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code/R_scripts/Cambuli_DifferentialExpression.R

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+###
+suppressMessages(library('DESeq2'))
+#
+ES_E14<-read.csv("ES_E14.count",header=FALSE,sep="\t")
+ES_E14<-ES_E14[grep('ENSMUS',ES_E14$V1),]
+colnames(ES_E14)<-c('ensGeneID','ES_E14')
+##
+ES_J1<-read.csv("ES_J1.count",header=FALSE,sep="\t")
+ES_J1<-ES_J1[grep('ENSMUS',ES_J1$V1),]
+colnames(ES_J1)<-c('ensGeneID','ES_J1')
+##
+iCDX2_ES_MEF<-read.csv("iCdx2_ES_MEF.count",header=FALSE,sep="\t")
+iCDX2_ES_MEF<-iCDX2_ES_MEF[grep('ENSMUS',iCDX2_ES_MEF$V1),]
+colnames(iCDX2_ES_MEF)<-c('ensGeneID','iCDX2_ES_MEF')
+##
+iCDX2_ES_TC<-read.csv("iCdx2_ES_TC_plastic.count",header=FALSE,sep="\t")
+iCDX2_ES_TC<-iCDX2_ES_TC[grep('ENSMUS',iCDX2_ES_TC$V1),]
+colnames(iCDX2_ES_TC)<-c('ensGeneID','iCDX2_ES_TC')
+##
+TS_EGFP<-read.csv("TS_EGFP.count",header=FALSE,sep="\t")
+TS_EGFP<-TS_EGFP[grep('ENSMUS',TS_EGFP$V1),]
+colnames(TS_EGFP)<-c('ensGeneID','TS_EGFP')
+##
+TS_Rs26<-read.csv("TS_Rs26.count",header=FALSE,sep="\t")
+TS_Rs26<-TS_Rs26[grep('ENSMUS',TS_Rs26$V1),]
+colnames(TS_Rs26)<-c('ensGeneID','TS_Rs26')
+##iCDX2-MEF vs ES-E14
+ESCdx2MEF_cambuli.data<-merge(ES_E14,iCDX2_ES_MEF,by='ensGeneID')
+rownames(ESCdx2MEF_cambuli.data)<-ESCdx2MEF_cambuli.data$ensGeneID
+ESCdx2MEF_cambuli.data<-ESCdx2MEF_cambuli.data[,-1]
+ESCdx2MEF_info.df<-data.frame('condition'=c('ES_E14','iCDX2_ES_MEF'),'type'=c('SR','SR'))
+rownames(ESCdx2MEF_info.df)<-colnames(ESCdx2MEF_cambuli.data)[c(1,2)]
+ESCdx2MEF_dds<-DESeqDataSetFromMatrix(countData=ESCdx2MEF_cambuli.data,colData=ESCdx2MEF_info.df,design = ~ condition)
+ESCdx2MEF_rld<-rlogTransformation(ESCdx2MEF_dds)
+ESCdx2MEF_res<-data.frame(assay(ESCdx2MEF_rld),avgLogExpr=(assay(ESCdx2MEF_rld)[,2]+assay(ESCdx2MEF_rld)[,1])/2,rLogFC=assay(ESCdx2MEF_rld)[,2]-assay(ESCdx2MEF_rld)[,1])
+ESCdx2MEF_res<-ESCdx2MEF_res[order(ESCdx2MEF_res$rLogFC,decreasing=TRUE),]
+colnames(ESCdx2MEF_res)<-paste('ESCdx2MEF',colnames(ESCdx2MEF_res),sep="_")
+#iCDX2-TC vs ES-J1
+ESCdx2TC_cambuli.data<-merge(ES_J1,iCDX2_ES_TC,by='ensGeneID')
+rownames(ESCdx2TC_cambuli.data)<-ESCdx2TC_cambuli.data$ensGeneID
+ESCdx2TC_cambuli.data<-ESCdx2TC_cambuli.data[,-1]
+ESCdx2TC_info.df<-data.frame('condition'=c('ES_J1','iCDX2_ES_TC'),'type'=c('SR','SR'))
