# HG changeset patch # User fubar # Date 1371020323 14400 # Node ID 82e0af5661602a9a6308e96904ead0d24c139844 Uploaded diff -r 000000000000 -r 82e0af566160 rgedgeR/datatypes_conf_xml.sample --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/datatypes_conf_xml.sample Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,2 @@ + + diff -r 000000000000 -r 82e0af566160 rgedgeR/installBioC.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/installBioC.R Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,4 @@ +source('http://bioconductor.org/biocLite.R') +installme=c('edgeR','limma','DESeq','DEXSeq','ggplot2','gplots') +biocLite() +biocLite(installme) diff -r 000000000000 -r 82e0af566160 rgedgeR/rgGSEA.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/rgGSEA.py Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,494 @@ +""" +April 2013 +eeesh GSEA does NOT respect the mode flag! + +Now realise that the creation of the input rank file for gsea needs to take the lowest p value for duplicate +feature names. To make Ish's life easier, remove duplicate gene ids from any gene set to stop GSEA from +barfing. + +October 14 2012 +Amazingly long time to figure out that GSEA fails with useless error message if any filename contains a dash "-" +eesh. + +Added history .gmt source - requires passing a faked name to gsea +Wrapper for GSEA http://www.broadinstitute.org/gsea/index.jsp +Started Feb 22 +Copyright 2012 Ross Lazarus +All rights reserved +Licensed under the LGPL + +called eg as + +#!/bin/sh +GALAXY_LIB="/data/extended/galaxy/lib" +if [ "$GALAXY_LIB" != "None" ]; then + if [ -n "$PYTHONPATH" ]; then + PYTHONPATH="$GALAXY_LIB:$PYTHONPATH" + else + PYTHONPATH="$GALAXY_LIB" + fi + export PYTHONPATH +fi + +cd /data/extended/galaxy/database/job_working_directory/027/27311 +python /data/extended/galaxy/tools/rgenetics/rgGSEA.py --input_tab "/data/extended/galaxy/database/files/033/dataset_33806.dat" --adjpvalcol "5" --signcol "2" +--idcol "1" --outhtml "/data/extended/galaxy/database/files/034/dataset_34455.dat" --input_name "actaearly-Controlearly-actalate-Controllate_topTable.xls" +--setMax "500" --setMin "15" --nPerm "1000" --plotTop "20" +--gsea_jar "/data/extended/galaxy/tool-data/shared/jars/gsea2-2.0.12.jar" +--output_dir "/data/extended/galaxy/database/job_working_directory/027/27311/dataset_34455_files" --mode "Max_probe" + --title " actaearly-Controlearly-actalate-Controllate_interpro_GSEA" --builtin_gmt "/data/genomes/gsea/3.1/IPR_DOMAIN.gmt" + + +""" +import optparse +import tempfile +import os +import sys +import subprocess +import time +import shutil +import glob +import math +import re + +KEEPSELECTION = False # detailed records for selection of multiple probes + +def timenow(): + """return current time as a string + """ + return time.strftime('%d/%m/%Y %H:%M:%S', time.localtime(time.time())) + + + +def fix_subdir(adir,destdir): + """ Galaxy wants everything in the same files_dir + if os.path.exists(adir): + for (d,dirs,files) in os.path.walk(adir): + for f in files: + sauce = os.path.join(d,f) + shutil.copy(sauce,destdir) + """ + + def fixAffycrap(apath=''): + """class='richTable'>RUNNING ESCORE ENRICHMENT1 + LBR1113 + 0.194-0.1065No2 + GGPS143090.014-0.4328 + No + """ + html = [] + try: + html = open(apath,'r').readlines() + except: + return html + for i,row in enumerate(html): + row = re.sub('https\:\/\/www.affymetrix.com\/LinkServlet\?probeset=',"http://www.genecards.org/index.php?path=/Search/keyword/",row) + html[i] = row + return html + + cleanup = False + if os.path.exists(adir): + flist = os.listdir(adir) # get all files created + for f in flist: + apath = os.path.join(adir,f) + dest = os.path.join(destdir,f) + if not os.path.isdir(apath): + if os.path.splitext(f)[1].lower() == '.html': + html = fixAffycrap(apath) + fixed = open(apath,'w') + fixed.write('\n'.join(html)) + fixed.write('\n') + fixed.close() + if not os.path.isfile(dest): + shutil.copy(apath,dest) + else: + fix_subdir(apath,destdir) + if cleanup: + try: + shutil.rmtree(path=adir,ignore_errors=True) + except: + pass + + + +def getFileString(fpath, outpath): + """ + format a nice file size string + """ + size = '' + fp = os.path.join(outpath, fpath) + s = fpath + if os.path.isfile(fp): + 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)) + s = '%s %s' % (fpath, size) + return s + +class gsea_wrapper: + """ + GSEA java desktop client has a CL interface. CL can be gleaned by clicking the 'command line' button after setting up an analysis + We don't want gsea to do the analysis but it can read .rnk files containing rows of identifiers and an evidence weight such as the signed t statistic from limma for differential expression +(vgalaxy)rlazarus@iaas1:~/public_html/idle_illumina_analysis$ cat gseaHumanREFSEQ.sh +#!/bin/bash +for RNK in `ls *.rnk` +do +DIRNAME=${RNK%.*} +echo $DIRNAME +qsub -cwd -b y java -Xmx4096m -cp /data/app/bin/gsea2-2.07.jar xtools.gsea.GseaPreranked -gmx ../msigdb.v3.0.symbols.gmt -collapse true -mode Max_probe -norm meandiv +-nperm 1000 -rnk $RNK -scoring_scheme weighted -rpt_label $RNK -chip ../RefSeq_human.chip -include_only_symbols true -make_sets true -plot_top_x 20 -rnd_seed timestamp +-set_max 500 -set_min 15 -zip_report false -out gseaout/${DIRNAME} -gui false +done + """ + + def __init__(self,myName=None,opts=None): + """ setup cl for gsea + """ + self.idcol = 0 + self.signcol = 0 + self.adjpvalcol = 0 + self.progname=myName + self.opts = opts + remove_duplicates=True + if not os.path.isdir(opts.output_dir): + try: + os.makedirs(opts.output_dir) + except: + print >> sys.stderr,'##Error: GSEA wrapper unable to create or find output directory %s. Stopping' % (opts.output_dir) + sys.exit(1) + fakeGMT = re.sub('[^a-zA-Z0-9_]+', '', opts.input_name) # gives a more useful title for the GSEA report + fakeGMT = os.path.join(opts.output_dir,fakeGMT) + fakeGMT = os.path.abspath(fakeGMT) + fakeRanks = '%s.rnk' % fakeGMT + if not fakeGMT.endswith('.gmt'): + fakeGMT = '%s.gmt' % fakeGMT + if opts.builtin_gmt and opts.history_gmt: + newfile = open(fakeGMT,'w') + subprocess.call(['cat',opts.builtin_gmt,opts.history_gmt],stdout=newfile) + newfile.close() + elif opts.history_gmt: + subprocess.call(['cp',opts.history_gmt,fakeGMT]) + else: + subprocess.call(['cp',opts.builtin_gmt,fakeGMT]) + # remove dupes from each gene set + gmt = open(fakeGMT,'r').readlines() + gmt = [x for x in gmt if len(x.split('\t')) > 3] + ugmt = [] + for i,row in enumerate(gmt): + rows = row.rstrip().split('\t') + gmtname = rows[0] + gmtcomment = rows[1] + glist = list(set(rows[2:])) + newgmt = [gmtname,gmtcomment] + newgmt += glist + ugmt.append('\t'.join(newgmt)) + gmt = open(fakeGMT,'w') + gmt.write('\n'.join(ugmt)) + gmt.write('\n') + gmt.close() + if opts.input_ranks: + infname = opts.input_ranks + rdat = open(opts.input_ranks,'r').readlines() # suck in and remove blank ids that cause gsea to barf rml april 10 2012 + rdat = [x.rstrip().split('\t') for x in rdat[1:]] # ignore head + dat = [[x[0],x[1],x[1]] for x in rdat] + # fake same structure as input tabular file + try: + pvals = [float(x[1]) for x in dat] + signs = [float(x[1]) for x in dat] + except: + print >> sys.stderr, '## error converting floating point - cannot process this input' + sys.exit(99) + else: # read tabular + self.idcol = int(opts.idcol) - 1 + self.signcol = int(opts.signcol) - 1 + self.adjpvalcol = int(opts.adjpvalcol) - 1 + maxcol = max(self.idcol,self.signcol,self.adjpvalcol) + infname = opts.input_tab + indat = open(opts.input_tab,'r').readlines() + dat = [x.rstrip().split('\t') for x in indat[1:]] + dat = [x for x in dat if len(x) > maxcol] + dat = [[x[self.idcol],x[self.adjpvalcol],x[self.signcol]] for x in dat] # reduce to rank form + pvals = [float(x[1]) for x in dat] + outofrange = [x for x in pvals if ((x < 0.0) or (x > 1.0))] + assert len(outofrange) == 0, '## p values outside 0-1 encountered - was that the right column for adjusted p value?' + signs = [float(x[2]) for x in dat] + outofrange = [i for i,x in enumerate(signs) if (not x) and (dat[i][self.signcol] <> '0')] + bad = [dat[x][2] for x in outofrange] + assert len(outofrange) == 0, '## null numeric values encountered for sign - was that the right column? %s' % bad + ids = [x[0] for x in dat] + res = [] + self.comments = [] + useme = [] + for i,row in enumerate(dat): + if row[1].upper() != 'NA' and row[2].upper() != 'NA' and row[0] != '' : + useme.append(i) + lost = len(dat) - len(useme) + if lost <> 0: + newdat = [dat[x] for x in useme] + del dat + dat = newdat + print >> sys.stdout, '## %d lost - NA values or null id' % lost + if remove_duplicates: + uids = list(set(ids)) # complex procedure to get min pval for each unique id + if len(uids) <> len(ids): # dupes - deal with mode + print >> sys.stdout,'## Dealing with %d uids in %d ids' % (len(uids),len(ids)) + ures = {} + for i,id in enumerate(ids): + p = pvals[i] + ures.setdefault(id,[]) + ures[id].append((p,signs[i])) + for id in uids: + tlist = ures[id] + tp = [x[0] for x in tlist] + ts = [x[1] for x in tlist] + if len(tp) == 1: + p = tp[0] + sign = ts[0] + else: + if opts.mode == "Max_probe": + p = min(tp) + sign = ts[tp.index(p)] + else: # guess median - too bad if even count + tp.sort() + ltp = len(tp) + ind = ltp/2 # yes, this is wrong for evens but what if sign wobbles? + if ltp % 2 == 1: # odd + ind += 1 # take the median + p = tp[ind] + sign = ts[ind] + if KEEPSELECTION: + self.comments.append('## for id=%s, got tp=%s, ts=%s, chose p=%f, sign =%f'\ + % (id,str(tp),str(ts),p,sign)) + if opts.input_ranks: # must be a rank file + res.append((id,'%f' % p)) + else: + if p == 0.0: + p = 1e-99 + try: + lp = -math.log10(p) # large positive if low p value + except ValueError: + lp = 0.0 + if sign < 0: + lp = -lp # if negative, swap p to negative + res.append((id,'%f' % lp)) + else: # no duplicates + for i,row in enumerate(dat): + (id,p,sign) = (row[0],float(row[1]),float(row[2])) + if opts.input_ranks: # must be a rank file + res.append((id,'%f' % p)) + else: + if p == 0.0: + p = 1e-99 + try: + lp = -math.log10(p) # large positive if low p value + except ValueError: + lp = 0.0 + if sign < 0: + lp = -lp # if negative, swap p to negative + res.append((id,'%f' % lp)) + else: + for i,row in enumerate(dat): + (id,p,sign) = (row[0],float(row[1]),float(row[2])) + if opts.input_ranks: # must be a rank file + res.append((id,'%f' % p)) + else: + if p == 0.0: + p = 1e-99 + try: + lp = -math.log10(p) # large positive if low p value + except ValueError: + lp = 0.0 + if sign < 0: + lp = -lp # if negative, swap p to negative + res.append((id,'%f' % lp)) + len1 = len(ids) + len2 = len(ranks) + delta = len1 - len2 + if delta <> 0: + print >> sys.stdout,'NOTE: %d of %d rank input file %s rows deleted - dup, null or NA IDs, pvals or logFCs' % (delta,len1,infname) + ranks = [(float(x[1]),x) for x in ranks] # decorate + ranks.sort() + ranks.reverse() + ranks = [x[1] for x in ranks] # undecorate + if opts.chip == '': # if mouse - need HUGO + ranks = [[x[0].upper(),x[1]] for x in ranks] + print >> sys.stdout, '## Fixed any lower case - now have',','.join([x[0] for x in ranks[:5]]) + ranks = ['\t'.join(x) for x in ranks] + if len(ranks) < 2: + print >> sys.stderr,'Input %s has 1 or less rows with two tab delimited fields - please check the tool documentation' % infname + sys.exit(1) + print '### opening %s and writing %s' % (fakeRanks,str(ranks[:10])) + rclean = open(fakeRanks,'w') + rclean.write('contig\tscore\n') + rclean.write('\n'.join(ranks)) + rclean.write('\n') + rclean.close() + cl = [] + a = cl.append + a('java -Xmx6G -cp') + a(opts.gsea_jar) + a('xtools.gsea.GseaPreranked') + a('-gmx %s' % fakeGMT) # ensure .gmt extension as required by GSEA - gene sets to use + a('-gui false') # use preranked file mode and no gui + a('-make_sets true -rnd_seed timestamp') # more things from the GUI command line display + a('-norm meandiv -zip_report false -scoring_scheme weighted') # ? need to set these? + a('-rnk %s' % fakeRanks) # input ranks file symbol (the chip file is the crosswalk for ids in first column) + a('-out %s' % opts.output_dir) + a('-set_max %s' % opts.setMax) + a('-set_min %s' % opts.setMin) + a('-mode %s' % opts.mode) + if opts.chip > '': + #a('-chip %s -collapse true -include_only_symbols true' % opts.chip) + a('-chip %s -collapse true' % opts.chip) + else: + a("-collapse false") # needed if no chip + a('-nperm %s' % opts.nPerm) + a('-rpt_label %s' % opts.title) + a('-plot_top_x %s' % opts.plotTop) + self.cl = cl + self.comments.append('## GSEA command line:') + self.comments.append(' '.join(self.cl)) + self.fakeRanks = fakeRanks + self.fakeGMT = fakeGMT + + def grepIds(self): + """ + """ + found = [] + allids = open(self.opts.input_ranks,'r').readlines() + allids = [x.strip().split() for x in allids] + allids = [x[0] for x in allids] # list of ids + gmtpath = os.path.split(self.opts.fakeGMT)[0] # get path to all chip + + def run(self): + """ + + """ + tlog = os.path.join(self.opts.output_dir,"gsea_runner.log") + sto = open(tlog,'w') + x = subprocess.Popen(' '.join(self.cl),shell=True,stdout=sto,stderr=sto,cwd=self.opts.output_dir) + retval = x.wait() + sto.close() + d = glob.glob(os.path.join(self.opts.output_dir,'%s*' % self.opts.title)) + if len(d) > 0: + fix_subdir(d[0],self.opts.output_dir) + htmlfname = os.path.join(self.opts.output_dir,'index.html') + try: + html = open(htmlfname,'r').readlines() + html = [x.strip() for x in html if len(x.strip()) > 0] + if len(self.comments) > 0: + s = ['
']
