# HG changeset patch
# User fubar
# Date 1409207585 14400
# Node ID c6fdf2c6d0f4ebaf765947140004024c379e8a43
# Parent ca60c96f0beb054f4bc52b5294e95db5d0213311
Citations added (thanks John!) and a few more output formats for Alistair Chilcott
diff -r ca60c96f0beb -r c6fdf2c6d0f4 old.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/old.xml Thu Aug 28 02:33:05 2014 -0400
@@ -0,0 +1,835 @@
+
+ 1 or 2 level models for count data
+
+ biocbasics
+ package_r3
+
+
+
+ rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "edgeR"
+ --output_dir "$html_file.files_path" --output_html "$html_file" --output_tab "$outtab" --make_HTML "yes"
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+ nsamp) {
+ dm =dm[1:nsamp,]
+ }
+ newcolnames = substr(colnames(dm),1,20)
+ colnames(dm) = newcolnames
+ pdf(outpdfname)
+ heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none',
+ Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5)
+ dev.off()
+}
+
+hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here")
+{
+ ## for 2 groups only was
+ ## col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"}
+ ## pcols = unlist(lapply(group,col.map))
+ gu = unique(group)
+ colours = rainbow(length(gu),start=0.3,end=0.6)
+ pcols = colours[match(group,gu)]
+ nrows = nrow(cmat)
+ mtitle = paste(myTitle,'Heatmap: n contigs =',nrows)
+ if (nrows > nsamp) {
+ cmat = cmat[c(1:nsamp),]
+ mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='')
+ }
+ newcolnames = substr(colnames(cmat),1,20)
+ colnames(cmat) = newcolnames
+ pdf(outpdfname)
+ heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols)
+ dev.off()
+}
+
+qqPlot = function(descr='Title',pvector, ...)
+## stolen from https://gist.github.com/703512
+{
+ o = -log10(sort(pvector,decreasing=F))
+ e = -log10( 1:length(o)/length(o) )
+ o[o==-Inf] = reallysmall
+ o[o==Inf] = reallybig
+ pdfname = paste(gsub(" ","", descr , fixed=TRUE),'pval_qq.pdf',sep='_')
+ maint = paste(descr,'QQ Plot')
+ pdf(pdfname)
+ plot(e,o,pch=19,cex=1, main=maint, ...,
+ xlab=expression(Expected~~-log[10](italic(p))),
+ ylab=expression(Observed~~-log[10](italic(p))),
+ xlim=c(0,max(e)), ylim=c(0,max(o)))
+ lines(e,e,col="red")
+ grid(col = "lightgray", lty = "dotted")
+ dev.off()
+}
+
+smearPlot = function(DGEList,deTags, outSmear, outMain)
+ {
+ pdf(outSmear)
+ plotSmear(DGEList,de.tags=deTags,main=outMain)
+ grid(col="blue")
+ dev.off()
+ }
+
+boxPlot = function(rawrs,cleanrs,maint,myTitle)
+{
+ nc = ncol(rawrs)
+ for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA}
+ fullnames = colnames(rawrs)
+ newcolnames = substr(colnames(rawrs),1,20)
+ colnames(rawrs) = newcolnames
+ newcolnames = substr(colnames(cleanrs),1,20)
+ colnames(cleanrs) = newcolnames
+ pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"sampleBoxplot.pdf",sep='_')
+ defpar = par(no.readonly=T)
+ pdf(pdfname)
+ l = layout(matrix(c(1,2),1,2,byrow=T))
+ print.noquote('raw contig counts by sample:')
+ print.noquote(summary(rawrs))
+ print.noquote('normalised contig counts by sample:')
+ print.noquote(summary(cleanrs))
+ boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('Raw:',maint))
+ grid(col="blue")
+ boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('After ',maint))
+ grid(col="blue")
+ dev.off()
+ pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"samplehistplot.pdf",sep='_')
+ nc = ncol(rawrs)
+ print.noquote(paste('Using ncol rawrs=',nc))
+ ncroot = round(sqrt(nc))
+ if (ncroot*ncroot < nc) { ncroot = ncroot + 1 }
+ m = c()
+ for (i in c(1:nc)) {
+ rhist = hist(rawrs[,i],breaks=100,plot=F)
+ m = append(m,max(rhist\$counts))
+ }
+ ymax = max(m)
+ pdf(pdfname)
+ par(mfrow=c(ncroot,ncroot))
+ for (i in c(1:nc)) {
+ hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon",
+ breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax))
+ }
+ dev.off()
+ par(defpar)
+
+}
+
+cumPlot = function(rawrs,cleanrs,maint,myTitle)
+{
+ pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_')
+ defpar = par(no.readonly=T)
+ pdf(pdfname)
+ par(mfrow=c(2,1))
+ lrs = log(rawrs,10)
+ lim = max(lrs)
+ hist(lrs,breaks=100,main=paste('Before:',maint),xlab="Reads (log)",
+ ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1)
+ grid(col="blue")
+ lrs = log(cleanrs,10)
+ hist(lrs,breaks=100,main=paste('After:',maint),xlab="Reads (log)",
+ ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1)
+ grid(col="blue")
+ dev.off()
+ par(defpar)
+}
+
+cumPlot1 = function(rawrs,cleanrs,maint,myTitle)
+{
+ pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_')
+ pdf(pdfname)
+ par(mfrow=c(2,1))
+ lastx = max(rawrs)
+ rawe = knots(ecdf(rawrs))
+ cleane = knots(ecdf(cleanrs))
+ cy = 1:length(cleane)/length(cleane)
+ ry = 1:length(rawe)/length(rawe)
+ plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads",
+ ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle)
+ grid(col="blue")
+ plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads",
+ ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle)
+ grid(col="blue")
+ dev.off()
+}
+
+
+
+doGSEA = function(y=NULL,design=NULL,histgmt="",
+ bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
+ ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH")
+{
+ genesets = c()
+ if (bigmt > "")
+ {
+ bigenesets = readLines(bigmt)
+ genesets = bigenesets
+ }
+ if (histgmt > "")
+ {
+ hgenesets = readLines(histgmt)
+ if (bigmt > "") {
+ genesets = rbind(genesets,hgenesets)
+ } else {
+ genesets = hgenesets
+ }
+ }
+ print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt))
+ genesets = strsplit(genesets,'\t')
+ ##### tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n
+ outf = outfname
+ head=paste(myTitle,'edgeR GSEA')
+ write(head,file=outfname,append=F)
+ ntest=length(genesets)
+ urownames = toupper(rownames(y))
+ upcam = c()
+ downcam = c()
+ for (i in 1:ntest) {
+ gs = unlist(genesets[i])
+ g = gs[1] #### geneset_id
+ u = gs[2]
+ if (u > "") { u = paste("",u,"",sep="") }
+ glist = gs[3:length(gs)] #### member gene symbols
+ glist = toupper(glist)
+ inglist = urownames %in% glist
+ nin = sum(inglist)
+ if ((nin > minnin) && (nin < maxnin)) {
+ ### print(paste('@@found',sum(inglist),'genes in glist'))
+ camres = camera(y=y,index=inglist,design=design)
+ if (camres) {
+ rownames(camres) = g
+ ##### gene set name
+ camres = cbind(GeneSet=g,URL=u,camres)
+ if (camres\$Direction == "Up")
+ {
+ upcam = rbind(upcam,camres) } else {
+ downcam = rbind(downcam,camres)
+ }
+ }
+ }
+ }
+ uscam = upcam[order(upcam\$PValue),]
+ unadjp = uscam\$PValue
+ uscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
+ nup = max(10,sum((uscam\$adjPValue < fdrthresh)))
+ dscam = downcam[order(downcam\$PValue),]
+ unadjp = dscam\$PValue
+ dscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
+ ndown = max(10,sum((dscam\$adjPValue < fdrthresh)))
+ write.table(uscam,file=paste('upCamera',outfname,sep='_'),quote=F,sep='\t',row.names=F)
