Mercurial > repos > sblanck > smagexp

cargs <- commandArgs()
cargs <- cargs[(which(cargs == "--args")+1):length(cargs)]
nbargs=length(cargs)
listfiles=vector()
listfilenames=vector()
for (i in seq(1,nbargs-6,2)) {
	listfiles=c(listfiles,cargs[[i]])
	listfilenames=c(listfilenames,cargs[[i+1]])
}
#mod<-cargs[[length(cargs) - 6]]
outputfile <- cargs[[length(cargs) - 5]]
result.html = cargs[[length(cargs) - 4]]
html.files.path=cargs[[length(cargs) - 3]]
result.template=cargs[[length(cargs) - 2]]

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)
fishcomb<-fishercomb(rawpval, BHth=alpha)
warning(length(rawpval))
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(DEresults, 2, FUN=function(x) which(x==1))
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=500,height=500)
grid.draw(venn.plot)
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)
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)
author	sblanck
date	Thu, 11 May 2017 10:20:35 -0400
parents	93451f832736
children	e5a94bc69bd6