+                s += self.comments
+                s.append('
') + try: + i = html.index('\n') + if len(fhtml) > 0: + fhtml.insert(0,'
\n') + fhtml.append('
Output File Name (click to view)Size

') + 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) + rlog = open(self.tlog,'r').readlines() + rlog = [x for x in rlog if x.strip() > ''] + if len(rlog) > 1: + html.append('
%s log
\n' % self.opts.interpreter)
+            html += rlog
+            html.append('
\n') + html.append(galhtmlattr % (self.toolname)) + 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: + 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=sto,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() + 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 "$scriptPath" --tool_name "foo" --interpreter "Rscript" + + """ + op = optparse.OptionParser() + a = op.add_option + a('--script_path',default=None) + a('--tool_name',default=None) + a('--interpreter',default=None) + a('--output_dir',default=None) + a('--output_html',default=None) + a('--input_tab',default="None") + a('--output_tab',default="None") + a('--user_email',default='Unknown') + a('--bad_user',default=None) + a('--make_Tool',default=None) + a('--make_HTML',default=None) + a('--help_text',default=None) + a('--tool_desc',default=None) + a('--new_tool',default=None) + a('--tool_version',default=None) + opts, args = op.parse_args() + assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user) + assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq' + assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript' + assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R' + if opts.output_dir: + try: + os.makedirs(opts.output_dir) + except: + pass + r = ScriptRunner(opts) + if opts.make_Tool: + retcode = r.makeTooltar() + else: + retcode = r.run() + os.unlink(r.sfile) + if retcode: + sys.exit(retcode) # indicate failure to job runner + + +if __name__ == "__main__": + main() + + diff -r 000000000000 -r 82e0af566160 rgedgeR/rgedgeR.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/rgedgeR.xml Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,504 @@ + + digital DGE between two groups of replicates + + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "edgeR" + --output_dir "$html_file.files_path" --output_html "$html_file" --output_tab "$outtab" --make_HTML "yes" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + makeRank == 'Yes' + + + + + +# 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 +sink(stdout(),append=T,type="message") +reallybig = log10(.Machine\$double.xmax) +reallysmall = log10(.Machine\$double.xmin) +require('stringr') +require('gplots') +library('ggplot2') +library('gridExtra') + +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)] } + print(paste('pcols',pcols)) + 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(rawdat,tmdat,maint,myTitle) + { + # give up on boxplot - it's just too buggy + rscolnames = substr(colnames(rawdat),1,25) + colnames(rawdat) = rscolnames + ccolnames = substr(colnames(tmdat),1,25) + colnames(tmdat) = ccolnames + print(paste('rawdat',paste(rscolnames,collapse=','))) + print(paste('tmdat',paste(ccolnames,collapse=','))) + pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"sampleBoxplot.pdf",sep='_') + raw = data.frame(rawdat) + cn = rscolnames + rdat = reshape(raw, direction="long",varying=list(cn),v.names="counts",times=cn) + rdat\$Sample = factor(rdat\$time,levels=cn) + rdat\$Counts = log(rdat\$counts + 0.1) + p1 = ggplot(rdat,aes(x=Sample,y=Counts)) + geom_boxplot(notch=T) + ylab("log Count") + p1 = p1 + theme(axis.text.x = element_text(angle=90, size=9)) + ggtitle('Raw Contig Counts') + raw = data.frame(tmdat) + cn = ccolnames + rdat = reshape(raw, direction="long",varying=list(cn),v.names="counts",times=cn) + rdat\$Sample = factor(rdat\$time,levels=cn) + rdat\$Counts = log(rdat\$counts + 0.1) + p2 = ggplot(rdat,aes(x=Sample,y=Counts)) + geom_boxplot(notch=T) + ylab("log Count") + p2 = p2 + theme(axis.text.x = element_text(angle=90, size=9)) + ggtitle('Normalised Contig Counts') + pdf(pdfname) + grid.arrange(p1,p2,nrow=1) + dev.off() +} + +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',priorn=5,fdrthresh=0.05,outputdir='.', + myTitle='edgeR',libSize=c(),useQuantile="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 (useQuantile == "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) + + print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F) + rsums = rowSums(workCM) + # Setup DGEList object + DGEList = DGEList(counts=workCM, group = group) + #Extract T v C names + TName=unique(group)[1] + CName=unique(group)[2] + if (length(subjects) == 0) { mydesign = model.matrix(~group) + } else { sf = factor(subjects) + mydesign = model.matrix(~sf+group) + } + print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=','))) + print.noquote('Using design matrix:') + print.noquote(mydesign) + print.noquote(paste("prior.df =",priorn)) + DGEList = calcNormFactors(DGEList) + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + comdisp = DGEList\$common.dispersion + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + DGLM = glmFit(DGEList,design=mydesign) + co = length(colnames(mydesign)) + DE = glmLRT(DGLM,coef=co) # always last one - subject is first if needed + goodness = gof(DGLM, pcutoff=fdrthresh) + if (sum(goodness\$outlier) > 0) { + print.noquote('GLM outliers:') + print.noquote(rownames(DE)[(goodness\$outlier != 