inst/shinyapp/server.R
In kuppal2/xMWAS: xMWAS: R package for data-integration and differential network analysis
options(shiny.maxRequestSize=100*1024^2)
options(shiny.sanitize.errors=FALSE)
# Server logic
server <- function(input, output) {
################################## Introduction Page #################################################
################################## Analysis Page #################################################
vals <- reactiveValues(count = 0)
done <- reactiveValues(count = 0)
go<- reactiveValues(count = 0)
observeEvent(input$exampledata,
{output$siderbar<-renderUI({
if(input$exampledata=="TRUE"){
sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))
}else{
if(vals$count==0){
sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 3, value = 0))
}else{
if(vals$count==1){
sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))
}else{
sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 8, value = 0))
}
}
}
})})
observeEvent(input$add,
{if(vals$count<2){vals$count=vals$count+1}
if(vals$count==1 | vals$count==2){
output$input_dataC<-renderUI({
column(12,tags$div(column(width=6,
fileInput("datasetC", "Input file for dataset C ('.csv' or '.txt', 100MB limit)",
multiple = FALSE,
width="350px",
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".txt"))
),
column(width=6,
textInput("Cname", "Name for dataset C:", value="",placeholder="datasetC")
)))
})
output$spls_dataC<-renderUI({
numericInput(width="380px","keepZ", "Maximum #datasetC variables to select in sPLS (change according to your dataset):", 1000, min = 1, max = 100000)
})
output$rsd_dataC<-renderUI({
numericInput(width="380px","max_zvar", "Maximum #datasetC variables to select based on RSD (change according to your dataset):", 10000, min = 1, max = 1000000)
})
if(vals$count==1){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}else{
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 8, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}
}else{
output$input_dataC<-renderUI({})
output$spls_dataC<-renderUI({})
output$rsd_dataC<-renderUI({})
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 3, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}
if(vals$count==2){
output$input_dataD<-renderUI({
column(12,tags$div(column(width=6,
fileInput("datasetD", "Input file for dataset D ('.csv' or '.txt', 100MB limit)",
multiple = FALSE,
width="350px",
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".txt"))
),
column(width=6,
textInput("Dname", "Name for dataset D:", value="",placeholder="datasetD")
)))
})
output$spls_dataD<-renderUI({
numericInput(width="380px","keepW", "Maximum #datasetD variables to select in sPLS (change according to your dataset):", 1000, min = 1, max = 100000)
})
output$rsd_dataD<-renderUI({
numericInput(width="380px","max_wvar", "Maximum #datasetD variables to select based on RSD (change according to your dataset):", 10000, min = 1, max = 1000000)
})
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 8, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}else{
output$input_dataD<-renderUI({})
output$spls_dataD<-renderUI({})
output$rsd_dataD<-renderUI({})
if(vals$count==1){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}else{
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 3, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}
}
})
observeEvent(input$delete,
{if(vals$count!=0){vals$count=vals$count-1};
if(vals$count==1 | vals$count==2){
output$input_dataC<-renderUI({
column(12,tags$div(column(width=6,
fileInput("datasetC", "Input file for dataset C ('.csv' or '.txt', 100MB limit)",
multiple = FALSE,
width="350px",
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".txt"))
),
column(width=6,
textInput("Cname", "Name for dataset C:", value="",placeholder="datasetC")
)))
})
output$spls_dataC<-renderUI({
numericInput(width="380px","keepZ", "Maximum #datasetC variables to select in sPLS (change according to your dataset):", 1000, min = 1, max = 100000)
})
output$rsd_dataC<-renderUI({
numericInput(width="380px","max_zvar", "Maximum #datasetC variables to select based on RSD (change according to your dataset):", 10000, min = 1, max = 1000000)
})
if(vals$count==1){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}else{
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 8, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}
}else{
output$input_dataC<-renderUI({})
output$spls_dataC<-renderUI({})
output$rsd_dataC<-renderUI({})
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 3, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}
if(vals$count==2){
output$input_dataD<-renderUI({
column(12,tags$div(column(width=6,
fileInput("datasetD", "Input file for dataset D ('.csv' or '.txt', 100MB limit)",
multiple = FALSE,
width="350px",
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".txt"))
),
column(width=6,
textInput("Dname", "Name for dataset D:", value="",placeholder="datasetD")
)))
})
output$spls_dataD<-renderUI({
numericInput(width="380px","keepW", "Maximum #datasetD variables to select in sPLS (change according to your dataset):", 1000, min = 1, max = 100000)
})
output$rsd_dataD<-renderUI({
numericInput(width="380px","max_wvar", "Maximum #datasetD variables to select based on RSD (change according to your dataset):", 10000, min = 1, max = 1000000)
})
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 8, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}else{
output$input_dataD<-renderUI({})
output$spls_dataD<-renderUI({})
output$rsd_dataD<-renderUI({})
if(vals$count==1){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}else{
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 3, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}
}
})
##############################################################
observeEvent(input$go,
{
if(vals$count==0 & input$exampledata=="FALSE"){
output$nText2 <- renderText({validate(
need(input$datasetA, "No datasetA provided. Please upload dataset A in 'Choose Files'."),
need(input$datasetA$type, "The format of datasetA is not correct. Please upload the file with correct format."),
need(input$datasetB, "No datasetB provided. Please upload dataset B in 'Choose Files'."),
need(input$datasetB$type, "The format of datasetB is not correct. Please upload the file with correct format."),
need(input$class_labels_file, "No class labels file provided. Please upload class labels file in 'Choose Files'."),
need(input$class_labels_file$type, "The format of class labels file is not correct. Please upload the file with correct format.")
)})
validate(
need(input$datasetA, "No datasetA provided. Please upload dataset A in 'Choose Files'."),
need(input$datasetA$type, "The format of datasetA is not correct. Please upload the file with correct format."),
need(input$datasetB, "No datasetB provided. Please upload dataset B in 'Choose Files'."),
need(input$datasetB$type, "The format of datasetB is not correct. Please upload the file with correct format."),
need(input$class_labels_file, "No class labels file provided. Please upload class labels file in 'Choose Files'."),
need(input$class_labels_file$type, "The format of class labels file is not correct. Please upload the file with correct format.")
)
showNotification("Starting processing now. Your results will be available for download shortly. The processing time depends on the number of variables. Please use the data filtering options to reduce the run time.", duration=3600)
}else{
if(vals$count==1 & input$exampledata=="FALSE"){
output$nText2 <- renderText({validate(
need(input$datasetA, "No datasetA provided. Please upload dataset A in 'Choose Files'."),
need(input$datasetA$type, "The format of datasetA is not correct. Please upload the file with correct format."),
need(input$datasetB, "No datasetB provided. Please upload dataset B in 'Choose Files'."),
need(input$datasetB$type, "The format of datasetB is not correct. Please upload the file with correct format."),
need(input$datasetC, "No datasetC provided. Please upload dataset C in 'Choose Files'."),
need(input$datasetC$type, "The format of datasetC is not correct. Please upload the file with correct format."),
need(input$class_labels_file, "No class labels file provided. Please upload class labels file in 'Choose Files'."),
need(input$class_labels_file$type, "The format of class labels file is not correct. Please upload the file with correct format.")
)})
validate(
need(input$datasetA, "No datasetA provided. Please upload dataset A in 'Choose Files'."),
need(input$datasetA$type, "The format of datasetA is not correct. Please upload the file with correct format."),
need(input$datasetB, "No datasetB provided. Please upload dataset B in 'Choose Files'."),
need(input$datasetB$type, "The format of datasetB is not correct. Please upload the file with correct format."),
need(input$datasetC, "No datasetC provided. Please upload dataset C in 'Choose Files'."),
need(input$datasetC$type, "The format of datasetC is not correct. Please upload the file with correct format."),
need(input$class_labels_file, "No class labels file provided. Please upload class labels file in 'Choose Files'."),
need(input$class_labels_file$type, "The format of class labels file is not correct. Please upload the file with correct format.")
)
showNotification("Starting processing now. Your results will be available for download shortly. The processing time depends on the number of variables. Please use the data filtering options to reduce the run time.", duration=600)
}else{
if(vals$count==2 & input$exampledata=="FALSE"){
output$nText2 <- renderText({validate(
need(input$datasetA, "No datasetA provided. Please upload dataset A in 'Choose Files'."),
need(input$datasetA$type, "The format of datasetA is not correct. Please upload the file with correct format."),
need(input$datasetB, "No datasetB provided. Please upload dataset B in 'Choose Files'."),
need(input$datasetB$type, "The format of datasetB is not correct. Please upload the file with correct format."),
need(input$datasetC, "No datasetC provided. Please upload dataset C in 'Choose Files'."),
need(input$datasetC$type, "The format of datasetC is not correct. Please upload the file with correct format."),
need(input$datasetD, "No datasetD provided. Please upload dataset D in 'Choose Files'."),
need(input$datasetD$type, "The format of datasetD is not correct. Please upload the file with correct format."),
need(input$class_labels_file, "No class labels file provided. Please upload class labels file in 'Choose Files'."),
need(input$class_labels_file$type, "The format of class labels file is not correct. Please upload the file with correct format.")
)})
validate(
need(input$datasetA, "No datasetA provided. Please upload dataset A in 'Choose Files'."),
need(input$datasetA$type, "The format of datasetA is not correct. Please upload the file with correct format."),
need(input$datasetB, "No datasetB provided. Please upload dataset B in 'Choose Files'."),
need(input$datasetB$type, "The format of datasetB is not correct. Please upload the file with correct format."),
need(input$datasetC, "No datasetC provided. Please upload dataset C in 'Choose Files'."),
need(input$datasetC$type, "The format of datasetC is not correct. Please upload the file with correct format."),
need(input$datasetD, "No datasetD provided. Please upload dataset D in 'Choose Files'."),
need(input$datasetD$type, "The format of datasetD is not correct. Please upload the file with correct format."),
need(input$class_labels_file, "No class labels file provided. Please upload class labels file in 'Choose Files'."),
need(input$class_labels_file$type, "The format of class labels file is not correct. Please upload the file with correct format.")
