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app.R
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# source('./library.r')
# source("./module1.r")
# source("./PCA.r")
# source("./Boxplot.r")
# source("./module2.r")
# source("./remove_samples.r")
# source("./QC_normalized_data.r")
# source("./module3.r")
# source("./functions.r")
# source("./Differential_expression.r")
# source("./MA_plot.r")
# source("./Volcano_plot.r")
# source("./WGCNA.r")
# source("./Kegg_module.r")
# source("./Biological_Process_module.r")
# source("./Hallmark_module.r")
# source("./FC-FC_plot_module.r")
# source("./Venn_diagram_module.r")
# source("./Enriched_markers_module.R")
# source("./Heatmap_module.R")
# source("./ANOVA_module.r")
# source("./Transcription_factor_prediction_module.r")
# source("./powerpoint_module.r")
source('library.R')
source("module1.R")
source("PCA.R")
source("Boxplot.R")
source("module2.R")
source("remove_samples.R")
source("QC_normalized_data.R")
source("module3.R")
source("functions.R")
source("Differential_expression.R")
source("MA_plot.R")
source("Volcano_plot.R")
source("WGCNA.R")
source("Kegg_module.R")
source("Biological_Process_module.R")
source("Hallmark_module.R")
source("FC-FC_plot_module.R")
source("Venn_diagram_module.R")
source("Enriched_markers_module.R")
source("Heatmap_module.R")
source("ANOVA_module.R")
source("Transcription_factor_prediction_module.R")
source("powerpoint_module.R")
# Define UI for application that draws a histogram
#shinyUI(fluidPage(
jsResetCode <- "shinyjs.reset = function() {history.go(0)}" # Define the js method that resets the page
ui<-navbarPage("Shiny-Seq",
navbarMenu("Raw Data",
tabPanel("Raw Data",
Module_Raw_data_UI("module"),
br(),
useShinyjs(),# Set up shinyjs
# Add a CSS class for red text colour
inlineCSS(list(.blue = "background: lightblue")),
uiOutput("button"),
#Alter user if the number of samples in count/expression data
#is not same as annotation table
fluidRow(column(1,downloadButton('anno_data', 'Download annotation')),
hr(),
column(1,downloadButton('exp_data', 'Download count data'))),
fluidRow(column(8,
bsAlert("alert_app"))),
br(),
br(),
hr(),
#Display annotation table (excluded due to download from table directly)
# downloadButton('downloadepData', 'Download full Data'),
fluidRow(column (10,DT::dataTableOutput("pData"))),
br(),
br(),
hr(),
#Display expression table
fluidRow(
column(1,
selectInput(inputId= "datachoice" ,label = h5("Select Data Type"),
choices = list("Excel" = 1, "CSV" = 2),
selected = 1)),
column(1,
br(),
br(),
downloadButton('downloadeData', 'Download Expression Data Table'))),
fluidRow(column(12,DT::dataTableOutput("edata")))
),
tabPanel("Boxplot",
Boxplot_module_UI("module")
),
tabPanel( "PCA",
PCA_UI("module")
)
),
navbarMenu("Normalization",
tabPanel("Normalized table",
conditionalPanel(condition="input.ok1==0",
# #p(input.ok1),
p("please press start in the unnormalized table tab")),
conditionalPanel(condition="input.ok1 >0",
fluidRow(column(4,uiOutput("condition"))),
fluidRow(column(5, uiOutput("design"))),
br(),
useShinyjs(), # Set up shinyjs
# Add a CSS class for red text colour
inlineCSS(list(.blue = "background: lightblue")),
uiOutput("button1"),
br(),
br(),
actionButton("rem_samp", "Detect outliers"),
actionButton("qc","Quality control"),
#actionButton("skip_bc","Skip Batch analysis"),
bsModal("modalqc","QC for normalized data","qc",size = "large",
conditionalPanel("input.ok2==0",
p("Please press start button in the normalized tab and wait for a few minutes prior to clicking this button")),
conditionalPanel("input.ok2>0",
QC_normalized_UI("module"))),
Module_Normalized_data_UI("module"),
bsModal("modaloutlier", "Detect and remove outlier samples in addition to identifying batch effects", "rem_samp",size = "large",#uiOutput("plots")),
conditionalPanel("input.ok2==0",
p("please press start button in the normalized tables tab and wait for a few minutes
prior to clicking this button")
),
conditionalPanel("input.ok2>0",
Remove_samples_UI("module"),
