diff --git a/diff_exp/DEA_htseq.R b/diff_exp/DEA_htseq.R
deleted file mode 100755
index 058dd1817f6d448baf3a9c84fdd22f8fc197ad3b..0000000000000000000000000000000000000000
--- a/diff_exp/DEA_htseq.R
+++ /dev/null
@@ -1,236 +0,0 @@
-#!/cm/shared/apps/R/intel/3.2.1/bin/Rscript
-library(edgeR)
-library(DESeq2)
-library("RColorBrewer")
-library("gplots")
-library(qusage)
-
-rowMax <- function(x) apply(x,1,max)
-col.grp <- function(n,b) {
- colorvec <- vector(mode="character", length=length(n))
- plotcols <- rainbow(length(b))
- for (i in 1:length(n)) {
- for (j in 1:length(b)) {
- if ( n[i] == b[j] ) {
- colorvec[i] = plotcols[j]
- }
- }
- }
- c(colorvec)
-}
-
-
-#################### Read in Data ################################
-genenames <- read.table(file="genenames.txt",header=TRUE,sep='\t')
-
-tbl <- read.table('countTable.txt',header=TRUE,sep="\t")
-tbl2 <- read.table('countTable.logCPM.txt',header=TRUE,sep="\t")
-ct <- tbl[,4:length(tbl)]
-row.names(ct) <- tbl$ENSEMBL
-
-samples<- names(ct)
-
-dtbl <- read.table('design.txt',header=TRUE,sep="\t")
-samtbl <- merge(as.data.frame(samples),dtbl,by.x="samples",by.y="SampleID",all.x=TRUE,sort=FALSE)
-grpnames <- levels(factor(as.character(samtbl$SampleGroup)))
-samtbl$SampleGroup <- factor(samtbl$SampleGroup, levels=grpnames)
-
-colData <- samtbl[c('SampleGroup','SubjectID')]
-row.names(colData) <- samtbl$samples
-
-dds <- DESeqDataSetFromMatrix(countData=ct,colData= colData,design= ~ SampleGroup)
-dds <- dds[ rowMax(counts(dds)) > 30, ]
-dds <- dds[ colSums(counts(dds)) > 1000000]
-
-if (nrow(counts(dds)) < 1) {
-print(paste("Samples are filtered if there is < 1M reads. There are less than no remaining sample(s) after this filter",sep=' '))
-q()
-}
-
-countTable <- counts(dds)
-#grps <- as.character(levels(colData(dds)$SampleGroup))
-grps <- as.character(colData(dds)$SampleGroup)
-col.blocks <-col.grp(grps,levels(factor(grps)))
-libSizes <- as.vector(colSums(countTable))
-
-logcpm <- tbl2[,4:length(tbl2)]
-row.names(logcpm) <- tbl2$SYMBOL
-
-tmp.tab <- aggregate(t(logcpm),by=list(as.character(samtbl$SampleGroup)),FUN=mean)
-row.names(tmp.tab) <- tmp.tab$Group.1
-mean.by.group <- round(log2(t(tmp.tab[,c(2:ncol(tmp.tab))])+1), digits = 2)
-
-#################### Run DESEQ2 ################################
-dds <- DESeq(dds)
-rld <- rlogTransformation(dds, blind=TRUE)
-sampleDists <- dist(t(assay(rld)))
-
-png(file="samples_heatmap.png",bg ="transparent",height=768,width=1024)
-heatmap.2(as.matrix(sampleDists), col = bluered(100),RowSideColors = col.blocks,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
-dev.off()
-
-#Compare Samples using PCA
-png(file="pca.png",bg ="transparent",height=768,width=1024)
-print(plotPCA(rld, intgroup="SampleGroup"),col.hab=col.blocks)
-dev.off()
-
-#Do all pairwise comparisons
-contrast <- resultsNames(dds)
-cond<- levels(colData(dds)$SampleGroup)
-a <- length(cond)-1
-for (i in 1:a) {
- for (j in 2:length(cond)) {
- if (i == j) {
- next
- } else {
- res <- as.data.frame(results(dds,contrast=c("SampleGroup",cond[i],cond[j])))
- res2 <- merge(genenames,res,by.x='ensembl',by.y='row.names',all.y=TRUE,all.x=FALSE)
- output <- merge(res2,mean.by.group,by.y="row.names",by.x='symbol')
- output$rawP <- output$pvalue
- output$logFC <- output$log2FoldChange
- output$fdr <- output$padj
- output$bonf <- p.adjust(output$rawP, method ='bonferroni')
- write.table(output,file=paste(cond[i],'_',cond[j],'.deseq2.txt',sep=""),quote=FALSE,row.names=FALSE,sep='\t')
- filt.out <- na.omit(output[output$fdr < 0.05,])
- if (nrow(filt.out) > 2) {
- subset <- logcpm[row.names(logcpm) %in% filt.out$symbol,]
- gnames <- filt.out[c('ensembl','symbol')]
- s <- merge(gnames,subset,by.x="ensembl",by.y="row.names",all.x=FALSE,all.y=TRUE,sort=FALSE)
- STREE <- hclust(dist(t(subset)))
- zscores <- scale(t(subset))
- ngenes <- length(colnames(zscores))
- textscale <- (1/(ngenes/30))
- if (textscale > 1) {
- textscale <-1
- }
- if (textscale < 0.1) {
- textscale <- 0.1
- }
- png(file=paste(cond[i],'_',cond[j],'.heatmap.deseq2.png',sep=""),height=768,width=1024)
- heatmap.2(zscores, col = bluered(100),Rowv = as.dendrogram(STREE), RowSideColors = col.blocks,dendrogram='row', cexCol=textscale,labCol=s$symbol,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
- legend("topright",legend=grpnames,col=rainbow(length(grpnames)),pch=20,cex=0.5)
- dev.off()
- }
- }
- }
-}
-
-###################### Run EdgeR ########################
-###################### Run QuSage ######################
-
-MSIG.geneSets <- read.gmt('geneset.gmt')
-
-design <- model.matrix(~grps)
-d <- DGEList(counts=countTable,group=grps,lib.size=libSizes)
-d <- calcNormFactors(d)
-d <- estimateCommonDisp(d)
-png(file="mds.png",bg ="transparent",height=768,width=1024)
-plotMDS(d, labels=grps,col=col.blocks)
-legend("topleft",legend=grpnames,col=rainbow(length(grpnames)),pch=20)
-dev.off()
-cond <-levels(d$samples$group)
-colnames(design) <- levels(d$samples$group)
-a <- length(cond)-1
-for (i in 1:a) {
- for (j in 2:length(cond)) {
- if (i == j) {
- next
- } else {
- c <- exactTest(d, pair=c(cond[j],cond[i]))
- res <- c$table
- res2 <- merge(genenames,res,by.x='ensembl',by.y='row.names',all.y=TRUE,all.x=FALSE)
- output <- merge(res2,mean.by.group,by.y="row.names",by.x='symbol')
- output$rawP <- output$PValue
- output$logFC <- output$logFC
- output$fdr <- p.adjust(output$rawP, method ='fdr')
- output$bonf <- p.adjust(output$rawP, method ='bonferroni')
- write.table(output,file=paste(cond[i],'_',cond[j],'.edgeR.txt',sep=""),quote=FALSE,row.names=FALSE,sep='\t')
- filt.out <- na.omit(output[output$fdr < 0.05,])
- if (nrow(filt.out) > 2) {
- subset <- logcpm[row.names(logcpm) %in% filt.out$symbol,]
- gnames <- filt.out[c('ensembl','symbol')]
- s <- merge(gnames,subset,by.x="ensembl",by.y="row.names",all.x=FALSE,all.y=TRUE,sort=FALSE)
