--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/src/ExprPlotsScript.R	Fri Jun 26 09:45:41 2020 -0400
@@ -0,0 +1,465 @@
+# A command-line interface to basic plots for use with Galaxy
+# written by Jimmy Vandel
+# one of these arguments is required:
+#
+#
+initial.options <- commandArgs(trailingOnly = FALSE)
+file.arg.name <- "--file="
+script.name <- sub(file.arg.name, "", initial.options[grep(file.arg.name, initial.options)])
+script.basename <- dirname(script.name)
+source(file.path(script.basename, "utils.R"))
+source(file.path(script.basename, "getopt.R"))
+
+#addComment("Welcome R!")
+
+# setup R error handling to go to stderr
+options( show.error.messages=F, error = function () { cat(geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
+
+# we need that to not crash galaxy with an UTF8 error on German LC settings.
+loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
+loc <- Sys.setlocale("LC_NUMERIC", "C")
+
+#get starting time
+start.time <- Sys.time()
+
+#get options
+options(stringAsfactors = FALSE, useFancyQuotes = FALSE)
+args <- commandArgs()
+
+
+# get options, using the spec as defined by the enclosed list.
+# we read the options from the default: commandArgs(TRUE).
+spec <- matrix(c(
+  "dataFile", "i", 1, "character",
+  "factorInfo","t", 1, "character",
+  "dataFileFormat","j",1,"character",
+  "conditionNames","c",1,"character",
+  "format", "f", 1, "character",
+  "quiet", "q", 0, "logical",
+  "log", "l", 1, "character",
+  "histo" , "h", 1, "character",
+  "maPlot" , "a", 1, "character",
+  "boxplot" , "b", 1, "character",
+  "microarray" , "m", 1, "character",
+  "acp" , "p" , 1, "character",
+  "screePlot" , "s" , 1, "character"),
+  byrow=TRUE, ncol=4)
+opt <- getopt(spec)
+
+# enforce the following required arguments
+if (is.null(opt$log)) {
+  addComment("[ERROR]'log file' is required")
+  q( "no", 1, F )
+}
+addComment("[INFO]Start of R script",T,opt$log,display=FALSE)
+if (is.null(opt$dataFile) || is.null(opt$dataFileFormat)) {
+  addComment("[ERROR]'dataFile' and it format are required",T,opt$log)
+  q( "no", 1, F )
+}
+if (is.null(opt$format)) {
+  addComment("[ERROR]'output format' is required",T,opt$log)
+  q( "no", 1, F )
+}
+if (is.null(opt$histo) & is.null(opt$maPlot) & is.null(opt$boxplot) & is.null(opt$microarray) & is.null(opt$acp)){
+  addComment("[ERROR]Select at least one plot to draw",T,opt$log)
+  q( "no", 1, F )
+}
+
+verbose <- if (is.null(opt$quiet)) {
+  TRUE
+}else{
+  FALSE}
+
+addComment("[INFO]Parameters checked!",T,opt$log,display=FALSE)
+
+addComment(c("[INFO]Working directory: ",getwd()),TRUE,opt$log,display=FALSE)
+addComment(c("[INFO]Command line: ",args),TRUE,opt$log,display=FALSE)
+
+#directory for plots
+dir.create(file.path(getwd(), "plotDir"))
+dir.create(file.path(getwd(), "plotLyDir"))
+
+#silent package loading
+suppressPackageStartupMessages({
+  library("oligo")
+  library("ff")
+  library("ggplot2")
+  library("plotly")
+})
+
+
+#chargement des fichiers en entrée
+#fichier de type CEL
+dataAreFromCel=FALSE
+if(toupper(opt$dataFileFormat)=="CEL"){
+  dataAreFromCel=TRUE
+  celData=read.celfiles(unlist(strsplit(opt$dataFile,",")))
+  #load all expressions
+  dataMatrix=exprs(celData)
+  #select "pm" probes
+  probeInfo=getProbeInfo(celData,probeType = c("pm"),target="probeset")
+  #reduce dataMatrix to log expression matrix for a randomly probe selection
+  dataMatrix=log2(dataMatrix[sample(unique(probeInfo[,1]),min(100000,length(unique(probeInfo[,1])))),])