0)]) + z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) + pdf(paste(outputdir,paste(mt,"GoodnessofFit.pdf",sep='_'),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) + #normData = (1e+06 * DGEList\$counts/expandAsMatrix(DGEList\$samples\$lib.size, dim(DGEList))) + 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(rawdat=nzd,tmdat=normData,maint='TMM Normalisation',myTitle=myTitle) + #Prepare our output file + output = 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 = output[order(output\$PVal),] # sorted into p value order - for quick toptable + nreads = soutput\$totreads # ordered same way + print('# writing output',quote=F) + write.table(soutput,outputfilename, quote=FALSE, sep="\t",row.names=F) + tt = topTags(DE,n=nrow(DE)) + rn = rownames(tt\$table) + 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) + if (sum(!is.na(testreg[,1]))/length(testreg[,1]) > 0.9) # is ucsc style string + { + urls = paste(ucsc,'&position=chr',testreg[,2],':',testreg[,3],"-",testreg[,4],"'>",rn,'</a>',sep='') + } else { + urls = paste(genecards,rn,"'>",rn,'</a>',sep="") + } + cat("# Top tags\n") + tt\$table = cbind(tt\$table,ntotreads=nreads,URL=urls) # add to end so table isn't laid out strangely + print(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 + fname = gsub(' ','_',myTitle,fixed=T) + deTags = rownames(output[output\$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(outputdir,paste(fname,"Smearplot.pdf",sep='_'),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) + # Plot MDS + ug = unique(group) + sample_colors = match(DGEList\$samples\$group,ug) #ifelse (DGEList\$samples\$group==group[1], 1, 2) + pdf(paste(outputdir,paste(fname,"MDSplot.pdf",sep='_'),sep='/')) + plotMDS.DGEList(DGEList,main=paste("MDS Plot for",TName,'Vs',CName),cex=0.5,col=sample_colors,pch=sample_colors) + legend(x="topleft", legend = c(group[1],group[length(group)]),col=c(1,2), pch=19) + grid(col="blue") + dev.off() + if (FALSE==TRUE) { + # need a design matrix and glm to use this + glmfit = glmFit(DGEList, design) + goodness = gof(glmfit, pcutoff=fdrpval) + sum(goodness\$outlier) + rownames(d)[goodness\$outlier] + z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) + pdf(paste(outputdir,paste(fname,"GoodnessofFit.pdf",sep='_'),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 + output + +} #Done + +options(width=512) +Out_Dir = "$html_file.files_path" +Input = "$input1" +ORG = "$input1.dbkey" +TreatmentName = "$treatment_name" +TreatmentCols = "$Treat_cols" +ControlName = "$control_name" +ControlCols= "$Control_cols" +outputfilename = "$outtab" +fdrtype = "$fdrtype" +priorn = $priorn +fdrthresh = $fdrthresh +useQuantile = "$useQuantile" +fQ = $fQ # non-differential centile cutoff +myTitle = "$title" +makeRank = "$makeRank" +outgsea = "" +if (makeRank > "") outgsea = "$outgsea" +#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') + + +# 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 +Count_Matrix = Count_Matrix[,c(TCols,CCols)] +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 +if (priorn <= 0) {priorn = ceiling(20/(length(group)-1))} # estimate prior.n if not provided +# see http://comments.gmane.org/gmane.comp.lang.r.sequencing/2009 +results = edgeIt(Count_Matrix=Count_Matrix,group=group,outputfilename=outputfilename,fdrtype=fdrtype,priorn=priorn,fdrthresh=fdrthresh, + outputdir=Out_Dir,myTitle=myTitle,libSize=c(),useQuantile=useQuantile,filterquantile=fQ) #Run the main function +# for the log + + +sessionInfo() + + + + + + + + + + + + + + + + + + + + +**What it does** + +Performs digital gene expression analysis between a treatment and control on a matrix. + +**Documentation** Please see documentation_ for methods and parameter details + +**Input** + +A matrix consisting of non-negative integers. The matrix must have a unique header row identifiying the samples, as well as a unique set of row names +as the first column. + +**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. + +**Attribution** Copyright Ross Lazarus (ross period lazarus at gmail period com) May 2012 +Derived from the implementation by Antony Kaspi and Sebastian Lunke at the BakerIDI + +All rights reserved. + +Licensed under the LGPL_ + +.. _LGPL: http://www.gnu.org/copyleft/lesser.html +.. _documentation: http://bioconductor.org/packages/release/bioc/html/edgeR.html + + + + + diff -r 000000000000 -r 82e0af566160 rgedgeR/rgedgeRglm.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/rgedgeRglm.xml Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,537 @@ + + + digital DGE glm + + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "edgeRglm" + --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +# edgeR.Rscript +# new nov 2012 for 2x2 factorial designs +# updated nov 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 +# + +reallybig = log10(.Machine\$double.xmax) +reallysmall = log10(.Machine\$double.xmin) +require('stringr') +require('gplots') +hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here') +{ + 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)] } + print(paste('pcols',pcols)) + gn = rownames(cmat) + dm = cmat[(! is.na(gn)),] + nprobes = nrow(dm) + if (nprobes > 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") +{ + 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, ...) +{ + 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) +{ + newcolnames = substr(colnames(rawrs),1,15) + colnames(rawrs) = newcolnames + newcolnames = substr(colnames(cleanrs),1,15) + 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)) + boxplot(rawrs,main=paste('Before:',maint),col="maroon",las=3,cex.axis=0.4) + grid(col="blue") + lrs = log(cleanrs,10) + boxplot(cleanrs,main=paste('After:',maint),col="maroon",las=3,cex.axis=0.4) + grid(col="blue") + 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() +} + + + +edgeIt = function (Count_Matrix,group,outtab1,outtab2,outtab3,fdrtype='fdr',priorn=5,fdrthresh=0.05,outputdir='.', + myTitle='edgeR',libSize=c(),useQuantile=T,filterquantile=0.2,org='hg19') +{ + + if (length(unique(group))!