+rownames(ESCdx2TC_info.df)<-colnames(ESCdx2TC_cambuli.data)[c(1,2)]
+ESCdx2TC_dds<-DESeqDataSetFromMatrix(countData=ESCdx2TC_cambuli.data,colData=ESCdx2TC_info.df,design = ~ condition)
+ESCdx2TC_rld<-rlogTransformation(ESCdx2TC_dds)
+ESCdx2TC_res<-data.frame(assay(ESCdx2TC_rld),avgLogExpr=(assay(ESCdx2TC_rld)[,2]+assay(ESCdx2TC_rld)[,1])/2,rLogFC=assay(ESCdx2TC_rld)[,2]-assay(ESCdx2TC_rld)[,1])
+ESCdx2TC_res<-ESCdx2TC_res[order(ESCdx2TC_res$rLogFC,decreasing=TRUE),]
+colnames(ESCdx2TC_res)<-paste('ESCdx2TC',colnames(ESCdx2TC_res),sep="_")
+#TS-eGFP vs ES-J1
+TS_cambuli.data<-merge(TS_EGFP,ES_J1,by='ensGeneID')
+rownames(TS_cambuli.data)<-TS_cambuli.data$ensGeneID
+TS_cambuli.data<-TS_cambuli.data[,-1]
+TS_info.df<-data.frame('condition'=c('ES_J1','TS_EGFP'),'type'=c('SR','SR'))
+rownames(TS_info.df)<-colnames(TS_cambuli.data)[c(1,2)]
+TS_dds<-DESeqDataSetFromMatrix(countData=TS_cambuli.data,colData=TS_info.df,design = ~ condition)
+TS_rld<-rlogTransformation(TS_dds)
+TS_res<-data.frame(assay(TS_rld),avgLogExpr=(assay(TS_rld)[,2]+assay(TS_rld)[,1])/2,rLogFC=assay(TS_rld)[,2]-assay(TS_rld)[,1])
+TS_res<-TS_res[order(TS_res$rLogFC,decreasing=TRUE),]
+colnames(TS_res)<-paste('TS',colnames(TS_res),sep="_")
+cambuli.res<-merge(ESCdx2MEF_res,ESCdx2TC_res,by="row.names")
+rownames(cambuli.res)<-cambuli.res$Row.names
+cambuli.res<-cambuli.res[,-1]
+cambuli.res<-merge(cambuli.res,TS_res,by="row.names")
+source("Annotation_Rfunctions.R")
+cambuli.res.anno<-AddAnnotation.FUN(df=cambuli.res,byEnsemblID=TRUE,EnsemblIDc=1,martID="mmusculus_gene_ensembl")
+cambuli.res.anno.su<-subset(cambuli.res.anno,
+cambuli.res.anno$ESCdx2MEF_ES_E14 >= 0 &
+cambuli.res.anno$ESCdx2MEF_iCDX2_ES_MEF >= 0 &
+cambuli.res.anno$ESCdx2TC_ES_J1 >= 0 &
+cambuli.res.anno$ESCdx2TC_iCDX2_ES_TC >= 0 &
+cambuli.res.anno$TS_TS_EGFP >= 0 &
+cambuli.res.anno$TS_ES_J1 >= 0
+)

code/R_scripts/Generic_DifferentialExpression.R

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+#!/usr/bin/env Rscript
+####################################
+#############PARAMETERS#############
+####################################
+annotation.file=""#two columns (Ensembl ID and gene symbol) tsv file
+wd=""#with count files from HTseq
+conditions=c("")
+comparison=c("condition","","")
+prefix=''
+Number.Samples=
+Number.Conditions=
+Qt.CutOff=
+minReplicates=
+pAdjustMethod=""#e.g. fdr, BH, ...