+  addComment("[INFO]Raw data are log2 transformed",TRUE,opt$log,display=FALSE)
+  remove(probeInfo)
+}else{
+  #fichier deja tabule
+  dataMatrix=read.csv(file=opt$dataFile,header=F,sep="\t",colClasses="character")
+  #remove first row to convert it as colnames (to avoid X before colnames with header=T)
+  colNamesData=dataMatrix[1,-1]
+  dataMatrix=dataMatrix[-1,]
+  #remove first colum to convert it as rownames
+  rowNamesData=dataMatrix[,1]
+  dataMatrix=dataMatrix[,-1]
+  if(is.data.frame(dataMatrix)){
+    dataMatrix=data.matrix(dataMatrix)
+  }else{
+    dataMatrix=data.matrix(as.numeric(dataMatrix))
+  }
+  dimnames(dataMatrix)=list(rowNamesData,colNamesData)
+  if(any(duplicated(rowNamesData)))addComment("[WARNING] several rows share the same probe/gene name, you should merge or rename them to avoid further analysis mistakes",TRUE,opt$log,display=FALSE)
+}
+
+addComment("[INFO]Input data loaded",TRUE,opt$log,display=FALSE)
+addComment(c("[INFO]Dim of data matrix:",dim(dataMatrix)),T,opt$log,display=FALSE)
+
+#get number of conditions
+nbConditions=ncol(dataMatrix)
+
+#get condition names if they are specified
+if(!is.null(opt$conditionNames) && length(opt$conditionNames)==nbConditions){
+  nameConditions=opt$conditionNames
+  colnames(dataMatrix)=nameConditions
+  #rownames(phenoData(celData)@data)=nameConditions
+  #rownames(protocolData(celData)@data)=nameConditions
+}else{
+  nameConditions=colnames(dataMatrix)
+}
+
+#create a correspondance table between plot file names and name displayed in figure legend and html items 
+correspondanceNameTable=matrix("",ncol=2,nrow=nbConditions)
+correspondanceNameTable[,1]=paste("Condition",1:nbConditions,sep="")
+correspondanceNameTable[,2]=nameConditions
+rownames(correspondanceNameTable)=correspondanceNameTable[,2]
+
+addComment("[INFO]Retreive condition names",TRUE,opt$log,display=FALSE)
+
+if(!is.null(opt$factorInfo)){
+  #chargement du fichier des facteurs
+  factorInfoMatrix=read.csv(file=file.path(getwd(), opt$factorInfo),header=F,sep="\t",colClasses="character")
+  #remove first row to convert it as colnames
+  colnames(factorInfoMatrix)=factorInfoMatrix[1,]
+  factorInfoMatrix=factorInfoMatrix[-1,]
+  #use first colum to convert it as rownames but not removing it to avoid conversion as vector in unique factor case
+  rownames(factorInfoMatrix)=factorInfoMatrix[,1]
+  
+  
+  if(length(setdiff(colnames(dataMatrix),rownames(factorInfoMatrix)))!=0){
+    addComment("[ERROR]Missing samples in factor file",T,opt$log)
+    q( "no", 1, F )
+  }
+  
+  #order sample as in expression matrix and remove spurious sample
+  factorInfoMatrix=factorInfoMatrix[colnames(dataMatrix),]
+  
+  addComment("[INFO]Factors OK",T,opt$log,display=FALSE)
+  addComment(c("[INFO]Dim of factorInfo matrix:",dim(factorInfoMatrix)),T,opt$log,display=FALSE)
+  
+}
+
+addComment("[INFO]Ready to plot",T,opt$log,display=FALSE)
+
+
+##----------------------
+
+###plot histograms###
+histogramPerFigure=50
+if (!is.null(opt$histo)) {
+  for(iToPlot in 1:(((nbConditions-1)%/%histogramPerFigure)+1)){
+    firstPlot=1+histogramPerFigure*(iToPlot-1)
+    lastPlot=min(nbConditions,histogramPerFigure*iToPlot)
+    dataToPlot=data.frame(x=c(dataMatrix[,firstPlot:lastPlot]),Experiment=rep(colnames(dataMatrix)[firstPlot:lastPlot],each=nrow(dataMatrix)))
+    p <- ggplot(data=dataToPlot, aes(x = x, color=Experiment)) + stat_density(geom="line", size=1, position="identity") +