bscols(column(3,actionButton("del", "Delete"),actionButton("reset", "Reset"))),
bscols(
column(10,DT::dataTableOutput("outliers")))
))
)
),
tabPanel("Boxplot",
conditionalPanel(condition = "input.ok2==0",
p("Please press start button in normalized table tab")),
conditionalPanel(condition="input.ok2 >0",
#p("hey"),
Boxplot_module_UI("module1"))
),
tabPanel("Exploratory data anlysis",
tabsetPanel(
tabPanel("PCA",
conditionalPanel(condition = "input.ok2==0",
p("Please press start button in normalized table tab")),
conditionalPanel(condition = "input.ok2>0",
PCA_UI("moduleX"))),
tabPanel("Heatmap of samples",
conditionalPanel(condition = "input.ok2==0",
p("Please press start button in normalized table tab")),
conditionalPanel(condition = "input.ok2>0",
plotOutput("heatmap_sample")))
)
)
),
tabPanel("Batch effect analysis",
tabsetPanel(
tabPanel("Compute batch effect",
br(),
actionButton("ok3","Start"),
conditionalPanel("input.ok3>0",
Module_Batch_effect_UI("module"))
),
tabPanel("Source of variation",
conditionalPanel(condition = "input.ok2==0",
p("Please press start button in normalized table tab")),
conditionalPanel(condition = "input.ok2>0",
uiOutput("sov_data_input"),
uiOutput("go_sov"),
plotlyOutput("sov")
)),
###insert
tabPanel("Heatmap of samples after batch correction ",
conditionalPanel(condition = "input.ok3==0",
p("Please press start button in normalized table tab")),
conditionalPanel(condition = "input.ok3>0",
plotOutput("heatmap_sample_batch"))
)
)
),
navbarMenu("Downstream Analysis",
tabPanel("Differential expression analysis",
tabsetPanel(
tabPanel("Get list of DE genes",
conditionalPanel("input.ok3>0",
Module_Differential_Expression_UI("module")),
conditionalPanel("input.ok3==0",
p("Please press start button in batch effect analysis tab"))
),
tabPanel("Get list of Transcription factors",
Enriched_markers_module_UI("module")
),
tabPanel("Get list of marker genes",
helpText("This tab enables you to search for any type of markers
in the file that are enriched in the dataset uploaded
please make sure that the first two columns in the file
correspond to gene names present in mouse(column 1) and human(column 2"),
fluidRow(
column(8,
fileInput("add_file", label = h3('Choose file to upload'),
accept = c(
'text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'text/plain',
'.csv',
'.tsv')
))),
Enriched_markers_module_UI("module1")
),
tabPanel("Transcription factor prediction",
predicted_TF_module_UI("module")
),
tabPanel("Visualization",
tabsetPanel(
tabPanel("MA plot",
MA_plot_UI("module")
),
tabPanel("Volcano plot",
Volcano_plot_UI("module")),
tabPanel("Venn Diagram",
Venn_diagram_module_UI("module")),
tabPanel("FC-FC plot",
FC_FC_plot_module_UI("module"))
))
)
),
tabPanel("Heatmap",
tabsetPanel(
tabPanel( "Heatmap of DE genes",
# actionButton("DE_go","GO"),
# conditionalPanel("input.DE_go>0",
heatmap_module_UI("module")
#)
),
tabPanel("heatmap of TF",
# actionButton("TF_go","GO"),
# conditionalPanel("input.TF_go>0",
heatmap_module_UI("module1")
# )
),
tabPanel("heatmap of marker genes",
heatmap_module_UI("module2")
)
)
),
tabPanel("Enrichment Analysis",
tabsetPanel(
tabPanel("Enrichment analysis: KEGG",
Kegg_module_UI("module")),
tabPanel("Enrichment analysis-GO:Biological processes",
Biological_process_module_UI("module")),
tabPanel("Hallmark plot",
Hallmark_module_UI("module"))
))
),
tabPanel("ANOVA",
tabsetPanel(
tabPanel("ANOVA Table",
conditionalPanel("input.ok3>0",
ANOVA_module_UI("module")
)
),
tabPanel("heatmap of 1000 most variable genes",
heatmap_module_UI("module3")
)
)
),
tabPanel("WGCNA",
fluidRow(column(3,
selectInput("option_wgcna", label = h5("Select choice"),
choices = list("","
Perform WGCNA independently" = 1,
"Perform as part of DE analysis" = 2),
selected = NULL))),
uiOutput("file_input1"),
uiOutput("file_input2"),
fluidRow(column (10,DT::dataTableOutput("pheno_table"))),
uiOutput("go_wgcna"),
conditionalPanel("input.go_wgcna>0",
WGCNA_module_UI("module"))
),
tabPanel("Summary and results ",
powerpoint_module_UI("module")
)
)