)
showNotification("Starting processing now. Your results will be available for download shortly. The processing time depends on the number of variables. Please use the data filtering options to reduce the run time.", duration=600)
}else{
if(input$exampledata=="TRUE" & is.null(input$datasetA$name) & is.null(input$datasetB$name) &
is.null(input$datasetC$name) & is.null(input$datasetD$name) & is.null(input$class_labels_file$name)){
showNotification("You're using example (NCI60) data and it is processing now. Your results will be available for download shortly (approx. 10 minutes).", duration=600)
}else{
showNotification("You selected TRUE for 'use example data'. You're not allowed to process both the example data and your datasets in the same time. Please select FALSE for 'use example data' if you want to analyze your datasets.", duration=15)
}
}
}
}
})
#########################################
session_outloc <- reactive({
if(( input$go!=0 & vals$count==0 & input$exampledata=="FALSE" & !is.null(input$datasetA$name) & !is.null(input$datasetB$name) & !is.null(input$class_labels_file$name)) &
(input$datasetA$type=="text/plain" || input$datasetA$type=="text/csv") &
(input$datasetB$type=="text/plain" || input$datasetB$type=="text/csv") &
(input$class_labels_file$type=="text/plain" || input$class_labels_file$type=="text/csv")){
cur_date<-Sys.time()
cur_date<-gsub(x=cur_date,pattern="-",replacement="")
cur_date<-gsub(x=cur_date,pattern=":",replacement="")
cur_date<-gsub(x=cur_date,pattern=" ",replacement="")
if(input$outloc==""){
outloc<-paste('~/xmwasresults',cur_date,sep="")
}else{
outloc<-paste('~/',input$outloc,cur_date,sep="")
}
outloc
}else{
if((input$go!=0 & vals$count==1 & input$exampledata=="FALSE" & !is.null(input$datasetA$name) & !is.null(input$datasetB$name) & !is.null(input$datasetC$name) & !is.null(input$class_labels_file$name)) &
(input$datasetA$type=="text/plain" || input$datasetA$type=="text/csv") &
(input$datasetB$type=="text/plain" || input$datasetB$type=="text/csv") &
(input$datasetC$type=="text/plain" || input$datasetC$type=="text/csv") &
(input$class_labels_file$type=="text/plain" || input$class_labels_file$type=="text/csv")){
cur_date<-Sys.time()
cur_date<-gsub(x=cur_date,pattern="-",replacement="")
cur_date<-gsub(x=cur_date,pattern=":",replacement="")
cur_date<-gsub(x=cur_date,pattern=" ",replacement="")
if(input$outloc==""){
outloc<-paste('~/xmwasresults',cur_date,sep="")
}else{
outloc<-paste('~/',input$outloc,cur_date,sep="")
}
outloc
}else{
if((input$go!=0 & vals$count==2 & input$exampledata=="FALSE" & !is.null(input$datasetA$name) & !is.null(input$datasetB$name) & !is.null(input$datasetC$name) & !is.null(input$datasetD$name) & !is.null(input$class_labels_file$name)) &
(input$datasetA$type=="text/plain" || input$datasetA$type=="text/csv") &
(input$datasetB$type=="text/plain" || input$datasetB$type=="text/csv") &
(input$datasetC$type=="text/plain" || input$datasetC$type=="text/csv") &
(input$datasetD$type=="text/plain" || input$datasetD$type=="text/csv") &
(input$class_labels_file$type=="text/plain" || input$class_labels_file$type=="text/csv")){
cur_date<-Sys.time()
cur_date<-gsub(x=cur_date,pattern="-",replacement="")
cur_date<-gsub(x=cur_date,pattern=":",replacement="")
cur_date<-gsub(x=cur_date,pattern=" ",replacement="")
if(input$outloc==""){
outloc<-paste('~/xmwasresults',cur_date,sep="")
}else{
outloc<-paste('~/',input$outloc,cur_date,sep="")
}
outloc
}else{
if(input$go!=0 & input$exampledata=="TRUE" & is.null(input$datasetA$name) & is.null(input$datasetB$name) &
is.null(input$datasetC$name) & is.null(input$datasetD$name) & is.null(input$class_labels_file$name)){
cur_date<-Sys.time()
cur_date<-gsub(x=cur_date,pattern="-",replacement="")
cur_date<-gsub(x=cur_date,pattern=":",replacement="")
cur_date<-gsub(x=cur_date,pattern=" ",replacement="")
if(input$outloc==""){
outloc<-paste('~/xmwasresults',cur_date,sep="")
}else{
outloc<-paste('~/',input$outloc,cur_date,sep="")
}
outloc
}else{
NULL
}
}
}
}
})
##########################################
#output$test<-renderPrint({c(vals$count,session_outloc())})
observeEvent(input$go,{go$count=1})
output$nText <- renderText({
if((input$go!=0 & !is.null(go$count) & vals$count==0 & input$exampledata=="FALSE" & !is.null(input$datasetA$name) & !is.null(input$datasetB$name) & !is.null(input$class_labels_file$name)) &
(input$datasetA$type=="text/plain" || input$datasetA$type=="text/csv") &
(input$datasetB$type=="text/plain" || input$datasetB$type=="text/csv") &
(input$class_labels_file$type=="text/plain" || input$class_labels_file$type=="text/csv")){
req(input$datasetA)
if(input$datasetA$type=="text/plain"){
xMat <- read.table(input$datasetA$datapath,header = TRUE,sep = "\t")
rownames(xMat)<-xMat[,1]
xMat<-xMat[,-c(1)]
xMat<-as.data.frame(xMat)
}else{
if(input$datasetA$type=="text/csv"){
xMat <- read.table(input$datasetA$datapath,header = TRUE,sep = ",")
rownames(xMat)<-xMat[,1]
xMat<-xMat[,-c(1)]
xMat<-as.data.frame(xMat)
}else{
# output warnings
}
}
if(input$Aname==""){
Xname="datasetA"
}else{
Xname=input$Aname
}
req(input$datasetB)
if(input$datasetB$type=="text/plain"){
yMat <- read.table(input$datasetB$datapath,header = TRUE,sep = "\t")
rownames(yMat)<-yMat[,1]
yMat<-yMat[,-c(1)]
yMat<-as.data.frame(yMat)
}else{
if(input$datasetB$type=="text/csv"){
yMat <- read.table(input$datasetB$datapath,header = TRUE,sep = ",")
rownames(yMat)<-yMat[,1]
yMat<-yMat[,-c(1)]
yMat<-as.data.frame(yMat)
}else{
# output warnings
}
}
if(input$Bname==""){
Yname="datasetB"
}else{
Yname=input$Bname
}
req(input$class_labels_file)
if(input$class_labels_file$type=="text/plain"){
class_labels_file <- read.table(input$class_labels_file$datapath,header = TRUE,sep = "\t")
}else{
if(input$class_labels_file$type=="text/csv"){
class_labels_file <- read.table(input$class_labels_file$datapath,header = TRUE,sep = ",")
}else{
# output warnings
}
}
req(input$xmwasmethod)
if(input$xmwasmethod=="PLS: Partial least squares"){
xmwasmethod="pls"
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}else{
if(input$xmwasmethod=="sPLS: sparse PLS"){
xmwasmethod="spls"
KeepX=round(input$keepX)
KeepY=round(input$keepY)
check_keepZ<-reactive({need(input$keepZ,"error")})
if(is.null(check_keepZ())){
KeepZ=round(input$keepZ)
}else{
KeepZ=1000
}
check_KeepW<-reactive({need(input$keepW,"error")})
if(is.null(check_KeepW())){
KeepW=round(input$keepW)
}else{
KeepW=1000
}
}else{
if(input$xmwasmethod=="OPLS: orthogonal PLS"){
xmwasmethod="o1pls"
}
else{
if(input$xmwasmethod=="RCC: regularized canonical correlation"){
xmwasmethod="rcc"
}
}
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}
}
max_xvar=round(input$max_xvar)
max_yvar=round(input$max_yvar)
check_max_zvar<-reactive({need(input$max_zvar,"error")})
if(is.null(check_max_zvar())){
max_zvar=round(input$max_zvar)
}else{
max_zvar=1000
}
check_max_wvar<-reactive({need(input$max_wvar,"error")})
if(is.null(check_max_wvar())){
max_wvar=round(input$max_wvar)
}else{
max_wvar=1000
}
maxnodesperclass=1000000 #round(input$maxnodesperclass)
numcomps=round(input$numcomps)
max_connections=round(input$max_connections)
if(max_connections<0){
max_connections<-NA
}
missing.val=input$missing.val
if(missing.val=="0"){
missing.val=0
}else{
missing.val=NA
}
###################
library('xMWAS')
#source("xMWAS_Rcode/xMWAS_v0.0.22.R")
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=NA,Wome_data=NA,outloc=session_outloc(),
classlabels=class_labels_file,xmwasmethod=xmwasmethod,plsmode=input$plsmode,
max_xvar=max_xvar,max_yvar=max_yvar,max_zvar=max_zvar,max_wvar=max_wvar,rsd.filt.thresh=input$rsd_filt_thresh,corthresh=input$corthresh,
keepX=KeepX,keepY=KeepY,keepZ=KeepZ,keepW=KeepW,pairedanalysis=input$pairedanalysis,optselect=input$optselect,rawPthresh=input$rawPthresh,
numcomps=numcomps,net_edge_colors=c("blue","red"),net_node_colors=c("orange", "green","cyan","gold"),
Xname=Xname,Yname=Yname,Zname=NA,Wname=NA,net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),
all.missing.thresh=input$all_missing_thresh,maxnodesperclass=maxnodesperclass,seednum=input$seednum,label.cex=input$label_cex,vertex.size=input$vertex_size,graphclustering=TRUE,
interactive=FALSE,max_connections=max_connections,centrality_method=input$centrality_method,use.X.reference=input$use_X_reference,removeRda=TRUE,compare.classes=input$compare.classes,missing.val=missing.val,
modularity.weighted=TRUE,
globalcomparison=TRUE,
plot.pairwise=TRUE,apply.sparse.class.comparison=FALSE)
done$count=1
#file.copy(paste(getwd(),'matrix_centrality.txt',sep='/'),session_outloc())
setwd(session_outloc())
zip(zipfile=paste(basename(session_outloc()),'zip',sep='.'), files='.')
print("Processing complete. Please click on Download to save the results.")