+      ggtitle("Intensity densities") + theme_bw() + ylab(label="Density") + 
+      theme(panel.border=element_blank(),plot.title = element_text(hjust = 0.5))
+    if(dataAreFromCel){
+      #original ploting function
+      #hist(celData[,firstPlot:lastPlot],lty=rep(1,nbConditions)[firstPlot:lastPlot],lwd=2,which='pm',target="probeset",transfo=log2,col=rainbow(nbConditions)[firstPlot:lastPlot])
+      p <- p + xlab(label="Log2 intensities") 
+    }else{
+      p <- p + xlab(label="Intensities") 
+    }
+    if(opt$format=="pdf"){
+      pdf(paste(c("./plotDir/",opt$histo,iToPlot,".pdf"),collapse=""))}else{
+        png(paste(c("./plotDir/",opt$histo,iToPlot,".png"),collapse=""))
+      }
+    print(p)
+    dev.off()
+    #save plotly files
+    pp <- ggplotly(p)
+    htmlwidgets::saveWidget(as_widget(pp), paste(c(file.path(getwd(), "plotLyDir"),"/",opt$histo,iToPlot,".html"),collapse=""),selfcontained = F)
+  }
+  remove(p,dataToPlot)
+  addComment("[INFO]Histograms drawn",T,opt$log,display=FALSE)
+}
+
+##----------------------
+
+###plot MAplots###
+MAplotPerPage=4
+if (!is.null(opt$maPlot)) {
+  iToPlot=1
+  plotVector=list()
+  toTake=sample(nrow(dataMatrix),min(200000,nrow(dataMatrix)))
+  refMedianColumn=rowMedians(as.matrix(dataMatrix[toTake,]))
+  if(length(toTake)>100000)addComment(c("[INFO]high number of input data rows ",length(toTake),"; the generation of MA plot can take a while, please be patient"),TRUE,opt$log,display=FALSE)
+  for (iCondition in 1:nbConditions){
+    #MAplot(celData,which=i,what=pm,transfo=log2)
+    #smoothScatter(x=xToPlot,y=yToPlot,main=nameConditions[iCondition])
+    dataA=dataMatrix[toTake,iCondition]
+    dataB=refMedianColumn####ATTENTION PAR DEFAUT
+    xToPlot=0.5*(dataA+dataB)
+    yToPlot=dataA-dataB
+    tempX=seq(min(xToPlot),max(xToPlot),0.1)
+    tempY=unlist(lapply(tempX,function(x){median(yToPlot[intersect(which(xToPlot>=(x-0.1/2)),which(xToPlot<(x+0.1/2)))])}))
+    
+    dataToPlot=data.frame(x=xToPlot,y=yToPlot)
+    dataMedianToPlot=data.frame(x=tempX,y=tempY)
+    p <- ggplot(data=dataToPlot, aes(x,y)) + stat_density2d(aes(fill = ..density..^0.25), geom = "tile", contour = FALSE, n = 100) +
+      scale_fill_continuous(low = "white", high = "dodgerblue4") + geom_smooth(data=dataMedianToPlot,colour="red", size=0.5, se=FALSE) +
+      ggtitle(correspondanceNameTable[iCondition,2]) + theme_bw() + xlab(label="") + ylab(label="") + 
+      theme(panel.border=element_blank(),plot.title = element_text(hjust = 0.5),legend.position = "none")
+    plotVector[[length(plotVector)+1]]=p
+  
+    #save plotly files   
+    pp <- ggplotly(p)
+    htmlwidgets::saveWidget(as_widget(pp), paste(c(file.path(getwd(), "plotLyDir"),"/",opt$maPlot,"_",correspondanceNameTable[iCondition,1],".html"),collapse=""),selfcontained = F)
+
+    if(iCondition==nbConditions || length(plotVector)==MAplotPerPage){
+      #define a new plotting file
+      if(opt$format=="pdf"){
+        pdf(paste(c("./plotDir/",opt$maPlot,iToPlot,".pdf"),collapse=""))}else{
+          png(paste(c("./plotDir/",opt$maPlot,iToPlot,".png"),collapse=""))
+        }
+      multiplot(plotlist=plotVector,cols=2)
+      dev.off()
+      if(iCondition<nbConditions){
+        #prepare for a new plotting file if necessary
+        plotVector=list()
+        iToPlot=iToPlot+1
+      }
+    }
+  }
+  remove(p,dataToPlot,dataA,dataB,toTake,xToPlot,yToPlot)
+  addComment("[INFO]MAplots drawn",T,opt$log,display=FALSE)
+}
+
+##----------------------
+
+###plot boxplots###
