view rgedgeR/rgedgeRpaired.xml @ 14:a4d7ec124c53 draft

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<tool id="rgedgeRpaired" name="edgeR" version="0.18">
  <description>1 or 2 level models for count data</description>
  <requirements>
      <requirement type="package" version="3.0.1">package_R</requirement>
      <requirement type="package" version="2.12">package_BioCBasics</requirement>
  </requirements>
  
  <command interpreter="python">
     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"
  </command>
  <inputs>
    <param name="input1"  type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample"
       help="Use the HTSeq based count matrix preparation tool to create these matrices from BAM/SAM files and a GTF file of genomic features"/>
    <param name="title" type="text" value="edgeR" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain">
      <sanitizer invalid_char="">
        <valid initial="string.letters,string.digits"><add value="_" /> </valid>
      </sanitizer>
    </param>
    <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/>
    <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True" 
         multiple="true" use_header_names="true" size="120" display="checkboxes">
        <validator type="no_options" message="Please select at least one column."/>
    </param>
    <param name="control_name" type="text" value="Control" size="50" label="Control Name"/>
    <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True" 
         multiple="true" use_header_names="true" size="120" display="checkboxes" optional="true">
    </param>
    <param name="subjectids" type="text" optional="true" size="120"
       label="IF SUBJECTS NOT ALL INDEPENDENT! Enter integers to indicate sample pairing for every column in input"
       help="Leave blank if no pairing, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter '1,2,1,2'">
      <sanitizer>
        <valid initial="string.digits"><add value="," /> </valid>
      </sanitizer>
    </param>
    <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs"
     help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/>
    <param name="useNDF" type="boolean" truevalue="T" checked='false' falsevalue="" size="1" label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples"
     help="May be a good or a bad idea depending on the biology and the question. This was the old default. Quantile based is available as an alternative"/>
    <param name="priordf" type="integer" value="20" size="3" label="prior.df for tagwise dispersion - lower value = more emphasis on each tag's variance. Replaces prior.n  and prior.df = prior.n * residual.df"
     help="Zero = Use edgeR default. Use a small value to 'smooth' small samples. See edgeR docs and note below"/>
    <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control"
     help="Conventional default value of 0.05 recommended"/>
    <param name="fdrtype" type="select" label="FDR (Type II error) control method" 
         help="Use fdr or bh typically to control for the number of tests in a reliable way">
            <option value="fdr" selected="true">fdr</option>
            <option value="BH">Benjamini Hochberg</option>
            <option value="BY">Benjamini Yukateli</option>
            <option value="bonferroni">Bonferroni</option>
            <option value="hochberg">Hochberg</option>
            <option value="holm">Holm</option>
            <option value="hommel">Hommel</option>
            <option value="none">no control for multiple tests</option>
    </param>
  </inputs>
  <outputs>
    <data format="tabular" name="outtab" label="${title}.xls"/>
    <data format="html" name="html_file" label="${title}.html"/>
  </outputs>
 <stdio>
     <exit_code range="4"   level="fatal"   description="Number of subject ids must match total number of samples in the input matrix" />
 </stdio>
 <tests>
<test>
<param name='input1' value='test_bams2mx.xls' ftype='tabular' />
 <param name='treatment_name' value='case' />
 <param name='title' value='edgeRtest' />
 <param name='fdrtype' value='fdr' />
 <param name='priordf' value="0" />
 <param name='fdrthresh' value="0.05" />
 <param name='control_name' value='control' />
 <param name='Treat_cols' value='3,4,5,9' />
 <param name='Control_cols' value='2,6,7,8' />
 <output name='outtab' file='edgeRtest1out.xls' ftype='tabular' compare='diff' />
 <output name='html_file' file='edgeRtest1out.html' ftype='html' compare='diff' lines_diff='20' />
</test>
</tests>

<configfiles>
<configfile name="runme">
<![CDATA[
# 
# edgeR.Rscript
# updated npv 2011 for R 2.14.0 and edgeR 2.4.0 by ross
# Performs DGE on a count table containing n replicates of two conditions
#
# Parameters
#
# 1 - Output Dir

