Nothing
inst/shiny-examples/MSstatsQCgui/server.R
In MSstatsQCgui: A graphical user interface for MSstatsQC package
source("plot-functions.R")
source("data-validation.R")
source("helper-functions.R")
source("QCMetrics.R")
shinyServer(function(input,output,session) {
#### Read data ##################################################################################################
data <- reactiveValues(df = NULL, metrics = NULL)
observeEvent(input$filein, {
file1 <- input$filein
data$df <- input_checking(read.csv(file=file1$datapath, sep=",", header=TRUE, stringsAsFactors=TRUE))
validate(
need(!is.null(data$df), "Please upload your data"),
need(is.data.frame(data$df), data$df)
)
data$metrics <- c(find_custom_metrics(data$df))
}, priority = 20)
observeEvent(input$sample_button, {
data$df <- input_checking(read.csv("./Datasets/Sampledata_CPTAC_Study_9_1_Site54.csv"))
validate(
need(!is.null(data$df), "Please upload your data"),
need(is.data.frame(data$df), data$df)
)
data$metrics <- c(find_custom_metrics(data$df))
}, priority = 20)
observeEvent(input$clear_button, {
data$df <- NULL
data$metrics <- NULL
}, priority = 20)
##### Precursor type selection #####################################################################################
output$pepSelect <- renderUI({
prodata <- data$df
validate(
need(!is.null(prodata), "Please upload your data.\n\n If your data contains min start time and max end time columns,the App will add a peak assymetry column automatically.\n\n Your data should contain a column named Annotation. Put all your metrics after this column.To see an example of a sample data click on the {Run with example data} button."),
need(is.data.frame(prodata), prodata)
)
selectInput("pepSelection","Choose peptide"
,choices = c(levels(prodata$Precursor),"all peptides")
)
})
######Show table of data #####################################################################################################
output$prodata_table <- renderDataTable({
validate(
need(!is.null(data$df), "Please upload your data.\n\n If your data contains min start time and max end time columns, the App will add a peak assymetry column automatically.\n\n Your data should contain a column named Annotation. Put all your metrics after this column.To see an example of a sample data click on the {Run with example data} button."),
need(is.data.frame(data$df), data$df)
)
data$df
}, options = list(pageLength = 25))
###### selection tab in Data Improt and selection #####################################################
output$selectMeanSD <- renderUI({
lapply(input$user_selected_metrics,
function(x){
fluidRow(
column(4,paste(x,":")),
column(4,
numericInput(paste0("selectMean@",x),"mean",value = 1)
),
column(4,
numericInput(paste0("selectSD@",x),"standard deviation",value = 1)
)
)
})
})
output$selectGuideSet <- renderUI({
fluidRow(
column(6,
numericInput("L","Lower bound of guide set",value = 1, min = 1, step = 1)
),
column(6,
numericInput("U","Upper bound of guide set", value = 5, min = 2, step = 1)
)
)
})
###### Tab for selecting decision rule and metrics ###############################################
output$metricThresholdRed <- renderUI({
numOfMetrics <- length(input$user_selected_metrics)
numericInput('threshold_metric_red', '', value = 2, min = 0, max = numOfMetrics, step = 1)
})
output$peptideThresholdYellow <- renderUI({
threshold_peptide_red <- input$threshold_peptide_red
numericInput('threshold_peptide_yellow', '', value = threshold_peptide_red - 1, min = 1, max = threshold_peptide_red, step = 1)
})
output$metricThresholdYellow <- renderUI({
numOfMetrics <- length(input$user_selected_metrics)
threshold_metric_red <- input$threshold_metric_red
validate(
need(!is.null(numOfMetrics),"loading..."),
need(!is.null(threshold_metric_red),"loading...")