=4) { + print.noquote("Number of conditions identified in experiment does not equal 4 - full 2x2 factorial not possible") + q() + } + require(edgeR) + require(limma) + mt = paste(unlist(strsplit(myTitle,' ')),collapse="") + expfact = factor(group,levels=unique(group)) + sampleTypes = levels(expfact) + mycontrast = NA + colnamesDesign = list() + 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.noquote('Quantiles for non-zero row counts:') + print.noquote(quantile(nzrs,probs=seq(0,1,0.1))) + if (useQuantile == "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.noquote(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs")) + 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.noquote(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs")) + maint = paste('Filter below',filterquantile,'quantile') + } + cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle) + print.noquote(paste("Total low count contigs per sample = ",paste(lo,collapse=','))) + rsums = rowSums(workCM) + allttn = c() + colnamesDesign = list() + l = sampleTypes + p1=paste(l[1],'-',l[2],sep='') + p2=paste(l[3],'-',l[4],sep='') + p3=paste('(',l[3],'-',l[4],') - (',l[1],'-',l[2],')',sep='') + colnamesDesign = list(comp1=p1,comp2=p2,diff=p3) + mydesign = model.matrix(~0+expfact) + colnames(mydesign) = sampleTypes + mycontrast = makeContrasts(contrasts=colnamesDesign,levels=mydesign) + print.noquote('Design matrix=') + print(mydesign,quote=F) + print.noquote('Contrast matrix=') + print(mycontrast,quote=F) + print.noquote('Samples=') + print(colnames(workCM),quote=F) + DGEList = DGEList(counts=workCM, group = group) + print.noquote(paste("prior.df =",priorn)) + DGEList = calcNormFactors(DGEList) + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + comdisp = DGEList\$common.dispersion + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + pdf(paste(mt,"BCV_vs_abundance.pdf",sep='_')) + plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance") + dev.off() + 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) + #normData = (1e+06 * DGEList\$counts/expandAsMatrix(DGEList\$samples\$lib.size, dim(DGEList))) + colnames(normData) = paste( colnames(normData),'N',sep="_") + boxPlot(rawrs=log(nonzerod),cleanrs=log(normData),maint='TMM Normalisation (log scale)',myTitle=myTitle) + ug = unique(group) + ncond = length(ug) + sample_colors = match(DGEList\$samples\$group,ug) + pdf(paste(outputdir,paste(mt,"MDSplot.pdf",sep='_'),sep='/')) + plotMDS.DGEList(DGEList,main=paste("MDS Plot for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors) + legend(x="topleft", legend = sampleTypes,col=sample_colors, pch=19) + grid(col="blue") + dev.off() + cn = colnames(Count_Matrix) + print('Using design matrix:') + print(mydesign) + DGLM = glmFit(DGEList, mydesign) + 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')} + outtabs = c(outtab1,outtab2,outtab3) + for (coeff in c(1:3)) + { + DE = glmLRT(DGLM,contrast=mycontrast[,coeff]) + cont = paste(unlist(strsplit(colnames(mycontrast)[coeff],' ')),collapse="") + output = 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 = output[order(output\$PVal),] + nreads = soutput\$totreads + write.table(soutput,outtabs[coeff], quote=FALSE, sep="\t",row.names=F) + print.noquote(paste("Top tags for",cont)) + 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='') + tt = topTags(DE,n=nrow(DE)) + rn = rownames(tt\$table) + reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" + testreg = str_match(rn,reg) + isucsc = F + if (!is.na(testreg[1,])) {isucsc = T} + if (isucsc == T) + { + urls = paste(ucsc,'&position=chr',testreg[,2],':',testreg[,3],"-",testreg[,4],"'>",rn,'</a>',sep='') + } else { + urls = paste(genecards,rn,"'>",rn,'</a>',sep="") + } + tt\$table = cbind(tt\$table,ntotreads=nreads,geneCardslink=urls) + print.noquote(tt[1:50,]) + deTags = rownames(output[output\$adj.p.value < fdrthresh,]) + nsig = length(deTags) + print(paste(nsig,'tags significant at adj p=',fdrthresh),quote=F) + outSmear = paste(outputdir,paste(mt,cont,"Smearplot.pdf",sep='_'),sep='/') + outMain = paste(cont,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') + smearPlot(DGEList=DE,deTags=deTags, outSmear=outSmear, outMain = outMain) + qqPlot(descr=paste(myTitle,cont),pvector=DE\$table\$PValue) + } +} + +rossDecide = function (object, adjust.method="BH", p.value=0.05,verbose=T) +{ + if (!is(object, "DGEExact") & !is(object, "DGELRT")) + stop("Need DGEExact or DGELRT object") + DE = decideTestsDGE(object, adjust.method = adjust.method, p.value = p.value) + sumDE = summary(DE) + rownames(sumDE)[1] = "DOWN" + rownames(sumDE)[3] = "UP" + sumDE = sumDE[c(3, 1), ] + if (verbose) { + cat("\n") + cat("------------------------------------------------------", + "\n") + cat(" Method for Selecting DEGs:", DEmethod, "\n") + cat(" Multiple Testing method: ", MTestmethod, "- pval", + PVcut, "\n") + cat("\n") + print.noquote(sumDE) + cat("------------------------------------------------------", + "\n") + } + return(DE) +} + +options(width=512) +Out_Dir = "$html_file.files_path" +Input = "$input1" +factor1name = "$factor1_name" +factor1 = as.factor(strsplit("$factor1",",")) +factor2name = "$factor2_name" +factor2 = as.factor(strsplit("$factor2",",")) +org = "$input1.dbkey" +outtabs = strsplit("$outtab",",") +fdrtype = "$fdrtype" +priorn = $priorn +fdrthresh = $fdrthresh +useNDfilt = "$useNDfilt" +fQ = $fQ +myTitle = "$title" + +print.noquote(paste('# got =',paste(CCols2,collapse=','))) +Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') +rn = rownames(Count_Matrix) +nsamp = length(colnames(Count_Matrix)) + +if (file.exists(Out_Dir) == F) dir.create(Out_Dir) +print.noquote(paste('Got nsamp = ',nsamp,'org=',org)) +islib = rn %in% c('librarySize','NotInBedRegions') +LibSizes = Count_Matrix[subset(rn,islib),][1] +Count_Matrix = Count_Matrix[subset(rn,! islib),] +print(paste('Got groups =',paste(group,collapse=' '))) +if (priorn <= 0) {priorn = ceiling(20/(length(group)-1))} +cn = colnames(Count_Matrix) +results = edgeIt(Count_Matrix,group=group,outtab1=outtab1,outtab2=outtab2,outtab3=outtab3,fdrtype,priorn,fdrthresh, + Out_Dir,myTitle,libSize=c(),useQuantile=useNDfilt,filterquantile=fQ,org=org) +options(width=80) +sessionInfo() + + + + + + + + + + + + + + + + + + + +**Executive Summary** +This is a Galaxy wrapper exposing the Bioconductor edgeR_ package. +There is a companion pairwise comparison tool but this is the two way model tool. +It will help you to analyse the main effects in a 2 way anova. + +**If things go wrong** +edgeR software is thoroughly documented at edgeR_Manual_ and the authors +provide excellent support on the Bioconductor users' mailing list for their code, but +if this tool fails to run properly in Galaxy, please make sure you +fill in the form and send the administrators a Galaxy bug report. +If you have questions about the edgeR code this tool wraps, please +try the Bioconductor users' list. + +**What data is it for?