+GLM=#TRUE or FALSE
+####################################
+####################################
+suppressMessages(library('DESeq2'))
+suppressMessages(library("gplots"))
+suppressMessages(library("ggplot2"))
+AddGeneName<-function(l){
+	a<-gDE$symbol[gDE$id %in% l[1]];
+	if(length(a) == 0){
+		return("NA")
+	}else{
+		(return(as.character(a[1])))
+	}
+}
+####################################
+####################################
+sampleTable<-as.data.frame(cbind('sampleName'=c(as.vector(sapply(as.vector(grep(".count",list.files(wd),value=TRUE)),function(x) unlist(strsplit(x,"[.]"))[1]))),'fileName'=grep(".count",list.files(wd),value=TRUE),'condition'=conditions))
+print(sampleTable)
+ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable,directory=wd,design = ~condition)
+if(GLM == TRUE){
+	ddsHTSeq<-DESeq(ddsHTSeq)
+}else{
+	cat("No GLM\n")
+	ddsHTSeq<-DESeq(ddsHTSeq,betaPrior=FALSE)
+}
+cat("Size factors\n")
+print(sizeFactors(ddsHTSeq))
+normCount<-counts(ddsHTSeq,normalized=TRUE)
+colnames(normCount)<-paste(colnames(normCount),'_nCOUNT',sep="")
+rawCount<-counts(ddsHTSeq)
+colnames(rawCount)<-paste(colnames(rawCount),'_rCOUNT',sep="")
+vsd<-assay(varianceStabilizingTransformation(ddsHTSeq))
+colnames(vsd)<-paste(colnames(vsd),'_vsd',sep="")
+rld<-assay(rlog(ddsHTSeq))
+colnames(rld)<-paste(colnames(rld),'_rld',sep="")
+diff.exp<-results(ddsHTSeq,contrast=comparison,pAdjustMethod=pAdjustMethod,cooksCutoff=FALSE)
+flag<-c()
+if(minReplicates >= 3){
+	cat("Flaging outliers\n")
+	ddsHTSeq.outliers<-replaceOutliers(ddsHTSeq, minReplicates = minReplicates,cooksCutoff=qf(Qt.CutOff,Number.Conditions,Number.Samples-Number.Conditions))
+	flag<-as.data.frame(mcols(ddsHTSeq.outliers)$replace)
+	rownames(flag)<-rownames(normCount)
+	colnames(flag)<-'FLAG'
+}else{
+	flag<-as.data.frame(rep(NA,times=nrow(diff.exp)))
+	rownames(flag)<-rownames(normCount)
+	colnames(flag)<-'FLAG'
+}
+merged.df<-merge(diff.exp,normCount,by="row.names",all=TRUE)
+rownames(merged.df)<-merged.df[,1]
+merged.df<-merged.df[,-1]
+merged.df<-merge(merged.df,vsd,by="row.names",all=TRUE)
+rownames(merged.df)<-merged.df[,1]
+merged.df<-merged.df[,-1]
+merged.df<-merge(merged.df,rld,by="row.names",all=TRUE)
+rownames(merged.df)<-merged.df[,1]
+merged.df<-merged.df[,-1]
+merged.df<-merge(merged.df,rawCount,by="row.names",all=TRUE)
+rownames(merged.df)<-merged.df[,1]
+merged.df<-merged.df[,-1]
+merged.df<-merge(merged.df,flag,by="row.names",all=TRUE)
+colnames(merged.df)[1]<-'GeneID'
+if(file.exists(annotation.file)){
+	cat("Adding Gene Names\n")
+	gDE<-read.csv(annotation.file,header=TRUE,sep="\t")
+	colnames(gDE)<-c('GeneID','symbol','name')
+	merged.df<-merge(merged.df,gDE[,c(1,2)],by="GeneID",all.x=TRUE)
+	#merged.df<-cbind(merged.df,apply(merged.df,1,function(l) AddGeneName(l)))
+}
+merged.df<-merged.df[order(merged.df$padj,decreasing=FALSE),]