#################################Begin of code#################
# By default, the file size limit is 5MB. It can be changed by
# setting this option. Here we'll raise limit to 19MB.
options(shiny.maxRequestSize = 19*1024^2)
server<-function(input, output,session) {
#Delete all plots generated for ppt in the previous session
do.call(file.remove,list(list.files("./plots",full.names = T)))
input_data<-callModule(Module_Raw_data_Input,"module")
print("inside app- line 98 module 1 pass")
#display output (count data and annotation file)
#
#V contains two reactive values wherein the variable v$data represents the count/expression table
# v$data is updated when either of the buttons "delete" or "reset" is clicked
v <- reactiveValues(data=NULL,click1=NULL,raw_pca=NULL,norm_pca=NULL,heat_de=NULL,heat_tf=NULL,heat_anova=NULL,
dds=NULL,batch=NULL,anova=NULL,kegg=NULL,bp=NULL,hallmark=NULL,
de=NULL,TF=NULL,marker=NULL,data_wgcna=NULL,ma=NULL,vol=NULL,
pheno_wgcna=NULL,
wgcna_output=NULL,
wgcna_click=FALSE)
#makeReactiveBinding("v")
ex_anno <- read.table("./example_data/annotation file.txt", header = TRUE,sep = "\t",check.names = FALSE)
ex_exp <- read.table("./example_data/expression data.txt", header = TRUE,sep = "\t",check.names = FALSE)
output$anno_data <- downloadHandler(
filename = function(){
paste("annotation file.txt")
},
content = function(file) {
write.table(ex_anno, file = file,quote = FALSE, row.names = FALSE, sep = "\t")
})
output$exp_data <- downloadHandler(
filename = function(){
paste("expression data.txt")
},
content = function(file) {
write.table(ex_exp, file = file,quote = FALSE, row.names = FALSE, sep = "\t")
})
#
observeEvent(input_data$file2(),{
if(!is.null(input_data$pData())) {
#print('k')
closeAlert(session,"exampleAlert_app")
print("inside app line 111")
#print(head(input_data$edata()))
data<-input_data$edata()#edata()
data2<-input_data$edata2()
pheno<-input_data$pData()#pData()
#set order of columns in expression data as same as order of sample ID in pheno data
# Create a Progress object
progress <- shiny::Progress$new()
# Make sure it closes when we exit this reactive, even if there's an error
on.exit(progress$close())
progress$set(message = "Processing Data", value = 0)
# Increment the progress bar, and update the detail text.
progress$inc(1/2, detail = paste("Doing part", 1,"/",2))
# Pause for 0.1 seconds to simulate a long computation.
Sys.sleep(0.1)
#print(as.vector(pheno[,1]))
#print(pheno)
data=data[,as.vector(pheno[,1])]
print("line 291 app")
print(head(data))
#progress$set(message = "Rlog transformation step", value = 0)
#v$data<-list(data,pheno,rlogTransformation(as.matrix(data))) #time consuming step
v$data<-list(data,pheno,as.matrix(data), data2)
# Increment the progress bar, and update the detail text.
progress$inc(2/2, detail = paste("Doing part", 2,"/",2))
# Pause for 0.1 seconds to simulate a long computation.
Sys.sleep(0.1)
#v$data<-list(data,pheno)
}
else {
createAlert(session,"alert_app", "exampleAlert_app", title = "Oops!",
content = paste0("Some samples in the expression/count table is absent in the annotation table!