- STREE <- hclust(dist(t(subset)))
- zscores <- scale(t(subset))
- ngenes <- length(colnames(zscores))
- textscale <- (1/(ngenes/30))
- if (textscale > 1) {
- textscale <-1
- }
- if (textscale < 0.1) {
- textscale <- 0.1
- }
- png(file=paste(cond[i],'_',cond[j],'.heatmap.edgeR.png',sep=""),height=768,width=1024)
- heatmap.2(zscores, col = bluered(100),Rowv = as.dendrogram(STREE), RowSideColors = col.blocks,dendrogram='row', cexCol=textscale,labCol=s$symbol,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
- legend("topright",legend=grpnames,col=rainbow(length(grpnames)),pch=20,cex=0.5)
- dev.off()
- gcont <- paste(cond[j],cond[i],sep='-')
- qs.results = qusage(logcpm, grps,gcont,MSIG.geneSets)
- save(qs.results,file=paste(cond[i],'_',cond[j],'.qusage.rda',sep=""))
- }
- }
- }
-}
-
-###################### Run Limma VOOM ########################
-
-# design <- model.matrix(~0+grps)
-# colnames(design) <- grpnames
-# a <- length(cond)-1
-# design.pairs <- c()
-# k <- 0
-# for (i in 1:a) {
-# for (j in 2:length(cond)) {
-# if (i == j) {
-# next
-# } else {
-# k <- k+1
-# design.pairs[k] <- paste(cond[i],'-',cond[j],sep='')
-# }
-# }
-# }
-
-#contrast.matrix <- makeContrasts(design.pairs,levels=design)
-
-#d <- DGEList(counts=countTable,group=grps,lib.size=libSizes)
-#d <- calcNormFactors(d)
-#d <- estimateCommonDisp(d)
-
-# v <- voom(d,design,plot=TRUE)
-# fit <- lmFit(v,design)
-# fit2 <- contrasts.fit(fit, contrast.matrix)
-# fit2 <- eBayes(fit2)
-
-# comps <- colnames(fit2$coefficients)
-# for (i in 1:length(comps)) {
-# res <- topTable(fit2,coef=i,number=Inf,sort.by="P")
-# res2 <- merge(genenames,res,by.x='ensembl',by.y='row.names',all.y=TRUE,all.x=FALSE)
-# output <- merge(res2,mean.by.group,by.y="row.names",by.x='symbol')
-# output$rawP <- output$P.Value
-# output$logFC <- output$adj.P.Val
-# output$fdr <- p.adjust(output$rawP, method ='fdr')
-# output$bonf <- p.adjust(output$rawP, method ='bonferroni')
-# write.table(output,file=paste(cond[i],'_',cond[j],'.voom.txt',sep=""),quote=FALSE,row.names=FALSE,sep='\t')
-# filt.out <- na.omit(output[output$fdr < 0.05,])
-# if (nrow(filt.out) > 2) {
-# subset <- logcpm[row.names(logcpm) %in% filt.out$ensembl,]
-# gnames <- filt.out[c('ensembl','symbol')]
-# s <- merge(gnames,subset,by.x="ensembl",by.y="row.names",all.x=FALSE,all.y=TRUE,sort=FALSE)
-# STREE <- hclust(dist(t(subset)))
-# zscores <- scale(t(subset))
-# ngenes <- length(colnames(zscores))
-# textscale <- (1/(ngenes/30))
-# if (textscale > 1) {
-# textscale <-1
-# }
-# if (textscale < 0.1) {
-# textscale <- 0.1
-# }
-# png(file=paste(cond[i],'_',cond[j],'.heatmap.voom.png',sep=""),height=768,width=1024)
-# heatmap.2(zscores, col = bluered(100),Rowv = as.dendrogram(STREE), RowSideColors = col.blocks,dendrogram='row', cexCol=textscale,labCol=s$symbol,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
-# legend("topright",legend=grpnames,col=rainbow(length(grpnames)),pch=20,cex=0.5)
-# dev.off()
-# }
-# }
-
-
diff --git a/diff_exp/DEA_htseq.R b/diff_exp/DEA_htseq.R
new file mode 120000
index 0000000000000000000000000000000000000000..c7e331c456c6c43b82300cb40a88762a0f849218
--- /dev/null
+++ b/diff_exp/DEA_htseq.R
@@ -0,0 +1 @@
+../genect_rnaseq/DEA_htseq.R
\ No newline at end of file
diff --git a/diff_exp/build_ballgown.R b/diff_exp/build_ballgown.R
deleted file mode 100755
index b74bfa29b69e8c491e23545e0bf249cd781ef7f0..0000000000000000000000000000000000000000
--- a/diff_exp/build_ballgown.R
+++ /dev/null
@@ -1,16 +0,0 @@
-library(ballgown)
-
-args<-commandArgs(TRUE)
-
-sample_dirs <- args
-samples <- gsub('_stringtie','',sample_dirs)
-design <- "design.txt"
-
-samtbl <- read.table(file=design,header=TRUE,sep='\t')
-
-bg <- ballgown(samples=sample_dirs, meas='all')
-samples <- gsub('_stringtie','',sampleNames(bg))
-mergetbl <- merge(as.data.frame(samples),samtbl,by.x="samples",by.y="SampleID",all.x=TRUE,sort=FALSE)
-pData(bg) = data.frame(id=samples, group=as.character(mergetbl$SampleGroup))
-
-save(bg,file="bg.rda")
diff --git a/diff_exp/build_ballgown.R b/diff_exp/build_ballgown.R
new file mode 120000
index 0000000000000000000000000000000000000000..9a7a90160726d8e6ead4cbb016e7de28473fae53
--- /dev/null
+++ b/diff_exp/build_ballgown.R
@@ -0,0 +1 @@
+../genect_rnaseq/build_ballgown.R
\ No newline at end of file
diff --git a/diff_exp/concat_edgeR.pl b/diff_exp/concat_edgeR.pl
deleted file mode 100755
index e736e822cc07a4225d19bdb936a7f6f3fc342055..0000000000000000000000000000000000000000
--- a/diff_exp/concat_edgeR.pl
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/perl -w
-#concat_edgeR.pl
-
-#symbol chrom start end ensembl type logFC logCPM PValue monocytes neutrophils rawP fdr bonf
-
-my @files = @ARGV;
-open OUT, ">edgeR.results.txt" or die $!;
-print OUT join("\t",'G1','G2','ENSEMBL','SYMBOL','TYPE','logFC','logCPM',
- 'pval','fdr','G1.Mean','G2.Mean'),"\n";
-foreach $f (@files) {
- open IN, "<$f" or die $!;
- my ($fname,$ext) = split(/\.edgeR\./,$f);
- $fname =~ s/edgeR\///;
- my ($g1,$g2) = split(/_/,$fname);
- my $head = <IN>;
- chomp($head);
- my @colnames = split(/\t/,$head);
- while (my $line = <IN>) {
- chomp($line);
- my @row = split(/\t/,$line);
- my %hash;
- foreach my $i (0..$#row) {
- $hash{$colnames[$i]} = $row[$i];
- }
- print OUT join("\t",$g1,$g2,$hash{ensembl},$hash{symbol},$hash{type},
- sprintf("%.2f",$hash{logFC}),
- sprintf("%.2f",$hash{logCPM}),
- sprintf("%.1e",$hash{rawP}),sprintf("%.1e",$hash{fdr}),
- sprintf("%.0f",$hash{$g1}),sprintf("%.0f",$hash{$g2})),"\n";
- }
-}
diff --git a/diff_exp/concat_edgeR.pl b/diff_exp/concat_edgeR.pl
new file mode 120000
index 0000000000000000000000000000000000000000..f6a17c415097ac34a5301c6452e7fc6a743efe26
--- /dev/null
+++ b/diff_exp/concat_edgeR.pl
@@ -0,0 +1 @@
+../genect_rnaseq/concat_edgeR.pl
\ No newline at end of file