)
numericInput('threshold_metric_yellow', '', value = threshold_metric_red , min = 0, max = threshold_metric_red, step = 1)
})
output$metricSelection <- renderUI({
checkboxGroupInput("user_selected_metrics","",
choices = c(data$metrics),
inline = TRUE)
})
################################################################# plots ###################################################
#################################################################################################################
output$XmR_tabset <- renderUI({
validate(
need(!is.null(data$df), "Please upload your data first"),
need(is.data.frame(data$df), data$df),
need(!is.null(input$user_selected_metrics),"Please first select metrics and create a decision rule")
)
is_guidset_selected <- FALSE
if(input$selectGuideSetOrMeanSD == "Mean and standard deviation estimated from guide set") {
is_guidset_selected <- TRUE
}
Tabs <- lapply(input$user_selected_metrics,
function(x) {
tabPanel(x,
tags$head(tags$style(type="text/css")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("It may take a while to load the plots, please wait...",
id="loadmessage")),
renderPlotly(render.QC.chart(data$df, input$pepSelection, input$L,
input$U, metric = x,
plot.method = "XmR", normalization = FALSE,
y.title1 = "Individual Value", y.title2 = "Moving Range",
selectMean = input[[paste0("selectMean@",x)]],selectSD = input[[paste0("selectSD@",x)]],
guidset_selected = is_guidset_selected)
)
)
})
do.call(tabsetPanel, Tabs)
})
################################################################################################################
#################################################################################################################
output$CUSUM_tabset <- renderUI({
validate(
need(!is.null(data$df), "Please upload your data first"),
need(is.data.frame(data$df), data$df),
need(!is.null(input$user_selected_metrics),"Please first select metrics and create a decision rule")
)
is_guidset_selected <- FALSE
if(input$selectGuideSetOrMeanSD == "Mean and standard deviation estimated from guide set") {
is_guidset_selected <- TRUE
}
Tabs <- lapply(input$user_selected_metrics,
function(x) {
tabPanel(x,
tags$head(tags$style(type="text/css")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("It may take a while to load the plots, please wait...",
id="loadmessage")),
renderPlotly(render.QC.chart(data$df, input$pepSelection, input$L, input$U, metric = x, plot.method = "CUSUM", normalization = TRUE, y.title1 = "CUSUM mean", y.title2 = "CUSUM variation",selectMean = input[[paste0("selectMean@",x)]],selectSD = input[[paste0("selectSD@",x)]],guidset_selected = is_guidset_selected))
)
})
do.call(tabsetPanel, Tabs)
})
################################################################################################################
#################################################################################################################
output$CP_tabset <- renderUI({
validate(
need(!is.null(data$df), "Please upload your data first"),
need(is.data.frame(data$df), data$df),
need(!is.null(input$user_selected_metrics),"Please first select metrics and create a decision rule")
)
is_guidset_selected <- FALSE
if(input$selectGuideSetOrMeanSD == "Mean and standard deviation estimated from guide set") {
is_guidset_selected <- TRUE
}
Tabs <- lapply(input$user_selected_metrics,
function(x) {
tabPanel(x,
tags$head(tags$style(type="text/css")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("It may take a while to load the plots, please wait...",
id="loadmessage")),
renderPlotly(render.QC.chart(data$df, input$pepSelection, input$L, input$U, metric = x, plot.method = "CP", normalization = TRUE, y.title1 = "Change point for mean", y.title2 = "Change point for variation",selectMean = input[[paste0("selectMean@",x)]],selectSD = input[[paste0("selectSD@",x)]],guidset_selected = is_guidset_selected))
)
})
do.call(tabsetPanel, Tabs)
})
######################################################### height and width in Summary tab ########################################
my_height <- reactive({
l <- length(input$user_selected_metrics)
k <- length(input$summary_controlChart_select)
if(l < 5) {
my_height <- ceiling(k)*700
}else if(l < 9) {
my_height <- ceiling(k)*1300