Please give correct input"), append = FALSE)
}
})
observeEvent(input$del, {
#v$data <- runif(100)
#input$del
print("inside app line 149")
idx<-input$outliers_rows_selected
data<-input_data$edata()
data2 <-input_data$edata2()
pheno<-input_data$pData()
#set order of columns in expression data as same as order of sample ID in pheno data
data=data[,as.vector(pheno[,1])]
# print('data tab')
#print(idx)
#data<-input_data$edata()
# pheno<-input_data$pData()
#set order of columns in expression data as same as order of sample ID in pheno data
data=data[,as.vector(pheno[,1])]
# print('data tab')
# print(idx)
#set order of columns in expression data as same as order of sample ID in pheno data
# Create a Progress object
progress <- shiny::Progress$new()
# Make sure it closes when we exit this reactive, even if there's an error
on.exit(progress$close())
progress$set(message = "Processing data", value = 0)
# Increment the progress bar, and update the detail text.
progress$inc(1/2, detail = paste("Doing part", 1,"/",2))
# Pause for 0.1 seconds to simulate a long computation.
Sys.sleep(0.1)
# Pause for 0.1 seconds to simulate a long computation.
# Sys.sleep(0.1)
if(!is.null(idx))
{
if(is.null(v$click1))
{
print(colnames(data2$counts))
print("inside app line 181, delete button clicked hence deleteing selected samples")
print(names(data2))
print(colnames(data2$counts))
if(!identical(data, data2)) {
data2$counts <- data2$counts[,-idx]
data2$abundance <- data2$abundance[,-idx]
data2$length <- data2$length[,-idx]}
else{
data2 <- data2[,-idx]
}
v$data<-list(data[,-idx],pheno[-idx,],rlogTransformation(as.matrix(data[,-idx])),data2)
print(ncol(v$data[[1]]))
v$click1<-1
}
else
{
v$data[[1]]<-v$data[[1]][,-idx]
v$data[[2]]<-v$data[[2]][-idx,]
v$data[[3]]<-rlogTransformation(as.matrix(v$data[[1]][,-idx]))
}
v$dds<-callModule(Module_Normalized_data,"module",reactive(v$data),input$conchoice,input$designchoice,reactive(v$click1))
normal<-list(v$dds$normal(),colData(v$dds$dds.fc()[[1]]),assay(v$dds$dds.fc()[[2]]))
callModule(Boxplot_module,"module1",normal)
callModule(PCA_module2,"moduleX",data=normal)
v$norm_pca<-callModule(PCA_module2,"moduleX",data=normal)
callModule(QC_normalized,"module",normal[[1]],zoom=TRUE)
v_list<-callModule(Remove_samples,"module",v$dds$dds.fc()[[1]],
v$dds$dds.fc()[[2]],
v$dds$normal())
callModule(QC_normalized,"module",normal[[1]],zoom=TRUE)
output$sov_data_input<-renderUI({
print("create Checkbox")
checklist = list()
for (i in seq_along(colnames(colData(v$dds$dds.fc()[[1]])))[-1]) {
checklist[[colnames(colData(v$dds$dds.fc()[[1]]))[[i]]]] = i}
selectizeInput("sov_inp", "Choose the variables to be included
to compute the amount of contribution to variance",
checklist,selected = NULL,multiple=T)
})
observeEvent(input$sov_inp,
{
output$go_sov<-renderUI({
actionButton("go_sov","Go")
})
observeEvent(input$go_sov,
{
output$sov<-renderPlotly({
id<-as.numeric(input$sov_inp)
print("inside app line 528")
print(id)
if(length(id)!=0){
df<-source_of_variation_op(v$dds$normal(),
colData(v$dds$dds.fc()[[1]]),
id)
if(sum(df$Percentage)<1) df<-rbind(df,c("Unknown",1-sum(df$Percentage)))
print(df)
p <- plot_ly(df, labels = ~Variation,
values = ~Percentage, type = 'pie',
textposition = 'inside',
textinfo = 'label+percent',
insidetextfont = list(color = '#FFFFFF')) %>%
layout(title = 'Source of variation plot',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
}
else plotly_empty()
})
})
})
#
# Increment the progress bar, and update the detail text.
progress$inc(2/2, detail = paste("Doing part", 2,"/",2))