}else{
if((input$go!=0 & !is.null(go$count) & vals$count==1 & input$exampledata=="FALSE" & !is.null(input$datasetA$name) & !is.null(input$datasetB$name) & !is.null(input$datasetC$name) & !is.null(input$class_labels_file$name)) &
(input$datasetA$type=="text/plain" || input$datasetA$type=="text/csv") &
(input$datasetB$type=="text/plain" || input$datasetB$type=="text/csv") &
(input$datasetC$type=="text/plain" || input$datasetC$type=="text/csv") &
(input$class_labels_file$type=="text/plain" || input$class_labels_file$type=="text/csv")){
req(input$datasetA)
if(input$datasetA$type=="text/plain"){
xMat <- read.table(input$datasetA$datapath,header = TRUE,sep = "\t")
rownames(xMat)<-xMat[,1]
xMat<-xMat[,-c(1)]
xMat<-as.data.frame(xMat)
}else{
if(input$datasetA$type=="text/csv"){
xMat <- read.table(input$datasetA$datapath,header = TRUE,sep = ",")
rownames(xMat)<-xMat[,1]
xMat<-xMat[,-c(1)]
xMat<-as.data.frame(xMat)
}else{
# output warnings
}
}
if(input$Aname==""){
Xname="datasetA"
}else{
Xname=input$Aname
}
req(input$datasetB)
if(input$datasetB$type=="text/plain"){
yMat <- read.table(input$datasetB$datapath,header = TRUE,sep = "\t")
rownames(yMat)<-yMat[,1]
yMat<-yMat[,-c(1)]
yMat<-as.data.frame(yMat)
}else{
if(input$datasetB$type=="text/csv"){
yMat <- read.table(input$datasetB$datapath,header = TRUE,sep = ",")
rownames(yMat)<-yMat[,1]
yMat<-yMat[,-c(1)]
yMat<-as.data.frame(yMat)
}else{
# output warnings
}
}
if(input$Bname==""){
Yname="datasetB"
}else{
Yname=input$Bname
}
req(input$datasetC)
if(input$datasetC$type=="text/plain"){
zMat <- read.table(input$datasetC$datapath,header = TRUE,sep = "\t")
rownames(zMat)<-zMat[,1]
zMat<-zMat[,-c(1)]
zMat<-as.data.frame(zMat)
}else{
if(input$datasetC$type=="text/csv"){
zMat <- read.table(input$datasetC$datapath,header = TRUE,sep = ",")
rownames(zMat)<-zMat[,1]
zMat<-zMat[,-c(1)]
zMat<-as.data.frame(zMat)
}else{
# output warnings
}
}
if(input$Cname==""){
Zname="datasetC"
}else{
Zname=input$Cname
}
req(input$class_labels_file)
if(input$class_labels_file$type=="text/plain"){
class_labels_file <- read.table(input$class_labels_file$datapath,header = TRUE,sep = "\t")
}else{
if(input$class_labels_file$type=="text/csv"){
class_labels_file <- read.table(input$class_labels_file$datapath,header = TRUE,sep = ",")
}else{
# output warnings
}
}
req(input$xmwasmethod)
if(input$xmwasmethod=="PLS: Partial least squares"){
xmwasmethod="pls"
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}else{
if(input$xmwasmethod=="sPLS: sparse PLS"){
xmwasmethod="spls"
KeepX=round(input$keepX)
KeepY=round(input$keepY)
check_keepZ<-reactive({need(input$keepZ,"error")})
if(is.null(check_keepZ())){
KeepZ=round(input$keepZ)
}else{
KeepZ=1000
}
check_KeepW<-reactive({need(input$keepW,"error")})
if(is.null(check_KeepW())){
KeepW=round(input$keepW)
}else{
KeepW=1000
}
}else{
if(input$xmwasmethod=="OPLS: orthogonal PLS"){
xmwasmethod="o1pls"
}
else{
if(input$xmwasmethod=="RCC: regularized canonical correlation"){
xmwasmethod="rcc"
}
}
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}
}
max_xvar=round(input$max_xvar)
max_yvar=round(input$max_yvar)
check_max_zvar<-reactive({need(input$max_zvar,"error")})
if(is.null(check_max_zvar())){
max_zvar=round(input$max_zvar)
}else{
max_zvar=10000
}
check_max_wvar<-reactive({need(input$max_wvar,"error")})
if(is.null(check_max_wvar())){
max_wvar=round(input$max_wvar)
}else{
max_wvar=10000
}
#maxnodesperclass=round(input$maxnodesperclass)
maxnodesperclass=1000000
numcomps=round(input$numcomps)
# c(max_zvar,session_outloc(),Xname,Yname,Zname,dim(xMat[1:5,1:5]),dim(yMat[1:5,1:5]),dim(zMat[1:5,1:5]),dim(class_labels_file[1:2,1:2]))
max_connections=round(input$max_connections)
if(max_connections<0){
max_connections<-NA
}
missing.val=input$missing.val
if(missing.val=="0"){
missing.val=0
}else{
missing.val=NA
}
###################
library('xMWAS')
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=NA,outloc=session_outloc(),
classlabels=class_labels_file,xmwasmethod=xmwasmethod,plsmode=input$plsmode,
max_xvar=max_xvar,max_yvar=max_yvar,max_zvar=max_zvar,max_wvar=max_wvar,rsd.filt.thresh=input$rsd_filt_thresh,corthresh=input$corthresh,
keepX=KeepX,keepY=KeepY,keepZ=KeepZ,keepW=KeepW,pairedanalysis=input$pairedanalysis,optselect=input$optselect,rawPthresh=input$rawPthresh,
numcomps=numcomps,net_edge_colors=c("blue","red"),net_node_colors=c("orange", "green","cyan","gold"),
Xname=Xname,Yname=Yname,Zname=Zname,Wname=NA,net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),
all.missing.thresh=input$all_missing_thresh,maxnodesperclass=maxnodesperclass,seednum=input$seednum,label.cex=input$label_cex,vertex.size=input$vertex_size,graphclustering=TRUE,
interactive=FALSE,max_connections=max_connections,centrality_method=input$centrality_method,use.X.reference=input$use_X_reference,removeRda=TRUE,compare.classes=input$compare.classes,missing.val=missing.val,
modularity.weighted=TRUE,
globalcomparison=TRUE,
plot.pairwise=TRUE,apply.sparse.class.comparison=FALSE)
if(FALSE){
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=NA,outloc=session_outloc(),
classlabels=class_labels_file,class_fname=NA,xmwasmethod="spls",plsmode="canonical",max_xvar=1000,max_yvar=1000,
max_zvar=1000,max_wvar=1000,rsd.filt.thresh=1,corthresh=0.8,keepX=100,keepY=100,keepZ=100,keepW=100,
pairedanalysis=FALSE,optselect=TRUE,rawPthresh=0.05,numcomps=10,net_edge_colors=c("blue","red"),
net_node_colors=c("orange", "green","cyan","gold"),Xname="X",Yname="Y",Zname="Z",Wname="W",
net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),all.missing.thresh=0.3,maxnodesperclass=100,
seednum=100,label.cex=0.2,vertex.size=6,graphclustering=TRUE,interactive=FALSE,max_connections=2000,
centrality_method="eigenvector",use.X.reference=FALSE,removeRda=TRUE,compare.classes=FALSE)
}
done$count=1
#file.copy(paste(getwd(),'matrix_centrality.txt',sep='/'),session_outloc())
setwd(session_outloc())
zip(zipfile=paste(basename(session_outloc()),'zip',sep='.'), files='.')
print("Processing complete. Please click on Download to save the results.")
}else{
if((input$go!=0 & !is.null(go$count) & vals$count==2 & input$exampledata=="FALSE" & !is.null(input$datasetA$name) & !is.null(input$datasetB$name) & !is.null(input$datasetC$name) & !is.null(input$datasetD$name) & !is.null(input$class_labels_file$name)) &
(input$datasetA$type=="text/plain" || input$datasetA$type=="text/csv") &
(input$datasetB$type=="text/plain" || input$datasetB$type=="text/csv") &
(input$datasetC$type=="text/plain" || input$datasetC$type=="text/csv") &
(input$datasetD$type=="text/plain" || input$datasetD$type=="text/csv") &
(input$class_labels_file$type=="text/plain" || input$class_labels_file$type=="text/csv")){
req(input$datasetA)
if(input$datasetA$type=="text/plain"){
xMat <- read.table(input$datasetA$datapath,header = TRUE,sep = "\t")
rownames(xMat)<-xMat[,1]
xMat<-xMat[,-c(1)]
xMat<-as.data.frame(xMat)
}else{
if(input$datasetA$type=="text/csv"){
xMat <- read.table(input$datasetA$datapath,header = TRUE,sep = ",")
rownames(xMat)<-xMat[,1]
xMat<-xMat[,-c(1)]
xMat<-as.data.frame(xMat)
}else{
# output warnings
}
}
if(input$Aname==""){
Xname="datasetA"
}else{
Xname=input$Aname
}
req(input$datasetB)
if(input$datasetB$type=="text/plain"){
yMat <- read.table(input$datasetB$datapath,header = TRUE,sep = "\t")
rownames(yMat)<-yMat[,1]
yMat<-yMat[,-c(1)]
yMat<-as.data.frame(yMat)
}else{
if(input$datasetB$type=="text/csv"){
yMat <- read.table(input$datasetB$datapath,header = TRUE,sep = ",")
rownames(yMat)<-yMat[,1]
yMat<-yMat[,-c(1)]
yMat<-as.data.frame(yMat)
}else{
# output warnings
}
}
if(input$Bname==""){
Yname="datasetB"
}else{
Yname=input$Bname
}
req(input$datasetC)
if(input$datasetC$type=="text/plain"){
zMat <- read.table(input$datasetC$datapath,header = TRUE,sep = "\t")
rownames(zMat)<-zMat[,1]
zMat<-zMat[,-c(1)]
zMat<-as.data.frame(zMat)
}else{
if(input$datasetC$type=="text/csv"){
zMat <- read.table(input$datasetC$datapath,header = TRUE,sep = ",")
rownames(zMat)<-zMat[,1]
zMat<-zMat[,-c(1)]
zMat<-as.data.frame(zMat)
}else{
# output warnings
}
}
if(input$Cname==""){
Zname="datasetC"
}else{
Zname=input$Cname
}
req(input$datasetD)
if(input$datasetD$type=="text/plain"){
wMat <- read.table(input$datasetD$datapath,header = TRUE,sep = "\t")
rownames(wMat)<-wMat[,1]
wMat<-wMat[,-c(1)]
wMat<-as.data.frame(wMat)
}else{
if(input$datasetD$type=="text/csv"){
wMat <- read.table(input$datasetD$datapath,header = TRUE,sep = ",")
rownames(wMat)<-wMat[,1]
wMat<-wMat[,-c(1)]
wMat<-as.data.frame(wMat)
}else{
# output warnings
}
}
if(input$Dname==""){
Wname="datasetD"
}else{
Wname=input$Dname
}
req(input$class_labels_file)
if(input$class_labels_file$type=="text/plain"){
class_labels_file <- read.table(input$class_labels_file$datapath,header = TRUE,sep = "\t")
}else{
if(input$class_labels_file$type=="text/csv"){
class_labels_file <- read.table(input$class_labels_file$datapath,header = TRUE,sep = ",")
}else{
# output warnings
}
}
req(input$xmwasmethod)
if(input$xmwasmethod=="PLS: Partial least squares"){
xmwasmethod="pls"
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}else{
if(input$xmwasmethod=="sPLS: sparse PLS"){
xmwasmethod="spls"
KeepX=round(input$keepX)
KeepY=round(input$keepY)
check_keepZ<-reactive({need(input$keepZ,"error")})
if(is.null(check_keepZ())){
KeepZ=round(input$keepZ)
}else{
KeepZ=1000
}
check_KeepW<-reactive({need(input$keepW,"error")})
if(is.null(check_KeepW())){
KeepW=round(input$keepW)
}else{
KeepW=1000
}
}else{
if(input$xmwasmethod=="OPLS: orthogonal PLS"){
xmwasmethod="o1pls"
}
else{
if(input$xmwasmethod=="RCC: regularized canonical correlation"){
xmwasmethod="rcc"
}
}
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}
}
#c(max_zvar,session_outloc(),Xname,Yname,Zname,dim(xMat[1:5,1:5]),dim(yMat[1:5,1:5]),dim(zMat[1:5,1:5]),dim(class_labels_file[1:2,1:2]))
max_xvar=round(input$max_xvar)
max_yvar=round(input$max_yvar)
check_max_zvar<-reactive({need(input$max_zvar,"error")})
if(is.null(check_max_zvar())){
max_zvar=round(input$max_zvar)
}else{
max_zvar=1000
}
check_max_wvar<-reactive({need(input$max_wvar,"error")})
if(is.null(check_max_wvar())){
max_wvar=round(input$max_wvar)
}else{
max_wvar=1000
}
#maxnodesperclass=round(input$maxnodesperclass)
maxnodesperclass=1000000
numcomps=round(input$numcomps)
max_connections=round(input$max_connections)
if(max_connections<0){
max_connections<-NA
}
missing.val=input$missing.val
if(missing.val=="0"){
missing.val=0
}else{
missing.val=NA
}
###################
library('xMWAS')
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=wMat,outloc=session_outloc(),
classlabels=class_labels_file,xmwasmethod=xmwasmethod,plsmode=input$plsmode,
max_xvar=max_xvar,max_yvar=max_yvar,max_zvar=max_zvar,max_wvar=max_wvar,rsd.filt.thresh=input$rsd_filt_thresh,corthresh=input$corthresh,
keepX=KeepX,keepY=KeepY,keepZ=KeepZ,keepW=KeepW,pairedanalysis=input$pairedanalysis,optselect=input$optselect,rawPthresh=input$rawPthresh,
numcomps=numcomps,net_edge_colors=c("blue","red"),net_node_colors=c("orange", "green","cyan","gold"),
Xname=Xname,Yname=Yname,Zname=Zname,Wname=Wname,net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),
all.missing.thresh=input$all_missing_thresh,maxnodesperclass=maxnodesperclass,seednum=input$seednum,label.cex=input$label_cex,vertex.size=input$vertex_size,graphclustering=TRUE,
interactive=FALSE,max_connections=max_connections,centrality_method=input$centrality_method,use.X.reference=input$use_X_reference,removeRda=TRUE,compare.classes=input$compare.classes,missing.val=missing.val,
modularity.weighted=TRUE,
globalcomparison=TRUE,
plot.pairwise=TRUE,apply.sparse.class.comparison=FALSE)
if(FALSE){
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=NA,outloc=session_outloc(),
classlabels=class_labels_file,class_fname=NA,xmwasmethod="spls",plsmode="canonical",max_xvar=1000,max_yvar=1000,
max_zvar=1000,max_wvar=1000,rsd.filt.thresh=1,corthresh=0.8,keepX=100,keepY=100,keepZ=100,keepW=100,
pairedanalysis=FALSE,optselect=TRUE,rawPthresh=0.05,numcomps=10,net_edge_colors=c("blue","red"),
net_node_colors=c("orange", "green","cyan","gold"),Xname="X",Yname="Y",Zname="Z",Wname="W",
net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),all.missing.thresh=0.3,maxnodesperclass=100,
seednum=100,label.cex=0.2,vertex.size=6,graphclustering=TRUE,interactive=FALSE,max_connections=2000,
centrality_method="eigenvector",use.X.reference=FALSE,removeRda=TRUE,compare.classes=FALSE)
}
done$count=1
#file.copy(paste(getwd(),'matrix_centrality.txt',sep='/'),session_outloc())
setwd(session_outloc())
zip(zipfile=paste(basename(session_outloc()),'zip',sep='.'), files='.')