+ write.table(dscam,file=paste('downCamera',outfname,sep='_'),quote=F,sep='\t',row.names=F)
+ print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:'))
+ write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F)
+ print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:'))
+ write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F)
+}
+
+
+
+edgeIt = function (Count_Matrix,group,outputfilename,fdrtype='fdr',priordf=5,
+ fdrthresh=0.05,outputdir='.', myTitle='edgeR',libSize=c(),useNDF=F,
+ filterquantile=0.2, subjects=c(),mydesign=NULL,
+ doDESeq=T,doVoom=T,doCamera=T,org='hg19',
+ histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
+ doCook=F,DESeq_fittype="parameteric")
+{
+ if (length(unique(group))!=2){
+ print("Number of conditions identified in experiment does not equal 2")
+ q()
+ }
+ require(edgeR)
+ options(width = 512)
+ mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ")
+ allN = nrow(Count_Matrix)
+ nscut = round(ncol(Count_Matrix)/2)
+ colTotmillionreads = colSums(Count_Matrix)/1e6
+ rawrs = rowSums(Count_Matrix)
+ nonzerod = Count_Matrix[(rawrs > 0),]
+ nzN = nrow(nonzerod)
+ nzrs = rowSums(nonzerod)
+ zN = allN - nzN
+ print('**** Quantiles for non-zero row counts:',quote=F)
+ print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F)
+ if (useNDF == "T")
+ {
+ gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut
+ lo = colSums(Count_Matrix[!gt1rpin3,])
+ workCM = Count_Matrix[gt1rpin3,]
+ cleanrs = rowSums(workCM)
+ cleanN = length(cleanrs)
+ meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="")
+ print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F)
+ maint = paste('Filter >= 1/million reads in >=',nscut,'samples')
+ } else {
+ useme = (nzrs > quantile(nzrs,filterquantile))
+ workCM = nonzerod[useme,]
+ lo = colSums(nonzerod[!useme,])
+ cleanrs = rowSums(workCM)
+ cleanN = length(cleanrs)
+ meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="")
+ print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F)
+ maint = paste('Filter below',filterquantile,'quantile')
+ }
+ cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle)
+ allgenes <- rownames(workCM)
+ print(paste("*** Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F)
+ rsums = rowSums(workCM)
+ TName=unique(group)[1]
+ CName=unique(group)[2]
+ DGEList = DGEList(counts=workCM, group = group)
+ DGEList = calcNormFactors(DGEList)
+
+ if (is.null(mydesign)) {
+ if (length(subjects) == 0)
+ {
+ mydesign = model.matrix(~group)
+ }
+ else {
+ subjf = factor(subjects)
+ mydesign = model.matrix(~subjf+group)
+ ### we block on subject so make group last to simplify finding it
+ }
+ }
+ print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=',')))
+ print.noquote('Using design matrix:')
+ print.noquote(mydesign)
+ DGEList = estimateGLMCommonDisp(DGEList,mydesign)
+ comdisp = DGEList\$common.dispersion
+ DGEList = estimateGLMTrendedDisp(DGEList,mydesign)
+ if (priordf > 0) {
+ print.noquote(paste("prior.df =",priordf))
+ DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = priordf)
+ } else {
+ DGEList = estimateGLMTagwiseDisp(DGEList,mydesign)
+ }
+ lastcoef=ncol(mydesign)
+ print.noquote(paste('*** lastcoef = ',lastcoef))
+ estpriorn = getPriorN(DGEList)
+ predLFC1 = predFC(DGEList,prior.count=1,design=mydesign,dispersion=DGEList\$tagwise.dispersion,offset=getOffset(DGEList))
+ predLFC3 = predFC(DGEList,prior.count=3,design=mydesign,dispersion=DGEList\$tagwise.dispersion,offset=getOffset(DGEList))
+ predLFC5 = predFC(DGEList,prior.count=5,design=mydesign,dispersion=DGEList\$tagwise.dispersion,offset=getOffset(DGEList))
+ DGLM = glmFit(DGEList,design=mydesign)
+ DE = glmLRT(DGLM)
+ #### always last one - subject is first if needed
+ logCPMnorm = cpm(DGEList,log=T,normalized.lib.sizes=T)
+ logCPMraw = cpm(DGEList,log=T,normalized.lib.sizes=F)
+ uoutput = cbind(
+ Name=as.character(rownames(DGEList\$counts)),
+ DE\$table,
+ adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype),
+ Dispersion=DGEList\$tagwise.dispersion,totreads=rsums,
+ predLFC1=predLFC1[,lastcoef],
+ predLFC3=predLFC3[,lastcoef],
+ predLFC5=predLFC5[,lastcoef],
+ logCPMnorm,
+ DGEList\$counts
+ )
+ soutput = uoutput[order(DE\$table\$PValue),]
+ heatlogcpmnorm = logCPMnorm[order(DE\$table\$PValue),]
+ goodness = gof(DGLM, pcutoff=fdrthresh)
+ noutl = (sum(goodness\$outlier) > 0)
+ if (noutl > 0) {
+ print.noquote(paste('***',noutl,'GLM outliers found'))
+ print(paste(rownames(DGLM)[(goodness\$outlier)],collapse=','),quote=F)
+ } else {
+ print('*** No GLM fit outlier genes found')
+ }
+ z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2)
+ pdf(paste(mt,"GoodnessofFit.pdf",sep='_'))
+ qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion")
+ abline(0,1,lwd=3)
+ points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon")
+ dev.off()
+ print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F)
+ uniqueg = unique(group)
+ sample_colors = match(group,levels(group))
+ pdf(paste(mt,"MDSplot.pdf",sep='_'))
+ sampleTypes = levels(factor(group))
+ print.noquote(sampleTypes)
+ plotMDS.DGEList(DGEList,main=paste("MDS Plot for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors)
+ legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19)
+ grid(col="blue")
+ dev.off()
+ colnames(logCPMnorm) = paste( colnames(logCPMnorm),'N',sep="_")
+ print(paste('Raw sample CPM',paste(colSums(logCPMraw,na.rm=T),collapse=',')))
+ try(boxPlot(rawrs=logCPMraw,cleanrs=logCPMnorm,maint='TMM Normalisation',myTitle=myTitle))
+ nreads = soutput\$totreads
+ print('*** writing output',quote=F)
+ write.table(soutput,outputfilename, quote=FALSE, sep="\t",row.names=F)
+ rn = row.names(workCM)
+ print.noquote('@@ rn')
+ print.noquote(head(rn))
+ reg = "^chr([0-9]+):([0-9]+)-([0-9]+)"
+ genecards=" 0.8)
+ {
+ print("@@ using ucsc substitution for urls")
+ urls = paste0(ucsc,"&position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",rn,"")
+ } else {
+ print("@@ using genecards substitution for urls")
+ urls = paste0(genecards,rn,"\'>",rn,"")
+ }
+ tt = uoutput
+ print.noquote("*** edgeR Top tags\n")
+ tt = cbind(tt,ntotreads=nreads,URL=urls)
+ tt = tt[order(DE\$table\$PValue),]
+ print.noquote(tt[1:50,])
+ ### Plot MAplot
+ deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,])
+ nsig = length(deTags)
+ print(paste('***',nsig,'tags significant at adj p=',fdrthresh),quote=F)
+ if (nsig > 0) {
+ print('*** deTags',quote=F)
+ print(head(deTags))
+ }
+ deColours = ifelse(deTags,'red','black')
+ pdf(paste(mt,"BCV_vs_abundance.pdf",sep='_'))
+ plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance")
+ dev.off()
+ dg = DGEList[order(DE\$table\$PValue),]
+ outpdfname=paste(mt,"heatmap.pdf",sep='_')
+ hmap2(heatlogcpmnorm,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=myTitle)
+ outSmear = paste(mt,"Smearplot.pdf",sep='_')
+ outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='')
+ smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain)
+ qqPlot(descr=myTitle,pvector=DE\$table\$PValue)
+ if (doDESeq == T)
+ {
+ ### DESeq2
+ require('DESeq2')
+ print.noquote(paste('****subjects=',subjects,'length=',length(subjects)))
+ if (length(subjects) == 0)
+ {
+ pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM))
+ deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ Rx))
+ } else {
+ pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM))
+ deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ subjects + Rx))
+ }
+ deSeqDatsizefac <- estimateSizeFactors(deSEQds)
+ deSeqDatdisp <- estimateDispersions(deSeqDatsizefac,fitType=DESeq_fittype)
+ resDESeq <- nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype)
+ rDESeq = as.data.frame(results(resDESeq))
+ srDESeq = rDESeq[order(rDESeq\$pvalue),]
+ write.table(srDESeq,paste(mt,'DESeq2_TopTable.xls',sep='_'), quote=FALSE, sep="\t",row.names=F)
+ topresults.DESeq <- rDESeq[which(rDESeq\$padj < fdrthresh), ]
+ DESeqcountsindex <- which(allgenes %in% rownames(topresults.DESeq))
+ DESeqcounts <- rep(0, length(allgenes))
+ DESeqcounts[DESeqcountsindex] <- 1
+ pdf(paste(mt,"DESeq2_dispersion_estimates.pdf",sep='_'))
+ plotDispEsts(resDESeq)
+ dev.off()
+ if (doCook) {
+ pdf(paste(mt,"DESeq2_cooks_distance.pdf",sep='_'))
+ W <- mcols(resDESeq)\$WaldStatistic_condition_treated_vs_untreated
+ maxCooks <- mcols(resDESeq)\$maxCooks
+ idx <- !is.na(W)
+ plot(rank(W[idx]), maxCooks[idx], xlab="rank of Wald statistic", ylab="maximum Cook's distance per gene",
+ ylim=c(0,5), cex=.4, col="maroon")
+ m <- ncol(dds)
+ p <- 3
+ abline(h=qf(.75, p, m - p),col="darkblue")
+ grid(col="lightgray",lty="dotted")
+ }
+ }
+ counts.dataframe = as.data.frame(c())
+ norm.factor = DGEList\$samples\$norm.factors
+ topresults.edgeR <- soutput[which(soutput\$adj.p.value < fdrthresh), ]
+ edgeRcountsindex <- which(allgenes %in% rownames(topresults.edgeR))
+ edgeRcounts <- rep(0, length(allgenes))
+ edgeRcounts[edgeRcountsindex] <- 1
+ if (doVoom == T) {
+ pdf(paste(mt,"voomplot.pdf",sep='_'))
+ dat.voomed <- voom(DGEList, mydesign, plot = TRUE, normalize.method="quantil", lib.size = NULL)
+ dev.off()
+ fit <- lmFit(dat.voomed, mydesign)
+ fit <- eBayes(fit)
+ rvoom <- topTable(fit, coef = length(colnames(mydesign)), adj = "BH", n = Inf)
+ write.table(rvoom,paste(mt,'VOOM_topTable.xls',sep='_'), quote=FALSE, sep="\t",row.names=F)
+ topresults.voom <- rvoom[which(rvoom\$adj.P.Val < fdrthresh), ]
+ voomcountsindex <- which(allgenes %in% rownames(topresults.voom))
+ voomcounts <- rep(0, length(allgenes))
+ voomcounts[voomcountsindex] <- 1
+ }
+ if ((doDESeq==T) || (doVoom==T)) {
+ if ((doVoom==T) && (doDESeq==T)) {
+ vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh)
+ counts.dataframe <- data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts,
+ VOOM_limma = voomcounts, row.names = allgenes)
+ } else if (doDESeq==T) {
+ vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh)
+ counts.dataframe <- data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes)
+ } else if (doVoom==T) {
+ vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh)
+ counts.dataframe <- data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes)
+ }
+
+ if (nrow(counts.dataframe > 1)) {
+ counts.venn <- vennCounts(counts.dataframe)
+ vennf = paste(mt,'venn.pdf',sep='_')
+ pdf(vennf)
+ vennDiagram(counts.venn,main=vennmain,col="maroon")
+ dev.off()
+ }
+ } ### doDESeq or doVoom
+ if (doDESeq==T) {
+ cat("*** DESeq top 50\n")
+ print(srDESeq[1:50,])
+ }
+ if (doVoom==T) {
+ cat("*** VOOM top 50\n")
+ print(rvoom[1:50,])
+ }
+ if (doCamera) {
+ doGSEA(y=DGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle,
+ outfname=paste(mt,"GSEA.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype)
+ }
+ uoutput
+
+}
+#### Done
+
+#### sink(stdout(),append=T,type="message")
+
+doDESeq = $DESeq.doDESeq
+### make these 'T' or 'F'
+doVoom = $doVoom
+doCamera = $camera.doCamera
+Out_Dir = "$html_file.files_path"
+Input = "$input1"
+TreatmentName = "$treatment_name"
+TreatmentCols = "$Treat_cols"
+ControlName = "$control_name"
+ControlCols= "$Control_cols"
+outputfilename = "$outtab"
+org = "$input1.dbkey"
+if (org == "") { org = "hg19"}
+fdrtype = "$fdrtype"
+priordf = $priordf
+fdrthresh = $fdrthresh
+useNDF = "$useNDF"
+fQ = $fQ
+myTitle = "$title"
+sids = strsplit("$subjectids",',')
+subjects = unlist(sids)
+nsubj = length(subjects)
+builtin_gmt=""
+history_gmt=""
+
+builtin_gmt = ""
+history_gmt = ""
+DESeq_fittype=""
+#if $DESeq.doDESeq == "T"
+ DESeq_fittype = "$DESeq.DESeq_fitType"
+#end if
+#if $camera.doCamera == 'T'
+ #if $camera.gmtSource.refgmtSource == "indexed" or $camera.gmtSource.refgmtSource == "both":
+ builtin_gmt = "${camera.gmtSource.builtinGMT.fields.path}"
+ #end if
+ #if $camera.gmtSource.refgmtSource == "history" or $camera.gmtSource.refgmtSource == "both":
+ history_gmt = "${camera.gmtSource.ownGMT}"
+ history_gmt_name = "${camera.gmtSource.ownGMT.name}"
+ #end if
+#end if
+if (nsubj > 0) {
+if (doDESeq) {
+ print('WARNING - cannot yet use DESeq2 for 2 way anova - see the docs')
+ doDESeq = F
+ }
+}
+TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1
+CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1
+cat('Got TCols=')
+cat(TCols)
+cat('; CCols=')
+cat(CCols)
+cat('\n')
+useCols = c(TCols,CCols)
+if (file.exists(Out_Dir) == F) dir.create(Out_Dir)
+Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header
+snames = colnames(Count_Matrix)
+nsamples = length(snames)
+if (nsubj > 0 & nsubj != nsamples) {
+options("show.error.messages"=T)
+mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','),
+ 'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=','))
+write(mess, stderr())
+quit(save="no",status=4)
+}
+
+Count_Matrix = Count_Matrix[,useCols] ### reorder columns
+if (length(subjects) != 0) {subjects = subjects[useCols]}
+rn = rownames(Count_Matrix)
+islib = rn %in% c('librarySize','NotInBedRegions')
+LibSizes = Count_Matrix[subset(rn,islib),][1] # take first
+Count_Matrix = Count_Matrix[subset(rn,! islib),]
+group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) )
+group = factor(group, levels=c(ControlName,TreatmentName))
+colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_")
+results = edgeIt(Count_Matrix=Count_Matrix,group=group,outputfilename=outputfilename,
+ fdrtype='BH',priordf=priordf,fdrthresh=fdrthresh,outputdir='.',
+ myTitle='edgeR',useNDF=F,libSize=c(),filterquantile=fQ,subjects=subjects,
+ doDESeq=doDESeq,doVoom=doVoom,doCamera=doCamera,org=org,
+ histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fittype=DESeq_fittype)
+sessionInfo()
+]]>
+
+
+
+
+**What it does**
+
+Performs digital gene expression analysis between a treatment and control on a count matrix.
+Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design.
+
+**Input**
+
+A matrix consisting of non-negative integers. The matrix must have a unique header row identifiying the samples, and a unique set of row names
+as the first column. Typically the row names are gene symbols or probe id's for downstream use in GSEA and other methods.
+
+If you have (eg) paired samples and wish to include a term in the GLM to account for some other factor (subject in the case of paired samples),
+put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or
+A list of integers, one for each subject or an empty string if samples are all independent.
+If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix.
+Integers for samples that are not in the analysis *must* be present in the string as filler even if not used.
+
+So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones
+eg if you had 6 samples with the first two independent but the second and third pairs each being from independent subjects. you might use
+8,9,1,1,2,2
+as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6
+
+**Output**
+
+A summary html page with links to the R source code and all the outputs, nice grids of helpful plot thumbnails, and lots
+of logging and the top 50 rows of the topTable.
+
+The main topTables of results are provided as separate excelish tabular files.
+
+They include adjusted p values and dispersions for each region, raw and cpm sample data counts and shrunken (predicted) log fold change estimates.
+These are provided for downstream analyses such as GSEA and are predictions of the logFC you might expect to see
+in an independent replication of your original experiment. Higher number means more shrinkage. Shrinkage is more extreme for low coverage features
+so downstream analyses are more robust against strong effect size estimates based on relatively little experimental information.
+
+**Note on prior.N**
+
+http://seqanswers.com/forums/showthread.php?t=5591 says:
+
+*prior.n*
+
+The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion.
+You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood
+in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your
+tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the
+common likelihood the weight of one observation.
+
+In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value,
+or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that
+you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation
+(squeezing) of the tagwise dispersions. How many samples do you have in your experiment?
+What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10.
+If you have more samples, then the tagwise dispersion estimates will be more reliable,
+so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5.
+
+
+From Bioconductor Digest, Vol 118, Issue 5, Gordon writes:
+
+Dear Dorota,
+
+The important settings are prior.df and trend.
+
+prior.n and prior.df are related through prior.df = prior.n * residual.df,
+and your experiment has residual.df = 36 - 12 = 24. So the old setting of
+prior.n=10 is equivalent for your data to prior.df = 240, a very large
+value. Going the other way, the new setting of prior.df=10 is equivalent
+to prior.n=10/24.
+
+To recover old results with the current software you would use
+
+ estimateTagwiseDisp(object, prior.df=240, trend="none")
+
+To get the new default from old software you would use
+
+ estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE)
+
+Actually the old trend method is equivalent to trend="loess" in the new
+software. You should use plotBCV(object) to see whether a trend is
+required.
+
+Note you could also use
+
+ prior.n = getPriorN(object, prior.df=10)
+
+to map between prior.df and prior.n.
+
+** Old rant on variable name changes in bioconductor versions**
+
+BioC authors sometimes make small mostly cosmetic changes to variable names (eg: from p.value to PValue)
+often to make them more internally consistent or self describing. Unfortunately, these improvements
+break existing code in ways that can take a while to track down that relies on the library in ways that can take a while to track down,
+increasing downstream tool maintenance effort uselessly.
+
+Please, don't do that. It hurts us.
+
+
+
+
+
+
+
diff -r ca60c96f0beb -r c6fdf2c6d0f4 rgToolFactoryMultIn.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgToolFactoryMultIn.py Thu Aug 28 02:33:05 2014 -0400
@@ -0,0 +1,736 @@
+# rgToolFactoryMultIn.py
+# see https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home
+#
+# copyright ross lazarus (ross stop lazarus at gmail stop com) May 2012
+#
+# all rights reserved
+# Licensed under the LGPL
+# suggestions for improvement and bug fixes welcome at https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home
+#
+# august 2014
+# Allows arbitrary number of input files
+# NOTE positional parameters are now passed to script
+# and output (may be "None") is *before* arbitrary number of inputs
+#
+# march 2014
+# had to remove dependencies because cross toolshed dependencies are not possible - can't pre-specify a toolshed url for graphicsmagick and ghostscript
+# grrrrr - night before a demo
+# added dependencies to a tool_dependencies.xml if html page generated so generated tool is properly portable
+#
+# added ghostscript and graphicsmagick as dependencies
+# fixed a wierd problem where gs was trying to use the new_files_path from universe (database/tmp) as ./database/tmp
+# errors ensued
+#
+# august 2013
+# found a problem with GS if $TMP or $TEMP missing - now inject /tmp and warn
+#
+# july 2013
+# added ability to combine images and individual log files into html output
+# just make sure there's a log file foo.log and it will be output
+# together with all images named like "foo_*.pdf
+# otherwise old format for html
+#
+# January 2013
+# problem pointed out by Carlos Borroto
+# added escaping for <>$ - thought I did that ages ago...
+#
+# August 11 2012
+# changed to use shell=False and cl as a sequence
+
+# This is a Galaxy tool factory for simple scripts in python, R or whatever ails ye.
+# It also serves as the wrapper for the new tool.
+#
+# you paste and run your script
+# Only works for simple scripts that read one input from the history.
+# Optionally can write one new history dataset,
+# and optionally collect any number of outputs into links on an autogenerated HTML page.
+
+# DO NOT install on a public or important site - please.
+
+# installed generated tools are fine if the script is safe.
+# They just run normally and their user cannot do anything unusually insecure
+# but please, practice safe toolshed.
+# Read the fucking code before you install any tool
+# especially this one
+
+# After you get the script working on some test data, you can
+# optionally generate a toolshed compatible gzip file
+# containing your script safely wrapped as an ordinary Galaxy script in your local toolshed for
+# safe and largely automated installation in a production Galaxy.
+
+# If you opt for an HTML output, you get all the script outputs arranged
+# as a single Html history item - all output files are linked, thumbnails for all the pdfs.
+# Ugly but really inexpensive.
+#
+# Patches appreciated please.