}else if(l <15) {
my_height <- ceiling(k)*1700
}else if(l < 20) {
my_height <- ceiling(k)*2000
}else if(l < 25) {
my_height <- ceiling(k)*2300
}else {
my_height <- ceiling(k)*2600
}
})
my_width <- reactive({
l = length(input$user_selected_metrics)
if(l == 1) {
my_width = 300
}else if(l == 2) {
my_width = 800
}else if(l == 3) {
my_width = 1200
}
my_width <- 1500
})
heatmap_height <- reactive({
l <- length(input$user_selected_metrics)
k <- length(input$heatmap_controlChart_select)
if(l == 1) {
heatmap_height <- ceiling(k)*300
}else {
heatmap_height <- ceiling(k)*ceiling(l)*200
}
})
heatmap_width <- reactive({
heatmap_width <- 1000
})
########################################################## box plot in Metric Summary tab ##########################################
output$box_plot <- renderPlotly({
prodata <- data$df
validate(
need(!is.null(prodata), "Please upload your data"),
need(is.data.frame(prodata), prodata),
need(!is.null(input$user_selected_metrics),"Please first select metrics and create a decision rule")
)
metrics_box.plot(prodata, data.metrics = input$user_selected_metrics)
})
############### summary plots and radar plots ############################################################################
output$plot_summary <- renderPlot({
prodata <- data$df
validate(
need(!is.null(prodata), "Please upload your data"),
need(is.data.frame(prodata), prodata),
need(!is.null(input$user_selected_metrics),"Please first select metrics and create a decision rule")
)
is_guidset_selected <- FALSE
if(input$selectGuideSetOrMeanSD == "Mean and standard deviation estimated from guide set") {
is_guidset_selected <- TRUE
}
listMean <- list()
listSD <- list()
for(metric in input$user_selected_metrics){
listMean[[metric]] <- input[[paste0("selectMean@",metric)]]
listSD[[metric]] <- input[[paste0("selectSD@",metric)]]
}
plots <- list()
i <- 1
for(method in input$summary_controlChart_select) {
p1 <- NULL
p2 <- NULL
if(method == "XmR") {
p1 <- XmR.Summary.plot(prodata, data.metrics = input$user_selected_metrics, input$L, input$U, listMean = listMean,listSD = listSD, guidset_selected = is_guidset_selected)
p2 <- XmR.Radar.Plot(prodata, data.metrics = input$user_selected_metrics,input$L,input$U,listMean = listMean,listSD = listSD, guidset_selected = is_guidset_selected)
} else if(method == "CUSUM") {
p1 <- CUSUM.Summary.plot(prodata, data.metrics = input$user_selected_metrics, input$L, input$U,listMean = listMean,listSD = listSD, guidset_selected = is_guidset_selected)
p2 <- CUSUM.Radar.Plot(prodata, data.metrics = input$user_selected_metrics, input$L,input$U,listMean = listMean,listSD = listSD, guidset_selected = is_guidset_selected)
}
plots[[i]] <- p1
plots[[i+1]] <- p2
i <- i+2
}
if(length(plots) > 0)
do.call("grid.arrange", c(plots, ncol = 1))
}, height = my_height, width = my_width)
############################# heat_map in Summary tab #############################################
output$heat_map <- renderPlot({
prodata <- data$df
validate(
need(!is.null(prodata), "Please upload your data"),
need(is.data.frame(prodata), prodata),
need(!is.null(input$user_selected_metrics),"Please first select metrics and create a decision rule"),
need(!is.null(prodata$AcquiredTime),"To view heatmaps, the dataset should include Acquired Time column.")
)
peptideThresholdRed <- (as.numeric(input$threshold_peptide_red))/100
peptideThresholdYellow <- (as.numeric(input$threshold_peptide_yellow))/100
if(is.null(prodata$AcquiredTime)) return(NULL)
is_guidset_selected <- FALSE
if(input$selectGuideSetOrMeanSD == "Mean and standard deviation estimated from guide set") {
is_guidset_selected <- TRUE
}
listMean <- list()
listSD <- list()
for(metric in input$user_selected_metrics){
listMean[[metric]] <- input[[paste0("selectMean@",metric)]]
listSD[[metric]] <- input[[paste0("selectSD@",metric)]]
}
plots <- list()
i <- 1
for(method in input$heatmap_controlChart_select) {
p1 <- metrics_heat.map(prodata,
data.metrics = input$user_selected_metrics, method = method,
peptideThresholdRed, peptideThresholdYellow,input$L, input$U, type = 1,
title = "Decision-map : mean",
listMean = listMean, listSD = listSD, guidset_selected = is_guidset_selected)
p2 <- metrics_heat.map(prodata,
data.metrics = input$user_selected_metrics, method = method,