+cat("Significant DEG: ",nrow(subset(merged.df,merged.df$padj <= 0.05)),"\n")
+write.table(merged.df,file=paste(wd,"/",prefix,"_All_MisRegulated_Genes.csv",sep=""),quote=FALSE,sep="\t",row.names=FALSE,col.names=TRUE)
+dists<-dist(t(as.matrix(vsd)))
+tm<-as.matrix(dists)
+colnames(tm)<-sampleTable$sampleName
+rownames(tm)<-sampleTable$sampleName
+pdf(paste(wd,"/",prefix,"_Expression_Heatmap.pdf", sep = ""))
+heatmap.2(tm,margins=c(15,15),trace="none",density.info="none")
+dev.off()
+cat("Done\n")
+save.image(file=paste(wd, "/env.RData", sep = ""),safe=TRUE)

code/R_scripts/Generic_Rfunctions.R

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+##R functions
+####
+##Find overlapp between genomic coordinates
+####
+suppressMessages(library('GenomicRanges'))
+suppressMessages(library('IRanges'))
+suppressMessages(library('GenomicFeatures'))
+oCT=10
+ByOverlap.FUN<-function(
+gr.object=NA,
+gr.annotation=NA,
+st=NA
+){
+	tmp.0<-as.data.frame(gr.annotation,row.names=NULL)
+	gr.df<-as.data.frame(gr.object)
+	gr.df<-cbind(gr.df,'id'=paste("tagid_",1:nrow(gr.df),sep=""))
+	annotation.df<-as.data.frame(gr.annotation)
+	annotation.df<-as.data.frame(findOverlaps(gr.object,gr.annotation,ignore.strand=st,type="any",select="arbitrary",minoverlap=oCT))
+	annotation.df<-cbind(annotation.df,'id'=paste("tagid_",1:nrow(annotation.df),sep=""))
+	tmp.1<-subset(tmp.0,rownames(tmp.0) %in% annotation.df[,1])
+	tmp.2<-cbind(rownames(tmp.1),tmp.1)
+	colnames(tmp.2)[1]<-colnames(annotation.df)[1]
+	M.0<-merge(annotation.df,tmp.2,by=colnames(annotation.df)[1],all.x=TRUE)
+	colnames(M.0)<-c('annoRoW',colnames(M.0)[2],paste('Annotation',colnames(M.0)[-c(1,2)],sep="_"))
+	M.1<-merge(gr.df,M.0[,-1],by='id',all.x=TRUE)
+	return(M.1[,-1])
+}
+##Annotate feature directly before genomic coordinates
+precede.FUN<-function(
+	gr.object=NA,
+	gr.annotation=NA
+){
+	tmp.0<-as.data.frame(gr.annotation,row.names=NULL)	
+	gr.df<-as.data.frame(gr.object)
+	gr.df<-cbind(gr.df,'id'=paste("tagid_",1:nrow(gr.df),sep=""))
+	annotation.df<-as.data.frame(gr.annotation)	
+	annotation.df<-as.data.frame(precede(gr.object,gr.annotation,ignore.strand=TRUE))
+	annotation.df<-cbind(annotation.df,'id'=paste("tagid_",1:nrow(annotation.df),sep=""))
+	tmp.1<-subset(tmp.0,rownames(tmp.0) %in% annotation.df[,1])
+	tmp.2<-cbind(rownames(tmp.1),tmp.1)
+	colnames(tmp.2)[1]<-colnames(annotation.df)[1]
+	M.0<-merge(annotation.df,tmp.2,by=colnames(annotation.df)[1],all.x=TRUE)
+	colnames(M.0)<-c('annoRoW',colnames(M.0)[2],paste('Annotation_following',colnames(M.0)[-c(1,2)],sep="_"))
+	M.1<-merge(gr.df,M.0[,-1],by='id',all.x=TRUE)
+	return(M.1)
+}
+##follow.FUN: to find the annotation feature righ after an intergenic query, MUST BE STRANDED!!!
+follow.FUN<-function(
+	gr.object=NA,
+	gr.annotation=NA
+){
+	tmp.0<-as.data.frame(gr.annotation,row.names=NULL)	