+#
+#
+# long route to June 2012 product
+# Behold the awesome power of Galaxy and the toolshed with the tool factory to bind them
+# derived from an integrated script model
+# called rgBaseScriptWrapper.py
+# Note to the unwary:
+# This tool allows arbitrary scripting on your Galaxy as the Galaxy user
+# There is nothing stopping a malicious user doing whatever they choose
+# Extremely dangerous!!
+# Totally insecure. So, trusted users only
+#
+# preferred model is a developer using their throw away workstation instance - ie a private site.
+# no real risk. The universe_wsgi.ini admin_users string is checked - only admin users are permitted to run this tool.
+#
+
+import sys
+import shutil
+import subprocess
+import os
+import time
+import tempfile
+import optparse
+import tarfile
+import re
+import shutil
+import math
+
+progname = os.path.split(sys.argv[0])[1]
+myversion = 'V001.1 March 2014'
+verbose = False
+debug = False
+toolFactoryURL = 'https://bitbucket.org/fubar/galaxytoolfactory'
+
+# if we do html we need these dependencies specified in a tool_dependencies.xml file and referred to in the generated
+# tool xml
+toolhtmldepskel = """
+
+
+
+
+
+
+
+
+ %s
+
+
+"""
+
+protorequirements = """
+ ghostscript
+ graphicsmagick
+ """
+
+def timenow():
+ """return current time as a string
+ """
+ return time.strftime('%d/%m/%Y %H:%M:%S', time.localtime(time.time()))
+
+html_escape_table = {
+ "&": "&",
+ ">": ">",
+ "<": "<",
+ "$": "\$"
+ }
+
+def html_escape(text):
+ """Produce entities within text."""
+ return "".join(html_escape_table.get(c,c) for c in text)
+
+def cmd_exists(cmd):
+ return subprocess.call("type " + cmd, shell=True,
+ stdout=subprocess.PIPE, stderr=subprocess.PIPE) == 0
+
+
+class ScriptRunner:
+ """class is a wrapper for an arbitrary script
+ """
+
+ def __init__(self,opts=None,treatbashSpecial=True):
+ """
+ cleanup inputs, setup some outputs
+
+ """
+ self.useGM = cmd_exists('gm')
+ self.useIM = cmd_exists('convert')
+ self.useGS = cmd_exists('gs')
+ self.temp_warned = False # we want only one warning if $TMP not set
+ self.treatbashSpecial = treatbashSpecial
+ if opts.output_dir: # simplify for the tool tarball
+ os.chdir(opts.output_dir)
+ self.thumbformat = 'png'
+ self.opts = opts
+ self.toolname = re.sub('[^a-zA-Z0-9_]+', '', opts.tool_name) # a sanitizer now does this but..
+ self.toolid = self.toolname
+ self.myname = sys.argv[0] # get our name because we write ourselves out as a tool later
+ self.pyfile = self.myname # crude but efficient - the cruft won't hurt much
+ self.xmlfile = '%s.xml' % self.toolname
+ s = open(self.opts.script_path,'r').readlines()
+ s = [x.rstrip() for x in s] # remove pesky dos line endings if needed
+ self.script = '\n'.join(s)
+ fhandle,self.sfile = tempfile.mkstemp(prefix=self.toolname,suffix=".%s" % (opts.interpreter))
+ tscript = open(self.sfile,'w') # use self.sfile as script source for Popen
+ tscript.write(self.script)
+ tscript.close()
+ self.indentedScript = '\n'.join([' %s' % html_escape(x) for x in s]) # for restructured text in help
+ self.escapedScript = '\n'.join([html_escape(x) for x in s])
+ self.elog = os.path.join(self.opts.output_dir,"%s_error.log" % self.toolname)
+ if opts.output_dir: # may not want these complexities
+ self.tlog = os.path.join(self.opts.output_dir,"%s_runner.log" % self.toolname)
+ art = '%s.%s' % (self.toolname,opts.interpreter)
+ artpath = os.path.join(self.opts.output_dir,art) # need full path
+ artifact = open(artpath,'w') # use self.sfile as script source for Popen
+ artifact.write(self.script)
+ artifact.close()
+ self.cl = []
+ self.html = []
+ self.test1Inputs = [] # now a list
+ a = self.cl.append
+ a(opts.interpreter)
+ if self.treatbashSpecial and opts.interpreter in ['bash','sh']:
+ a(self.sfile)
+ else:
+ a('-') # stdin
+ # if multiple inputs - positional or need to distinguish them with cl params
+ if opts.output_tab:
+ a('%s' % opts.output_tab)
+ if opts.input_tab:
+ tests = []
+ for i,intab in enumerate(opts.input_tab): # if multiple, make tests
+ if intab.find(',') <> -1:
+ (gpath,uname) = intab.split(',')
+ else:
+ gpath = uname = intab
+ a('"%s"' % (intab))
+ tests.append(os.path.basename(gpath))
+ self.test1Inputs = '' % (','.join(tests))
+ else:
+ self.test1Inputs = ''
+ self.outFormats = opts.output_format
+ self.inputFormats = opts.input_formats
+ self.test1Output = '%s_test1_output.xls' % self.toolname
+ self.test1HTML = '%s_test1_output.html' % self.toolname
+
+ def makeXML(self):
+ """
+ Create a Galaxy xml tool wrapper for the new script as a string to write out
+ fixme - use templating or something less fugly than this example of what we produce
+
+
+ a tabular file
+
+ reverse.py --script_path "$runMe" --interpreter "python"
+ --tool_name "reverse" --input_tab "$input1" --output_tab "$tab_file"
+
+
+
+
+
+
+
+
+
+
+
+**What it Does**
+
+Reverse the columns in a tabular file
+
+
+
+
+
+# reverse order of columns in a tabular file
+import sys
+inp = sys.argv[1]
+outp = sys.argv[2]
+i = open(inp,'r')
+o = open(outp,'w')
+for row in i:
+ rs = row.rstrip().split('\t')
+ rs.reverse()
+ o.write('\t'.join(rs))
+ o.write('\n')
+i.close()
+o.close()
+
+
+
+
+
+
+ """
+ newXML="""
+%(tooldesc)s
+%(requirements)s
+
+%(command)s
+
+
+%(inputs)s
+
+
+%(outputs)s
+
+
+
+%(script)s
+
+
+
+%(tooltests)s
+
+
+
+%(help)s
+
+
+""" # needs a dict with toolname, toolid, interpreter, scriptname, command, inputs as a multi line string ready to write, outputs ditto, help ditto
+
+ newCommand="""
+ %(toolname)s.py --script_path "$runMe" --interpreter "%(interpreter)s"
+ --tool_name "%(toolname)s"
+ %(command_inputs)s
+ %(command_outputs)s
+ """
+ # may NOT be an input or htmlout - appended later
+ tooltestsTabOnly = """
+
+ %(test1Inputs)s
+
+
+
+
+
+ """