diff --git a/diff_exp/dea.R b/diff_exp/dea.R
deleted file mode 100755
index 058dd1817f6d448baf3a9c84fdd22f8fc197ad3b..0000000000000000000000000000000000000000
--- a/diff_exp/dea.R
+++ /dev/null
@@ -1,236 +0,0 @@
-#!/cm/shared/apps/R/intel/3.2.1/bin/Rscript
-library(edgeR)
-library(DESeq2)
-library("RColorBrewer")
-library("gplots")
-library(qusage)
-
-rowMax <- function(x) apply(x,1,max)
-col.grp <- function(n,b) {
- colorvec <- vector(mode="character", length=length(n))
- plotcols <- rainbow(length(b))
- for (i in 1:length(n)) {
- for (j in 1:length(b)) {
- if ( n[i] == b[j] ) {
- colorvec[i] = plotcols[j]
- }
- }
- }
- c(colorvec)
-}
-
-
-#################### Read in Data ################################
-genenames <- read.table(file="genenames.txt",header=TRUE,sep='\t')
-
-tbl <- read.table('countTable.txt',header=TRUE,sep="\t")
-tbl2 <- read.table('countTable.logCPM.txt',header=TRUE,sep="\t")
-ct <- tbl[,4:length(tbl)]
-row.names(ct) <- tbl$ENSEMBL
-
-samples<- names(ct)
-
-dtbl <- read.table('design.txt',header=TRUE,sep="\t")
-samtbl <- merge(as.data.frame(samples),dtbl,by.x="samples",by.y="SampleID",all.x=TRUE,sort=FALSE)
-grpnames <- levels(factor(as.character(samtbl$SampleGroup)))
-samtbl$SampleGroup <- factor(samtbl$SampleGroup, levels=grpnames)
-
-colData <- samtbl[c('SampleGroup','SubjectID')]
-row.names(colData) <- samtbl$samples
-
-dds <- DESeqDataSetFromMatrix(countData=ct,colData= colData,design= ~ SampleGroup)
-dds <- dds[ rowMax(counts(dds)) > 30, ]
-dds <- dds[ colSums(counts(dds)) > 1000000]
-
-if (nrow(counts(dds)) < 1) {
-print(paste("Samples are filtered if there is < 1M reads. There are less than no remaining sample(s) after this filter",sep=' '))
-q()
-}
-
-countTable <- counts(dds)
-#grps <- as.character(levels(colData(dds)$SampleGroup))
-grps <- as.character(colData(dds)$SampleGroup)
-col.blocks <-col.grp(grps,levels(factor(grps)))
-libSizes <- as.vector(colSums(countTable))
-
-logcpm <- tbl2[,4:length(tbl2)]
-row.names(logcpm) <- tbl2$SYMBOL
-
-tmp.tab <- aggregate(t(logcpm),by=list(as.character(samtbl$SampleGroup)),FUN=mean)
-row.names(tmp.tab) <- tmp.tab$Group.1
-mean.by.group <- round(log2(t(tmp.tab[,c(2:ncol(tmp.tab))])+1), digits = 2)
-
-#################### Run DESEQ2 ################################
-dds <- DESeq(dds)
-rld <- rlogTransformation(dds, blind=TRUE)
-sampleDists <- dist(t(assay(rld)))
-
-png(file="samples_heatmap.png",bg ="transparent",height=768,width=1024)
-heatmap.2(as.matrix(sampleDists), col = bluered(100),RowSideColors = col.blocks,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
-dev.off()
-
-#Compare Samples using PCA
-png(file="pca.png",bg ="transparent",height=768,width=1024)
-print(plotPCA(rld, intgroup="SampleGroup"),col.hab=col.blocks)
-dev.off()
-
-#Do all pairwise comparisons
-contrast <- resultsNames(dds)
-cond<- levels(colData(dds)$SampleGroup)
-a <- length(cond)-1
-for (i in 1:a) {
- for (j in 2:length(cond)) {
- if (i == j) {
- next
- } else {
- res <- as.data.frame(results(dds,contrast=c("SampleGroup",cond[i],cond[j])))
- res2 <- merge(genenames,res,by.x='ensembl',by.y='row.names',all.y=TRUE,all.x=FALSE)
- output <- merge(res2,mean.by.group,by.y="row.names",by.x='symbol')
- output$rawP <- output$pvalue
- output$logFC <- output$log2FoldChange
- output$fdr <- output$padj
- output$bonf <- p.adjust(output$rawP, method ='bonferroni')
- write.table(output,file=paste(cond[i],'_',cond[j],'.deseq2.txt',sep=""),quote=FALSE,row.names=FALSE,sep='\t')
- filt.out <- na.omit(output[output$fdr < 0.05,])
- if (nrow(filt.out) > 2) {
- subset <- logcpm[row.names(logcpm) %in% filt.out$symbol,]
- gnames <- filt.out[c('ensembl','symbol')]
- s <- merge(gnames,subset,by.x="ensembl",by.y="row.names",all.x=FALSE,all.y=TRUE,sort=FALSE)
- STREE <- hclust(dist(t(subset)))
- zscores <- scale(t(subset))
- ngenes <- length(colnames(zscores))
- textscale <- (1/(ngenes/30))
- if (textscale > 1) {
- textscale <-1
- }
- if (textscale < 0.1) {
- textscale <- 0.1
- }
- png(file=paste(cond[i],'_',cond[j],'.heatmap.deseq2.png',sep=""),height=768,width=1024)
- heatmap.2(zscores, col = bluered(100),Rowv = as.dendrogram(STREE), RowSideColors = col.blocks,dendrogram='row', cexCol=textscale,labCol=s$symbol,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
- legend("topright",legend=grpnames,col=rainbow(length(grpnames)),pch=20,cex=0.5)
- dev.off()
- }
- }
- }
-}
-
-###################### Run EdgeR ########################
-###################### Run QuSage ######################
-
-MSIG.geneSets <- read.gmt('geneset.gmt')
-
-design <- model.matrix(~grps)
-d <- DGEList(counts=countTable,group=grps,lib.size=libSizes)
-d <- calcNormFactors(d)
-d <- estimateCommonDisp(d)
-png(file="mds.png",bg ="transparent",height=768,width=1024)
-plotMDS(d, labels=grps,col=col.blocks)
-legend("topleft",legend=grpnames,col=rainbow(length(grpnames)),pch=20)
-dev.off()
-cond <-levels(d$samples$group)
-colnames(design) <- levels(d$samples$group)
-a <- length(cond)-1
-for (i in 1:a) {
- for (j in 2:length(cond)) {
- if (i == j) {
- next
- } else {
- c <- exactTest(d, pair=c(cond[j],cond[i]))
- res <- c$table
- res2 <- merge(genenames,res,by.x='ensembl',by.y='row.names',all.y=TRUE,all.x=FALSE)
- output <- merge(res2,mean.by.group,by.y="row.names",by.x='symbol')
- output$rawP <- output$PValue
- output$logFC <- output$logFC
- output$fdr <- p.adjust(output$rawP, method ='fdr')
- output$bonf <- p.adjust(output$rawP, method ='bonferroni')
- write.table(output,file=paste(cond[i],'_',cond[j],'.edgeR.txt',sep=""),quote=FALSE,row.names=FALSE,sep='\t')
- filt.out <- na.omit(output[output$fdr < 0.05,])
- if (nrow(filt.out) > 2) {
- subset <- logcpm[row.names(logcpm) %in% filt.out$symbol,]
- gnames <- filt.out[c('ensembl','symbol')]
- s <- merge(gnames,subset,by.x="ensembl",by.y="row.names",all.x=FALSE,all.y=TRUE,sort=FALSE)
- STREE <- hclust(dist(t(subset)))