** +Like the pairwise version, it takes a count matrix with columns containing reads per contig (NOT transformed!) for +multiple replicates in comparison groups. The counts can be any kind of counts. It was designed for +short read sequence data - RNA-seq, miR-seq,...ChIP-seq. + +**Slightly Longer Version** +Performs digital gene expression factorial analysis for a 2x2 design. +Factorial designs are old but good if you want to get at the individual effects and interaction between two factors. +EG: a treatment applied to 2 different cell types, or two treatments applied in all 4 possible combinations (-a-b,-a,+b,+a-b,+a+b). +Data with replicates in the four groups is supplied as a count matrix. +The two primary comparisons are defined by selecting two sets of samples for comparison +best thought of as control and treatment (whatever that means) for each of the main comparisons. + +The interaction is defined as the difference between those two comparisons and is reported as a topTable as are the +primary comparisons. + +All comparisons are reported as separate tabular spreadsheets ordered by p value and a comprehensive summary is +provided in the html output. + +This code essentially embelishes the code described by Gordon Smythe in the limma documentation for a factorial +analysis. + +**Input** + +A matrix consisting of non-negative integers. The matrix must have a unique header row identifiying the samples, as well as a unique set of row names +as the first column. + +**Output** + +Tabular files which contain the statistical results and the raw and transformed counts and some colourful +and 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 - now replaced with prior.df** + +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. + + + .. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html + .. _edgeR_Manual: http://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf + + + + + + diff -r 000000000000 -r 82e0af566160 rgedgeR/rgedgeRpaired.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/rgedgeRpaired.xml Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,627 @@ + + 2 level Anova for counts + + 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" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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="",rn,'',sep='') + } else { + urls = paste(genecards,rn,"'>",rn,'',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() +]]> + + + +**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. + + + + + + diff -r 000000000000 -r 82e0af566160 rgedgeR/rgedgeRpaired.xml.iaas1 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/rgedgeRpaired.xml.iaas1 Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,627 @@ + + 2 level Anova for counts + + 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" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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="",rn,'',sep='') + } else { + urls = paste(genecards,rn,"'>",rn,'',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() +]]> + + + +**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. + + + + + + diff -r 000000000000 -r 82e0af566160 rgedgeR/test-data/edgeRtest1out.html --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/test-data/edgeRtest1out.html Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,813 @@ + + + + + + + + +
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Galaxy Tool "edgeR" run at 12/06/2013 16:42:51

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Image called edgeRtest_BCV_vs_abundance.pdfImage called edgeRtest_MDSplot.pdfImage called edgeRtest_RowsumCum.pdf
Image called edgeRtest_Smearplot.pdfImage called edgeRtest_heatmap.pdfImage called edgeRtest_pval_qq.pdf
Image called edgeRtest_sampleBoxplot.pdfImage called edgeRtest_samplehistplot.pdfImage called edgeRtest_venn.pdf
Image called edgeRtest_voomplot.pdf  
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Output File Name (click to view)Size
edgeR.Rscript19.7 KB)
edgeR_runner.log79.2 KB)
edgeRtest_BCV_vs_abundance.pdf16.4 KB)
edgeRtest_DESeq_TopTable.xls135.1 KB)
edgeRtest_MDSplot.pdf4.9 KB)
edgeRtest_RowsumCum.pdf6.3 KB)
edgeRtest_Smearplot.pdf16.7 KB)
edgeRtest_VOOM_topTable.xls131.4 KB)
edgeRtest_heatmap.pdf11.3 KB)
edgeRtest_pval_qq.pdf15.9 KB)
edgeRtest_sampleBoxplot.pdf7.8 KB)
edgeRtest_samplehistplot.pdf10.9 KB)
edgeRtest_venn.pdf9.7 KB)
edgeRtest_voomplot.pdf16.7 KB)

+
Rscript log
+
+## Toolfactory generated command line = Rscript - None /data/home/rlazarus/galaxy-central/database/files/000/dataset_2.dat
+
+Loading required package: gtools
+
+Loading required package: gdata
+
+gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
+
+gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
+
+Attaching package: ‘gdata’
+
+The following object(s) are masked from ‘package:stats’:
+
+    nobs
+
+The following object(s) are masked from ‘package:utils’:
+
+    object.size
+
+Loading required package: caTools
+
+Loading required package: grid
+
+Loading required package: KernSmooth
+
+KernSmooth 2.23 loaded
+
+Copyright M. P. Wand 1997-2009
+
+Loading required package: MASS
+
+Attaching package: ‘gplots’
+
+The following object(s) are masked from ‘package:stats’:
+
+    lowess
+
+Loading required package: Biobase
+
+Loading required package: BiocGenerics
+
+Loading required package: methods
+
+Attaching package: ‘BiocGenerics’
+
+The following object(s) are masked from ‘package:gdata’:
+
+    combine
+
+The following object(s) are masked from ‘package:stats’:
+
+    xtabs
+
+The following object(s) are masked from ‘package:base’:
+
+    anyDuplicated, cbind, colnames, duplicated, eval, Filter, Find,
+
+    get, intersect, lapply, Map, mapply, mget, order, paste, pmax,
+
+    pmax.int, pmin, pmin.int, Position, rbind, Reduce, rep.int,
+
+    rownames, sapply, setdiff, table, tapply, union, unique
+
+Welcome to Bioconductor
+
+    Vignettes contain introductory material; view with
+
+    'browseVignettes()'. To cite Bioconductor, see
+
+    'citation("Biobase")', and for packages 'citation("pkgname")'.