+ tooltestsHTMLOnly = """
+
+ %(test1Inputs)s
+
+
+
+
+
+ """
+ tooltestsBoth = """
+
+ %(test1Inputs)s
+
+
+
+
+
+
+ """
+ xdict = {}
+ xdict['requirements'] = ''
+ if self.opts.make_HTML:
+ if self.opts.include_dependencies == "yes":
+ xdict['requirements'] = protorequirements
+ xdict['tool_version'] = self.opts.tool_version
+ xdict['test1HTML'] = self.test1HTML
+ xdict['test1Output'] = self.test1Output
+ xdict['test1Inputs'] = self.test1Inputs
+ if self.opts.make_HTML and self.opts.output_tab <> 'None':
+ xdict['tooltests'] = tooltestsBoth % xdict
+ elif self.opts.make_HTML:
+ xdict['tooltests'] = tooltestsHTMLOnly % xdict
+ else:
+ xdict['tooltests'] = tooltestsTabOnly % xdict
+ xdict['script'] = self.escapedScript
+ # configfile is least painful way to embed script to avoid external dependencies
+ # but requires escaping of <, > and $ to avoid Mako parsing
+ if self.opts.help_text:
+ helptext = open(self.opts.help_text,'r').readlines()
+ helptext = [html_escape(x) for x in helptext] # must html escape here too - thanks to Marius van den Beek
+ xdict['help'] = ''.join([x for x in helptext])
+ else:
+ xdict['help'] = 'Please ask the tool author (%s) for help as none was supplied at tool generation\n' % (self.opts.user_email)
+ coda = ['**Script**','Pressing execute will run the following code over your input file and generate some outputs in your history::']
+ coda.append('\n')
+ coda.append(self.indentedScript)
+ coda.append('\n**Attribution**\nThis Galaxy tool was created by %s at %s\nusing the Galaxy Tool Factory.\n' % (self.opts.user_email,timenow()))
+ coda.append('See %s for details of that project' % (toolFactoryURL))
+ coda.append('Please cite: Creating re-usable tools from scripts: The Galaxy Tool Factory. Ross Lazarus; Antony Kaspi; Mark Ziemann; The Galaxy Team. ')
+ coda.append('Bioinformatics 2012; doi: 10.1093/bioinformatics/bts573\n')
+ xdict['help'] = '%s\n%s' % (xdict['help'],'\n'.join(coda))
+ if self.opts.tool_desc:
+ xdict['tooldesc'] = '%s' % self.opts.tool_desc
+ else:
+ xdict['tooldesc'] = ''
+ xdict['command_outputs'] = ''
+ xdict['outputs'] = ''
+ if self.opts.input_tab <> 'None':
+ cins = ['\n',]
+ cins.append('#for intab in $input1:')
+ cins.append('--input_tab "$intab"')
+ cins.append('#end for\n')
+ xdict['command_inputs'] = '\n'.join(cins)
+ xdict['inputs'] = ''' \n''' % self.inputFormats
+ else:
+ xdict['command_inputs'] = '' # assume no input - eg a random data generator
+ xdict['inputs'] = ''
+ xdict['inputs'] += ' \n' % self.toolname
+ xdict['toolname'] = self.toolname
+ xdict['toolid'] = self.toolid
+ xdict['interpreter'] = self.opts.interpreter
+ xdict['scriptname'] = self.sfile
+ if self.opts.make_HTML:
+ xdict['command_outputs'] += ' --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"'
+ xdict['outputs'] += ' \n'
+ else:
+ xdict['command_outputs'] += ' --output_dir "./"'
+ if self.opts.output_tab <> 'None':
+ xdict['command_outputs'] += ' --output_tab "$tab_file"'
+ xdict['outputs'] += ' \n' % self.outFormats
+ xdict['command'] = newCommand % xdict
+ xmls = newXML % xdict
+ xf = open(self.xmlfile,'w')
+ xf.write(xmls)
+ xf.write('\n')
+ xf.close()
+ # ready for the tarball
+
+
+ def makeTooltar(self):
+ """
+ a tool is a gz tarball with eg
+ /toolname/tool.xml /toolname/tool.py /toolname/test-data/test1_in.foo ...
+ """
+ retval = self.run()
+ if retval:
+ print >> sys.stderr,'## Run failed. Cannot build yet. Please fix and retry'
+ sys.exit(1)
+ tdir = self.toolname
+ os.mkdir(tdir)
+ self.makeXML()
+ if self.opts.make_HTML:
+ if self.opts.help_text:
+ hlp = open(self.opts.help_text,'r').read()
+ else:
+ hlp = 'Please ask the tool author for help as none was supplied at tool generation\n'
+ if self.opts.include_dependencies == "yes":
+ tooldepcontent = toolhtmldepskel % hlp
+ depf = open(os.path.join(tdir,'tool_dependencies.xml'),'w')
+ depf.write(tooldepcontent)
+ depf.write('\n')
+ depf.close()
+ if self.opts.input_tab <> 'None': # no reproducible test otherwise? TODO: maybe..
+ testdir = os.path.join(tdir,'test-data')
+ os.mkdir(testdir) # make tests directory
+ for i,intab in enumerate(self.opts.input_tab):
+ si = self.opts.input_tab[i]
+ if si.find(',') <> -1:
+ s = si.split(',')[0]
+ si = s
+ dest = os.path.join(testdir,os.path.basename(si))
+ if si <> dest:
+ shutil.copyfile(si,dest)
+ if self.opts.output_tab <> 'None':
+ shutil.copyfile(self.opts.output_tab,os.path.join(testdir,self.test1Output))
+ if self.opts.make_HTML:
+ shutil.copyfile(self.opts.output_html,os.path.join(testdir,self.test1HTML))
+ if self.opts.output_dir:
+ shutil.copyfile(self.tlog,os.path.join(testdir,'test1_out.log'))
+ outpif = '%s.py' % self.toolname # new name
+ outpiname = os.path.join(tdir,outpif) # path for the tool tarball
+ pyin = os.path.basename(self.pyfile) # our name - we rewrite ourselves (TM)
+ notes = ['# %s - a self annotated version of %s generated by running %s\n' % (outpiname,pyin,pyin),]
+ notes.append('# to make a new Galaxy tool called %s\n' % self.toolname)
+ notes.append('# User %s at %s\n' % (self.opts.user_email,timenow()))
+ pi = open(self.pyfile,'r').readlines() # our code becomes new tool wrapper (!) - first Galaxy worm
+ notes += pi
+ outpi = open(outpiname,'w')
+ outpi.write(''.join(notes))
+ outpi.write('\n')
+ outpi.close()
+ stname = os.path.join(tdir,self.sfile)
+ if not os.path.exists(stname):
+ shutil.copyfile(self.sfile, stname)
+ xtname = os.path.join(tdir,self.xmlfile)
+ if not os.path.exists(xtname):
+ shutil.copyfile(self.xmlfile,xtname)
+ tarpath = "%s.gz" % self.toolname
+ tar = tarfile.open(tarpath, "w:gz")
+ tar.add(tdir,arcname=self.toolname)
+ tar.close()
+ shutil.copyfile(tarpath,self.opts.new_tool)
+ shutil.rmtree(tdir)
+ ## TODO: replace with optional direct upload to local toolshed?