- zscores <- scale(t(subset))
- ngenes <- length(colnames(zscores))
- textscale <- (1/(ngenes/30))
- if (textscale > 1) {
- textscale <-1
- }
- if (textscale < 0.1) {
- textscale <- 0.1
- }
- png(file=paste(cond[i],'_',cond[j],'.heatmap.edgeR.png',sep=""),height=768,width=1024)
- heatmap.2(zscores, col = bluered(100),Rowv = as.dendrogram(STREE), RowSideColors = col.blocks,dendrogram='row', cexCol=textscale,labCol=s$symbol,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
- legend("topright",legend=grpnames,col=rainbow(length(grpnames)),pch=20,cex=0.5)
- dev.off()
- gcont <- paste(cond[j],cond[i],sep='-')
- qs.results = qusage(logcpm, grps,gcont,MSIG.geneSets)
- save(qs.results,file=paste(cond[i],'_',cond[j],'.qusage.rda',sep=""))
- }
- }
- }
-}
-
-###################### Run Limma VOOM ########################
-
-# design <- model.matrix(~0+grps)
-# colnames(design) <- grpnames
-# a <- length(cond)-1
-# design.pairs <- c()
-# k <- 0
-# for (i in 1:a) {
-# for (j in 2:length(cond)) {
-# if (i == j) {
-# next
-# } else {
-# k <- k+1
-# design.pairs[k] <- paste(cond[i],'-',cond[j],sep='')
-# }
-# }
-# }
-
-#contrast.matrix <- makeContrasts(design.pairs,levels=design)
-
-#d <- DGEList(counts=countTable,group=grps,lib.size=libSizes)
-#d <- calcNormFactors(d)
-#d <- estimateCommonDisp(d)
-
-# v <- voom(d,design,plot=TRUE)
-# fit <- lmFit(v,design)
-# fit2 <- contrasts.fit(fit, contrast.matrix)
-# fit2 <- eBayes(fit2)
-
-# comps <- colnames(fit2$coefficients)
-# for (i in 1:length(comps)) {
-# res <- topTable(fit2,coef=i,number=Inf,sort.by="P")
-# res2 <- merge(genenames,res,by.x='ensembl',by.y='row.names',all.y=TRUE,all.x=FALSE)
-# output <- merge(res2,mean.by.group,by.y="row.names",by.x='symbol')
-# output$rawP <- output$P.Value
-# output$logFC <- output$adj.P.Val
-# output$fdr <- p.adjust(output$rawP, method ='fdr')
-# output$bonf <- p.adjust(output$rawP, method ='bonferroni')
-# write.table(output,file=paste(cond[i],'_',cond[j],'.voom.txt',sep=""),quote=FALSE,row.names=FALSE,sep='\t')
-# filt.out <- na.omit(output[output$fdr < 0.05,])
-# if (nrow(filt.out) > 2) {
-# subset <- logcpm[row.names(logcpm) %in% filt.out$ensembl,]
-# gnames <- filt.out[c('ensembl','symbol')]
-# s <- merge(gnames,subset,by.x="ensembl",by.y="row.names",all.x=FALSE,all.y=TRUE,sort=FALSE)
-# STREE <- hclust(dist(t(subset)))
-# zscores <- scale(t(subset))
-# ngenes <- length(colnames(zscores))
-# textscale <- (1/(ngenes/30))
-# if (textscale > 1) {
-# textscale <-1
-# }
-# if (textscale < 0.1) {
-# textscale <- 0.1
-# }
-# png(file=paste(cond[i],'_',cond[j],'.heatmap.voom.png',sep=""),height=768,width=1024)
-# heatmap.2(zscores, col = bluered(100),Rowv = as.dendrogram(STREE), RowSideColors = col.blocks,dendrogram='row', cexCol=textscale,labCol=s$symbol,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
-# legend("topright",legend=grpnames,col=rainbow(length(grpnames)),pch=20,cex=0.5)
-# dev.off()
-# }
-# }
-
-
diff --git a/diff_exp/dea.R b/diff_exp/dea.R
new file mode 120000
index 0000000000000000000000000000000000000000..3c564e6b4a23f0fcba9f3d0287b5ad404759b834
--- /dev/null
+++ b/diff_exp/dea.R
@@ -0,0 +1 @@
+../genect_rnaseq/dea.R
\ No newline at end of file
diff --git a/diff_exp/gencode_genename.pl b/diff_exp/gencode_genename.pl
deleted file mode 100755
index f2a81f5a6b3214dfad1de511839c8a53b49a8024..0000000000000000000000000000000000000000
--- a/diff_exp/gencode_genename.pl
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/usr/bin/perl -w
-#parse_gencode.pl
-
-open OUT, ">genenames.txt" or die $!;
-print OUT join("\t",'chrom','start','end','ensembl','symbol','type'),"\n";
-my $gtf = shift @ARGV;
-open GCODE, "<$gtf" or die $!;
-while (my $line = <GCODE>) {
- chomp($line);
- next if ($line =~ m/^#/);
- my ($chrom,$source,$feature,$start,$end,$filter,$phase,$frame,$info) =
- split(/\t/,$line);
- next unless ($feature eq 'gene');
- $info =~ s/\"//g;
- my %hash;
- foreach $a (split(/;\s*/,$info)) {
- my ($key,$val) = split(/ /,$a);
- $hash{$key} = $val;
- }
- $hash{gene_id} =~ s/\.\d+//;
- print OUT join("\t",$chrom,$start,$end,$hash{gene_id},$hash{gene_name},$hash{gene_type}),"\n";
-}
diff --git a/diff_exp/gencode_genename.pl b/diff_exp/gencode_genename.pl
new file mode 120000
index 0000000000000000000000000000000000000000..2cd704644eb079851e1fa46f634b0c4660f8f721
--- /dev/null
+++ b/diff_exp/gencode_genename.pl
@@ -0,0 +1 @@
+../genect_rnaseq/gencode_genename.pl
\ No newline at end of file
diff --git a/diff_exp/geneabundance.sh b/diff_exp/geneabundance.sh
deleted file mode 100644
index e763ef7c10a6e3ec2f595fd1deb97f99d435b1c7..0000000000000000000000000000000000000000
--- a/diff_exp/geneabundance.sh
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/bin/bash
-#rnaseqalign.sh
-
-usage() {
- echo "-h Help documentation for hisat.sh"
- echo "-b --BAM File"
- echo "-g --GTF File"
- echo "-s --stranded"
- echo "-p --Prefix for output file name"
- echo "Example: bash geneabudance.sh genect_rnaseq/geneabundance.sh -s stranded -g gtf_file -p pair_id -b bam"
- exit 1
-}
-OPTIND=1 # Reset OPTIND
-while getopts :b:g:p:s:h opt
-do
- case $opt in
- b) sbam=$OPTARG;;
- g) gtf=$OPTARG;;
- p) pair_id=$OPTARG;;
- s) stranded=$OPTARG;;
- h) usage;;
- esac
-done
-
-shift $(($OPTIND -1))
-
-# Check for mandatory options
-if [[ -z $pair_id ]] || [[ -z $sbam ]]
-then
- usage
-fi
-if [[ -z $SLURM_CPUS_ON_NODE ]]
-then
- SLURM_CPUS_ON_NODE=1
-fi
-if [[ $SLURM_CPUS_ON_NODE > 64 ]]
-then
- SLURM_CPUS_ON_NODE=64
-fi
-source /etc/profile.d/modules.sh
-module load subread/1.6.1 stringtie/1.3.2d-intel
-featureCounts -s $stranded -M --fraction -J --ignoreDup -T $SLURM_CPUS_ON_NODE -p -g gene_name -a ${gtf} -o ${pair_id}.cts ${sbam}
-mkdir ${pair_id}_stringtie
-cd ${pair_id}_stringtie
-stringtie ../${sbam} -p $SLURM_CPUS_ON_NODE -G ${gtf} -B -e -o denovo.gtf -A ../${pair_id}.fpkm.txt