print("Processing complete. Please click on Download to save the results.")
}else{
if((input$go!=0 & !is.null(go$count) & input$exampledata=="TRUE" & is.null(input$datasetA$name) & is.null(input$datasetB$name) &
is.null(input$datasetC$name)) & is.null(input$datasetD$name) & is.null(input$class_labels_file$name)){
library('xMWAS')
#source("xMWAS_Rcode/xMWAS_v0.0.22.R")
data(exnci60)
xMat<-exnci60$mrna
yMat<-exnci60$miRNA
zMat<-exnci60$prot
class_labels_file<-exnci60$classlabels
Xname="mrna"
Yname="miRNA"
Zname="prot"
req(input$xmwasmethod)
if(input$xmwasmethod=="PLS: Partial least squares"){
xmwasmethod="pls"
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}else{
if(input$xmwasmethod=="sPLS: sparse PLS"){
xmwasmethod="spls"
KeepX=round(input$keepX)
KeepY=round(input$keepY)
check_keepZ<-reactive({need(input$keepZ,"error")})
if(is.null(check_keepZ())){
KeepZ=round(input$keepZ)
}else{
KeepZ=1000
}
check_KeepW<-reactive({need(input$keepW,"error")})
if(is.null(check_KeepW())){
KeepW=round(input$keepW)
}else{
KeepW=1000
}
}else{
if(input$xmwasmethod=="OPLS: orthogonal PLS"){
xmwasmethod="o1pls"
}
else{
if(input$xmwasmethod=="RCC: regularized canonical correlation"){
xmwasmethod="rcc"
}
}
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}
}
max_xvar=round(input$max_xvar)
max_yvar=round(input$max_yvar)
check_max_zvar<-reactive({need(input$max_zvar,"error")})
if(is.null(check_max_zvar())){
max_zvar=round(input$max_zvar)
}else{
max_zvar=1000
}
check_max_wvar<-reactive({need(input$max_wvar,"error")})
if(is.null(check_max_wvar())){
max_wvar=round(input$max_wvar)
}else{
max_wvar=1000
}
maxnodesperclass=1000000 #round(input$maxnodesperclass)
numcomps=round(input$numcomps)
# c(max_zvar,session_outloc(),Xname,Yname,Zname,dim(xMat[1:5,1:5]),dim(yMat[1:5,1:5]),dim(zMat[1:5,1:5]),dim(class_labels_file[1:2,1:2]))
max_connections=round(input$max_connections)
if(max_connections<0){
max_connections<-NA
}
missing.val=input$missing.val
if(missing.val=="0"){
missing.val=0
}else{
missing.val=NA
}
###################
#xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=NA,outloc=session_outloc(),
# classlabels=class_labels_file,xmwasmethod=xmwasmethod,plsmode=input$plsmode,
# max_xvar=max_xvar,max_yvar=max_yvar,max_zvar=max_zvar,max_wvar=NA,rsd.filt.thresh=input$rsd_filt_thresh,corthresh=input$corthresh,
# keepX=KeepX,keepY=KeepY,keepZ=KeepZ,keepW=NA,pairedanalysis=input$pairedanalysis,optselect=input$optselect,rawPthresh=input$rawPthresh,
# numcomps=numcomps,net_edge_colors=c("blue","red"),net_node_colors=c("orange", "green","cyan","gold"),
# Xname=Xname,Yname=Yname,Zname=Zname,Wname=NA,net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),
# all.missing.thresh=input$all_missing_thresh,maxnodesperclass=maxnodesperclass,seednum=input$seednum,label.cex=input$label_cex,vertex.size=input$vertex_size,graphclustering=TRUE,
# interactive=input$interactive,max_connections=max_connections,centrality_method=input$centrality_method,use.X.reference=input$use_X_reference,removeRda=input$removeRda)
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=NA,outloc=session_outloc(),
classlabels=class_labels_file,xmwasmethod=xmwasmethod,plsmode=input$plsmode,
max_xvar=max_xvar,max_yvar=max_yvar,max_zvar=max_zvar,max_wvar=max_wvar,rsd.filt.thresh=input$rsd_filt_thresh,corthresh=input$corthresh,
keepX=KeepX,keepY=KeepY,keepZ=KeepZ,keepW=KeepW,pairedanalysis=input$pairedanalysis,optselect=input$optselect,rawPthresh=input$rawPthresh,
numcomps=numcomps,net_edge_colors=c("blue","red"), net_node_colors=c("orange","green","cyan","gold"),
Xname=Xname,Yname=Yname,Zname=Zname,Wname=NA, net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),
all.missing.thresh=input$all_missing_thresh,maxnodesperclass=maxnodesperclass,seednum=input$seednum,label.cex=input$label_cex,
vertex.size=input$vertex_size,graphclustering=TRUE,interactive=FALSE,max_connections=max_connections,
centrality_method=input$centrality_method,use.X.reference=input$use_X_reference,removeRda=TRUE,compare.classes=input$compare.classes,missing.val=missing.val,modularity.weighted=TRUE,
globalcomparison=TRUE,
plot.pairwise=TRUE,apply.sparse.class.comparison=FALSE)
if(FALSE){
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=NA,outloc=session_outloc(),
classlabels=class_labels_file,class_fname=NA,xmwasmethod="spls",plsmode="canonical",max_xvar=1000,max_yvar=1000,
max_zvar=1000,max_wvar=1000,rsd.filt.thresh=1,corthresh=0.8,keepX=100,keepY=100,keepZ=100,keepW=100,
pairedanalysis=FALSE,optselect=TRUE,rawPthresh=0.05,numcomps=10,net_edge_colors=c("blue","red"),
net_node_colors=c("orange", "green","cyan","gold"),Xname="X",Yname="Y",Zname="Z",Wname="W",
net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),all.missing.thresh=0.3,maxnodesperclass=100,
seednum=100,label.cex=0.2,vertex.size=6,graphclustering=TRUE,interactive=FALSE,max_connections=2000,
centrality_method="eigenvector",use.X.reference=FALSE,removeRda=TRUE,compare.classes=FALSE)
}
done$count=1
#file.copy(paste(getwd(),'matrix_centrality.txt',sep='/'),session_outloc())
setwd(session_outloc())
zip(zipfile=paste(basename(session_outloc()),'zip',sep='.'), files='.')
print("Processing complete. Please click on Download to save the results.")
}else{
go$count=NULL
}
}
}
}
})
##########################################
observeEvent(#{if(done$count==1) TRUE else return()}
req(done$count==1),{
output$myImage <- renderImage({
req(input$obs)
l1<-list.files(session_outloc(),".png",recursive=TRUE,full.names=FALSE)
if(length(grep("Rplot",l1,perl=TRUE))==0){
}else{
l1<-l1[-grep("Rplot",l1,perl=TRUE)]
}
l1<-l1[grep("Multidata",l1,perl=TRUE)]
if(input$obs>0 && input$obs<=length(l1))
{
filename <- normalizePath(file.path(session_outloc(),l1[input$obs]))
list(src = filename,width=600,height=600,
alt = "This is an image")
}else{
filename <- normalizePath(file.path(session_outloc(),l1[length(l1)]))
list(src = filename,width=600,height=600,
alt = "This is an image")
}
}, deleteFile = FALSE)
})
output$downloadData <- downloadHandler(
#if(input$go!=0 && input$featselmethod!="-" && input$feature_table_file!="" && input$class_labels_file!=""){
filename <- function() {
paste(basename(session_outloc()), "zip", sep=".")
},
content <- function(file) {
fname1<-paste(session_outloc(),"/",basename(session_outloc()), ".zip", sep="")
file.copy(fname1, file)
},
contentType = "application/zip"
#}
)
################################## Help Page #################################################
#output$table_dataset <- renderPrint({
#library(htmlTable)
#library('xMWAS')
#data(exnci60)
#output<-exnci60$mrna[1:5,1:6]
#output<-htmlTable(output,align='c',caption="<strong>Dataset File Format:</strong>",css.table="width:100%;")
#output
#})
}
kuppal2/xMWAS documentation built on April 15, 2021, 5:47 a.m.