# Pause for 0.1 seconds to simulate a long computation.
Sys.sleep(0.1)
}
})
#when reset button is clicked
observeEvent(input$reset, {
#v$data <- rnorm(100)
data<-input_data$edata()
data2 <- input_data$edata2()
pheno<-input_data$pData()
#set order of columns in expression data as same as order of sample ID in pheno data
data=data[,as.vector(pheno[,1])]
#set order of columns in expression data as same as order of sample ID in pheno data
# Create a Progress object
progress <- shiny::Progress$new()
# Make sure it closes when we exit this reactive, even if there's an error
on.exit(progress$close())
progress$set(message = "Processing data", value = 0)
# Increment the progress bar, and update the detail text.
progress$inc(1/2, detail = paste("Doing part", 1,"/",2))
# Pause for 0.1 seconds to simulate a long computation.
Sys.sleep(0.1)
v$data<-list(data,pheno,rlogTransformation(as.matrix(data)),data2)
v$dds<-callModule(Module_Normalized_data,"module",reactive(v$data),input$conchoice,input$designchoice,reactive(v$click1))
normal<-list(v$dds$normal(),colData(v$dds$dds.fc()[[1]]),assay(v$dds$dds.fc()[[2]]))
callModule(Boxplot_module,"module1",normal)
v$norm_pca<-callModule(PCA_module2,"moduleX",data=normal)
callModule(PCA_module2,"moduleX",data=normal)
callModule(QC_normalized,"module",normal[[1]],zoom=TRUE)
callModule(Remove_samples,"module",v$dds$dds.fc()[[1]],
v$dds$dds.fc()[[2]],
v$dds$normal())
callModule(QC_normalized,"module",normal[[1]],zoom=TRUE)
output$sov_data_input<-renderUI({
print("create Checkbox")
checklist = list()
for (i in seq_along(colnames(colData(v$dds$dds.fc()[[1]])))[-1]) {
checklist[[colnames(colData(v$dds$dds.fc()[[1]]))[[i]]]] = i}
selectizeInput("sov_inp", "Choose the variables to be included
to compute the amount of contribution to variance",
checklist,selected = NULL,multiple=T)
})
observeEvent(input$sov_inp,
{
output$go_sov<-renderUI({
actionButton("go_sov","Go")
})
observeEvent(input$go_sov,
{
output$sov<-renderPlotly({
id<-as.numeric(input$sov_inp)
print("inside app line 499")
print(id)
if(length(id)!=0){
edata<-v$dds$normal()
if(v$batch$batch_choice()>1)
{
print("line 505 inside app")
print(head(v$batch$batch_corrected_data()))
edata<-2^v$batch$batch_corrected_data()
print(head(edata))
}
df<-source_of_variation_op(v$dds$normal(),
colData(v$dds$dds.fc()[[1]]),
id)
if(sum(df$Percentage)<1) df<-rbind(df,c("Unknown",1-sum(df$Percentage)))
print(df)
p <- plot_ly(df, labels = ~Variation,
values = ~Percentage, type = 'pie',
textposition = 'inside',
textinfo = 'label+percent',
insidetextfont = list(color = '#FFFFFF')) %>%
layout(title = 'Source of variation plot',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
}
else plotly_empty()
})
})
})
# Increment the progress bar, and update the detail text.
progress$inc(2/2, detail = paste("Doing part", 2,"/",2))
