diff --git a/scripts/edgeR_output.R b/scripts/edgeR_output.R
new file mode 100644
index 0000000..05f1b4a
--- /dev/null
+++ b/scripts/edgeR_output.R
@@ -0,0 +1,138 @@
+
+library(edgeR)
+
+isopret.input=NULL
+
+args=commandArgs(trailingOnly = TRUE)
+
+#args[1] - path to RSEM control samples
+#args[2] - path to RSEM case samples
+
+#Assemble RSEM output files into a matrix of counts in 'countsData'
+
+countsData=NULL
+
+labels=c()
+
+for (next.file.name in list.files(args[1],full.names = TRUE))
+{
+
+  if (is.null(countsData))
+  {
+    countsData=read.table(next.file.name,header=TRUE)[c(2,1)]
+  }
+    next.file=read.table(next.file.name,header=TRUE)
+
+    countsData=cbind(countsData,next.file$expected_count)
+
+    labels=c(labels,1)
+}
+
+for (next.file.name in list.files(args[2],full.names = TRUE))
+{
+   next.file=read.table(next.file.name,header=TRUE)
+
+   countsData=cbind(countsData,next.file$expected_count)
+
+   labels=c(labels,2)
+}
+
+countsData=countsData[rowSums(countsData[,-c(1,2)]>0)==ncol(countsData)-2,]
+#run edgeR on isoform counts
+
+isoform.counts=countsData[,-c(1,2)]
+
+rownames(isoform.counts)=paste(countsData[,1],countsData[,2])
+
+y=DGEList(counts=isoform.counts,group=labels)
+
+keep=filterByExpr(y)
+
+y=y[keep,,keep.lib.size=FALSE]
+
+y=calcNormFactors(y)
+
+design=model.matrix(~labels)
+
+y=estimateDisp(y,design)
+
+fit=glmFit(y,design)
+
+lrt=glmLRT(fit,coef=2)
+
+edger.out=(topTags(lrt,n = nrow(y))$table)
+
+isopret.input=edger.out[,c(1,5)]
+
+isopret.input[,1]=2^isopret.input[,1]
+
+isopret.input=cbind(do.call(rbind,lapply(rownames(edger.out),
+                              function(x)c(unlist(strsplit(x,split=' '))[1],unlist(strsplit(x,split=' '))[2]))),isopret.input)
+
+rownames(isopret.input)=NULL
+
+num.iso=unlist(lapply(isopret.input[,1],function(x){sum(isopret.input[,1]==x)}))
+
+no.iso.genes=unlist(lapply(split(isopret.input,isopret.input[,1]),function(m)nrow(m)==1))
+
+isopret.input=isopret.input[isopret.input[,1] %in% names(no.iso.genes)[no.iso.genes==FALSE],]
+
+countsData=countsData[countsData[,1] %in% isopret.input[,1],]
+
+#run edgeR on gene counts
+
+gene.counts=do.call(rbind,lapply(split(countsData,countsData[,1]),function(m){
+
+  res=matrix(colSums(m[,-c(1,2)]),nrow=1)
+
+  rownames(res)=m[1,1]
+
+  res
+
+}))
+
+y=DGEList(counts=gene.counts,group=labels)
+
+keep=filterByExpr(y)
+
+y=y[keep,,keep.lib.size=FALSE]
+
+y=calcNormFactors(y)
+
+design=model.matrix(~labels)
+
+y=estimateDisp(y,design)
+
+fit=glmFit(y,design)
+
+lrt=glmLRT(fit,coef=2)
+
+edger.out=(topTags(lrt,n = nrow(y))$table)
+
+gene.results=do.call(rbind,lapply(rownames(edger.out),function(x){
+
+  v=edger.out[rownames(edger.out)==x,]
+
+  matrix(unlist(c(rownames(v),'Expression',v[1],v[5])),nrow=1)
+
+}))
+
+colnames(gene.results)=colnames(isopret.input)
+
+isopret.input=rbind(isopret.input,gene.results)
+
+isopret.input=isopret.input[order(isopret.input[,1]),]
+
+colnames(isopret.input)=c('Gene','Isoform','ExplogFC/FC','BH')
+
+isopret.input$`ExplogFC/FC`=as.numeric(isopret.input$`ExplogFC/FC`)
+
+isopret.input$BH=as.numeric(isopret.input$BH)
+
+isopret.input$BH[abs(isopret.input$`ExplogFC/FC`)<log2(1.5)]=1
+
+isopret.input$`ExplogFC/FC`[isopret.input$Isoform!='Expression']=2^isopret.input$`ExplogFC/FC`[isopret.input$Isoform!='Expression']
+
+write.table(isopret.input,'Isopret_Input.txt',
+            sep='\t',quote = F,col.names = T,row.names = F)
+