R Package Documentation
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options(shiny.maxRequestSize=100*1024^2)
options(shiny.sanitize.errors=FALSE)
# Server logic
server <- function(input, output) {
################################## Introduction Page #################################################
################################## Analysis Page #################################################
vals <- reactiveValues(count = 0)
done <- reactiveValues(count = 0)
go<- reactiveValues(count = 0)
observeEvent(input$exampledata,
{output$siderbar<-renderUI({
if(input$exampledata=="TRUE"){
sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))
}else{
if(vals$count==0){
sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 3, value = 0))
}else{
if(vals$count==1){
sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))
}else{
sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 8, value = 0))
}
}
}
})})
observeEvent(input$add,
{if(vals$count<2){vals$count=vals$count+1}
if(vals$count==1 | vals$count==2){
output$input_dataC<-renderUI({
column(12,tags$div(column(width=6,
fileInput("datasetC", "Input file for dataset C ('.csv' or '.txt', 100MB limit)",
multiple = FALSE,
width="350px",
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".txt"))
),
column(width=6,
textInput("Cname", "Name for dataset C:", value="",placeholder="datasetC")
)))
})
output$spls_dataC<-renderUI({
numericInput(width="380px","keepZ", "Maximum #datasetC variables to select in sPLS (change according to your dataset):", 1000, min = 1, max = 100000)
})
output$rsd_dataC<-renderUI({
numericInput(width="380px","max_zvar", "Maximum #datasetC variables to select based on RSD (change according to your dataset):", 10000, min = 1, max = 1000000)
})
if(vals$count==1){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}else{
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 8, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}
}else{
output$input_dataC<-renderUI({})
output$spls_dataC<-renderUI({})
output$rsd_dataC<-renderUI({})
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 3, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}
if(vals$count==2){
output$input_dataD<-renderUI({
column(12,tags$div(column(width=6,
fileInput("datasetD", "Input file for dataset D ('.csv' or '.txt', 100MB limit)",
multiple = FALSE,
width="350px",
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".txt"))
),
column(width=6,
textInput("Dname", "Name for dataset D:", value="",placeholder="datasetD")
)))
})
output$spls_dataD<-renderUI({
numericInput(width="380px","keepW", "Maximum #datasetD variables to select in sPLS (change according to your dataset):", 1000, min = 1, max = 100000)
})
output$rsd_dataD<-renderUI({
numericInput(width="380px","max_wvar", "Maximum #datasetD variables to select based on RSD (change according to your dataset):", 10000, min = 1, max = 1000000)
})
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 8, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}else{
output$input_dataD<-renderUI({})
output$spls_dataD<-renderUI({})
output$rsd_dataD<-renderUI({})
if(vals$count==1){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}else{
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 3, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}
}
})
observeEvent(input$delete,
{if(vals$count!=0){vals$count=vals$count-1};
if(vals$count==1 | vals$count==2){
output$input_dataC<-renderUI({
column(12,tags$div(column(width=6,
fileInput("datasetC", "Input file for dataset C ('.csv' or '.txt', 100MB limit)",
multiple = FALSE,
width="350px",
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".txt"))
),
column(width=6,
textInput("Cname", "Name for dataset C:", value="",placeholder="datasetC")
)))
})
output$spls_dataC<-renderUI({
numericInput(width="380px","keepZ", "Maximum #datasetC variables to select in sPLS (change according to your dataset):", 1000, min = 1, max = 100000)
})
output$rsd_dataC<-renderUI({
numericInput(width="380px","max_zvar", "Maximum #datasetC variables to select based on RSD (change according to your dataset):", 10000, min = 1, max = 1000000)
})
if(vals$count==1){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}else{
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 8, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}
}else{
output$input_dataC<-renderUI({})
output$spls_dataC<-renderUI({})
output$rsd_dataC<-renderUI({})
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 3, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}
if(vals$count==2){
output$input_dataD<-renderUI({
column(12,tags$div(column(width=6,
fileInput("datasetD", "Input file for dataset D ('.csv' or '.txt', 100MB limit)",
multiple = FALSE,
width="350px",
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".txt"))
),
column(width=6,
textInput("Dname", "Name for dataset D:", value="",placeholder="datasetD")
)))
})
output$spls_dataD<-renderUI({
numericInput(width="380px","keepW", "Maximum #datasetD variables to select in sPLS (change according to your dataset):", 1000, min = 1, max = 100000)
})
output$rsd_dataD<-renderUI({
numericInput(width="380px","max_wvar", "Maximum #datasetD variables to select based on RSD (change according to your dataset):", 10000, min = 1, max = 1000000)
})
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 8, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}else{
output$input_dataD<-renderUI({})
output$spls_dataD<-renderUI({})
output$rsd_dataD<-renderUI({})
if(vals$count==1){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}else{
if(input$exampledata=="FALSE"){
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 3, value = 0))})
}else{
output$siderbar<-renderUI({sidebarPanel(sliderInput("obs", "Slide to go to next figure:", min = 0, max = 5, value = 0))})
}
}
}
})
##############################################################
observeEvent(input$go,
{
if(vals$count==0 & input$exampledata=="FALSE"){
output$nText2 <- renderText({validate(
need(input$datasetA, "No datasetA provided. Please upload dataset A in 'Choose Files'."),
need(input$datasetA$type, "The format of datasetA is not correct. Please upload the file with correct format."),
need(input$datasetB, "No datasetB provided. Please upload dataset B in 'Choose Files'."),
need(input$datasetB$type, "The format of datasetB is not correct. Please upload the file with correct format."),
need(input$class_labels_file, "No class labels file provided. Please upload class labels file in 'Choose Files'."),
need(input$class_labels_file$type, "The format of class labels file is not correct. Please upload the file with correct format.")
)})
validate(
need(input$datasetA, "No datasetA provided. Please upload dataset A in 'Choose Files'."),
need(input$datasetA$type, "The format of datasetA is not correct. Please upload the file with correct format."),
need(input$datasetB, "No datasetB provided. Please upload dataset B in 'Choose Files'."),
need(input$datasetB$type, "The format of datasetB is not correct. Please upload the file with correct format."),
need(input$class_labels_file, "No class labels file provided. Please upload class labels file in 'Choose Files'."),
need(input$class_labels_file$type, "The format of class labels file is not correct. Please upload the file with correct format.")
)
showNotification("Starting processing now. Your results will be available for download shortly. The processing time depends on the number of variables. Please use the data filtering options to reduce the run time.", duration=3600)
}else{
if(vals$count==1 & input$exampledata=="FALSE"){
output$nText2 <- renderText({validate(
need(input$datasetA, "No datasetA provided. Please upload dataset A in 'Choose Files'."),
need(input$datasetA$type, "The format of datasetA is not correct. Please upload the file with correct format."),
need(input$datasetB, "No datasetB provided. Please upload dataset B in 'Choose Files'."),
need(input$datasetB$type, "The format of datasetB is not correct. Please upload the file with correct format."),
need(input$datasetC, "No datasetC provided. Please upload dataset C in 'Choose Files'."),
need(input$datasetC$type, "The format of datasetC is not correct. Please upload the file with correct format."),
need(input$class_labels_file, "No class labels file provided. Please upload class labels file in 'Choose Files'."),
need(input$class_labels_file$type, "The format of class labels file is not correct. Please upload the file with correct format.")
)})
validate(
need(input$datasetA, "No datasetA provided. Please upload dataset A in 'Choose Files'."),
need(input$datasetA$type, "The format of datasetA is not correct. Please upload the file with correct format."),
need(input$datasetB, "No datasetB provided. Please upload dataset B in 'Choose Files'."),
need(input$datasetB$type, "The format of datasetB is not correct. Please upload the file with correct format."),
need(input$datasetC, "No datasetC provided. Please upload dataset C in 'Choose Files'."),
need(input$datasetC$type, "The format of datasetC is not correct. Please upload the file with correct format."),
need(input$class_labels_file, "No class labels file provided. Please upload class labels file in 'Choose Files'."),
need(input$class_labels_file$type, "The format of class labels file is not correct. Please upload the file with correct format.")
)
showNotification("Starting processing now. Your results will be available for download shortly. The processing time depends on the number of variables. Please use the data filtering options to reduce the run time.", duration=600)
}else{
if(vals$count==2 & input$exampledata=="FALSE"){
output$nText2 <- renderText({validate(
need(input$datasetA, "No datasetA provided. Please upload dataset A in 'Choose Files'."),
need(input$datasetA$type, "The format of datasetA is not correct. Please upload the file with correct format."),
need(input$datasetB, "No datasetB provided. Please upload dataset B in 'Choose Files'."),
need(input$datasetB$type, "The format of datasetB is not correct. Please upload the file with correct format."),
need(input$datasetC, "No datasetC provided. Please upload dataset C in 'Choose Files'."),
need(input$datasetC$type, "The format of datasetC is not correct. Please upload the file with correct format."),
need(input$datasetD, "No datasetD provided. Please upload dataset D in 'Choose Files'."),
need(input$datasetD$type, "The format of datasetD is not correct. Please upload the file with correct format."),
need(input$class_labels_file, "No class labels file provided. Please upload class labels file in 'Choose Files'."),
need(input$class_labels_file$type, "The format of class labels file is not correct. Please upload the file with correct format.")
)})
validate(
need(input$datasetA, "No datasetA provided. Please upload dataset A in 'Choose Files'."),
need(input$datasetA$type, "The format of datasetA is not correct. Please upload the file with correct format."),
need(input$datasetB, "No datasetB provided. Please upload dataset B in 'Choose Files'."),
need(input$datasetB$type, "The format of datasetB is not correct. Please upload the file with correct format."),
need(input$datasetC, "No datasetC provided. Please upload dataset C in 'Choose Files'."),
need(input$datasetC$type, "The format of datasetC is not correct. Please upload the file with correct format."),
need(input$datasetD, "No datasetD provided. Please upload dataset D in 'Choose Files'."),
need(input$datasetD$type, "The format of datasetD is not correct. Please upload the file with correct format."),
need(input$class_labels_file, "No class labels file provided. Please upload class labels file in 'Choose Files'."),
need(input$class_labels_file$type, "The format of class labels file is not correct. Please upload the file with correct format.")