+ return retval
+
+
+ def compressPDF(self,inpdf=None,thumbformat='png'):
+ """need absolute path to pdf
+ note that GS gets confoozled if no $TMP or $TEMP
+ so we set it
+ """
+ assert os.path.isfile(inpdf), "## Input %s supplied to %s compressPDF not found" % (inpdf,self.myName)
+ hlog = os.path.join(self.opts.output_dir,"compress_%s.txt" % os.path.basename(inpdf))
+ sto = open(hlog,'a')
+ our_env = os.environ.copy()
+ our_tmp = our_env.get('TMP',None)
+ if not our_tmp:
+ our_tmp = our_env.get('TEMP',None)
+ if not (our_tmp and os.path.exists(our_tmp)):
+ newtmp = os.path.join(self.opts.output_dir,'tmp')
+ try:
+ os.mkdir(newtmp)
+ except:
+ sto.write('## WARNING - cannot make %s - it may exist or permissions need fixing\n' % newtmp)
+ our_env['TEMP'] = newtmp
+ if not self.temp_warned:
+ sto.write('## WARNING - no $TMP or $TEMP!!! Please fix - using %s temporarily\n' % newtmp)
+ self.temp_warned = True
+ outpdf = '%s_compressed' % inpdf
+ cl = ["gs", "-sDEVICE=pdfwrite", "-dNOPAUSE", "-dUseCIEColor", "-dBATCH","-dPDFSETTINGS=/printer", "-sOutputFile=%s" % outpdf,inpdf]
+ x = subprocess.Popen(cl,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env)
+ retval1 = x.wait()
+ sto.close()
+ if retval1 == 0:
+ os.unlink(inpdf)
+ shutil.move(outpdf,inpdf)
+ os.unlink(hlog)
+ hlog = os.path.join(self.opts.output_dir,"thumbnail_%s.txt" % os.path.basename(inpdf))
+ sto = open(hlog,'w')
+ outpng = '%s.%s' % (os.path.splitext(inpdf)[0],thumbformat)
+ if self.useGM:
+ cl2 = ['gm', 'convert', inpdf, outpng]
+ else: # assume imagemagick
+ cl2 = ['convert', inpdf, outpng]
+ x = subprocess.Popen(cl2,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env)
+ retval2 = x.wait()
+ sto.close()
+ if retval2 == 0:
+ os.unlink(hlog)
+ retval = retval1 or retval2
+ return retval
+
+
+ def getfSize(self,fpath,outpath):
+ """
+ format a nice file size string
+ """
+ size = ''
+ fp = os.path.join(outpath,fpath)
+ if os.path.isfile(fp):
+ size = '0 B'
+ n = float(os.path.getsize(fp))
+ if n > 2**20:
+ size = '%1.1f MB' % (n/2**20)
+ elif n > 2**10:
+ size = '%1.1f KB' % (n/2**10)
+ elif n > 0:
+ size = '%d B' % (int(n))
+ return size
+
+ def makeHtml(self):
+ """ Create an HTML file content to list all the artifacts found in the output_dir
+ """
+
+ galhtmlprefix = """
+
+
+
+
+
+
+
+
\n"""
+
+ flist = os.listdir(self.opts.output_dir)
+ flist = [x for x in flist if x <> 'Rplots.pdf']
+ flist.sort()
+ html = []
+ html.append(galhtmlprefix % progname)
+ html.append('
Galaxy Tool "%s" run at %s
' % (self.toolname,timenow()))
+ fhtml = []
+ if len(flist) > 0:
+ logfiles = [x for x in flist if x.lower().endswith('.log')] # log file names determine sections
+ logfiles.sort()
+ logfiles = [x for x in logfiles if os.path.abspath(x) <> os.path.abspath(self.tlog)]
+ logfiles.append(os.path.abspath(self.tlog)) # make it the last one
+ pdflist = []
+ npdf = len([x for x in flist if os.path.splitext(x)[-1].lower() == '.pdf'])
+ for rownum,fname in enumerate(flist):
+ dname,e = os.path.splitext(fname)
+ sfsize = self.getfSize(fname,self.opts.output_dir)
+ if e.lower() == '.pdf' : # compress and make a thumbnail
+ thumb = '%s.%s' % (dname,self.thumbformat)
+ pdff = os.path.join(self.opts.output_dir,fname)
+ retval = self.compressPDF(inpdf=pdff,thumbformat=self.thumbformat)
+ if retval == 0:
+ pdflist.append((fname,thumb))
+ else:
+ pdflist.append((fname,fname))
+ if (rownum+1) % 2 == 0:
+ fhtml.append('
' % (fname,fname,sfsize))
+ for logfname in logfiles: # expect at least tlog - if more
+ if os.path.abspath(logfname) == os.path.abspath(self.tlog): # handled later
+ sectionname = 'All tool run'
+ if (len(logfiles) > 1):
+ sectionname = 'Other'
+ ourpdfs = pdflist
+ else:
+ realname = os.path.basename(logfname)
+ sectionname = os.path.splitext(realname)[0].split('_')[0] # break in case _ added to log
+ ourpdfs = [x for x in pdflist if os.path.basename(x[0]).split('_')[0] == sectionname]
+ pdflist = [x for x in pdflist if os.path.basename(x[0]).split('_')[0] <> sectionname] # remove
+ nacross = 1
+ npdf = len(ourpdfs)
+
+ if npdf > 0:
+ nacross = math.sqrt(npdf) ## int(round(math.log(npdf,2)))
+ if int(nacross)**2 != npdf:
+ nacross += 1
+ nacross = int(nacross)
+ width = min(400,int(1200/nacross))
+ html.append('
%s images and outputs
' % sectionname)
+ html.append('(Click on a thumbnail image to download the corresponding original PDF image) ')
+ ntogo = nacross # counter for table row padding with empty cells
+ html.append('
\n
')
+ for i,paths in enumerate(ourpdfs):
+ fname,thumb = paths
+ s= """
\n""" % (fname,thumb,fname,width,fname)
+ if ((i+1) % nacross == 0):
+ s += '
\n'
+ ntogo = 0
+ if i < (npdf - 1): # more to come
+ s += '
\n')
+ else:
+ if ntogo > 0: # pad
+ html.append('
'*ntogo)
+ html.append('\n')
+ logt = open(logfname,'r').readlines()
+ logtext = [x for x in logt if x.strip() > '']
+ html.append('
%s log output
' % sectionname)
+ if len(logtext) > 1:
+ html.append('\n
\n')
+ html += logtext
+ html.append('\n
\n')
+ else:
+ html.append('%s is empty ' % logfname)
+ if len(fhtml) > 0:
+ fhtml.insert(0,'
Output File Name (click to view)
Size
\n')
+ fhtml.append('
')
+ html.append('
All output files available for downloading
\n')
+ html += fhtml # add all non-pdf files to the end of the display
+ else:
+ html.append('
### Error - %s returned no files - please confirm that parameters are sane
' % self.opts.interpreter)
+ html.append(galhtmlpostfix)
+ htmlf = file(self.opts.output_html,'w')
+ htmlf.write('\n'.join(html))
+ htmlf.write('\n')
+ htmlf.close()
+ self.html = html
+
+
+ def run(self):
+ """
+ scripts must be small enough not to fill the pipe!