+
+Loading required package: locfit
+
+locfit 1.5-9.1 	 2013-03-22
+
+Loading required package: lattice
+
+Warning message:
+
+found methods to import for function ‘eapply’ but not the generic itself 
+
+Loading required package: limma
+
+Attaching package: ‘limma’
+
+The following object(s) are masked from ‘package:DESeq’:
+
+    plotMA
+
+# got TCols=2 3 4 8; CCols=1 5 6 7
+
+[1] # Quantiles for non-zero row counts:
+
+       0%       10%       20%       30%       40%       50%       60%       70%       80%       90%      100% 
+
+      1.0       1.0       2.0       3.0       4.0       8.0      13.0      24.0      86.6     753.0 2325567.0 
+
+[1] Read 3242 contigs. Removed 1494 with no reads. After filtering at count quantile =0.3, there are 1141 contigs
+
+[1] # Total low count contigs per sample =  145,111,155,120,170,67,203,86
+
+Calculating library sizes from column totals.
+
+[1] Using samples: case_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam,case_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam,case_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam,case_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam,control_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam,control_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam,control_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam,control_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam
+
+[1] Using design matrix:
+
+  (Intercept) groupcase
+
+1           1         1
+
+2           1         1
+
+3           1         1
+
+4           1         1
+
+5           1         0
+
+6           1         0
+
+7           1         0
+
+8           1         0
+
+attr(,"assign")
+
+[1] 0 1
+
+attr(,"contrasts")
+
+attr(,"contrasts")$group
+
+[1] contr.treatment
+
+[1] "No GLM fit outlier genes found\n"
+
+[1] Common Dispersion = 0.24088423096811 CV =  0.490799583300669 getPriorN =  3.33333333333333
+
+[1] "Raw sample read totals 1440960,1341813,2888924,1428365,2443751,1644652,1682104,1806045"
+
+[1] raw contig counts by sample:
+
+ case_X11706He_AGTTCC case_X11699He_GGCTAC case_X11698He_TAGCTT case_X11700He_CTTGTA control_X11706Liv_CA control_X11700Liv_AT control_X11698Liv_AC control_X11699Liv_AT
+
+ Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043      
+
+ 1st Qu.:0.0043       1st Qu.:0.0043       1st Qu.:0.0043       1st Qu.:0.0043       1st Qu.:0.3032       1st Qu.:0.0043       1st Qu.:0.3032       1st Qu.:0.0043      
+
+ Median :0.4786       Median :0.4786       Median :0.4786       Median :0.4786       Median :0.7789       Median :0.6031       Median :0.6998       Median :0.6031      
+
+ Mean   :0.9210       Mean   :0.9428       Mean   :1.0205       Mean   :0.9753       Mean   :1.0519       Mean   :1.0343       Mean   :0.9855       Mean   :0.9966      
+
+ 3rd Qu.:1.5770       3rd Qu.:1.5855       3rd Qu.:1.7161       3rd Qu.:1.6022       3rd Qu.:1.5410       3rd Qu.:1.6335       3rd Qu.:1.4473       3rd Qu.:1.5534      
+
+ Max.   :5.3609       Max.   :5.3589       Max.   :5.6967       Max.   :5.3702       Max.   :5.8209       Max.   :5.6905       Max.   :5.7999       Max.   :5.7215      
+
+ NA's   :902          NA's   :957          NA's   :821          NA's   :950          NA's   :650          NA's   :969          NA's   :664          NA's   :886         
+
+[1] normalised contig counts by sample:
+
+ case_X11706He_AGTTCC case_X11699He_GGCTAC case_X11698He_TAGCTT case_X11700He_CTTGTA control_X11706Liv_CA control_X11700Liv_AT control_X11698Liv_AC control_X11699Liv_AT
+
+ Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf        
+
+ 1st Qu.:-Inf         1st Qu.:-Inf         1st Qu.:-Inf         1st Qu.:-Inf         1st Qu.:   0         1st Qu.:-Inf         1st Qu.:   0         1st Qu.:-Inf        
+
+ Median :   0         Median :   0         Median :   0         Median :   0         Median :   0         Median :   0         Median :   0         Median :   0        
+
+ Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf        
+
+ 3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1        
+
+ Max.   :   5         Max.   :   5         Max.   :   6         Max.   :   5         Max.   :   6         Max.   :   6         Max.   :   6         Max.   :   5        
+
+[1] Using ncol rawrs= 8
+
+[1] # writing output
+
+[1] # urls
+
+0610005C13Rik 0610007N19Rik 0610008F07Rik 0610009L18Rik 0610012G03Rik 0610031O16Rik# Top tags
+
+                       Name      logFC    logCPM        LR       PValue  adj.p.value Dispersion totreads case_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam control_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam
+
+Mir122a             Mir122a -10.661348 12.537949 445.91203 5.594743e-99 6.383602e-96 0.10205510    90428                                                   6.644669                                               2.864648e+00                                               2.012369e+01                                               4.756523e+00                                                   2.084166e+04                                                   1.681895e+04
+
+Mir192               Mir192  -7.109019 17.247415 366.13166 1.301558e-81 7.425390e-79 0.07765487  2325567                                                1779.663879                                               1.294412e+03                                               4.493546e+03                                               1.933526e+03                                                   4.635990e+05                                                   3.403123e+05
+
+Mir208b             Mir208b  13.423720  9.822338 355.41782 2.801296e-79 1.065426e-76 0.12444059    14756                                                1245.907022                                               5.701390e+02                                               4.930344e+03                                               8.819024e+02                                                   7.453220e-01                                                   0.000000e+00
+
+Mir208a             Mir208a  11.977586  8.388290 348.81540 7.675451e-78 2.189422e-75 0.09936798     4638                                                 433.789590                                               7.125278e+02                                               1.006599e+03                                               6.497485e+02                                                   0.000000e+00                                                   0.000000e+00
+
+Mir499               Mir499  14.488216  8.612770 291.12794 2.823507e-65 6.443244e-63 0.13481620     6527                                                 608.439028                                               5.066577e+02                                               2.521609e+03                                               2.703574e+02                                                   0.000000e+00                                                   0.000000e+00
+
+Mir149               Mir149   6.869731  8.785665 253.01986 5.702930e-57 1.084507e-54 0.09834684     6164                                                 774.657675                                               4.595714e+02                                               1.710514e+03                                               7.663947e+02                                                   1.750400e+01                                                   6.246465e+00
+
+Mir490               Mir490   8.453930  6.909596 251.05610 1.528281e-56 2.491098e-54 0.09745005     1741                                                 209.881557                                               2.177498e+02                                               5.205387e+02                                               1.957941e+02                                                   1.038505e+00                                                   5.537224e-01
+
+Mir802               Mir802 -12.499678  6.629391 237.56161 1.337812e-53 1.908055e-51 0.10939981     1514                                                   0.000000                                               0.000000e+00                                               0.000000e+00                                               0.000000e+00                                                   3.056548e+02                                                   1.641034e+02
+