)
showNotification("Starting processing now. Your results will be available for download shortly. The processing time depends on the number of variables. Please use the data filtering options to reduce the run time.", duration=600)
}else{
if(input$exampledata=="TRUE" & is.null(input$datasetA$name) & is.null(input$datasetB$name) &
is.null(input$datasetC$name) & is.null(input$datasetD$name) & is.null(input$class_labels_file$name)){
showNotification("You're using example (NCI60) data and it is processing now. Your results will be available for download shortly (approx. 10 minutes).", duration=600)
}else{
showNotification("You selected TRUE for 'use example data'. You're not allowed to process both the example data and your datasets in the same time. Please select FALSE for 'use example data' if you want to analyze your datasets.", duration=15)
}
}
}
}
})
#########################################
session_outloc <- reactive({
if(( input$go!=0 & vals$count==0 & input$exampledata=="FALSE" & !is.null(input$datasetA$name) & !is.null(input$datasetB$name) & !is.null(input$class_labels_file$name)) &
(input$datasetA$type=="text/plain" || input$datasetA$type=="text/csv") &
(input$datasetB$type=="text/plain" || input$datasetB$type=="text/csv") &
(input$class_labels_file$type=="text/plain" || input$class_labels_file$type=="text/csv")){
cur_date<-Sys.time()
cur_date<-gsub(x=cur_date,pattern="-",replacement="")
cur_date<-gsub(x=cur_date,pattern=":",replacement="")
cur_date<-gsub(x=cur_date,pattern=" ",replacement="")
if(input$outloc==""){
outloc<-paste('~/xmwasresults',cur_date,sep="")
}else{
outloc<-paste('~/',input$outloc,cur_date,sep="")
}
outloc
}else{
if((input$go!=0 & vals$count==1 & input$exampledata=="FALSE" & !is.null(input$datasetA$name) & !is.null(input$datasetB$name) & !is.null(input$datasetC$name) & !is.null(input$class_labels_file$name)) &
(input$datasetA$type=="text/plain" || input$datasetA$type=="text/csv") &
(input$datasetB$type=="text/plain" || input$datasetB$type=="text/csv") &
(input$datasetC$type=="text/plain" || input$datasetC$type=="text/csv") &
(input$class_labels_file$type=="text/plain" || input$class_labels_file$type=="text/csv")){
cur_date<-Sys.time()
cur_date<-gsub(x=cur_date,pattern="-",replacement="")
cur_date<-gsub(x=cur_date,pattern=":",replacement="")
cur_date<-gsub(x=cur_date,pattern=" ",replacement="")
if(input$outloc==""){
outloc<-paste('~/xmwasresults',cur_date,sep="")
}else{
outloc<-paste('~/',input$outloc,cur_date,sep="")
}
outloc
}else{
if((input$go!=0 & vals$count==2 & input$exampledata=="FALSE" & !is.null(input$datasetA$name) & !is.null(input$datasetB$name) & !is.null(input$datasetC$name) & !is.null(input$datasetD$name) & !is.null(input$class_labels_file$name)) &
(input$datasetA$type=="text/plain" || input$datasetA$type=="text/csv") &
(input$datasetB$type=="text/plain" || input$datasetB$type=="text/csv") &
(input$datasetC$type=="text/plain" || input$datasetC$type=="text/csv") &
(input$datasetD$type=="text/plain" || input$datasetD$type=="text/csv") &
(input$class_labels_file$type=="text/plain" || input$class_labels_file$type=="text/csv")){
cur_date<-Sys.time()
cur_date<-gsub(x=cur_date,pattern="-",replacement="")
cur_date<-gsub(x=cur_date,pattern=":",replacement="")
cur_date<-gsub(x=cur_date,pattern=" ",replacement="")
if(input$outloc==""){
outloc<-paste('~/xmwasresults',cur_date,sep="")
}else{
outloc<-paste('~/',input$outloc,cur_date,sep="")
}
outloc
}else{
if(input$go!=0 & input$exampledata=="TRUE" & is.null(input$datasetA$name) & is.null(input$datasetB$name) &
is.null(input$datasetC$name) & is.null(input$datasetD$name) & is.null(input$class_labels_file$name)){
cur_date<-Sys.time()
cur_date<-gsub(x=cur_date,pattern="-",replacement="")
cur_date<-gsub(x=cur_date,pattern=":",replacement="")
cur_date<-gsub(x=cur_date,pattern=" ",replacement="")
if(input$outloc==""){
outloc<-paste('~/xmwasresults',cur_date,sep="")
}else{
outloc<-paste('~/',input$outloc,cur_date,sep="")
}
outloc
}else{
NULL
}
}
}
}
})
##########################################
#output$test<-renderPrint({c(vals$count,session_outloc())})
observeEvent(input$go,{go$count=1})
output$nText <- renderText({
if((input$go!=0 & !is.null(go$count) & vals$count==0 & input$exampledata=="FALSE" & !is.null(input$datasetA$name) & !is.null(input$datasetB$name) & !is.null(input$class_labels_file$name)) &
(input$datasetA$type=="text/plain" || input$datasetA$type=="text/csv") &
(input$datasetB$type=="text/plain" || input$datasetB$type=="text/csv") &
(input$class_labels_file$type=="text/plain" || input$class_labels_file$type=="text/csv")){
req(input$datasetA)
if(input$datasetA$type=="text/plain"){
xMat <- read.table(input$datasetA$datapath,header = TRUE,sep = "\t")
rownames(xMat)<-xMat[,1]
xMat<-xMat[,-c(1)]
xMat<-as.data.frame(xMat)
}else{
if(input$datasetA$type=="text/csv"){
xMat <- read.table(input$datasetA$datapath,header = TRUE,sep = ",")
rownames(xMat)<-xMat[,1]
xMat<-xMat[,-c(1)]
xMat<-as.data.frame(xMat)
}else{
# output warnings
}
}
if(input$Aname==""){
Xname="datasetA"
}else{
Xname=input$Aname
}
req(input$datasetB)
if(input$datasetB$type=="text/plain"){
yMat <- read.table(input$datasetB$datapath,header = TRUE,sep = "\t")
rownames(yMat)<-yMat[,1]
yMat<-yMat[,-c(1)]
yMat<-as.data.frame(yMat)
}else{
if(input$datasetB$type=="text/csv"){
yMat <- read.table(input$datasetB$datapath,header = TRUE,sep = ",")
rownames(yMat)<-yMat[,1]
yMat<-yMat[,-c(1)]
yMat<-as.data.frame(yMat)
}else{
# output warnings
}
}
if(input$Bname==""){
Yname="datasetB"
}else{
Yname=input$Bname
}
req(input$class_labels_file)
if(input$class_labels_file$type=="text/plain"){
class_labels_file <- read.table(input$class_labels_file$datapath,header = TRUE,sep = "\t")
}else{
if(input$class_labels_file$type=="text/csv"){
class_labels_file <- read.table(input$class_labels_file$datapath,header = TRUE,sep = ",")
}else{
# output warnings
}
}
req(input$xmwasmethod)
if(input$xmwasmethod=="PLS: Partial least squares"){
xmwasmethod="pls"
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}else{
if(input$xmwasmethod=="sPLS: sparse PLS"){
xmwasmethod="spls"
KeepX=round(input$keepX)
KeepY=round(input$keepY)
check_keepZ<-reactive({need(input$keepZ,"error")})
if(is.null(check_keepZ())){
KeepZ=round(input$keepZ)
}else{
KeepZ=1000
}
check_KeepW<-reactive({need(input$keepW,"error")})
if(is.null(check_KeepW())){
KeepW=round(input$keepW)
}else{
KeepW=1000
}
}else{
if(input$xmwasmethod=="OPLS: orthogonal PLS"){
xmwasmethod="o1pls"
}
else{
if(input$xmwasmethod=="RCC: regularized canonical correlation"){
xmwasmethod="rcc"
}
}
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}
}
max_xvar=round(input$max_xvar)
max_yvar=round(input$max_yvar)
check_max_zvar<-reactive({need(input$max_zvar,"error")})
if(is.null(check_max_zvar())){
max_zvar=round(input$max_zvar)
}else{
max_zvar=1000
}
check_max_wvar<-reactive({need(input$max_wvar,"error")})
if(is.null(check_max_wvar())){
max_wvar=round(input$max_wvar)
}else{
max_wvar=1000
}
maxnodesperclass=1000000 #round(input$maxnodesperclass)
numcomps=round(input$numcomps)
max_connections=round(input$max_connections)
if(max_connections<0){
max_connections<-NA
}
missing.val=input$missing.val
if(missing.val=="0"){
missing.val=0
}else{
missing.val=NA
}
###################
library('xMWAS')
#source("xMWAS_Rcode/xMWAS_v0.0.22.R")
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=NA,Wome_data=NA,outloc=session_outloc(),
classlabels=class_labels_file,xmwasmethod=xmwasmethod,plsmode=input$plsmode,
max_xvar=max_xvar,max_yvar=max_yvar,max_zvar=max_zvar,max_wvar=max_wvar,rsd.filt.thresh=input$rsd_filt_thresh,corthresh=input$corthresh,
keepX=KeepX,keepY=KeepY,keepZ=KeepZ,keepW=KeepW,pairedanalysis=input$pairedanalysis,optselect=input$optselect,rawPthresh=input$rawPthresh,
numcomps=numcomps,net_edge_colors=c("blue","red"),net_node_colors=c("orange", "green","cyan","gold"),
Xname=Xname,Yname=Yname,Zname=NA,Wname=NA,net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),
all.missing.thresh=input$all_missing_thresh,maxnodesperclass=maxnodesperclass,seednum=input$seednum,label.cex=input$label_cex,vertex.size=input$vertex_size,graphclustering=TRUE,
interactive=FALSE,max_connections=max_connections,centrality_method=input$centrality_method,use.X.reference=input$use_X_reference,removeRda=TRUE,compare.classes=input$compare.classes,missing.val=missing.val,
modularity.weighted=TRUE,
globalcomparison=TRUE,
plot.pairwise=TRUE,apply.sparse.class.comparison=FALSE)
done$count=1
#file.copy(paste(getwd(),'matrix_centrality.txt',sep='/'),session_outloc())
setwd(session_outloc())
zip(zipfile=paste(basename(session_outloc()),'zip',sep='.'), files='.')
print("Processing complete. Please click on Download to save the results.")