+boxplotPerFigure=50
+if (!is.null(opt$boxplot)) {
+  for(iToPlot in 1:(((nbConditions-1)%/%boxplotPerFigure)+1)){
+    firstPlot=1+boxplotPerFigure*(iToPlot-1)
+    lastPlot=min(nbConditions,boxplotPerFigure*iToPlot)
+    dataToPlot=data.frame(intensities=c(dataMatrix[,firstPlot:lastPlot]),Experiment=rep(colnames(dataMatrix)[firstPlot:lastPlot],each=nrow(dataMatrix)))
+    #to make HTML file lighter, sampling will be done amongst outliers
+    #get outliers for each boxplot
+    boxplotsOutliers=apply(dataMatrix[,firstPlot:lastPlot],2,function(x)boxplot.stats(x)$out)
+    #sample amongst them to keep at maximum of 1000 points and include both min and max outliers values
+    boxplotsOutliers=lapply(boxplotsOutliers,function(x)if(length(x)>0)c(sample(c(x),min(length(x),1000)),max(c(x)),min(c(x))))
+    dataOutliers=data.frame(yVal=unlist(boxplotsOutliers),xVal=unlist(lapply(seq_along(boxplotsOutliers),function(x)rep(names(boxplotsOutliers)[x],length(boxplotsOutliers[[x]])))))
+    #plot boxplots without outliers
+    p <- ggplot(data=dataToPlot, aes(y = intensities, x=Experiment ,color=Experiment)) + geom_boxplot(outlier.colour=NA,outlier.shape =NA) +
+      ggtitle("Intensities") + theme_bw() + xlab(label="") + 
+      theme(panel.border=element_blank(),plot.title = element_text(hjust = 0.5),axis.text.x = element_text(angle = 45, hjust = 1),plot.margin=unit(c(10,10,max(unlist(lapply(dataToPlot$Experiment,function(x)nchar(as.character(x))))),15+max(unlist(lapply(dataToPlot$Experiment,function(x)nchar(as.character(x)))))),"mm"))
+    #add to plot sampled outliers
+    p <- p + geom_point(data=dataOutliers,aes(x=xVal,y=yVal,color=xVal),inherit.aes = F)
+    if(dataAreFromCel){
+      #original plotting function
+  	  #boxplot(celData[,firstPlot:lastPlot],which='pm',col=rainbow(nbConditions)[firstPlot:lastPlot],target="probeset",transfo=log2,names=nameConditions[firstPlot:lastPlot],main="Intensities") 
+      p <- p + ylab(label="Log2 intensities")
+    }else{
+      p <- p + ylab(label="Intensities")
+    }
+    if(opt$format=="pdf"){
+      pdf(paste(c("./plotDir/",opt$boxplot,iToPlot,".pdf"),collapse=""))}else{
+        png(paste(c("./plotDir/",opt$boxplot,iToPlot,".png"),collapse=""))
+      }
+    print(p)
+    dev.off()
+    #save plotly files    
+    pp <- ggplotly(p)
+    
+    #modify plotly object to get HTML file not too heavy for loading
+    for(iData in 1:length(pp$x$data)){
+      ##get kept outliers y values
+      #yPointsToKeep=dataOutliers$yVal[which(dataOutliers$xVal==pp$x$data[[iData]]$name)]
+      if(pp$x$data[[iData]]$type=="scatter"){
+        ##scatter plot represent outliers points added to boxplot through geom_point
+        ##nothing to do as outliers have been sampled allready, just have to modify hover text
+        #if(length(yPointsToKeep)>0){
+          #pointsToKeep=which(pp$x$data[[iData]]$y %in% yPointsToKeep)
+          #pp$x$data[[iData]]$x=pp$x$data[[iData]]$x[pointsToKeep]
+          #pp$x$data[[iData]]$y=pp$x$data[[iData]]$y[pointsToKeep]
+          #pp$x$data[[iData]]$text=pp$x$data[[iData]]$text[pointsToKeep]
+        #}else{
+          #pp$x$data[[iData]]$x=NULL
+          #pp$x$data[[iData]]$y=NULL
+          #pp$x$data[[iData]]$marker$opacity=0
+          #pp$x$data[[iData]]$hoverinfo=NULL
+          #pp$x$data[[iData]]$text=NULL
+        #}
+        #modify text to display
+        if(dataAreFromCel){