peptideThresholdRed, peptideThresholdYellow,input$L, input$U, type = 2,
title = "Decision-map : variability",
listMean = listMean, listSD = listSD, guidset_selected = is_guidset_selected)
plots[[i]] <- p1
plots[[i+1]] <- p2
i <- i+2
}
if(length(plots) > 0)
do.call("grid.arrange", c(plots, ncol = 1))
}, height = heatmap_height, width = heatmap_width)
session$onSessionEnded(function() {
stopApp()
})
############################################################################################################################
})
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source("plot-functions.R")
source("data-validation.R")
source("helper-functions.R")
source("QCMetrics.R")
shinyServer(function(input,output,session) {
#### Read data ##################################################################################################
data <- reactiveValues(df = NULL, metrics = NULL)
observeEvent(input$filein, {
file1 <- input$filein
data$df <- input_checking(read.csv(file=file1$datapath, sep=",", header=TRUE, stringsAsFactors=TRUE))
validate(
need(!is.null(data$df), "Please upload your data"),
need(is.data.frame(data$df), data$df)
)
data$metrics <- c(find_custom_metrics(data$df))
}, priority = 20)
observeEvent(input$sample_button, {
data$df <- input_checking(read.csv("./Datasets/Sampledata_CPTAC_Study_9_1_Site54.csv"))
validate(
need(!is.null(data$df), "Please upload your data"),
need(is.data.frame(data$df), data$df)
)
data$metrics <- c(find_custom_metrics(data$df))
}, priority = 20)
observeEvent(input$clear_button, {
data$df <- NULL
data$metrics <- NULL
}, priority = 20)
##### Precursor type selection #####################################################################################
output$pepSelect <- renderUI({
prodata <- data$df
validate(
need(!is.null(prodata), "Please upload your data.\n\n If your data contains min start time and max end time columns,the App will add a peak assymetry column automatically.\n\n Your data should contain a column named Annotation. Put all your metrics after this column.To see an example of a sample data click on the {Run with example data} button."),
need(is.data.frame(prodata), prodata)
)
selectInput("pepSelection","Choose peptide"
,choices = c(levels(prodata$Precursor),"all peptides")
)
})
######Show table of data #####################################################################################################
output$prodata_table <- renderDataTable({
validate(
need(!is.null(data$df), "Please upload your data.\n\n If your data contains min start time and max end time columns, the App will add a peak assymetry column automatically.\n\n Your data should contain a column named Annotation. Put all your metrics after this column.To see an example of a sample data click on the {Run with example data} button."),
need(is.data.frame(data$df), data$df)
)
data$df
}, options = list(pageLength = 25))
###### selection tab in Data Improt and selection #####################################################
output$selectMeanSD <- renderUI({
lapply(input$user_selected_metrics,
function(x){
fluidRow(
column(4,paste(x,":")),
column(4,
numericInput(paste0("selectMean@",x),"mean",value = 1)
),
column(4,
numericInput(paste0("selectSD@",x),"standard deviation",value = 1)
)
)
})
})
output$selectGuideSet <- renderUI({
fluidRow(
column(6,
numericInput("L","Lower bound of guide set",value = 1, min = 1, step = 1)
),
column(6,
numericInput("U","Upper bound of guide set", value = 5, min = 2, step = 1)
)
)
})
###### Tab for selecting decision rule and metrics ###############################################
output$metricThresholdRed <- renderUI({
numOfMetrics <- length(input$user_selected_metrics)
numericInput('threshold_metric_red', '', value = 2, min = 0, max = numOfMetrics, step = 1)
})
output$peptideThresholdYellow <- renderUI({
threshold_peptide_red <- input$threshold_peptide_red
numericInput('threshold_peptide_yellow', '', value = threshold_peptide_red - 1, min = 1, max = threshold_peptide_red, step = 1)
})
output$metricThresholdYellow <- renderUI({
numOfMetrics <- length(input$user_selected_metrics)
threshold_metric_red <- input$threshold_metric_red
validate(
need(!is.null(numOfMetrics),"loading..."),
need(!is.null(threshold_metric_red),"loading...")