# Pause for 0.1 seconds to simulate a long computation.
Sys.sleep(0.1)
})
#Display expression table
output$edata <- DT::renderDataTable({
#print(head(v$data[[3]]))
DT::datatable(v$data[[1]],class = 'cell-border stripe',
selection = list(target = 'column'),
extensions = list('Scroller'=NULL,'Buttons'=NULL),
options = list(deferRender = TRUE,scrollX = TRUE,scrollY = 500,scroller = TRUE,dom = 'Bfrtip',
buttons = list('copy')#I('colvis')extend = 'collection',
)#list('copy',list(extend = 'collection',buttons = c('csv', 'excel'),text = 'Download'))
)
})
# download of expression table, either as excel or csv
output$downloadeData <- downloadHandler(
filename = function(){
if(as.numeric(input$datachoice==1)){paste("Expression Table.xlsx")}
else if (as.numeric(input$datachoice==2)){paste("Expression Table.csv")}
},
content = function (file){
if(as.numeric(input$datachoice==1)){
wb <- createWorkbook()
addWorksheet(wb, sheetName = "Expression data")
writeData(wb = wb, sheet = 1, x = v$data[[1]], colNames = T, rowNames = T)
saveWorkbook(wb, file)
# write.xlsx2(v$data[[1]], file, sheetName = "Expression data",
# col.names = TRUE, row.names = TRUE, append = FALSE)
}
else if (as.numeric(input$datachoice==2)){write.csv(v$data[[1]], file)}
}
)
# #Display annotation table
output$pData <- DT::renderDataTable(server=FALSE,{
DT::datatable(v$data[[2]],class = 'cell-border stripe',
selection = list(target = 'column'),
extensions = list('Scroller'=NULL,'Buttons'=NULL),
options = list(deferRender = TRUE,scrollX = TRUE,scrollY = 200,scroller = TRUE,dom = 'Bfrtip',
buttons = list('copy',list(extend = 'collection',buttons = c('csv', 'excel', 'pdf'),text = 'Download annotation data'))#I('colvis')
)#buttons = list('copy',list(extend = 'collection'
)
})
# #outputOptions(output, 'pData', suspendWhenHidden=FALSE)
#
#dowmload annotation data (changed to download from table!)
# output$downloadepData <- downloadHandler(
# filename = function() { 'annotation.xlsx' },
# content = function(file) {
# #write.csv(v$data[[2]], file)
# write.xlsx2(v$data[[2]], file, sheetName = "Annotation",
# col.names = TRUE, row.names = TRUE, append = FALSE)
# }
# )
#start button in raw data tab
observeEvent(input_data$file2(),{
#div(style="background-color:orange;",input$ok1)
#toggleClass("ok1", "grey")
output$button<-renderUI({
actionButton("ok1", label = "Start", icon("paper-plane"))
})
})
#start button turns blue on click
#call boxplot and PCA for raw data input
observeEvent(input$ok1,{
toggleClass("ok1", "blue")
callModule(Boxplot_module,"module",v$data)
v$raw_pca<-callModule(PCA_module,"module",data=v$data) # save for ppt module
callModule(PCA_module,"module",data=v$data) # show plot in app
# creates a checkbox widget to select the important condition
output$condition <-
renderUI({
#if (!is.null(input$file2)) {
#print("create Checkbox condition")
checklist = list()
for (i in seq_along(colnames(v$data[[2]]))[-1]) {
checklist[[colnames(v$data[[2]])[[i]]]] = i
}
radioButtons("conchoice", "Choose the conditon", checklist,selected = 2)
#}
})
# creates a checkbox widget to select the variables to be included in design
output$design <-
renderUI({
req(input$conchoice)
checklist = list()
for (i in seq_along(colnames(v$data[[2]]))[-1]) {
# disply all variables except condition variable
if(i!=as.numeric(input$conchoice)) checklist[[colnames(v$data[[2]])[[i]]]] = i
}
#print(checklist)
if(length(checklist)!=0){
checkboxGroupInput(session$ns("designchoice"), "Choose the variables to be included in design", checklist)
}
})
output$button1<-renderUI({
req(input$conchoice)
actionButton("ok2", label = "Start")
})
#req(input$conchoice)
})
observeEvent(input$ok2,
{
toggleClass("ok2", "blue")
print("inside app line 324 calling normalized table module")