+          pp$x$data[[iData]]$text=unlist(lapply(seq_along(pp$x$data[[iData]]$y),function(x)return(paste(c("log2(intensity) ",prettyNum(pp$x$data[[iData]]$y[x],digits=4)),collapse = ""))))
+        }else{
+          pp$x$data[[iData]]$text=unlist(lapply(seq_along(pp$x$data[[iData]]$y),function(x)return(paste(c("intensity ",prettyNum(pp$x$data[[iData]]$y[x],digits=4)),collapse = ""))))
+        }
+      }else{
+        ##disable marker plotting to keep only box and whiskers plot (outliers are displayed through scatter plot)
+        pp$x$data[[iData]]$marker$opacity=0
+        
+        #sample 50000 points amongst all data to get a lighter html file, sampling size should not be too low to avoid modifying limit of boxplots
+        pp$x$data[[iData]]$y=c(sample(dataMatrix[,pp$x$data[[iData]]$name],min(length(dataMatrix[,pp$x$data[[iData]]$name]),50000)),min(dataMatrix[,pp$x$data[[iData]]$name]),max(dataMatrix[,pp$x$data[[iData]]$name]))
+        pp$x$data[[iData]]$x=rep(pp$x$data[[iData]]$x[1],length(pp$x$data[[iData]]$y))
+        
+        ##first remove outliers info
+        #downUpValues=boxplot.stats(dataMatrix[,pp$x$data[[iData]]$name])$stats
+        #if(verbose)addComment(c("filter values for boxplot",pp$x$data[[iData]]$name,"between",min(downUpValues),"and",max(downUpValues)),T,opt$log)
+        #pointsToRemove=which(pp$x$data[[iData]]$y<min(downUpValues))
+        #if(length(pointsToRemove)>0)pp$x$data[[iData]]$y=pp$x$data[[iData]]$y[-pointsToRemove]
+        #pointsToRemove=which(pp$x$data[[iData]]$y>max(downUpValues))
+        #if(length(pointsToRemove)>0)pp$x$data[[iData]]$y=pp$x$data[[iData]]$y[-pointsToRemove]
+        #then add sampled outliers info
+        #pp$x$data[[iData]]$y=c(yPointsToKeep,pp$x$data[[iData]]$y)
+        #pp$x$data[[iData]]$x=rep(pp$x$data[[iData]]$x[1],length(pp$x$data[[iData]]$y))
+      }
+    }
+    
+    htmlwidgets::saveWidget(as_widget(pp), paste(c(file.path(getwd(), "plotLyDir"),"/",opt$boxplot,iToPlot,".html"),collapse=""),selfcontained = F)
+  }
+  remove(p,dataToPlot)
+  addComment("[INFO]Boxplots drawn",T,opt$log,display=FALSE)
+  
+}
+
+##----------------------
+
+###plot microarrays (only for .CEL files)###
+if (!is.null(opt$microarray) && dataAreFromCel) {
+  for (iCondition in 1:nbConditions){
+    if(opt$format=="pdf"){
+      pdf(paste(c("./plotDir/",opt$microarray,"_",correspondanceNameTable[iCondition,1],".pdf"),collapse=""),onefile = F,width = 5,height = 5)}else{
+        png(paste(c("./plotDir/",opt$microarray,"_",correspondanceNameTable[iCondition,1],".png"),collapse=""))
+      }
+    image(celData[,iCondition],main=correspondanceNameTable[iCondition,2])
+    dev.off()
+  }
+  addComment("[INFO]Microarray drawn",T,opt$log,display=FALSE)
+}
+
+##----------------------
+
+###plot PCA plot###
+if (!is.null(opt$acp)){
+  ##to avoid error when nrow is too large, results quite stable with 200k random selected rows
+  randomSelection=sample(nrow(dataMatrix),min(200000,nrow(dataMatrix)))
+  #remove constant variables
+  
+  dataFiltered=dataMatrix[randomSelection,]
+  toRemove=which(unlist(apply(dataFiltered,1,var))==0)
+  if(length(toRemove)>0){
+    dataFiltered=dataFiltered[-toRemove,]
+  }
+  ##geom_text(aes(label=Experiments,hjust=1, vjust=1.3), y = PC2+0.01)
+  PACres = prcomp(t(dataFiltered),scale.=TRUE)
+  
+  if(!is.null(opt$screePlot)){
+    #scree plot
+    #p <- fviz_eig(PACres)
+    dataToPlot=data.frame(compo=seq(1,length(PACres$sdev)),var=(PACres$sdev^2/sum(PACres$sdev^2))*100)
+    p<-ggplot(data=dataToPlot, aes(x=compo, y=var)) + geom_bar(stat="identity", fill="steelblue") + geom_line() + geom_point() +