-
diff --git a/diff_exp/geneabundance.sh b/diff_exp/geneabundance.sh
new file mode 120000
index 0000000000000000000000000000000000000000..831e0e8fbadd7a08da89004eaf6fe13319109849
--- /dev/null
+++ b/diff_exp/geneabundance.sh
@@ -0,0 +1 @@
+../genect_rnaseq/geneabundance.sh
\ No newline at end of file
diff --git a/genect_rnaseq/DEA_htseq.R b/genect_rnaseq/DEA_htseq.R
new file mode 100755
index 0000000000000000000000000000000000000000..058dd1817f6d448baf3a9c84fdd22f8fc197ad3b
--- /dev/null
+++ b/genect_rnaseq/DEA_htseq.R
@@ -0,0 +1,236 @@
+#!/cm/shared/apps/R/intel/3.2.1/bin/Rscript
+library(edgeR)
+library(DESeq2)
+library("RColorBrewer")
+library("gplots")
+library(qusage)
+
+rowMax <- function(x) apply(x,1,max)
+col.grp <- function(n,b) {
+ colorvec <- vector(mode="character", length=length(n))
+ plotcols <- rainbow(length(b))
+ for (i in 1:length(n)) {
+ for (j in 1:length(b)) {
+ if ( n[i] == b[j] ) {
+ colorvec[i] = plotcols[j]
+ }
+ }
+ }
+ c(colorvec)
+}
+
+
+#################### Read in Data ################################
+genenames <- read.table(file="genenames.txt",header=TRUE,sep='\t')
+
+tbl <- read.table('countTable.txt',header=TRUE,sep="\t")
+tbl2 <- read.table('countTable.logCPM.txt',header=TRUE,sep="\t")
+ct <- tbl[,4:length(tbl)]
+row.names(ct) <- tbl$ENSEMBL
+
+samples<- names(ct)
+
+dtbl <- read.table('design.txt',header=TRUE,sep="\t")
+samtbl <- merge(as.data.frame(samples),dtbl,by.x="samples",by.y="SampleID",all.x=TRUE,sort=FALSE)
+grpnames <- levels(factor(as.character(samtbl$SampleGroup)))
+samtbl$SampleGroup <- factor(samtbl$SampleGroup, levels=grpnames)
+
+colData <- samtbl[c('SampleGroup','SubjectID')]
+row.names(colData) <- samtbl$samples
+
+dds <- DESeqDataSetFromMatrix(countData=ct,colData= colData,design= ~ SampleGroup)
+dds <- dds[ rowMax(counts(dds)) > 30, ]
+dds <- dds[ colSums(counts(dds)) > 1000000]
+
+if (nrow(counts(dds)) < 1) {
+print(paste("Samples are filtered if there is < 1M reads. There are less than no remaining sample(s) after this filter",sep=' '))
+q()
+}
+
+countTable <- counts(dds)
+#grps <- as.character(levels(colData(dds)$SampleGroup))
+grps <- as.character(colData(dds)$SampleGroup)
+col.blocks <-col.grp(grps,levels(factor(grps)))
+libSizes <- as.vector(colSums(countTable))
+
+logcpm <- tbl2[,4:length(tbl2)]
+row.names(logcpm) <- tbl2$SYMBOL
+
+tmp.tab <- aggregate(t(logcpm),by=list(as.character(samtbl$SampleGroup)),FUN=mean)
+row.names(tmp.tab) <- tmp.tab$Group.1
+mean.by.group <- round(log2(t(tmp.tab[,c(2:ncol(tmp.tab))])+1), digits = 2)
+
+#################### Run DESEQ2 ################################
+dds <- DESeq(dds)
+rld <- rlogTransformation(dds, blind=TRUE)
+sampleDists <- dist(t(assay(rld)))
+
+png(file="samples_heatmap.png",bg ="transparent",height=768,width=1024)
+heatmap.2(as.matrix(sampleDists), col = bluered(100),RowSideColors = col.blocks,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
+dev.off()
+
+#Compare Samples using PCA
+png(file="pca.png",bg ="transparent",height=768,width=1024)
+print(plotPCA(rld, intgroup="SampleGroup"),col.hab=col.blocks)
+dev.off()
+
+#Do all pairwise comparisons
+contrast <- resultsNames(dds)
+cond<- levels(colData(dds)$SampleGroup)
+a <- length(cond)-1
+for (i in 1:a) {
+ for (j in 2:length(cond)) {
+ if (i == j) {
+ next
+ } else {
+ res <- as.data.frame(results(dds,contrast=c("SampleGroup",cond[i],cond[j])))
+ res2 <- merge(genenames,res,by.x='ensembl',by.y='row.names',all.y=TRUE,all.x=FALSE)
+ output <- merge(res2,mean.by.group,by.y="row.names",by.x='symbol')
+ output$rawP <- output$pvalue
+ output$logFC <- output$log2FoldChange
+ output$fdr <- output$padj
+ output$bonf <- p.adjust(output$rawP, method ='bonferroni')
+ write.table(output,file=paste(cond[i],'_',cond[j],'.deseq2.txt',sep=""),quote=FALSE,row.names=FALSE,sep='\t')
+ filt.out <- na.omit(output[output$fdr < 0.05,])
+ if (nrow(filt.out) > 2) {
+ subset <- logcpm[row.names(logcpm) %in% filt.out$symbol,]
+ gnames <- filt.out[c('ensembl','symbol')]
+ s <- merge(gnames,subset,by.x="ensembl",by.y="row.names",all.x=FALSE,all.y=TRUE,sort=FALSE)
+ STREE <- hclust(dist(t(subset)))
+ zscores <- scale(t(subset))
+ ngenes <- length(colnames(zscores))
+ textscale <- (1/(ngenes/30))
+ if (textscale > 1) {
+ textscale <-1
+ }
+ if (textscale < 0.1) {
+ textscale <- 0.1
+ }
+ png(file=paste(cond[i],'_',cond[j],'.heatmap.deseq2.png',sep=""),height=768,width=1024)
+ heatmap.2(zscores, col = bluered(100),Rowv = as.dendrogram(STREE), RowSideColors = col.blocks,dendrogram='row', cexCol=textscale,labCol=s$symbol,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
+ legend("topright",legend=grpnames,col=rainbow(length(grpnames)),pch=20,cex=0.5)
+ dev.off()
+ }
+ }
+ }
+}
+
+###################### Run EdgeR ########################
+###################### Run QuSage ######################
+
+MSIG.geneSets <- read.gmt('geneset.gmt')
+
+design <- model.matrix(~grps)
+d <- DGEList(counts=countTable,group=grps,lib.size=libSizes)
+d <- calcNormFactors(d)
+d <- estimateCommonDisp(d)
+png(file="mds.png",bg ="transparent",height=768,width=1024)
+plotMDS(d, labels=grps,col=col.blocks)
+legend("topleft",legend=grpnames,col=rainbow(length(grpnames)),pch=20)
+dev.off()
+cond <-levels(d$samples$group)
+colnames(design) <- levels(d$samples$group)
+a <- length(cond)-1
+for (i in 1:a) {
+ for (j in 2:length(cond)) {
+ if (i == j) {
+ next
+ } else {
+ c <- exactTest(d, pair=c(cond[j],cond[i]))
+ res <- c$table
+ res2 <- merge(genenames,res,by.x='ensembl',by.y='row.names',all.y=TRUE,all.x=FALSE)
+ output <- merge(res2,mean.by.group,by.y="row.names",by.x='symbol')
+ output$rawP <- output$PValue
+ output$logFC <- output$logFC
+ output$fdr <- p.adjust(output$rawP, method ='fdr')
+ output$bonf <- p.adjust(output$rawP, method ='bonferroni')