+	gr.df<-as.data.frame(gr.object)
+	gr.df<-cbind(gr.df,'id'=paste("tagid_",1:nrow(gr.df),sep=""))
+	annotation.df<-as.data.frame(gr.annotation)	
+	annotation.df<-as.data.frame(follow(gr.object,gr.annotation,ignore.strand=TRUE))
+	annotation.df<-cbind(annotation.df,'id'=paste("tagid_",1:nrow(annotation.df),sep=""))
+	tmp.1<-subset(tmp.0,rownames(tmp.0) %in% annotation.df[,1])
+	tmp.2<-cbind(rownames(tmp.1),tmp.1)
+	colnames(tmp.2)[1]<-colnames(annotation.df)[1]
+	M.0<-merge(annotation.df,tmp.2,by=colnames(annotation.df)[1],all.x=TRUE)
+	colnames(M.0)<-c('annoRoW',colnames(M.0)[2],paste('Annotation_preceding',colnames(M.0)[-c(1,2)],sep="_"))
+	M.1<-merge(gr.df,M.0[,-1],by='id',all.x=TRUE)
+	return(M.1)
+}

code/R_scripts/Rhee_DifferentialExpression.R

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+###
+suppressMessages(library(DESeq2))
+#
+ES_cells<-read.csv("ES_cells.count",header=FALSE,sep="\t")
+ES_cells<-ES_cells[grep('ENSMUS',ES_cells$V1),]
+colnames(ES_cells)<-c('ensGeneID','ES')
+OECdx2_ES<-read.csv("OECdx2_ES.count",header=FALSE,sep="\t")
+OECdx2_ES<-OECdx2_ES[grep('ENSMUS',OECdx2_ES$V1),]
+colnames(OECdx2_ES)<-c('ensGeneID','OECdx2')
+TS_cells<-read.csv("TS_cells.count",header=FALSE,sep="\t")
+TS_cells<-TS_cells[grep('ENSMUS',TS_cells$V1),]
+colnames(TS_cells)<-c('ensGeneID','TS')
+OECdx2_TS<-read.csv("OECdx2_TS.count",header=FALSE,sep="\t")
+OECdx2_TS<-OECdx2_TS[grep('ENSMUS',OECdx2_TS$V1),]
+colnames(OECdx2_TS)<-c('ensGeneID','OECdx2')
+#ESCdx2 vs ES
+ESCdx2_rhee.data<-merge(ES_cells,OECdx2_ES,by='ensGeneID')
+rownames(ESCdx2_rhee.data)<-ESCdx2_rhee.data$ensGeneID
+ESCdx2_rhee.data<-ESCdx2_rhee.data[,-1]
+ESCdx2_info.df<-data.frame('condition'=c('ES','ESCdx2'),'type'=c('SR','SR'))
+rownames(ESCdx2_info.df)<-colnames(ESCdx2_rhee.data)[c(1,2)]
+ESCdx2_dds<-DESeqDataSetFromMatrix(countData=ESCdx2_rhee.data,colData=ESCdx2_info.df,design = ~ condition)
+ESCdx2_rld<-rlogTransformation(ESCdx2_dds)
+ESCdx2_res<-data.frame(assay(ESCdx2_rld),avgLogExpr=(assay(ESCdx2_rld)[,2]+assay(ESCdx2_rld)[,1])/2,rLogFC=assay(ESCdx2_rld)[,2]-assay(ESCdx2_rld)[,1])
+ESCdx2_res<-ESCdx2_res[order(ESCdx2_res$rLogFC,decreasing=TRUE),]
+colnames(ESCdx2_res)<-paste('ESCdx2',colnames(ESCdx2_res),sep="_")
+#TSCdx2 vs ES
+TSCdx2_rhee.data<-merge(ES_cells,OECdx2_TS,by='ensGeneID')
+rownames(TSCdx2_rhee.data)<-TSCdx2_rhee.data$ensGeneID
+TSCdx2_rhee.data<-TSCdx2_rhee.data[,-1]
+TSCdx2_info.df<-data.frame('condition'=c('ES','TSCdx2'),'type'=c('SR','SR'))
+rownames(TSCdx2_info.df)<-colnames(TSCdx2_rhee.data)[c(1,2)]
+TSCdx2_dds<-DESeqDataSetFromMatrix(countData=TSCdx2_rhee.data,colData=TSCdx2_info.df,design = ~ condition)
+TSCdx2_rld<-rlogTransformation(TSCdx2_dds)
+TSCdx2_res<-data.frame(assay(TSCdx2_rld),avgLogExpr=(assay(TSCdx2_rld)[,2]+assay(TSCdx2_rld)[,1])/2,rLogFC=assay(TSCdx2_rld)[,2]-assay(TSCdx2_rld)[,1])