}else{
if((input$go!=0 & !is.null(go$count) & vals$count==1 & input$exampledata=="FALSE" & !is.null(input$datasetA$name) & !is.null(input$datasetB$name) & !is.null(input$datasetC$name) & !is.null(input$class_labels_file$name)) &
(input$datasetA$type=="text/plain" || input$datasetA$type=="text/csv") &
(input$datasetB$type=="text/plain" || input$datasetB$type=="text/csv") &
(input$datasetC$type=="text/plain" || input$datasetC$type=="text/csv") &
(input$class_labels_file$type=="text/plain" || input$class_labels_file$type=="text/csv")){
req(input$datasetA)
if(input$datasetA$type=="text/plain"){
xMat <- read.table(input$datasetA$datapath,header = TRUE,sep = "\t")
rownames(xMat)<-xMat[,1]
xMat<-xMat[,-c(1)]
xMat<-as.data.frame(xMat)
}else{
if(input$datasetA$type=="text/csv"){
xMat <- read.table(input$datasetA$datapath,header = TRUE,sep = ",")
rownames(xMat)<-xMat[,1]
xMat<-xMat[,-c(1)]
xMat<-as.data.frame(xMat)
}else{
# output warnings
}
}
if(input$Aname==""){
Xname="datasetA"
}else{
Xname=input$Aname
}
req(input$datasetB)
if(input$datasetB$type=="text/plain"){
yMat <- read.table(input$datasetB$datapath,header = TRUE,sep = "\t")
rownames(yMat)<-yMat[,1]
yMat<-yMat[,-c(1)]
yMat<-as.data.frame(yMat)
}else{
if(input$datasetB$type=="text/csv"){
yMat <- read.table(input$datasetB$datapath,header = TRUE,sep = ",")
rownames(yMat)<-yMat[,1]
yMat<-yMat[,-c(1)]
yMat<-as.data.frame(yMat)
}else{
# output warnings
}
}
if(input$Bname==""){
Yname="datasetB"
}else{
Yname=input$Bname
}
req(input$datasetC)
if(input$datasetC$type=="text/plain"){
zMat <- read.table(input$datasetC$datapath,header = TRUE,sep = "\t")
rownames(zMat)<-zMat[,1]
zMat<-zMat[,-c(1)]
zMat<-as.data.frame(zMat)
}else{
if(input$datasetC$type=="text/csv"){
zMat <- read.table(input$datasetC$datapath,header = TRUE,sep = ",")
rownames(zMat)<-zMat[,1]
zMat<-zMat[,-c(1)]
zMat<-as.data.frame(zMat)
}else{
# output warnings
}
}
if(input$Cname==""){
Zname="datasetC"
}else{
Zname=input$Cname
}
req(input$class_labels_file)
if(input$class_labels_file$type=="text/plain"){
class_labels_file <- read.table(input$class_labels_file$datapath,header = TRUE,sep = "\t")
}else{
if(input$class_labels_file$type=="text/csv"){
class_labels_file <- read.table(input$class_labels_file$datapath,header = TRUE,sep = ",")
}else{
# output warnings
}
}
req(input$xmwasmethod)
if(input$xmwasmethod=="PLS: Partial least squares"){
xmwasmethod="pls"
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}else{
if(input$xmwasmethod=="sPLS: sparse PLS"){
xmwasmethod="spls"
KeepX=round(input$keepX)
KeepY=round(input$keepY)
check_keepZ<-reactive({need(input$keepZ,"error")})
if(is.null(check_keepZ())){
KeepZ=round(input$keepZ)
}else{
KeepZ=1000
}
check_KeepW<-reactive({need(input$keepW,"error")})
if(is.null(check_KeepW())){
KeepW=round(input$keepW)
}else{
KeepW=1000
}
}else{
if(input$xmwasmethod=="OPLS: orthogonal PLS"){
xmwasmethod="o1pls"
}
else{
if(input$xmwasmethod=="RCC: regularized canonical correlation"){
xmwasmethod="rcc"
}
}
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}
}
max_xvar=round(input$max_xvar)
max_yvar=round(input$max_yvar)
check_max_zvar<-reactive({need(input$max_zvar,"error")})
if(is.null(check_max_zvar())){
max_zvar=round(input$max_zvar)
}else{
max_zvar=10000
}
check_max_wvar<-reactive({need(input$max_wvar,"error")})
if(is.null(check_max_wvar())){
max_wvar=round(input$max_wvar)
}else{
max_wvar=10000
}
#maxnodesperclass=round(input$maxnodesperclass)
maxnodesperclass=1000000
numcomps=round(input$numcomps)
# c(max_zvar,session_outloc(),Xname,Yname,Zname,dim(xMat[1:5,1:5]),dim(yMat[1:5,1:5]),dim(zMat[1:5,1:5]),dim(class_labels_file[1:2,1:2]))
max_connections=round(input$max_connections)
if(max_connections<0){
max_connections<-NA
}
missing.val=input$missing.val
if(missing.val=="0"){
missing.val=0
}else{
missing.val=NA
}
###################
library('xMWAS')
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=NA,outloc=session_outloc(),
classlabels=class_labels_file,xmwasmethod=xmwasmethod,plsmode=input$plsmode,
max_xvar=max_xvar,max_yvar=max_yvar,max_zvar=max_zvar,max_wvar=max_wvar,rsd.filt.thresh=input$rsd_filt_thresh,corthresh=input$corthresh,
keepX=KeepX,keepY=KeepY,keepZ=KeepZ,keepW=KeepW,pairedanalysis=input$pairedanalysis,optselect=input$optselect,rawPthresh=input$rawPthresh,
numcomps=numcomps,net_edge_colors=c("blue","red"),net_node_colors=c("orange", "green","cyan","gold"),
Xname=Xname,Yname=Yname,Zname=Zname,Wname=NA,net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),
all.missing.thresh=input$all_missing_thresh,maxnodesperclass=maxnodesperclass,seednum=input$seednum,label.cex=input$label_cex,vertex.size=input$vertex_size,graphclustering=TRUE,
interactive=FALSE,max_connections=max_connections,centrality_method=input$centrality_method,use.X.reference=input$use_X_reference,removeRda=TRUE,compare.classes=input$compare.classes,missing.val=missing.val,
modularity.weighted=TRUE,
globalcomparison=TRUE,
plot.pairwise=TRUE,apply.sparse.class.comparison=FALSE)
if(FALSE){
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=NA,outloc=session_outloc(),
classlabels=class_labels_file,class_fname=NA,xmwasmethod="spls",plsmode="canonical",max_xvar=1000,max_yvar=1000,
max_zvar=1000,max_wvar=1000,rsd.filt.thresh=1,corthresh=0.8,keepX=100,keepY=100,keepZ=100,keepW=100,
pairedanalysis=FALSE,optselect=TRUE,rawPthresh=0.05,numcomps=10,net_edge_colors=c("blue","red"),
net_node_colors=c("orange", "green","cyan","gold"),Xname="X",Yname="Y",Zname="Z",Wname="W",
net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),all.missing.thresh=0.3,maxnodesperclass=100,
seednum=100,label.cex=0.2,vertex.size=6,graphclustering=TRUE,interactive=FALSE,max_connections=2000,
centrality_method="eigenvector",use.X.reference=FALSE,removeRda=TRUE,compare.classes=FALSE)
}
done$count=1
#file.copy(paste(getwd(),'matrix_centrality.txt',sep='/'),session_outloc())
setwd(session_outloc())
zip(zipfile=paste(basename(session_outloc()),'zip',sep='.'), files='.')
print("Processing complete. Please click on Download to save the results.")
}else{
if((input$go!=0 & !is.null(go$count) & vals$count==2 & input$exampledata=="FALSE" & !is.null(input$datasetA$name) & !is.null(input$datasetB$name) & !is.null(input$datasetC$name) & !is.null(input$datasetD$name) & !is.null(input$class_labels_file$name)) &
(input$datasetA$type=="text/plain" || input$datasetA$type=="text/csv") &
(input$datasetB$type=="text/plain" || input$datasetB$type=="text/csv") &
(input$datasetC$type=="text/plain" || input$datasetC$type=="text/csv") &
(input$datasetD$type=="text/plain" || input$datasetD$type=="text/csv") &
(input$class_labels_file$type=="text/plain" || input$class_labels_file$type=="text/csv")){
req(input$datasetA)
if(input$datasetA$type=="text/plain"){
xMat <- read.table(input$datasetA$datapath,header = TRUE,sep = "\t")
rownames(xMat)<-xMat[,1]
xMat<-xMat[,-c(1)]
xMat<-as.data.frame(xMat)
}else{
if(input$datasetA$type=="text/csv"){
xMat <- read.table(input$datasetA$datapath,header = TRUE,sep = ",")
rownames(xMat)<-xMat[,1]
xMat<-xMat[,-c(1)]
xMat<-as.data.frame(xMat)
}else{
# output warnings
}
}
if(input$Aname==""){
Xname="datasetA"
}else{
Xname=input$Aname
}
req(input$datasetB)
if(input$datasetB$type=="text/plain"){
yMat <- read.table(input$datasetB$datapath,header = TRUE,sep = "\t")
rownames(yMat)<-yMat[,1]
yMat<-yMat[,-c(1)]
yMat<-as.data.frame(yMat)
}else{
if(input$datasetB$type=="text/csv"){
yMat <- read.table(input$datasetB$datapath,header = TRUE,sep = ",")
rownames(yMat)<-yMat[,1]
yMat<-yMat[,-c(1)]
yMat<-as.data.frame(yMat)
}else{
# output warnings
}
}
if(input$Bname==""){
Yname="datasetB"
}else{
Yname=input$Bname
}
req(input$datasetC)
if(input$datasetC$type=="text/plain"){
zMat <- read.table(input$datasetC$datapath,header = TRUE,sep = "\t")
rownames(zMat)<-zMat[,1]
zMat<-zMat[,-c(1)]
zMat<-as.data.frame(zMat)
}else{
if(input$datasetC$type=="text/csv"){
zMat <- read.table(input$datasetC$datapath,header = TRUE,sep = ",")
rownames(zMat)<-zMat[,1]
zMat<-zMat[,-c(1)]
zMat<-as.data.frame(zMat)
}else{
# output warnings
}
}
if(input$Cname==""){
Zname="datasetC"
}else{
Zname=input$Cname
}
req(input$datasetD)
if(input$datasetD$type=="text/plain"){
wMat <- read.table(input$datasetD$datapath,header = TRUE,sep = "\t")
rownames(wMat)<-wMat[,1]
wMat<-wMat[,-c(1)]
wMat<-as.data.frame(wMat)
}else{
if(input$datasetD$type=="text/csv"){
wMat <- read.table(input$datasetD$datapath,header = TRUE,sep = ",")
rownames(wMat)<-wMat[,1]
wMat<-wMat[,-c(1)]
wMat<-as.data.frame(wMat)
}else{
# output warnings
}
}
if(input$Dname==""){
Wname="datasetD"
}else{
Wname=input$Dname
}
req(input$class_labels_file)
if(input$class_labels_file$type=="text/plain"){
class_labels_file <- read.table(input$class_labels_file$datapath,header = TRUE,sep = "\t")
}else{
if(input$class_labels_file$type=="text/csv"){
class_labels_file <- read.table(input$class_labels_file$datapath,header = TRUE,sep = ",")
}else{
# output warnings
}
}
req(input$xmwasmethod)
if(input$xmwasmethod=="PLS: Partial least squares"){
xmwasmethod="pls"
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}else{
if(input$xmwasmethod=="sPLS: sparse PLS"){
xmwasmethod="spls"
KeepX=round(input$keepX)
KeepY=round(input$keepY)
check_keepZ<-reactive({need(input$keepZ,"error")})
if(is.null(check_keepZ())){
KeepZ=round(input$keepZ)
}else{
KeepZ=1000
}
check_KeepW<-reactive({need(input$keepW,"error")})
if(is.null(check_KeepW())){
KeepW=round(input$keepW)
}else{
KeepW=1000
}
}else{
if(input$xmwasmethod=="OPLS: orthogonal PLS"){
xmwasmethod="o1pls"
}
else{
if(input$xmwasmethod=="RCC: regularized canonical correlation"){
xmwasmethod="rcc"
}
}
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}
}
#c(max_zvar,session_outloc(),Xname,Yname,Zname,dim(xMat[1:5,1:5]),dim(yMat[1:5,1:5]),dim(zMat[1:5,1:5]),dim(class_labels_file[1:2,1:2]))
max_xvar=round(input$max_xvar)
max_yvar=round(input$max_yvar)
check_max_zvar<-reactive({need(input$max_zvar,"error")})
if(is.null(check_max_zvar())){
max_zvar=round(input$max_zvar)
}else{
max_zvar=1000
}
check_max_wvar<-reactive({need(input$max_wvar,"error")})
if(is.null(check_max_wvar())){
max_wvar=round(input$max_wvar)
}else{
max_wvar=1000
}
#maxnodesperclass=round(input$maxnodesperclass)
maxnodesperclass=1000000
numcomps=round(input$numcomps)
max_connections=round(input$max_connections)
if(max_connections<0){
max_connections<-NA
}
missing.val=input$missing.val
if(missing.val=="0"){
missing.val=0
}else{
missing.val=NA
}
###################
library('xMWAS')
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=wMat,outloc=session_outloc(),
classlabels=class_labels_file,xmwasmethod=xmwasmethod,plsmode=input$plsmode,
max_xvar=max_xvar,max_yvar=max_yvar,max_zvar=max_zvar,max_wvar=max_wvar,rsd.filt.thresh=input$rsd_filt_thresh,corthresh=input$corthresh,
keepX=KeepX,keepY=KeepY,keepZ=KeepZ,keepW=KeepW,pairedanalysis=input$pairedanalysis,optselect=input$optselect,rawPthresh=input$rawPthresh,
numcomps=numcomps,net_edge_colors=c("blue","red"),net_node_colors=c("orange", "green","cyan","gold"),
Xname=Xname,Yname=Yname,Zname=Zname,Wname=Wname,net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),
all.missing.thresh=input$all_missing_thresh,maxnodesperclass=maxnodesperclass,seednum=input$seednum,label.cex=input$label_cex,vertex.size=input$vertex_size,graphclustering=TRUE,
interactive=FALSE,max_connections=max_connections,centrality_method=input$centrality_method,use.X.reference=input$use_X_reference,removeRda=TRUE,compare.classes=input$compare.classes,missing.val=missing.val,
modularity.weighted=TRUE,
globalcomparison=TRUE,
plot.pairwise=TRUE,apply.sparse.class.comparison=FALSE)
if(FALSE){
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=NA,outloc=session_outloc(),
classlabels=class_labels_file,class_fname=NA,xmwasmethod="spls",plsmode="canonical",max_xvar=1000,max_yvar=1000,
max_zvar=1000,max_wvar=1000,rsd.filt.thresh=1,corthresh=0.8,keepX=100,keepY=100,keepZ=100,keepW=100,
pairedanalysis=FALSE,optselect=TRUE,rawPthresh=0.05,numcomps=10,net_edge_colors=c("blue","red"),
net_node_colors=c("orange", "green","cyan","gold"),Xname="X",Yname="Y",Zname="Z",Wname="W",
net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),all.missing.thresh=0.3,maxnodesperclass=100,
seednum=100,label.cex=0.2,vertex.size=6,graphclustering=TRUE,interactive=FALSE,max_connections=2000,
centrality_method="eigenvector",use.X.reference=FALSE,removeRda=TRUE,compare.classes=FALSE)
}
done$count=1
#file.copy(paste(getwd(),'matrix_centrality.txt',sep='/'),session_outloc())
setwd(session_outloc())
zip(zipfile=paste(basename(session_outloc()),'zip',sep='.'), files='.')