# Original edgeR code by: S.Lunke and A.Kaspi
reallybig = log10(.Machine\$double.xmax)
reallysmall = log10(.Machine\$double.xmin)
library('stringr')
library('gplots')
library('DESeq')
library('edgeR')
hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here')
{
# Perform clustering for significant pvalues after controlling FWER
    samples = colnames(cmat)
    gu = unique(group)
    if (length(gu) == 2) {
        col.map = function(g) {if (g==gu[1]) "#FF0000" else "#0000FF"}
        pcols = unlist(lapply(group,col.map))        
        } else {
        colours = rainbow(length(gu),start=0,end=4/6)
        pcols = colours[match(group,gu)]        }
    gn = rownames(cmat)
    dm = cmat[(! is.na(gn)),] 
    # remove unlabelled hm rows
    nprobes = nrow(dm)
    # sub = paste('Showing',nprobes,'contigs ranked for evidence of differential abundance')
    if (nprobes > nsamp) {
      dm =dm[1:nsamp,]
      #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total')
    }
    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,height=6,width=8)
        #par(mfrow=c(1,2)) # 1 rows 2 col
        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)
{   # updated to use ecdf
        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)
{   # updated to use ecdf
        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()
}



edgeIt = function (Count_Matrix,group,outputfilename,fdrtype='fdr',priordf=5,fdrthresh=0.05,outputdir='.',
    myTitle='edgeR',libSize=c(),useNDF="T",filterquantile=0.2,subjects=c()) {

        # Error handling
        if (length(unique(group))!=2){
                print("Number of conditions identified in experiment does not equal 2")
                q()
        }
        require(edgeR)
        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),] # remove all zero count genes
        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]
        # Setup DGEList object
        DGEList = DGEList(counts=workCM, group = group)
        if (length(subjects) == 0) 
            {
               doDESEQ = T
               mydesign = model.matrix(~group)
            } 
            else { 
              doDESEQ = F
              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)
        }
        DGLM = glmFit(DGEList,design=mydesign)
        efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors
        normData = (1e+06*DGEList\$counts/efflib)
        co = length(colnames(mydesign))
        DE = glmLRT(DGLM,coef=co) # always last one - subject is first if needed
        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,normData,
                DGEList\$counts
        )
        soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable
        goodness = gof(DGLM, pcutoff=fdrthresh)
        if (sum(goodness\$outlier) > 0) {
            print.noquote('GLM outliers:')
            print(paste(rownames(DGLM)[(goodness\$outlier != 0)],collapse=','),quote=F)
            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="dodgerblue")
            dev.off()
            } else { print('No GLM fit outlier genes found\n')}
        estpriorn = getPriorN(DGEList)
        print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F)
        efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors
        normData = (1e+06*DGEList\$counts/efflib)
        uniqueg = unique(group)
        # Plot MDS
        sample_colors =  match(group,levels(group))
        pdf(paste(mt,"MDSplot.pdf",sep='_'))
        sampleTypes = levels(group)
        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(normData) = paste( colnames(normData),'N',sep="_")
        print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=',')))
        nzd = data.frame(log(nonzerod + 1e-2,10))
        boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle)
        if (doDESEQ)
        {
        # DESeq
        deSeqDatcount <- newCountDataSet(workCM, group)
        deSeqDatsizefac <- estimateSizeFactors(deSeqDatcount)
        deSeqDatdisp <- estimateDispersions(deSeqDatsizefac)
        rDESeq <- nbinomTest(deSeqDatdisp, levels(group)[1], levels(group)[2])
        rDESeq <- rDESeq[order(rDESeq\$pval), ]
        write.table(rDESeq,paste(mt,'DESeq_TopTable.xls',sep='_'), quote=FALSE, sep="\t",row.names=F)
        topresults.DESeq <- rDESeq[which(rDESeq\$padj < fdrthresh), ]
        DESeqcountsindex <- which(allgenes %in% topresults.DESeq\$id)
        DESeqcounts <- rep(0, length(allgenes))
        DESeqcounts[DESeqcountsindex] <- 1
        }
        DGEList = calcNormFactors(DGEList)
        norm.factor = DGEList\$samples\$norm.factors
        pdf(paste(mt,"voomplot.pdf",sep='_'))
        dat.voomed <- voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor)
        dev.off()
        # Use limma to fit data
        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)
        # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma
        topresults.voom <- rvoom[which(rvoom\$adj.P.Val < fdrthresh), ]
        topresults.edgeR <- soutput[which(soutput\$adj.p.value < fdrthresh), ]
        # Create venn diagram of hits
        edgeRcountsindex <- which(allgenes %in% rownames(topresults.edgeR))
        voomcountsindex <- which(allgenes %in% topresults.voom\$ID)
        edgeRcounts <- rep(0, length(allgenes))
        edgeRcounts[edgeRcountsindex] <- 1
        voomcounts <- rep(0, length(allgenes))
        voomcounts[voomcountsindex] <- 1
        if (doDESEQ) {
            vennmain = paste(mt,'Voom,edgeR and DESeq overlap at FDR=',fdrthresh)
            counts.dataframe <- data.frame(edgeRcounts = edgeRcounts, DESeqcounts = DESeqcounts, 
                voomcounts = voomcounts, row.names = allgenes)
        } else {
            vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh)
            counts.dataframe <- data.frame(edgeRcounts = edgeRcounts, voomcounts = voomcounts, row.names = allgenes)
        }
        counts.venn <- vennCounts(counts.dataframe)
        vennf = paste(mt,'venn.pdf',sep='_')
        pdf(vennf)
        vennDiagram(counts.venn,main=vennmain,col="maroon")
        dev.off()
        #Prepare our output file
        nreads = soutput\$totreads # ordered same way
        print('# writing output',quote=F)
        write.table(soutput,outputfilename, quote=FALSE, sep="\t",row.names=F)
        rn = uoutput\$Name
        reg = "^chr([0-9]+):([0-9]+)-([0-9]+)"
        org="hg19"
        genecards="<a href='http://www.genecards.org/index.php?path=/Search/keyword/"
        ucsc = paste("<a href='http://genome.ucsc.edu/cgi-bin/hgTracks?db=",org,sep='')
        testreg = str_match(rn,reg)
        nreads = uoutput\$totreads # ordered same way
        if (sum(!is.na(testreg[,1]))/length(testreg[,1]) > 0.8) # is ucsc style string
        {
          urls = paste(ucsc,'&amp;position=chr',testreg[,2],':',testreg[,3],"-",testreg[,4],"'>",rn,'</a>',sep='')
        } else {
          urls = paste(genecards,rn,"'></a>",rn,'</a>',sep="")
        }
        print.noquote('# urls')
        cat(head(urls))
        tt = uoutput
        cat("# Top tags\n")
        tt = cbind(tt,ntotreads=nreads,URL=urls) # add to end so table isn't laid out strangely
        tt = tt[order(DE\$table\$PValue),] 
        options(width = 500) 
        print.noquote(tt[1:50,])
        pdf(paste(mt,"BCV_vs_abundance.pdf",sep='_'))
        plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance")
        dev.off()
        # Plot MAplot
        deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,])
        nsig = length(deTags)
        print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F)
        print('# deTags',quote=F)
        print(head(deTags))
        dg = DGEList[order(DE\$table\$PValue),]
        #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg)))
        efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors
        normData = (1e+06*dg\$counts/efflib)
        outpdfname=paste(mt,"heatmap.pdf",sep='_')
        hmap2(normData,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) {
           cat("# DESeq top 50\n")
           print(rDESeq[1:50,])
        }
        cat("# VOOM top 50\n")
        print(rvoom[1:50,])
        # need a design matrix and glm to use this
        goodness = gof(DGLM, pcutoff=fdrthresh)
        nout = sum(goodness\$outlier)
        if (nout > 0) {
            print.noquote(paste('Found',nout,'Goodness of fit outliers'))
            rownames(DGLM)[goodness\$outlier]
            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="dodgerblue")
            dev.off()
            }
        #Return our main table
        uoutput 
  