+      ggtitle("Scree plot") + theme_bw() + theme(panel.border=element_blank(),plot.title = element_text(hjust = 0.5)) +
+      xlab(label="Dimensions") + ylab(label="% explained variances") + scale_x_discrete(limits=dataToPlot$compo)
+    pp <- ggplotly(p)
+    
+    if(opt$format=="pdf"){
+        pdf(paste(c("./plotDir/",opt$screePlot,".pdf"),collapse=""))}else{
+        png(paste(c("./plotDir/",opt$screePlot,".png"),collapse=""))
+      }
+    plot(p)
+    dev.off()
+    htmlwidgets::saveWidget(as_widget(pp), paste(c(file.path(getwd(), "plotLyDir"),"/",opt$screePlot,".html"),collapse=""),selfcontained = F)
+  }
+  
+  #now plot pca plots
+  
+  if(!is.null(opt$factorInfo)){
+    fileIdent=""
+    symbolset = c("circle","cross","square","diamond","circle-open","square-open","diamond-open","x")
+    
+    #save equivalence between real factor names and generic ones in correspondanceNameTable
+    correspondanceNameTable=rbind(correspondanceNameTable,matrix(c(paste("Factor",1:(ncol(factorInfoMatrix)-1),sep=""),colnames(factorInfoMatrix)[-1]),ncol=2,nrow=ncol(factorInfoMatrix)-1))
+    rownames(correspondanceNameTable)=correspondanceNameTable[,2]
+    
+    #first order factors from decreasing groups number
+    orderedFactors=colnames(factorInfoMatrix)[-1][order(unlist(lapply(colnames(factorInfoMatrix)[-1],function(x)length(table(factorInfoMatrix[,x])))),decreasing = T)]
+    allFactorsBigger=length(table(factorInfoMatrix[,orderedFactors[length(orderedFactors)]]))>length(symbolset)
+    if(allFactorsBigger)addComment("[WARNING]All factors are composed of too many groups to display two factors at same time, each PCA plot will display only one factor groups",T,opt$log,display=FALSE) 
+    for(iFactor in 1:length(orderedFactors)){
+      #if it is the last factor of the list or if all factor 
+      if(iFactor==length(orderedFactors) || allFactorsBigger){
+       if(length(orderedFactors)==1 || allFactorsBigger){ 
+          dataToPlot=data.frame(PC1=PACres$x[,1],PC2=PACres$x[,2],PC3=PACres$x[,3],Experiments=rownames(PACres$x), Attribute1=factorInfoMatrix[rownames(PACres$x),orderedFactors[iFactor]], hoverLabel=unlist(lapply(rownames(PACres$x),function(x)paste(factorInfoMatrix[x,-1],collapse=","))))
+          p <- plot_ly(dataToPlot,x = ~PC1, y = ~PC2, z = ~PC3, type = 'scatter3d', mode="markers", color=~Attribute1,colors=rainbow(length(levels(dataToPlot$Attribute1))+2),hoverinfo = 'text', text = ~paste(Experiments,"\n",hoverLabel),marker=list(size=5))%>%
+            layout(title = "Principal Component Analysis", scene = list(xaxis = list(title = "Component 1"),yaxis = list(title = "Component 2"),zaxis = list(title = "Component 3")),
+                   legend=list(font = list(family = "sans-serif",size = 15,color = "#000")))
+          fileIdent=correspondanceNameTable[orderedFactors[iFactor],1]
+          #add text label to plot
+          ##p <- add_text(p,x = dataToPlot$PC1, y = dataToPlot$PC2 + (max(PACres$x[,2])-min(PACres$x[,2]))*0.02, z = dataToPlot$PC3, mode = 'text', inherit = F, text=rownames(PACres$x), hoverinfo='skip', showlegend = FALSE, color=I('black'))
+          #save the plotly plot
+          htmlwidgets::saveWidget(as_widget(p), paste(c(file.path(getwd(), "plotLyDir"),"/",opt$acp,"_",fileIdent,".html"),collapse=""),selfcontained = F)
+        }
+      }else{
+        for(iFactorBis in (iFactor+1):length(orderedFactors)){