+ write.table(output,file=paste(cond[i],'_',cond[j],'.edgeR.txt',sep=""),quote=FALSE,row.names=FALSE,sep='\t')
+ filt.out <- na.omit(output[output$fdr < 0.05,])
+ if (nrow(filt.out) > 2) {
+ subset <- logcpm[row.names(logcpm) %in% filt.out$symbol,]
+ gnames <- filt.out[c('ensembl','symbol')]
+ s <- merge(gnames,subset,by.x="ensembl",by.y="row.names",all.x=FALSE,all.y=TRUE,sort=FALSE)
+ STREE <- hclust(dist(t(subset)))
+ zscores <- scale(t(subset))
+ ngenes <- length(colnames(zscores))
+ textscale <- (1/(ngenes/30))
+ if (textscale > 1) {
+ textscale <-1
+ }
+ if (textscale < 0.1) {
+ textscale <- 0.1
+ }
+ png(file=paste(cond[i],'_',cond[j],'.heatmap.edgeR.png',sep=""),height=768,width=1024)
+ heatmap.2(zscores, col = bluered(100),Rowv = as.dendrogram(STREE), RowSideColors = col.blocks,dendrogram='row', cexCol=textscale,labCol=s$symbol,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
+ legend("topright",legend=grpnames,col=rainbow(length(grpnames)),pch=20,cex=0.5)
+ dev.off()
+ gcont <- paste(cond[j],cond[i],sep='-')
+ qs.results = qusage(logcpm, grps,gcont,MSIG.geneSets)
+ save(qs.results,file=paste(cond[i],'_',cond[j],'.qusage.rda',sep=""))
+ }
+ }
+ }
+}
+
+###################### Run Limma VOOM ########################
+
+# design <- model.matrix(~0+grps)
+# colnames(design) <- grpnames
+# a <- length(cond)-1
+# design.pairs <- c()
+# k <- 0
+# for (i in 1:a) {
+# for (j in 2:length(cond)) {
+# if (i == j) {
+# next
+# } else {
+# k <- k+1
+# design.pairs[k] <- paste(cond[i],'-',cond[j],sep='')
+# }
+# }
+# }
+
+#contrast.matrix <- makeContrasts(design.pairs,levels=design)
+
+#d <- DGEList(counts=countTable,group=grps,lib.size=libSizes)
+#d <- calcNormFactors(d)
+#d <- estimateCommonDisp(d)
+
+# v <- voom(d,design,plot=TRUE)
+# fit <- lmFit(v,design)
+# fit2 <- contrasts.fit(fit, contrast.matrix)
+# fit2 <- eBayes(fit2)
+
+# comps <- colnames(fit2$coefficients)
+# for (i in 1:length(comps)) {
+# res <- topTable(fit2,coef=i,number=Inf,sort.by="P")
+# res2 <- merge(genenames,res,by.x='ensembl',by.y='row.names',all.y=TRUE,all.x=FALSE)
+# output <- merge(res2,mean.by.group,by.y="row.names",by.x='symbol')
+# output$rawP <- output$P.Value
+# output$logFC <- output$adj.P.Val
+# output$fdr <- p.adjust(output$rawP, method ='fdr')
+# output$bonf <- p.adjust(output$rawP, method ='bonferroni')
+# write.table(output,file=paste(cond[i],'_',cond[j],'.voom.txt',sep=""),quote=FALSE,row.names=FALSE,sep='\t')
+# filt.out <- na.omit(output[output$fdr < 0.05,])
+# if (nrow(filt.out) > 2) {
+# subset <- logcpm[row.names(logcpm) %in% filt.out$ensembl,]
+# gnames <- filt.out[c('ensembl','symbol')]
+# s <- merge(gnames,subset,by.x="ensembl",by.y="row.names",all.x=FALSE,all.y=TRUE,sort=FALSE)
+# STREE <- hclust(dist(t(subset)))
+# zscores <- scale(t(subset))
+# ngenes <- length(colnames(zscores))
+# textscale <- (1/(ngenes/30))
+# if (textscale > 1) {
+# textscale <-1
+# }
+# if (textscale < 0.1) {
+# textscale <- 0.1
+# }
+# png(file=paste(cond[i],'_',cond[j],'.heatmap.voom.png',sep=""),height=768,width=1024)
+# heatmap.2(zscores, col = bluered(100),Rowv = as.dendrogram(STREE), RowSideColors = col.blocks,dendrogram='row', cexCol=textscale,labCol=s$symbol,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
+# legend("topright",legend=grpnames,col=rainbow(length(grpnames)),pch=20,cex=0.5)
+# dev.off()
+# }
+# }
+
+
diff --git a/genect_rnaseq/build_ballgown.R b/genect_rnaseq/build_ballgown.R
new file mode 100755
index 0000000000000000000000000000000000000000..b74bfa29b69e8c491e23545e0bf249cd781ef7f0
--- /dev/null
+++ b/genect_rnaseq/build_ballgown.R
@@ -0,0 +1,16 @@
+library(ballgown)
+
+args<-commandArgs(TRUE)
+
+sample_dirs <- args
+samples <- gsub('_stringtie','',sample_dirs)
+design <- "design.txt"
+
+samtbl <- read.table(file=design,header=TRUE,sep='\t')
+
+bg <- ballgown(samples=sample_dirs, meas='all')
+samples <- gsub('_stringtie','',sampleNames(bg))
+mergetbl <- merge(as.data.frame(samples),samtbl,by.x="samples",by.y="SampleID",all.x=TRUE,sort=FALSE)
+pData(bg) = data.frame(id=samples, group=as.character(mergetbl$SampleGroup))
+
+save(bg,file="bg.rda")
diff --git a/genect_rnaseq/concat_edgeR.pl b/genect_rnaseq/concat_edgeR.pl
new file mode 100755
index 0000000000000000000000000000000000000000..e736e822cc07a4225d19bdb936a7f6f3fc342055
--- /dev/null
+++ b/genect_rnaseq/concat_edgeR.pl
@@ -0,0 +1,31 @@
+#!/usr/bin/perl -w
+#concat_edgeR.pl
+
+#symbol chrom start end ensembl type logFC logCPM PValue monocytes neutrophils rawP fdr bonf
+
+my @files = @ARGV;
+open OUT, ">edgeR.results.txt" or die $!;
+print OUT join("\t",'G1','G2','ENSEMBL','SYMBOL','TYPE','logFC','logCPM',
+ 'pval','fdr','G1.Mean','G2.Mean'),"\n";
+foreach $f (@files) {
+ open IN, "<$f" or die $!;
+ my ($fname,$ext) = split(/\.edgeR\./,$f);
+ $fname =~ s/edgeR\///;
+ my ($g1,$g2) = split(/_/,$fname);
+ my $head = <IN>;
+ chomp($head);
+ my @colnames = split(/\t/,$head);
+ while (my $line = <IN>) {
+ chomp($line);
+ my @row = split(/\t/,$line);
+ my %hash;
+ foreach my $i (0..$#row) {
+ $hash{$colnames[$i]} = $row[$i];
+ }
+ print OUT join("\t",$g1,$g2,$hash{ensembl},$hash{symbol},$hash{type},
+ sprintf("%.2f",$hash{logFC}),
+ sprintf("%.2f",$hash{logCPM}),
+ sprintf("%.1e",$hash{rawP}),sprintf("%.1e",$hash{fdr}),
+ sprintf("%.0f",$hash{$g1}),sprintf("%.0f",$hash{$g2})),"\n";
+ }
+}
diff --git a/genect_rnaseq/dea.R b/genect_rnaseq/dea.R
new file mode 100755
index 0000000000000000000000000000000000000000..058dd1817f6d448baf3a9c84fdd22f8fc197ad3b
--- /dev/null
+++ b/genect_rnaseq/dea.R
@@ -0,0 +1,236 @@
+#!/cm/shared/apps/R/intel/3.2.1/bin/Rscript
+library(edgeR)
+library(DESeq2)
+library("RColorBrewer")
+library("gplots")
+library(qusage)
+
+rowMax <- function(x) apply(x,1,max)
+col.grp <- function(n,b) {
+ colorvec <- vector(mode="character", length=length(n))
+ plotcols <- rainbow(length(b))
+ for (i in 1:length(n)) {
