Mercurial > repos > sblanck > smagexp
view MetaRNASeq.R @ 28:49ce6fbe11de draft
planemo upload for repository https://github.com/sblanck/smagexp/tree/master/smagexp_tools commit cb90fdea2a64908e301355aef89de403e107764d
| author | sblanck |
|---|---|
| date | Thu, 21 Jun 2018 04:28:19 -0400 |
| parents | cb29ad7d75a5 |
| children | 102e4ef8fed2 |
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#!/usr/bin/env Rscript # 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") library("optparse") ##### Read options option_list=list( make_option("--input",type="character",default="NULL",help="list of rdata objects containing eset objects"), make_option("--result",type="character",default=NULL,help="text file containing result of the meta-analysis"), make_option("--htmloutput",type="character",default=NULL,help="Output html report"), make_option("--htmloutputpath",type="character",default="NULL",help="Path of output html report"), make_option("--htmltemplate",type="character",default=NULL,help="html template)") ); opt_parser = OptionParser(option_list=option_list); opt = parse_args(opt_parser); if(is.null(opt$input)){ print_help(opt_parser) stop("input required.", call.=FALSE) } #loading libraries suppressPackageStartupMessages(require(affy)) suppressPackageStartupMessages(require(annaffy)) suppressPackageStartupMessages(require(VennDiagram)) suppressPackageStartupMessages(require(GEOquery)) listInput <- trimws( unlist( strsplit(trimws(opt$input), ",") ) ) listfiles=vector() listfilenames=vector() for (i in 1:length(listInput)) { inputFileInfo <- unlist( strsplit( listInput[i], ';' ) ) listfiles=c(listfiles,inputFileInfo[1]) listfilenames=c(listfilenames,inputFileInfo[2]) } outputfile <- opt$result result.html = opt$htmloutput html.files.path=opt$htmloutputpath result.template=opt$htmltemplate alpha=0.05 #print(comparison) listData=lapply(listfiles,read.table) orderData=lapply(listData, function(x) x[order(x[1]), ]) rawpval=lapply(orderData,function(x) x[6]) rawpval=lapply(rawpval, function(x) as.numeric(unlist(x))) DE=list() DE=lapply(orderData, function(x) ifelse(x[7]<=0.05,1,0)) FC=list() FC=lapply(orderData, function(x) x[3]) DE=as.data.frame(DE) colnames(DE)=listfilenames FC=as.data.frame(FC) colnames(FC)=listfilenames # the comparison must only have two values and the conds must # be a vector from those values, at least one of each. #if (length(comparison) != 2) { # stop("Comparison type must be a tuple: ", cargs[length(cargs) - 8]) #} sink("/dev/null") dir.create(html.files.path, recursive=TRUE) #library(DESeq) #library(HTSFilter) #DE=list() #FC=list() #i=1 # Open the html output file #file.conn = file(diag.html, open="w") #writeLines( c("<html><body>"), file.conn) # Perform deseq analysis on each study #for(i in 1:length(listfiles)) #{ # f=listfiles[i] # fname=listfilenames[i] # study_name=unlist(strsplit(fname,"[.]"))[1] # print(paste0("study.name ",study_name)) # d <- read.table(f, sep=" ", header=TRUE, row.names=1) # conds<-sapply(strsplit(colnames(d),"[.]"),FUN=function(x) x[1]) # if (length(unique(conds)) != 2) { # warning(as.data.frame(strsplit(colnames(d),"[.]"))) # stop("You can only have two columns types: ", paste(conds,collapse=" ")) # } # if (!identical(sort(comparison), sort(unique(conds)))) { # stop("Column types must use the two names from Comparison type, and vice versa. Must have at least one of each in the Column types.\nColumn types: ", cargs[2], "\n", "Comparison type: ", cargs[3]) # } # if (length(d) != length(conds)) { # stop("Number of total sample columns in counts file must correspond to the columns types field. E.g. if column types is 'kidney,kidney,liver,liver' then number of sample columns in counts file must be 4 as well.") # } # # cds <- newCountDataSet(d, conds) # cds <- estimateSizeFactors(cds) # # cdsBlind <- estimateDispersions( cds, method="blind" ) # # if (length(conds) != 2) { # cds <- estimateDispersions( cds ) # norep = FALSE # } # # if (length(conds) == 2) { # cds <- estimateDispersions( cds, method=method, sharingMode=mod, fitType="parametric" ) # norep = TRUE # } # # filter<-HTSFilter(cds, plot=FALSE) # cds.filter<-filter$filteredData # on.index<-which(filter$on==1) # # res<-as.data.frame(matrix(NA,nrow=nrow(cds),ncol=ncol(cds))) # nbT <- nbinomTest(cds.filter, comparison[1], comparison[2]) # colnames(res)<-colnames(nbT) # res[on.index,]<-nbT # #write.table(res[order(res$padj), ], file=outputfile, quote=FALSE, row.names=FALSE, sep="\t") # # # temp.pval.plot = file.path( html.files.path, paste("PvalHist",i,".png",sep="")) # png( temp.pval.plot, width=500, height=500 ) # hist(res$pval, breaks=100, col="skyblue", border="slateblue", main="") # dev.off() # # writeLines( c("<h2>P-value histogram for ",study_name,"</h2>"), file.conn) # writeLines( c("<img src='PvalHist",i,".png'><br/><br/>"), file.conn) # # #on enregistre la p-value # rawpval[[study_name]]<-res$pval # DE[[study_name]]<-ifelse(res$padj<=alpha,1,0) # FC[[study_name]]<-res$log2FoldChange # # i=i+1 #} # combinations library(metaRNASeq) library(UpSetR) fishcomb<-fishercomb(rawpval, BHth=alpha) warning(length(rawpval)) #nrep=c(length(listFiles)) invnormcomb<-invnorm(rawpval, nrep=c(8,8), BHth=alpha) #DE[["fishercomb"]]<-ifelse(fishcomb$adjpval<=alpha,1,0) #DE[["invnormcomb"]]<-ifelse(invnormcomb$adjpval<=alpha,1,0) signsFC<-mapply(FC,FUN=function(x) sign(x)) sumsigns<-apply(signsFC,1,sum) commonsgnFC<-ifelse(abs(sumsigns)==dim(signsFC)[2],sign(sumsigns),0) DEresults <- data.frame(DE=DE,"DE.fishercomb"=ifelse(fishcomb$adjpval<=alpha,1,0),"DE.invnorm"=ifelse(invnormcomb$adjpval<=alpha,1,0)) unionDE <- unique(c(fishcomb$DEindices,invnormcomb$DEindices)) FC.selecDE <- data.frame(DEresults[unionDE,],FC[unionDE,],signFC=commonsgnFC[unionDE]) keepDE <- FC.selecDE[which(abs(FC.selecDE$signFC)==1),] fishcomb_de <- rownames(keepDE)[which(keepDE[,"DE.fishercomb"]==1)] invnorm_de <- rownames(keepDE)[which(keepDE[,"DE.invnorm"]==1)] indstudy_de = list() for (i in 1:length(listfiles)) { currentIndstudy_de = rownames(keepDE)[which(keepDE[,i]==1)] indstudy_de[[listfilenames[i]]]=currentIndstudy_de } IDDIRRfishcomb=IDD.IRR(fishcomb_de,indstudy_de) IDDIRRinvnorm=IDD.IRR(invnorm_de,indstudy_de) #conflits<-data.frame(ID=listData[[1]][rownames(DEresults),1],Fishercomb=DEresults[["DE.fishercomb"]],Invnormcomb=DEresults[["DE.invnorm"]],sign=commonsgnFC) conflits<-data.frame(ID=listData[[1]][rownames(DEresults),1],DE=DEresults,FC=FC,signFC=commonsgnFC) #write DE outputfile write.table(conflits, outputfile,sep="\t",,row.names=FALSE) library(VennDiagram) DE_num=apply(keepDE[,1:(length(listfiles)+2)], 2, FUN=function(x) which(x==1)) #DE_num=apply(DEresults, 2, FUN=function(x) which(x==1)) if (length(listfiles<=2)) { title="VENN DIAGRAM" width=500 venn.plot<-venn.diagram(x=as.list(DE_num),filename=NULL, col="black", fill=1:length(DE_num)+1,alpha=0.6) temp.venn.plot = file.path( html.files.path, paste("venn.png")) png(temp.venn.plot,width=width,height=500) grid.draw(venn.plot) dev.off() } else { title="UPSETR DIAGRAM" width=1000 png(temp.venn.plot,width=width,height=500) upset(fromList(as.list(DE_num)), order.by = "freq") dev.off() } library(jsonlite) matrixConflits=as.matrix(conflits) datajson=toJSON(matrixConflits,pretty = TRUE) summaryFishcombjson=toJSON(as.matrix(t(IDDIRRfishcomb)),pretty = TRUE) summaryinvnormjson=toJSON(as.matrix(t(IDDIRRinvnorm)),pretty = TRUE) #vennsplit=strsplit(result.venn,split="/")[[1]] #venn=paste0("./",vennsplit[length(vennsplit)]) vennFilename="venn.png" vennFile=file.path(html.files.path,vennFilename) htmlfile=readChar(result.template, file.info(result.template)$size) htmlfile=gsub(x=htmlfile,pattern = "###DATAJSON###",replacement = datajson, fixed = TRUE) htmlfile=gsub(x=htmlfile,pattern = "###FISHSUMMARYJSON###",replacement = summaryFishcombjson, fixed = TRUE) htmlfile=gsub(x=htmlfile,pattern = "###INVSUMMARYJSON###",replacement = summaryinvnormjson, fixed = TRUE) htmlfile=gsub(x=htmlfile,pattern = "###VENN###",replacement = vennFilename, fixed = TRUE) htmlfile=gsub(x=htmlfile,pattern = "###WIDTH##",replacement = as.character(width), fixed = TRUE) htmlfile=gsub(x=htmlfile,pattern = "###TITLE##",replacement = as.character(width), fixed = TRUE) write(htmlfile,result.html) #library(VennDiagram) #flog.threshold(ERROR) # ##venn.plot<-venn.diagram(x = c(res[c(1:(length(res)-3))],meta=list(res$Meta)),filename = v, col = "black", fill = c(1:(length(res)-2)), margin=0.05, alpha = 0.6,imagetype = "png") #dir.create(result.path, showWarnings = TRUE, recursive = FALSE) # #showVenn<-function(liste,file) #{ # venn.plot<-venn.diagram(x = liste, # filename = vennFilename, col = "black", # fill = 1:length(liste)+1, # margin=0.05, alpha = 0.6,imagetype = "png") ## png(file); ## grid.draw(venn.plot); ## dev.off(); # #} # #l=list() #for(i in 1:length(esets)) #{ # l[[paste("study",i,sep="")]]<-res[[i]] #} #l[["Meta"]]=res[[length(res)-1]] #showVenn(l,vennFile) #file.copy(vennFilename,result.path) #writeLines( c("<h2>Venn Plot</h2>"), file.conn) #writeLines( c("<img src='venn.png'><br/><br/>"), file.conn) #writeLines( c("</body></html>"), file.conn) #close(file.conn) #print("passe6") #sink(NULL)