation"), min = 1,
max = 20, value = 6),
sliderInput("Segmentation_variance_coverage", label = ("Set variance coverage"), min = 0,
max = 1, value = 0.8),
selectInput("Segmentation", label = ("Segmentation method"),
choices = list("Spatial kMeans" = "spatialKMeans", "Spatial Shrunken Centroids" = "spatialShrunkenCentroids", "none" = "none","def_file"="def_file"),
selected = "spatialKMeans"),
selectInput("Segmentation_ncomp", label = ("Segmentation nComp"),
choices = list("auto-detect" = "auto-detect", "3" = 3, "6" = 6, "9" = 9),
selected = "auto-detect"),
selectizeInput(
'Segmentation_def', 'Select one Segmentation label file', choices = NULL,
multiple = F, options = list(maxItems = 1000)
)
# "missedCleavages","Substitute_AA","Decoy_search","Decoy_mode","Decoy_adducts",
# "use_previous_candidates","IMS_analysis","FDR_cutoff","peptide_ID_filter","plot_matching_score",
# "Protein_feature_summary","Peptide_feature_summary","Region_feature_summary","parallel"
#
),
column(4,
br(),h3("IMS analysis"),br(),
sliderInput("mz_tolerance_ppm", label = ("Precusor tolerance (ppm)"), min = 1,
max = 100, value = 5),
sliderInput("threshold", label = ("Set relative signal threshold"), min = 0,
max = 0.2, value = 0.005),
sliderInput("FDR_cutoff", label = ("False discovery rate cut-off"), min = 0,
max = 1, value = 0.05),
sliderInput("peptide_ID_filter", label = ("Minimum peptide number for Prtoein ID"), min = 1,
max = 10, value = 2),
checkboxInput("Decoy_search", label = "Decoy search", value = TRUE),
selectInput("Decoy_mode", label = ("Decoy Mode"),
choices = list("Isotope" = "isotope", "Elements" = "elements", "Adducts"="adducts"),
selected = 1),
selectizeInput("Decoy_adducts", label = ("Decoy adducts"),
choices = c("M+ACN+H","M+IsoProp+H","M+DMSO+H","M+Co","M+Ag","M+Cu","M+He","M+Ne","M+Ar","M+Kr","M+Xe","M+Rn"),
multiple = TRUE, options = list(maxItems = 10)),
checkboxInput("IMS_analysis", label = "IMS analysis", value = TRUE),
checkboxInput("PMFsearch", label = "PMF analysis", value = TRUE),
checkboxInput("Protein_feature_summary", label = "Protein feature summary", value = TRUE),
checkboxInput("Peptide_feature_summary", label = "Peptide feature summary list", value = TRUE),
br()
))
}
),icon = icon("fingerprint")),
tabPanel("Cluster image rendering", wellPanel(
{
sidebarLayout(
sidebarPanel(
# img(src = "tm.bmp", height =72 , width = 260), br(),h5("MALDI-MSI Proteomics annotation"),br(),
# shinyDirButton("Projectdir", "Chose or creat Project", "Creat and choose a Project folder"),
# br(),h4("Project"),
# verbatimTextOutput("Projectdir"),
# br(),
# verbatimTextOutput("imzmlibd"),
# br(),
selectizeInput(
'imzml_file_Pre_processing', 'Select result file for rendering', choices = NULL,
multiple = TRUE, options = list(maxItems = 1000)
),
h3("Pre-processing"),br(),
sliderInput("mzrange", label = ("Set m/z Range"), min = 0,
max = 4000, value = c(500, 4000)),
selectInput("Smooth_range", label = ("Denoising level"),
choices = list("None" = 0, "Weak R1" = 1, "Medium R2" = 2, "Strong R3" = 3),
selected = 2),
checkboxInput("force_Pre_process", label = "Force pre-process data", value = TRUE),
checkboxInput("use_Pre_processRDS", label = "Use pre-processed RDS", value = TRUE),
selectInput("normalize", label = ("Normalize method"),
choices = list("tic" = "tic", "rms" = "rms", "reference" = "reference"),
selected = 2),
selectInput("smoothSignal", label = ("Smooth Signal method"),
choices = list("gaussian" = "gaussian", "ma" = "ma", "sgolay" = "sgolay"),
selected = 3),
selectInput("reduceBaseline", label = ("Reduce Baseline method"),
choices = list("locmin" = "locmin", "median" = "median"),
selected = 1),
sliderInput("peakAlign", label = ("Set peakAlign tolerance (ppm)"), min = 1,
max = 100, value = 8)
),
mainPanel(
h3("Cluster image"),
verbatimTextOutput("input_print")
))}
),icon = icon("layer-group")),
# tabPanel("Server", wellPanel(
# serverCreator()
# )),
# tabPanel("Global", wellPanel(
# globalCreator()
# )),
# tabPanel("Export result", wellPanel(
# downloadButton(outputId = "creatorSavebtn", label = "Save shinyWYSIWYG data"),
# fileInput(inputId = "creatorLoadbtn", label = "", accept = ".RData", buttonLabel = "Load shinyWYSIWYG data"),
# actionButton("input_print_button","input"),
# verbatimTextOutput("rotate_pre")
#
# ),icon = icon("file-export
tabPanel("Target selection", wellPanel(
{
fluidRow(
column(3,
selectizeInput(
'Target_table_file', 'Select Target ID', choices = NULL,
multiple = F, options = list(maxItems = 1000)
),br(),
selectizeInput(
'ID_table_file', 'Select ID input', choices = NULL,
multiple = F, options = list(maxItems = 1000)
),br(),
selectInput("Analysis_pipeline", label = ("Analysis pipeline"),
choices = list("ProteinPilot" = "ProteinPilot", "Fragpipe" = "Fragpipe", "UserTable" = "UserTable"),
selected = "ProteinPilot"),
sliderInput("pep_length", label = ("Set peptide length range"), min = 5,
max = 50, value = c(7, 40)),
sliderInput("Score_cutoff", label = ("Set score cut-off"), min = 0,
max = 100, value = 50),
sliderInput("ppm_cutoff", label = ("Set tolerance in ppm"), min = 0,
max = 100, value = 15),
sliderInput("TopN_Feat", label = ("Set TopN Features to report"), min = 0,
max = 100, value = 5),
checkboxInput("Peptideatlas_mapping", label = "Use Peptideatlas Data", value = TRUE),
),column(3,actionButton(inputId="PRM_processing_run", icon=NULL, label="Start PRM candidate selection"),
br())
)
}
),icon = icon("circle-dot")),
tabPanel("Task manager", wellPanel(
h5(paste0('Available Cores: ', future::availableCores())),
column(3,h5('Running task:')),column(3,actionButton('Refresh_Running_task',"Refresh")),
DT::dataTableOutput(NUM_ASYNC_TASKS_RUNNING),
numericInput("duration", "Duration", value = 5, min = 0),
actionButton("start_proc_future", h5("get data using future")),
h5("Task console (most recent first)"),
DT::dataTableOutput(DEBUG_CONSOLE_OUTPUT),
uiOutput("hidden_downloads")
),icon = icon("stack-overflow"))
)
)
}
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