+          if(length(table(factorInfoMatrix[,orderedFactors[iFactorBis]]))<=length(symbolset)){
+            dataToPlot=data.frame(PC1=PACres$x[,1],PC2=PACres$x[,2],PC3=PACres$x[,3],Experiments=rownames(PACres$x), Attribute1=factorInfoMatrix[rownames(PACres$x),orderedFactors[iFactor]], Attribute2=factorInfoMatrix[rownames(PACres$x),orderedFactors[iFactorBis]], hoverLabel=unlist(lapply(rownames(PACres$x),function(x)paste(factorInfoMatrix[x,-1],collapse=","))))
+            p <- plot_ly(dataToPlot,x = ~PC1, y = ~PC2, z = ~PC3, type = 'scatter3d', mode="markers", color=~Attribute1,colors=rainbow(length(levels(dataToPlot$Attribute1))+2),symbol=~Attribute2,symbols = symbolset,hoverinfo = 'text', text = ~paste(Experiments,"\n",hoverLabel),marker=list(size=5))%>%
+              layout(title = "Principal Component Analysis", scene = list(xaxis = list(title = "Component 1"),yaxis = list(title = "Component 2"),zaxis = list(title = "Component 3")),
+                     legend=list(font = list(family = "sans-serif",size = 15,color = "#000")))
+            fileIdent=paste(correspondanceNameTable[orderedFactors[c(iFactor,iFactorBis)],1],collapse="_AND_")
+            #add text label to plot
+            ##p <- add_text(p,x = dataToPlot$PC1, y = dataToPlot$PC2 + (max(PACres$x[,2])-min(PACres$x[,2]))*0.02, z = dataToPlot$PC3, mode = 'text', inherit = F, text=rownames(PACres$x), hoverinfo='skip', showlegend = FALSE, color=I('black'))
+            #save the plotly plot
+            htmlwidgets::saveWidget(as_widget(p), paste(c(file.path(getwd(), "plotLyDir"),"/",opt$acp,"_",fileIdent,".html"),collapse=""),selfcontained = F)
+          }else{
+            addComment(c("[WARNING]PCA with",orderedFactors[iFactor],"and",orderedFactors[iFactorBis],"groups cannot be displayed, too many groups (max",length(symbolset),")"),T,opt$log,display=FALSE) 
+          }
+        }
+      }
+    }
+  }else{
+    dataToPlot=data.frame(PC1=PACres$x[,1],PC2=PACres$x[,2],PC3=PACres$x[,3],Experiments=rownames(PACres$x))
+    p <- plot_ly(dataToPlot,x = ~PC1, y = ~PC2, z = ~PC3, type = 'scatter3d', mode="markers",marker=list(size=5,color="salmon"),hoverinfo = 'text',text = ~paste(Experiments))%>%
+      layout(title = "Principal Component Analysis", scene = list(xaxis = list(title = "Component 1"),yaxis = list(title = "Component 2"),zaxis = list(title = "Component 3")),
+             legend=list(font = list(family = "sans-serif",size = 15,color = "#000")))
+    ##p <- add_text(p,x = dataToPlot$PC1, y = dataToPlot$PC2 + (max(PACres$x[,2])-min(PACres$x[,2]))*0.02, z = dataToPlot$PC3, mode = 'text', inherit = F, text=rownames(PACres$x), hoverinfo='skip', showlegend = FALSE, color=I('black'))
+    
+    #save plotly files 
+    htmlwidgets::saveWidget(as_widget(p), paste(c(file.path(getwd(), "plotLyDir"),"/",opt$acp,"_plot.html"),collapse=""),selfcontained = F)
+  }
+  remove(p,dataToPlot,dataFiltered)
+  addComment("[INFO]ACP plot drawn",T,opt$log,display=FALSE)
+}
+
+#write correspondances between plot file names and displayed names in figure legends, usefull to define html items in xml file
+write.table(correspondanceNameTable,file=file.path(getwd(), "correspondanceFileNames.csv"),quote=FALSE,sep="\t",col.names = F,row.names = F)
+
+end.time <- Sys.time()
+addComment(c("[INFO]Total execution time for R script:",as.numeric(end.time - start.time,units="mins"),"mins"),T,opt$log,display=FALSE)
+
+addComment("[INFO]End of R script",T,opt$log,display=FALSE)
+
+printSessionInfo(opt$log)
+#sessionInfo()