v$dds<-callModule(Module_Normalized_data,"module",reactive(v$data),input$conchoice,input$designchoice,reactive(v$click1))
# print("dat")
normal<-list(v$dds$normal(),colData(v$dds$dds.fc()[[1]]),assay(v$dds$dds.fc()[[2]]))
#normalized table,annotation table,rlog transformed data for PCA
callModule(Boxplot_module2,"module1",normal)
v$norm_pca<-callModule(PCA_module2,"moduleX",data=normal) # save for ppt module
callModule(PCA_module2,"moduleX",data=normal) # show plot in app
callModule(QC_normalized,"module",normal[[1]],zoom=TRUE)
output$heatmap_sample<-renderPlot({
sampleDists <- dist(t(assay(v$dds$dds.fc()[[2]])))
var<-as.numeric(input$conchoice)
print("inside app line 368")
#print(colData(v$dds$dds.fc()[[1]])[,var])
heatmap_sample(sampleDists,v$dds$dds.fc()[[2]])
})
output$sov_data_input<-renderUI({
print("create Checkbox")
checklist = list()
for (i in seq_along(colnames(colData(v$dds$dds.fc()[[1]])))[-1]) {
checklist[[colnames(colData(v$dds$dds.fc()[[1]]))[[i]]]] = i}
selectizeInput("sov_inp", "Choose the variables to be included
to compute the amount of contribution to variance",
checklist,selected = NULL,multiple=T)
})
observeEvent(input$sov_inp,
{
output$go_sov<-renderUI({
actionButton("go_sov","Go")
})
observeEvent(input$go_sov,
{
output$sov<-renderPlotly({
id<-as.numeric(input$sov_inp)
print("inside app line 528")
print(id)
if(length(id)!=0){
edata<-v$dds$normal()
if(v$batch$batch_choice()>1)
{
print("line 675 inside app")
print(head(v$batch$batch_corrected_data()))
edata<-2^v$batch$batch_corrected_data()
print(head(edata))
}
df<-source_of_variation_op(edata,
colData(v$dds$dds.fc()[[1]]),
id)
if(sum(df$Percentage)<1) df<-rbind(df,c("Unknown",1-sum(df$Percentage)))
print(df)
p <- plot_ly(df, labels = ~Variation,
values = ~Percentage, type = 'pie',
textposition = 'inside',
textinfo = 'label+percent',
insidetextfont = list(color = '#FFFFFF')) %>%
layout(title = 'Source of variation plot',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
}
else plotly_empty()
})
})
})
observeEvent(input$rem_samp,
{
v_list<-callModule(Remove_samples,"module",v$dds$dds.fc()[[1]],
v$dds$dds.fc()[[2]],
v$dds$normal())
print("inside app line 328 bar")
# print(v_list$d())
# print(v_list$bar())
#display annotation table after removing outliers
output$outliers <- DT::renderDataTable({
print("inside app line 334 outab")
dat <- as.data.frame(colData(v$dds$dds.fc()[[2]]))
# print("dat")
# print(head(dat))
# print(typeof(dat))
d <- v_list$d()
v1_clickBar_list<-v_list$bar()
idx<-NULL
if (!is.null(d) && is.null(v1_clickBar_list))#is.null(input$clickBar))
{
idx<-which(dat[,1] %in% d)#d$key)
}
else if(is.null(d) && !is.null(v1_clickBar_list))
{
idx<-round(v1_clickBar_list)
#print(idx)
}
else if(!is.null(d) && !is.null(v1_clickBar_list))
{
id<-c(which(dat[,1] %in% d),round(v1_clickBar_list))
#print(id)
idx<-unique(id)
}
library(DT)
DT::datatable(dat,class = 'cell-border stripe',
selection = list(mode='multiple',selected=idx,target='row'),
extensions = list('Scroller'=NULL,'Buttons'=NULL),
options = list(deferRender = TRUE,scrollX = TRUE,scrollY = 350,scroller = TRUE,dom = 'Bfrtip',
buttons = list()))#buttons = list('copy',list(extend = 'collection',
})
print("inside app line 372")
})
observeEvent(input$ok3,
{
print("inside app line 406")
print(head(v$dds$dds.fc()[[1]]))
v$batch<-callModule(Module_Batch_effect,"module",input$conchoice,input$designchoice,reactive({v$dds$dds.fc()}),reactive({v$dds$normal()}))
normal<-list(v$dds$normal(),colData(v$dds$dds.fc()[[1]]),assay(v$dds$dds.fc()[[2]]))
observeEvent(input$go_wgcna,
{
#Estimate size factors
dds.norm=estimateSizeFactors(v$dds$dds.fc()[[1]])
if(v$batch$batch_choice()>1)
{