+ """
+ if self.treatbashSpecial and self.opts.interpreter in ['bash','sh']:
+ retval = self.runBash()
+ else:
+ if self.opts.output_dir:
+ ste = open(self.elog,'w')
+ sto = open(self.tlog,'w')
+ sto.write('## Toolfactory generated command line = %s\n' % ' '.join(self.cl))
+ sto.flush()
+ p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=ste,stdin=subprocess.PIPE,cwd=self.opts.output_dir)
+ else:
+ p = subprocess.Popen(self.cl,shell=False,stdin=subprocess.PIPE)
+ p.stdin.write(self.script)
+ p.stdin.close()
+ retval = p.wait()
+ if self.opts.output_dir:
+ sto.close()
+ ste.close()
+ err = open(self.elog,'r').readlines()
+ if retval <> 0 and err: # problem
+ print >> sys.stderr,err
+ if self.opts.make_HTML:
+ self.makeHtml()
+ return retval
+
+ def runBash(self):
+ """
+ cannot use - for bash so use self.sfile
+ """
+ if self.opts.output_dir:
+ s = '## Toolfactory generated command line = %s\n' % ' '.join(self.cl)
+ sto = open(self.tlog,'w')
+ sto.write(s)
+ sto.flush()
+ p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=sto,cwd=self.opts.output_dir)
+ else:
+ p = subprocess.Popen(self.cl,shell=False)
+ retval = p.wait()
+ if self.opts.output_dir:
+ sto.close()
+ if self.opts.make_HTML:
+ self.makeHtml()
+ return retval
+
+
+def main():
+ u = """
+ This is a Galaxy wrapper. It expects to be called by a special purpose tool.xml as:
+ rgBaseScriptWrapper.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript"
+
+ """
+ op = optparse.OptionParser()
+ a = op.add_option
+ a('--script_path',default=None)
+ a('--tool_name',default=None)
+ a('--interpreter',default=None)
+ a('--output_dir',default='./')
+ a('--output_html',default=None)
+ a('--input_tab',default=[], action="append")
+ a("--input_formats",default="tabular")
+ a('--output_tab',default="None")
+ a('--output_format',default='tabular')
+ a('--user_email',default='Unknown')
+ a('--bad_user',default=None)
+ a('--make_Tool',default=None)
+ a('--make_HTML',default=None)
+ a('--help_text',default=None)
+ a('--tool_desc',default=None)
+ a('--new_tool',default=None)
+ a('--tool_version',default=None)
+ a('--include_dependencies',default=None)
+ opts, args = op.parse_args()
+ assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user)
+ assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq'
+ assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript'
+ assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R'
+ if opts.output_dir:
+ try:
+ os.makedirs(opts.output_dir)
+ except:
+ pass
+ opts.input_tab = [x.replace('"','').replace("'",'') for x in opts.input_tab]
+ r = ScriptRunner(opts)
+ if opts.make_Tool:
+ retcode = r.makeTooltar()
+ else:
+ retcode = r.run()
+ os.unlink(r.sfile)
+ if retcode:
+ sys.exit(retcode) # indicate failure to job runner
+
+
+if __name__ == "__main__":
+ main()
+
+
diff -r ca60c96f0beb -r c6fdf2c6d0f4 rgToolFactoryMultIn.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgToolFactoryMultIn.xml Thu Aug 28 02:33:05 2014 -0400
@@ -0,0 +1,343 @@
+
+ Makes scripts into tools
+
+ ghostscript
+ graphicsmagick
+
+
+#if ( $__user_email__ not in $__admin_users__ ):
+ rgToolFactoryMultIn.py --bad_user $__user_email__
+#else:
+ rgToolFactoryMultIn.py --script_path "$runme" --interpreter "$interpreter"
+ --tool_name "$tool_name" --user_email "$__user_email__"
+ #if $make_TAB.value=="yes":
+ --output_tab "$output1"
+ --output_format "$output_format"
+ #end if
+ #if $makeMode.make_Tool=="yes":
+ --make_Tool "$makeMode.make_Tool"
+ --tool_desc "$makeMode.tool_desc"
+ --tool_version "$makeMode.tool_version"
+ --new_tool "$new_tool"
+ --help_text "$helpme"
+ #if $make_HTML.value=="yes":
+ #if $makeMode.include_deps.value=="yes":
+ --include_dependencies "yes"
+ #end if
+ #end if
+ #end if
+ #if $make_HTML.value=="yes":
+ --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"
+ #else:
+ --output_dir "."
+ #end if
+ #if $input1 != 'None':
+ --input_formats "$input_formats"
+ #for intab in $input1:
+ #if $add_names.value == "yes":
+ --input_tab "$intab,$intab.name"
+ #else:
+ --input_tab "$intab"
+ #end if
+ #end for
+ --input_formats = "$input_formats"
+ #end if
+#end if
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ make_TAB=="yes"
+
+
+
+
+
+
+
+ make_HTML == "yes"
+
+
+ makeMode['make_Tool'] == "yes"
+
+
+
+$dynScript
+
+#if $makeMode.make_Tool == "yes":
+${makeMode.help_text}
+#end if
+
+
+
+
+.. class:: warningmark
+
+**Details and attribution** GTF_
+
+**Local Admins ONLY** Only users whose IDs found in the local admin_user configuration setting in universe_wsgi.ini can run this tool.
+
+**If you find a bug** please raise an issue at the bitbucket repository GTFI_
+
+**What it does** This tool enables a user to paste and submit an arbitrary R/python/perl script to Galaxy.
+
+**Input options** This version is limited to simple transformation or reporting requiring only a single input file selected from the history.
+
+**Output options** Optional script outputs include one single new history tabular file, or for scripts that create multiple outputs,
+a new HTML report linking all the files and images created by the script can be automatically generated.
+
+**Tool Generation option** Once the script is working with test data, this tool will optionally generate a new Galaxy tool in a gzip file
+ready to upload to your local toolshed for sharing and installation. Provide a small sample input when you run generate the tool because
+it will become the input for the generated functional test.
+
+.. class:: warningmark
+
+**Note to system administrators** This tool offers *NO* built in protection against malicious scripts. It should only be installed on private/personnal Galaxy instances.
+Admin_users will have the power to do anything they want as the Galaxy user if you install this tool.
+
+.. class:: warningmark
+
+**Use on public servers** is STRONGLY discouraged for obvious reasons
+
+The tools generated by this tool will run just as securely as any other normal installed Galaxy tool but like any other new tools, should always be checked carefully before installation.
+We recommend that you follow the good code hygiene practices associated with safe toolshed.
+
+**Scripting conventions** The pasted script will be executed with the path to the (optional) input tabular data file path or NONE if you do not select one, and the path to the optional
+output file or None if none is wanted, as the first and second command line parameters. The script must deal appropriately with these - see Rscript examples below.
+Note that if an optional HTML output is selected, all the output files created by the script will be nicely presented as links, with pdf images linked as thumbnails in that output.
+This can be handy for complex scripts creating lots of output.
+
+**Examples**
+ $OUTF
+
+A trivial perl script example to show that even perl works::
+
+ #
+ # change all occurances of a string in a file to another string
+ #
+ $oldfile = $ARGV[0];
+ $newfile = $ARGV[1];
+ $old = "gene";
+ $new = "foo";
+ open(OF, $oldfile);
+ open(NF, ">$newfile");
+ # read in each line of the file
+ while ($line = ) {
+ $line =~ s/$old/$new/;
+ print NF $line;
+ }
+ close(OF);
+ close(NF);
+
+]]>
+
+**Citation**
+
+
+Paper_ :
+
+Creating re-usable tools from scripts: The Galaxy Tool Factory
+Ross Lazarus; Antony Kaspi; Mark Ziemann; The Galaxy Team
+Bioinformatics 2012; doi: 10.1093/bioinformatics/bts573
+
+
+**Licensing**
+
+Copyright Ross Lazarus (ross period lazarus at gmail period com) May 2012
+All rights reserved.
+Licensed under the LGPL_
+
+.. _LGPL: http://www.gnu.org/copyleft/lesser.html
+.. _GTF: https://bitbucket.org/fubar/galaxytoolfactory
+.. _GTFI: https://bitbucket.org/fubar/galaxytoolfactory/issues
+.. _Paper: http://bioinformatics.oxfordjournals.org/cgi/reprint/bts573?ijkey=lczQh1sWrMwdYWJ&keytype=ref
+
+
+
+
+
+
+