+TSCdx2_res<-TSCdx2_res[order(TSCdx2_res$rLogFC,decreasing=TRUE),]
+colnames(TSCdx2_res)<-paste('TSCdx2',colnames(TSCdx2_res),sep="_")
+#TS vs ES
+TS_rhee.data<-merge(ES_cells,TS_cells,by='ensGeneID')
+rownames(TS_rhee.data)<-TS_rhee.data$ensGeneID
+TS_rhee.data<-TS_rhee.data[,-1]
+TS_info.df<-data.frame('condition'=c('ES','TS'),'type'=c('SR','SR'))
+rownames(TS_info.df)<-colnames(TS_rhee.data)[c(1,2)]
+TS_dds<-DESeqDataSetFromMatrix(countData=TS_rhee.data,colData=TS_info.df,design = ~ condition)
+TS_rld<-rlogTransformation(TS_dds)
+TS_res<-data.frame(assay(TS_rld),avgLogExpr=(assay(TS_rld)[,2]+assay(TS_rld)[,1])/2,rLogFC=assay(TS_rld)[,2]-assay(TS_rld)[,1])
+TS_res<-TS_res[order(TS_res$rLogFC,decreasing=TRUE),]
+colnames(TS_res)<-paste('TS',colnames(TS_res),sep="_")
+Rhee.res<-merge(ESCdx2_res,TSCdx2_res,by="row.names")
+rownames(Rhee.res)<-Rhee.res$Row.names
+Rhee.res<-Rhee.res[,-1]
+Rhee.res<-merge(Rhee.res,TS_res,by="row.names")
+Rhee.res.anno<-AddAnnotation.FUN(df=Rhee.res,byEnsemblID=TRUE,EnsemblIDc=1,martID="mmusculus_gene_ensembl")
+Rhee.res.anno.su<-subset(Rhee.res.anno,
+Rhee.res.anno$ESCdx2_ES >=0 &
+Rhee.res.anno$ESCdx2_OECdx2 >=0 &
+Rhee.res.anno$TSCdx2_ES >=0 &
+Rhee.res.anno$TSCdx2_OECdx2 >=0 &
+Rhee.res.anno$TS_ES >=0 &
+Rhee.res.anno$TS_TS >=0
+)
+############################
+############################
+#ESCdx2 vs TS
+ESCdx2_rhee.data<-merge(TS_cells,OECdx2_ES,by='ensGeneID')
+rownames(ESCdx2_rhee.data)<-ESCdx2_rhee.data$ensGeneID
+ESCdx2_rhee.data<-ESCdx2_rhee.data[,-1]
+ESCdx2_info.df<-data.frame('condition'=c('TS','ESCdx2'),'type'=c('SR','SR'))
+rownames(ESCdx2_info.df)<-colnames(ESCdx2_rhee.data)[c(1,2)]
+ESCdx2_dds<-DESeqDataSetFromMatrix(countData=ESCdx2_rhee.data,colData=ESCdx2_info.df,design = ~ condition)
+ESCdx2_rld<-rlogTransformation(ESCdx2_dds)
+ESCdx2_res<-data.frame(assay(ESCdx2_rld),avgLogExpr=(assay(ESCdx2_rld)[,2]+assay(ESCdx2_rld)[,1])/2,rLogFC=assay(ESCdx2_rld)[,2]-assay(ESCdx2_rld)[,1])
+ESCdx2_res<-ESCdx2_res[order(ESCdx2_res$rLogFC,decreasing=TRUE),]
+colnames(ESCdx2_res)<-paste('ESCdx2',colnames(ESCdx2_res),sep="_")
+#TSCdx2 vs TS
+TSCdx2_rhee.data<-merge(TS_cells,OECdx2_TS,by='ensGeneID')
+rownames(TSCdx2_rhee.data)<-TSCdx2_rhee.data$ensGeneID
+TSCdx2_rhee.data<-TSCdx2_rhee.data[,-1]
+TSCdx2_info.df<-data.frame('condition'=c('TS','TSCdx2'),'type'=c('SR','SR'))
+rownames(TSCdx2_info.df)<-colnames(TSCdx2_rhee.data)[c(1,2)]
+TSCdx2_dds<-DESeqDataSetFromMatrix(countData=TSCdx2_rhee.data,colData=TSCdx2_info.df,design = ~ condition)
+TSCdx2_rld<-rlogTransformation(TSCdx2_dds)
+TSCdx2_res<-data.frame(assay(TSCdx2_rld),avgLogExpr=(assay(TSCdx2_rld)[,2]+assay(TSCdx2_rld)[,1])/2,rLogFC=assay(TSCdx2_rld)[,2]-assay(TSCdx2_rld)[,1])
+TSCdx2_res<-TSCdx2_res[order(TSCdx2_res$rLogFC,decreasing=TRUE),]
+colnames(TSCdx2_res)<-paste('TSCdx2',colnames(TSCdx2_res),sep="_")