#callModule(WGCNA_module,"module",infile=reactive({v$data_wgcna}),de=TRUE,condition)
condition<-colData(v$batch$batch_data_for_DESeq()[[1]])[,as.numeric(input$conchoice)]
v$wgcna_output<-callModule(WGCNA_module,"module",
infile=reactive({v$batch$batch_corrected_data()}),
perform_voom=FALSE,condition,
reactive({v$dds$dds.fc()[[1]]}),
reactive({v$batch$batch_data_for_DESeq()[[1]]}),
reactive({input$conchoice}),
reactive({input_data$organism()}),
reactive({v$de$ok3()}),
reactive({v$de$combination}),
reactive({v$anova$anova_table()}),
reactive({v$batch$batch_choice()}),
reactive({v$batch$batch_corrected_data()}),
reactive({v$dds$normal()})
)
}
else
{
#callModule(WGCNA_module,"module",infile=reactive({v$data_wgcna}),de=TRUE,condition)
condition<-colData(v$batch$batch_data_for_DESeq()[[1]])[,as.numeric(input$conchoice)]
v$wgcna_output<-callModule(WGCNA_module,"module",
infile=reactive({v$batch$batch_data_for_DESeq()[[1]]}),
perform_voom=TRUE,condition,
reactive({v$dds$dds.fc()[[1]]}),
reactive({v$batch$batch_data_for_DESeq()[[1]]}),
reactive({input$conchoice}),
reactive({input_data$organism()}),
reactive({v$de$ok3()}),
reactive({v$de$combination}),
reactive({v$anova$anova_table()}),
reactive({v$batch$batch_choice()}),
NULL,
reactive({v$dds$normal()})
)
}
print("inside app line 484")
})
v$de<-callModule(Module_Differential_Expression,"module",input$conchoice,
reactive({v$batch$batch_data_for_DESeq()}),
#reactive({v$wgcna_click}),
reactive({v$wgcna_output}),
reactive({v$dds$normal()}),
reactive({v$batch$batch_choice()}),
reactive({v$batch$batch_corrected_data()}),
reactive({v$anova$anova_table()})
)
callModule(FC_FC_plot_module,"module",reactive({v$de$de_genes}),
reactive({v$de$combination})
)
#
v$ma<-callModule(MA_plot,"module",reactive({v$de$combination}),
reactive({v$de$de_genes()}),
reactive({v$de$p_value()}))
v$vol<-callModule(Volcano_plot,"module",
reactive({v$de$combination()}),
reactive({v$de$de_genes()}),
reactive({v$de$p_value()}),
reactive({v$de$hypothesis_choice()}))
callModule(Venn_diagram_module,"module",reactive({v$de$de_genes}),
reactive({v$de$combination}),
reactive({v$wgcna_output}))
v$heat_de<-callModule(heatmap_module,"module","DE",NULL,
reactive({v$batch}),
reactive({v$de$de_genes}),
reactive({v$de$combination}),
reactive({v$wgcna_output})
# reactive({v$batch$batch_choice}),
# reactive({v$batch$batch_corrected_data})
)
#}
#})
observeEvent(input$add_file,
{
filepath<-input$add_file$datapath
TF_list<-read.csv(filepath, header = TRUE,sep = "\t",check.names = FALSE,quote = "\"")
v$marker<-callModule(Enriched_markers_module,"module1",reactive({TF_list}),
reactive({input_data$organism()}),
reactive({v$de$de_genes}),
reactive({v$de$combination}),
reactive({v$wgcna_output}),
input$conchoice,
reactive({v$dds$normal()}),
reactive({v$batch$batch_data_for_DESeq()}),
reactive({v$batch$batch_choice()}),
reactive({v$batch$batch_corrected_data()}),
reactive({v$anova$anova_table()}),
reactive({v$dds$dds.fc()[[1]]})
)
callModule(heatmap_module,"module2","TF",NULL,
reactive({v$batch}),
reactive({v$marker$de_genes}),
reactive({v$de$combination}),
reactive({v$wgcna_output})
# reactive({v$batch$batch_choice}),
# reactive({v$batch$batch_corrected_data})
)
})
TF_list<-read.csv("./www/Transcriptome_TFcat.txt", header = TRUE,sep = "\t",check.names = FALSE,quote = "\"")
v$TF<-callModule(Enriched_markers_module,"module",
reactive({TF_list}),
reactive({input_data$organism()}),
reactive({v$de$de_genes}),
reactive({v$de$combination}),
reactive({v$wgcna_output}),
input$conchoice,
reactive({v$dds$normal()}),
reactive({v$batch$batch_data_for_DESeq()}),
reactive({v$batch$batch_choice()}),
reactive({v$batch$batch_corrected_data()}),
reactive({v$anova$anova_table()}),
reactive({v$dds$dds.fc()[[1]]})