)
numericInput('threshold_metric_yellow', '', value = threshold_metric_red , min = 0, max = threshold_metric_red, step = 1)
})
output$metricSelection <- renderUI({
checkboxGroupInput("user_selected_metrics","",
choices = c(data$metrics),
inline = TRUE)
})
################################################################# plots ###################################################
#################################################################################################################
output$XmR_tabset <- renderUI({
validate(
need(!is.null(data$df), "Please upload your data first"),
need(is.data.frame(data$df), data$df),
need(!is.null(input$user_selected_metrics),"Please first select metrics and create a decision rule")
)
is_guidset_selected <- FALSE
if(input$selectGuideSetOrMeanSD == "Mean and standard deviation estimated from guide set") {
is_guidset_selected <- TRUE
}
Tabs <- lapply(input$user_selected_metrics,
function(x) {
tabPanel(x,
tags$head(tags$style(type="text/css")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("It may take a while to load the plots, please wait...",
id="loadmessage")),
renderPlotly(render.QC.chart(data$df, input$pepSelection, input$L,
input$U, metric = x,
plot.method = "XmR", normalization = FALSE,
y.title1 = "Individual Value", y.title2 = "Moving Range",
selectMean = input[[paste0("selectMean@",x)]],selectSD = input[[paste0("selectSD@",x)]],
guidset_selected = is_guidset_selected)
)
)
})
do.call(tabsetPanel, Tabs)
})
################################################################################################################
#################################################################################################################
output$CUSUM_tabset <- renderUI({
validate(
need(!is.null(data$df), "Please upload your data first"),
need(is.data.frame(data$df), data$df),
need(!is.null(input$user_selected_metrics),"Please first select metrics and create a decision rule")
)
is_guidset_selected <- FALSE
if(input$selectGuideSetOrMeanSD == "Mean and standard deviation estimated from guide set") {
is_guidset_selected <- TRUE
}
Tabs <- lapply(input$user_selected_metrics,
function(x) {
tabPanel(x,
tags$head(tags$style(type="text/css")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("It may take a while to load the plots, please wait...",
id="loadmessage")),
renderPlotly(render.QC.chart(data$df, input$pepSelection, input$L, input$U, metric = x, plot.method = "CUSUM", normalization = TRUE, y.title1 = "CUSUM mean", y.title2 = "CUSUM variation",selectMean = input[[paste0("selectMean@",x)]],selectSD = input[[paste0("selectSD@",x)]],guidset_selected = is_guidset_selected))
)
})
do.call(tabsetPanel, Tabs)
})
################################################################################################################
#################################################################################################################
output$CP_tabset <- renderUI({
validate(
need(!is.null(data$df), "Please upload your data first"),
need(is.data.frame(data$df), data$df),
need(!is.null(input$user_selected_metrics),"Please first select metrics and create a decision rule")
)
is_guidset_selected <- FALSE
if(input$selectGuideSetOrMeanSD == "Mean and standard deviation estimated from guide set") {
is_guidset_selected <- TRUE
}
Tabs <- lapply(input$user_selected_metrics,
function(x) {
tabPanel(x,
tags$head(tags$style(type="text/css")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("It may take a while to load the plots, please wait...",
id="loadmessage")),
renderPlotly(render.QC.chart(data$df, input$pepSelection, input$L, input$U, metric = x, plot.method = "CP", normalization = TRUE, y.title1 = "Change point for mean", y.title2 = "Change point for variation",selectMean = input[[paste0("selectMean@",x)]],selectSD = input[[paste0("selectSD@",x)]],guidset_selected = is_guidset_selected))
)
})
do.call(tabsetPanel, Tabs)
})
######################################################### height and width in Summary tab ########################################
my_height <- reactive({
l <- length(input$user_selected_metrics)
k <- length(input$summary_controlChart_select)
if(l < 5) {
my_height <- ceiling(k)*700
}else if(l < 9) {
my_height <- ceiling(k)*1300
}else if(l <15) {
my_height <- ceiling(k)*1700
}else if(l < 20) {
my_height <- ceiling(k)*2000
}else if(l < 25) {
my_height <- ceiling(k)*2300
}else {
my_height <- ceiling(k)*2600
}
})
my_width <- reactive({
l = length(input$user_selected_metrics)
if(l == 1) {
my_width = 300
}else if(l == 2) {