}       #Done
sink(stdout(),append=T,type="message")
options(width=512)
Out_Dir = "$html_file.files_path"
Input =  "$input1"
TreatmentName = "$treatment_name"
TreatmentCols = "$Treat_cols"
ControlName = "$control_name"
ControlCols= "$Control_cols"
outputfilename = "$outtab"
fdrtype = "$fdrtype"
priordf = $priordf
fdrthresh = $fdrthresh
useNDF = "$useNDF"
fQ = $fQ # non-differential centile cutoff
myTitle = "$title"
subjects = c($subjectids)
nsubj = length(subjects)
#Set our columns 
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)
# Create output dir if non existent
  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())
#print(mess)
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)) )             #Build a group descriptor
group = factor(group, levels=c(ControlName,TreatmentName))
colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_")                   #Relable columns
results = edgeIt(Count_Matrix=Count_Matrix,group=group,outputfilename=outputfilename,fdrtype=fdrtype,priordf=priordf,fdrthresh=fdrthresh,
   outputdir=Out_Dir,myTitle=myTitle,libSize=c(),useNDF=useNDF,filterquantile=fQ,subjects=subjects) 
#Run the main function
# for the log
sessionInfo()
]]>
</configfile>
</configfiles>
<help>
**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 matrix which consists the original data and relative expression levels and some helpful plots

**Note on edgeR versions**

The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue) 
breaking this and all other code that assumed the old name for this variable, 
between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). 
This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing 
to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly
when their old scripts break. This tool currently now works with 2.4.6.

**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.

</help>

</tool>