+ for (j in 1:length(b)) {
+ if ( n[i] == b[j] ) {
+ colorvec[i] = plotcols[j]
+ }
+ }
+ }
+ c(colorvec)
+}
+
+
+#################### Read in Data ################################
+genenames <- read.table(file="genenames.txt",header=TRUE,sep='\t')
+
+tbl <- read.table('countTable.txt',header=TRUE,sep="\t")
+tbl2 <- read.table('countTable.logCPM.txt',header=TRUE,sep="\t")
+ct <- tbl[,4:length(tbl)]
+row.names(ct) <- tbl$ENSEMBL
+
+samples<- names(ct)
+
+dtbl <- read.table('design.txt',header=TRUE,sep="\t")
+samtbl <- merge(as.data.frame(samples),dtbl,by.x="samples",by.y="SampleID",all.x=TRUE,sort=FALSE)
+grpnames <- levels(factor(as.character(samtbl$SampleGroup)))
+samtbl$SampleGroup <- factor(samtbl$SampleGroup, levels=grpnames)
+
+colData <- samtbl[c('SampleGroup','SubjectID')]
+row.names(colData) <- samtbl$samples
+
+dds <- DESeqDataSetFromMatrix(countData=ct,colData= colData,design= ~ SampleGroup)
+dds <- dds[ rowMax(counts(dds)) > 30, ]
+dds <- dds[ colSums(counts(dds)) > 1000000]
+
+if (nrow(counts(dds)) < 1) {
+print(paste("Samples are filtered if there is < 1M reads. There are less than no remaining sample(s) after this filter",sep=' '))
+q()
+}
+
+countTable <- counts(dds)
+#grps <- as.character(levels(colData(dds)$SampleGroup))
+grps <- as.character(colData(dds)$SampleGroup)
+col.blocks <-col.grp(grps,levels(factor(grps)))
+libSizes <- as.vector(colSums(countTable))
+
+logcpm <- tbl2[,4:length(tbl2)]
+row.names(logcpm) <- tbl2$SYMBOL
+
+tmp.tab <- aggregate(t(logcpm),by=list(as.character(samtbl$SampleGroup)),FUN=mean)
+row.names(tmp.tab) <- tmp.tab$Group.1
+mean.by.group <- round(log2(t(tmp.tab[,c(2:ncol(tmp.tab))])+1), digits = 2)
+
+#################### Run DESEQ2 ################################
+dds <- DESeq(dds)
+rld <- rlogTransformation(dds, blind=TRUE)
+sampleDists <- dist(t(assay(rld)))
+
+png(file="samples_heatmap.png",bg ="transparent",height=768,width=1024)
+heatmap.2(as.matrix(sampleDists), col = bluered(100),RowSideColors = col.blocks,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
+dev.off()
+
+#Compare Samples using PCA
+png(file="pca.png",bg ="transparent",height=768,width=1024)
+print(plotPCA(rld, intgroup="SampleGroup"),col.hab=col.blocks)
+dev.off()
+
+#Do all pairwise comparisons
+contrast <- resultsNames(dds)
+cond<- levels(colData(dds)$SampleGroup)
+a <- length(cond)-1
+for (i in 1:a) {
+ for (j in 2:length(cond)) {
+ if (i == j) {
+ next
+ } else {
+ res <- as.data.frame(results(dds,contrast=c("SampleGroup",cond[i],cond[j])))
+ res2 <- merge(genenames,res,by.x='ensembl',by.y='row.names',all.y=TRUE,all.x=FALSE)
+ output <- merge(res2,mean.by.group,by.y="row.names",by.x='symbol')
+ output$rawP <- output$pvalue
+ output$logFC <- output$log2FoldChange
+ output$fdr <- output$padj
+ output$bonf <- p.adjust(output$rawP, method ='bonferroni')
+ write.table(output,file=paste(cond[i],'_',cond[j],'.deseq2.txt',sep=""),quote=FALSE,row.names=FALSE,sep='\t')
+ filt.out <- na.omit(output[output$fdr < 0.05,])
+ if (nrow(filt.out) > 2) {
+ subset <- logcpm[row.names(logcpm) %in% filt.out$symbol,]
+ gnames <- filt.out[c('ensembl','symbol')]
+ s <- merge(gnames,subset,by.x="ensembl",by.y="row.names",all.x=FALSE,all.y=TRUE,sort=FALSE)
+ STREE <- hclust(dist(t(subset)))
+ zscores <- scale(t(subset))
+ ngenes <- length(colnames(zscores))
+ textscale <- (1/(ngenes/30))
+ if (textscale > 1) {
+ textscale <-1
+ }
+ if (textscale < 0.1) {
+ textscale <- 0.1
+ }
+ png(file=paste(cond[i],'_',cond[j],'.heatmap.deseq2.png',sep=""),height=768,width=1024)
+ heatmap.2(zscores, col = bluered(100),Rowv = as.dendrogram(STREE), RowSideColors = col.blocks,dendrogram='row', cexCol=textscale,labCol=s$symbol,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
+ legend("topright",legend=grpnames,col=rainbow(length(grpnames)),pch=20,cex=0.5)
+ dev.off()
+ }
+ }
+ }
+}
+
+###################### Run EdgeR ########################
+###################### Run QuSage ######################
+
+MSIG.geneSets <- read.gmt('geneset.gmt')
+
+design <- model.matrix(~grps)
+d <- DGEList(counts=countTable,group=grps,lib.size=libSizes)
+d <- calcNormFactors(d)
+d <- estimateCommonDisp(d)
+png(file="mds.png",bg ="transparent",height=768,width=1024)
+plotMDS(d, labels=grps,col=col.blocks)
+legend("topleft",legend=grpnames,col=rainbow(length(grpnames)),pch=20)
+dev.off()
+cond <-levels(d$samples$group)
+colnames(design) <- levels(d$samples$group)
+a <- length(cond)-1
+for (i in 1:a) {
+ for (j in 2:length(cond)) {
+ if (i == j) {
+ next
+ } else {
+ c <- exactTest(d, pair=c(cond[j],cond[i]))
+ res <- c$table
+ res2 <- merge(genenames,res,by.x='ensembl',by.y='row.names',all.y=TRUE,all.x=FALSE)
+ output <- merge(res2,mean.by.group,by.y="row.names",by.x='symbol')
+ output$rawP <- output$PValue
+ output$logFC <- output$logFC
+ output$fdr <- p.adjust(output$rawP, method ='fdr')
+ output$bonf <- p.adjust(output$rawP, method ='bonferroni')
+ write.table(output,file=paste(cond[i],'_',cond[j],'.edgeR.txt',sep=""),quote=FALSE,row.names=FALSE,sep='\t')
+ filt.out <- na.omit(output[output$fdr < 0.05,])
+ if (nrow(filt.out) > 2) {
+ subset <- logcpm[row.names(logcpm) %in% filt.out$symbol,]
+ gnames <- filt.out[c('ensembl','symbol')]
+ s <- merge(gnames,subset,by.x="ensembl",by.y="row.names",all.x=FALSE,all.y=TRUE,sort=FALSE)
+ STREE <- hclust(dist(t(subset)))
+ zscores <- scale(t(subset))
+ ngenes <- length(colnames(zscores))
+ textscale <- (1/(ngenes/30))
+ if (textscale > 1) {
+ textscale <-1
+ }
+ if (textscale < 0.1) {
+ textscale <- 0.1
+ }
+ png(file=paste(cond[i],'_',cond[j],'.heatmap.edgeR.png',sep=""),height=768,width=1024)
+ heatmap.2(zscores, col = bluered(100),Rowv = as.dendrogram(STREE), RowSideColors = col.blocks,dendrogram='row', cexCol=textscale,labCol=s$symbol,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