print("Processing complete. Please click on Download to save the results.")
}else{
if((input$go!=0 & !is.null(go$count) & input$exampledata=="TRUE" & is.null(input$datasetA$name) & is.null(input$datasetB$name) &
is.null(input$datasetC$name)) & is.null(input$datasetD$name) & is.null(input$class_labels_file$name)){
library('xMWAS')
#source("xMWAS_Rcode/xMWAS_v0.0.22.R")
data(exnci60)
xMat<-exnci60$mrna
yMat<-exnci60$miRNA
zMat<-exnci60$prot
class_labels_file<-exnci60$classlabels
Xname="mrna"
Yname="miRNA"
Zname="prot"
req(input$xmwasmethod)
if(input$xmwasmethod=="PLS: Partial least squares"){
xmwasmethod="pls"
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}else{
if(input$xmwasmethod=="sPLS: sparse PLS"){
xmwasmethod="spls"
KeepX=round(input$keepX)
KeepY=round(input$keepY)
check_keepZ<-reactive({need(input$keepZ,"error")})
if(is.null(check_keepZ())){
KeepZ=round(input$keepZ)
}else{
KeepZ=1000
}
check_KeepW<-reactive({need(input$keepW,"error")})
if(is.null(check_KeepW())){
KeepW=round(input$keepW)
}else{
KeepW=1000
}
}else{
if(input$xmwasmethod=="OPLS: orthogonal PLS"){
xmwasmethod="o1pls"
}
else{
if(input$xmwasmethod=="RCC: regularized canonical correlation"){
xmwasmethod="rcc"
}
}
KeepX=1000
KeepY=1000
KeepZ=1000
KeepW=1000
}
}
max_xvar=round(input$max_xvar)
max_yvar=round(input$max_yvar)
check_max_zvar<-reactive({need(input$max_zvar,"error")})
if(is.null(check_max_zvar())){
max_zvar=round(input$max_zvar)
}else{
max_zvar=1000
}
check_max_wvar<-reactive({need(input$max_wvar,"error")})
if(is.null(check_max_wvar())){
max_wvar=round(input$max_wvar)
}else{
max_wvar=1000
}
maxnodesperclass=1000000 #round(input$maxnodesperclass)
numcomps=round(input$numcomps)
# c(max_zvar,session_outloc(),Xname,Yname,Zname,dim(xMat[1:5,1:5]),dim(yMat[1:5,1:5]),dim(zMat[1:5,1:5]),dim(class_labels_file[1:2,1:2]))
max_connections=round(input$max_connections)
if(max_connections<0){
max_connections<-NA
}
missing.val=input$missing.val
if(missing.val=="0"){
missing.val=0
}else{
missing.val=NA
}
###################
#xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=NA,outloc=session_outloc(),
# classlabels=class_labels_file,xmwasmethod=xmwasmethod,plsmode=input$plsmode,
# max_xvar=max_xvar,max_yvar=max_yvar,max_zvar=max_zvar,max_wvar=NA,rsd.filt.thresh=input$rsd_filt_thresh,corthresh=input$corthresh,
# keepX=KeepX,keepY=KeepY,keepZ=KeepZ,keepW=NA,pairedanalysis=input$pairedanalysis,optselect=input$optselect,rawPthresh=input$rawPthresh,
# numcomps=numcomps,net_edge_colors=c("blue","red"),net_node_colors=c("orange", "green","cyan","gold"),
# Xname=Xname,Yname=Yname,Zname=Zname,Wname=NA,net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),
# all.missing.thresh=input$all_missing_thresh,maxnodesperclass=maxnodesperclass,seednum=input$seednum,label.cex=input$label_cex,vertex.size=input$vertex_size,graphclustering=TRUE,
# interactive=input$interactive,max_connections=max_connections,centrality_method=input$centrality_method,use.X.reference=input$use_X_reference,removeRda=input$removeRda)
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=NA,outloc=session_outloc(),
classlabels=class_labels_file,xmwasmethod=xmwasmethod,plsmode=input$plsmode,
max_xvar=max_xvar,max_yvar=max_yvar,max_zvar=max_zvar,max_wvar=max_wvar,rsd.filt.thresh=input$rsd_filt_thresh,corthresh=input$corthresh,
keepX=KeepX,keepY=KeepY,keepZ=KeepZ,keepW=KeepW,pairedanalysis=input$pairedanalysis,optselect=input$optselect,rawPthresh=input$rawPthresh,
numcomps=numcomps,net_edge_colors=c("blue","red"), net_node_colors=c("orange","green","cyan","gold"),
Xname=Xname,Yname=Yname,Zname=Zname,Wname=NA, net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),
all.missing.thresh=input$all_missing_thresh,maxnodesperclass=maxnodesperclass,seednum=input$seednum,label.cex=input$label_cex,
vertex.size=input$vertex_size,graphclustering=TRUE,interactive=FALSE,max_connections=max_connections,
centrality_method=input$centrality_method,use.X.reference=input$use_X_reference,removeRda=TRUE,compare.classes=input$compare.classes,missing.val=missing.val,modularity.weighted=TRUE,
globalcomparison=TRUE,
plot.pairwise=TRUE,apply.sparse.class.comparison=FALSE)
if(FALSE){
xmwas_res<-run_xmwas(Xome_data=xMat,Yome_data=yMat,Zome_data=zMat,Wome_data=NA,outloc=session_outloc(),
classlabels=class_labels_file,class_fname=NA,xmwasmethod="spls",plsmode="canonical",max_xvar=1000,max_yvar=1000,
max_zvar=1000,max_wvar=1000,rsd.filt.thresh=1,corthresh=0.8,keepX=100,keepY=100,keepZ=100,keepW=100,
pairedanalysis=FALSE,optselect=TRUE,rawPthresh=0.05,numcomps=10,net_edge_colors=c("blue","red"),
net_node_colors=c("orange", "green","cyan","gold"),Xname="X",Yname="Y",Zname="Z",Wname="W",
net_node_shape=c(input$net_node_shapeA,input$net_node_shapeB,input$net_node_shapeC,input$net_node_shapeD),all.missing.thresh=0.3,maxnodesperclass=100,
seednum=100,label.cex=0.2,vertex.size=6,graphclustering=TRUE,interactive=FALSE,max_connections=2000,
centrality_method="eigenvector",use.X.reference=FALSE,removeRda=TRUE,compare.classes=FALSE)
}
done$count=1
#file.copy(paste(getwd(),'matrix_centrality.txt',sep='/'),session_outloc())
setwd(session_outloc())
zip(zipfile=paste(basename(session_outloc()),'zip',sep='.'), files='.')
print("Processing complete. Please click on Download to save the results.")
}else{
go$count=NULL
}
}
}
}
})
##########################################
observeEvent(#{if(done$count==1) TRUE else return()}
req(done$count==1),{
output$myImage <- renderImage({
req(input$obs)
l1<-list.files(session_outloc(),".png",recursive=TRUE,full.names=FALSE)
if(length(grep("Rplot",l1,perl=TRUE))==0){
}else{
l1<-l1[-grep("Rplot",l1,perl=TRUE)]
}
l1<-l1[grep("Multidata",l1,perl=TRUE)]
if(input$obs>0 && input$obs<=length(l1))
{
filename <- normalizePath(file.path(session_outloc(),l1[input$obs]))
list(src = filename,width=600,height=600,
alt = "This is an image")
}else{
filename <- normalizePath(file.path(session_outloc(),l1[length(l1)]))
list(src = filename,width=600,height=600,
alt = "This is an image")
}
}, deleteFile = FALSE)
})
output$downloadData <- downloadHandler(
#if(input$go!=0 && input$featselmethod!="-" && input$feature_table_file!="" && input$class_labels_file!=""){
filename <- function() {
paste(basename(session_outloc()), "zip", sep=".")
},
content <- function(file) {
fname1<-paste(session_outloc(),"/",basename(session_outloc()), ".zip", sep="")
file.copy(fname1, file)
},
contentType = "application/zip"
#}
)
################################## Help Page #################################################
#output$table_dataset <- renderPrint({
#library(htmlTable)
#library('xMWAS')
#data(exnci60)
#output<-exnci60$mrna[1:5,1:6]
#output<-htmlTable(output,align='c',caption="<strong>Dataset File Format:</strong>",css.table="width:100%;")
#output
#})
}
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