my_width = 800
}else if(l == 3) {
my_width = 1200
}
my_width <- 1500
})
heatmap_height <- reactive({
l <- length(input$user_selected_metrics)
k <- length(input$heatmap_controlChart_select)
if(l == 1) {
heatmap_height <- ceiling(k)*300
}else {
heatmap_height <- ceiling(k)*ceiling(l)*200
}
})
heatmap_width <- reactive({
heatmap_width <- 1000
})
########################################################## box plot in Metric Summary tab ##########################################
output$box_plot <- renderPlotly({
prodata <- data$df
validate(
need(!is.null(prodata), "Please upload your data"),
need(is.data.frame(prodata), prodata),
need(!is.null(input$user_selected_metrics),"Please first select metrics and create a decision rule")
)
metrics_box.plot(prodata, data.metrics = input$user_selected_metrics)
})
############### summary plots and radar plots ############################################################################
output$plot_summary <- renderPlot({
prodata <- data$df
validate(
need(!is.null(prodata), "Please upload your data"),
need(is.data.frame(prodata), prodata),
need(!is.null(input$user_selected_metrics),"Please first select metrics and create a decision rule")
)
is_guidset_selected <- FALSE
if(input$selectGuideSetOrMeanSD == "Mean and standard deviation estimated from guide set") {
is_guidset_selected <- TRUE
}
listMean <- list()
listSD <- list()
for(metric in input$user_selected_metrics){
listMean[[metric]] <- input[[paste0("selectMean@",metric)]]
listSD[[metric]] <- input[[paste0("selectSD@",metric)]]
}
plots <- list()
i <- 1
for(method in input$summary_controlChart_select) {
p1 <- NULL
p2 <- NULL
if(method == "XmR") {
p1 <- XmR.Summary.plot(prodata, data.metrics = input$user_selected_metrics, input$L, input$U, listMean = listMean,listSD = listSD, guidset_selected = is_guidset_selected)
p2 <- XmR.Radar.Plot(prodata, data.metrics = input$user_selected_metrics,input$L,input$U,listMean = listMean,listSD = listSD, guidset_selected = is_guidset_selected)
} else if(method == "CUSUM") {
p1 <- CUSUM.Summary.plot(prodata, data.metrics = input$user_selected_metrics, input$L, input$U,listMean = listMean,listSD = listSD, guidset_selected = is_guidset_selected)
p2 <- CUSUM.Radar.Plot(prodata, data.metrics = input$user_selected_metrics, input$L,input$U,listMean = listMean,listSD = listSD, guidset_selected = is_guidset_selected)
}
plots[[i]] <- p1
plots[[i+1]] <- p2
i <- i+2
}
if(length(plots) > 0)
do.call("grid.arrange", c(plots, ncol = 1))
}, height = my_height, width = my_width)
############################# heat_map in Summary tab #############################################
output$heat_map <- renderPlot({
prodata <- data$df
validate(
need(!is.null(prodata), "Please upload your data"),
need(is.data.frame(prodata), prodata),
need(!is.null(input$user_selected_metrics),"Please first select metrics and create a decision rule"),
need(!is.null(prodata$AcquiredTime),"To view heatmaps, the dataset should include Acquired Time column.")
)
peptideThresholdRed <- (as.numeric(input$threshold_peptide_red))/100
peptideThresholdYellow <- (as.numeric(input$threshold_peptide_yellow))/100
if(is.null(prodata$AcquiredTime)) return(NULL)
is_guidset_selected <- FALSE
if(input$selectGuideSetOrMeanSD == "Mean and standard deviation estimated from guide set") {
is_guidset_selected <- TRUE
}
listMean <- list()
listSD <- list()
for(metric in input$user_selected_metrics){
listMean[[metric]] <- input[[paste0("selectMean@",metric)]]
listSD[[metric]] <- input[[paste0("selectSD@",metric)]]
}
plots <- list()
i <- 1
for(method in input$heatmap_controlChart_select) {
p1 <- metrics_heat.map(prodata,
data.metrics = input$user_selected_metrics, method = method,
peptideThresholdRed, peptideThresholdYellow,input$L, input$U, type = 1,
title = "Decision-map : mean",
listMean = listMean, listSD = listSD, guidset_selected = is_guidset_selected)
p2 <- metrics_heat.map(prodata,
data.metrics = input$user_selected_metrics, method = method,
peptideThresholdRed, peptideThresholdYellow,input$L, input$U, type = 2,
title = "Decision-map : variability",
listMean = listMean, listSD = listSD, guidset_selected = is_guidset_selected)
plots[[i]] <- p1
plots[[i+1]] <- p2
i <- i+2
}
if(length(plots) > 0)
do.call("grid.arrange", c(plots, ncol = 1))
}, height = heatmap_height, width = heatmap_width)
session$onSessionEnded(function() {
stopApp()
})
############################################################################################################################
})
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