+ legend("topright",legend=grpnames,col=rainbow(length(grpnames)),pch=20,cex=0.5)
+ dev.off()
+ gcont <- paste(cond[j],cond[i],sep='-')
+ qs.results = qusage(logcpm, grps,gcont,MSIG.geneSets)
+ save(qs.results,file=paste(cond[i],'_',cond[j],'.qusage.rda',sep=""))
+ }
+ }
+ }
+}
+
+###################### Run Limma VOOM ########################
+
+# design <- model.matrix(~0+grps)
+# colnames(design) <- grpnames
+# a <- length(cond)-1
+# design.pairs <- c()
+# k <- 0
+# for (i in 1:a) {
+# for (j in 2:length(cond)) {
+# if (i == j) {
+# next
+# } else {
+# k <- k+1
+# design.pairs[k] <- paste(cond[i],'-',cond[j],sep='')
+# }
+# }
+# }
+
+#contrast.matrix <- makeContrasts(design.pairs,levels=design)
+
+#d <- DGEList(counts=countTable,group=grps,lib.size=libSizes)
+#d <- calcNormFactors(d)
+#d <- estimateCommonDisp(d)
+
+# v <- voom(d,design,plot=TRUE)
+# fit <- lmFit(v,design)
+# fit2 <- contrasts.fit(fit, contrast.matrix)
+# fit2 <- eBayes(fit2)
+
+# comps <- colnames(fit2$coefficients)
+# for (i in 1:length(comps)) {
+# res <- topTable(fit2,coef=i,number=Inf,sort.by="P")
+# res2 <- merge(genenames,res,by.x='ensembl',by.y='row.names',all.y=TRUE,all.x=FALSE)
+# output <- merge(res2,mean.by.group,by.y="row.names",by.x='symbol')
+# output$rawP <- output$P.Value
+# output$logFC <- output$adj.P.Val
+# output$fdr <- p.adjust(output$rawP, method ='fdr')
+# output$bonf <- p.adjust(output$rawP, method ='bonferroni')
+# write.table(output,file=paste(cond[i],'_',cond[j],'.voom.txt',sep=""),quote=FALSE,row.names=FALSE,sep='\t')
+# filt.out <- na.omit(output[output$fdr < 0.05,])
+# if (nrow(filt.out) > 2) {
+# subset <- logcpm[row.names(logcpm) %in% filt.out$ensembl,]
+# gnames <- filt.out[c('ensembl','symbol')]
+# s <- merge(gnames,subset,by.x="ensembl",by.y="row.names",all.x=FALSE,all.y=TRUE,sort=FALSE)
+# STREE <- hclust(dist(t(subset)))
+# zscores <- scale(t(subset))
+# ngenes <- length(colnames(zscores))
+# textscale <- (1/(ngenes/30))
+# if (textscale > 1) {
+# textscale <-1
+# }
+# if (textscale < 0.1) {
+# textscale <- 0.1
+# }
+# png(file=paste(cond[i],'_',cond[j],'.heatmap.voom.png',sep=""),height=768,width=1024)
+# heatmap.2(zscores, col = bluered(100),Rowv = as.dendrogram(STREE), RowSideColors = col.blocks,dendrogram='row', cexCol=textscale,labCol=s$symbol,srtRow=45,srtCol=45,trace="none", margins=c(5, 5))
+# legend("topright",legend=grpnames,col=rainbow(length(grpnames)),pch=20,cex=0.5)
+# dev.off()
+# }
+# }
+
+
diff --git a/genect_rnaseq/gencode_genename.pl b/genect_rnaseq/gencode_genename.pl
new file mode 100755
index 0000000000000000000000000000000000000000..f2a81f5a6b3214dfad1de511839c8a53b49a8024
--- /dev/null
+++ b/genect_rnaseq/gencode_genename.pl
@@ -0,0 +1,22 @@
+#!/usr/bin/perl -w
+#parse_gencode.pl
+
+open OUT, ">genenames.txt" or die $!;
+print OUT join("\t",'chrom','start','end','ensembl','symbol','type'),"\n";
+my $gtf = shift @ARGV;
+open GCODE, "<$gtf" or die $!;
+while (my $line = <GCODE>) {
+ chomp($line);
+ next if ($line =~ m/^#/);
+ my ($chrom,$source,$feature,$start,$end,$filter,$phase,$frame,$info) =
+ split(/\t/,$line);
+ next unless ($feature eq 'gene');
+ $info =~ s/\"//g;
+ my %hash;
+ foreach $a (split(/;\s*/,$info)) {
+ my ($key,$val) = split(/ /,$a);
+ $hash{$key} = $val;
+ }
+ $hash{gene_id} =~ s/\.\d+//;
+ print OUT join("\t",$chrom,$start,$end,$hash{gene_id},$hash{gene_name},$hash{gene_type}),"\n";
+}
diff --git a/genect_rnaseq/geneabundance.sh b/genect_rnaseq/geneabundance.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e763ef7c10a6e3ec2f595fd1deb97f99d435b1c7
--- /dev/null
+++ b/genect_rnaseq/geneabundance.sh
@@ -0,0 +1,46 @@
+#!/bin/bash
+#rnaseqalign.sh
+
+usage() {
+ echo "-h Help documentation for hisat.sh"
+ echo "-b --BAM File"
+ echo "-g --GTF File"
+ echo "-s --stranded"
+ echo "-p --Prefix for output file name"
+ echo "Example: bash geneabudance.sh genect_rnaseq/geneabundance.sh -s stranded -g gtf_file -p pair_id -b bam"
+ exit 1
+}
+OPTIND=1 # Reset OPTIND
+while getopts :b:g:p:s:h opt
+do
+ case $opt in
+ b) sbam=$OPTARG;;
+ g) gtf=$OPTARG;;
+ p) pair_id=$OPTARG;;
+ s) stranded=$OPTARG;;
+ h) usage;;
+ esac
+done
+
+shift $(($OPTIND -1))
+
+# Check for mandatory options
+if [[ -z $pair_id ]] || [[ -z $sbam ]]
+then
+ usage
+fi
+if [[ -z $SLURM_CPUS_ON_NODE ]]
+then
+ SLURM_CPUS_ON_NODE=1
+fi
+if [[ $SLURM_CPUS_ON_NODE > 64 ]]
+then
+ SLURM_CPUS_ON_NODE=64
+fi
+source /etc/profile.d/modules.sh
+module load subread/1.6.1 stringtie/1.3.2d-intel
+featureCounts -s $stranded -M --fraction -J --ignoreDup -T $SLURM_CPUS_ON_NODE -p -g gene_name -a ${gtf} -o ${pair_id}.cts ${sbam}
+mkdir ${pair_id}_stringtie
+cd ${pair_id}_stringtie
+stringtie ../${sbam} -p $SLURM_CPUS_ON_NODE -G ${gtf} -B -e -o denovo.gtf -A ../${pair_id}.fpkm.txt
+
diff --git a/genect_rnaseq/statanal.sh b/genect_rnaseq/statanal.sh
index e221f1b8bd7b0fa72964cbf07a515065cef81988..ad5cab388e0f1edbe81cb8af30c4515dc6b6d14a 100644
--- a/genect_rnaseq/statanal.sh
+++ b/genect_rnaseq/statanal.sh
@@ -17,6 +17,7 @@ do
done
shift $(($OPTIND -1))
+baseDir="`dirname \"$0\"`"
# Check for mandatory options
if [[ -z $SLURM_CPUS_ON_NODE ]]
@@ -25,9 +26,9 @@ then
fi
source /etc/profile.d/modules.sh
-perl $baseDir/scripts/concat_cts.pl -o ./ *.cts
-perl $baseDir/scripts/concat_fpkm.pl -o ./ *.fpkm.txt
-perl $baseDir/scripts/concat_ctsum.pl -o ./ *.cts.summary
+perl $baseDir/concat_cts.pl -o ./ *.cts
+perl $baseDir/concat_fpkm.pl -o ./ *.fpkm.txt
+perl $baseDir/concat_ctsum.pl -o ./ *.cts.summary
cp design.txt design.shiny.txt
cp geneset.gmt geneset.shiny.gmt
@@ -37,7 +38,7 @@ then
touch bg.rda
else
module load R/3.2.1-intel
- Rscript $baseDir/scripts/dea.R
- Rscript $baseDir/scripts/build_ballgown.R *_stringtie
- perl $baseDir/scripts/concat_edgeR.pl *.edgeR.txt
+ Rscript $baseDir/dea.R
+ Rscript $baseDir/build_ballgown.R *_stringtie
+ perl $baseDir/concat_edgeR.pl *.edgeR.txt
fi