+Mir204               Mir204   7.371179  7.558996 237.12302 1.667365e-53 2.113848e-51 0.10292005     2601                                                 425.578854                                               3.264164e+02                                               6.462477e+02                                               3.754819e+02                                                   3.040281e+00                                                   6.923364e-01
+
+Mir1948             Mir1948  -7.583326  7.293456 204.79641 1.875897e-46 2.140399e-44 0.11833999     2404                                                   1.216112                                               6.923364e-01                                               1.661167e+00                                               1.636942e+00                                                   6.476848e+02                                                   3.852783e+02
+
+Mir3073             Mir3073 -11.748556  5.881462 200.03350 2.053625e-45 2.130170e-43 0.11068562      904                                                   0.000000                                               0.000000e+00                                               0.000000e+00                                               0.000000e+00                                                   2.073471e+02                                                   7.546467e+01
+
+Mir194-2           Mir194-2  -5.439827  7.860048 165.57239 6.859199e-38 6.521955e-36 0.10671020     3570                                                  10.520726                                               6.138532e+00                                               1.490644e+01                                               9.513045e+00                                                   9.214105e+02                                                   6.003547e+02
+
+Mir10b               Mir10b   5.032898 13.880770 160.06580 1.094640e-36 9.607570e-35 0.10795434   197340                                               25838.822630                                               3.216361e+04                                               3.631730e+04                                               3.446522e+04                                                   1.825385e+03                                                   3.738617e+02
+
+Mir101b             Mir101b  -3.925287 11.936090 120.62427 4.618143e-28 3.763787e-26 0.09663075    59019                                                 219.816815                                               3.012250e+02                                               8.119232e+02                                               4.270695e+02                                                   1.014507e+04                                                   7.747970e+03
+
+Mir182               Mir182  -5.244384  8.942708 118.92815 1.085926e-27 8.260276e-26 0.14993443     7189                                                  16.962242                                               1.439678e+01                                               2.209871e+01                                               3.130352e+01                                                   9.840056e+02                                                   6.154406e+02
+
+Mir200b             Mir200b  -6.725348  5.765187 115.73189 5.440989e-27 3.880105e-25 0.16712490      888                                                   2.432225                                               0.000000e+00                                               1.661167e+00                                               0.000000e+00                                                   3.696797e+02                                                   9.750871e+01
+
+Mir181c             Mir181c   3.611867 10.569815 107.82011 2.943153e-25 1.975375e-23 0.09385270    23605                                                2420.852087                                               1.892879e+03                                               4.093093e+03                                               1.902590e+03                                                   2.320365e+02                                                   2.727878e+02
+
+Mir181d             Mir181d   3.489976  7.232064 102.18661 5.053019e-24 3.203053e-22 0.08738498     2139                                                 310.053946                                               2.021522e+02                                               5.398233e+02                                               2.956660e+02                                                   3.648337e+01                                                   1.453906e+01
+
+Mir21                 Mir21  -3.077771 13.881500  91.37324 1.189745e-21 7.144733e-20 0.08494272   229120                                                3207.212585                                               2.190826e+03                                               4.348911e+03                                               2.010566e+03                                                   2.715400e+04                                                   3.322794e+04
+
+Mir199b             Mir199b   5.411476  4.695869  86.70372 1.260596e-20 7.191701e-19 0.15656183      370                                                  43.403268                                               4.481024e+01                                               9.924938e+01                                               4.925255e+01                                                   1.384673e+00                                                   5.537224e-01
+
+Mir193               Mir193  -5.360157  4.775477  84.66127 3.541205e-20 1.924055e-18 0.15546497      421                                                   1.490644                                               1.189131e+00                                               2.800640e+00                                               6.940516e-01                                                   1.015454e+02                                                   3.496299e+01
+
+Mir23a               Mir23a   3.003071  9.467420  82.28684 1.177029e-19 6.104501e-18 0.08920345    10118                                                1149.889322                                               1.127958e+03                                               2.471518e+03                                               1.096325e+03                                                   1.505832e+02                                                   1.268024e+02
+
+Mir195               Mir195   3.060546  8.152797  81.39727 1.846126e-19 9.158392e-18 0.09133016     3962                                                 429.134881                                               2.831909e+02                                               9.227086e+02                                               4.708957e+02                                                   1.106253e+02                                                   7.773378e+01
+
+Ahsg                   Ahsg -10.101343  6.638589  79.82133 4.098447e-19 1.923759e-17 0.42092199     1524                                                   0.000000                                               5.537224e-01                                               0.000000e+00                                               0.000000e+00                                                   3.484153e+02                                                   1.260288e+01
+
+Mir148a             Mir148a  -2.789375 16.047169  79.76589 4.215073e-19 1.923759e-17 0.08196054  1002397                                               15925.598251                                               8.528200e+03                                               2.207082e+04                                               1.059020e+04                                                   1.927239e+05                                                   1.054456e+05
+
+Dnm3os               Dnm3os   3.189443  6.618834  79.19614 5.623979e-19 2.468062e-17 0.09496531     1401                                                 134.000827                                               8.716715e+01                                               3.652078e+02                                               1.825316e+02                                                   3.710848e+01                                                   2.498586e+01
+
+Mir27a               Mir27a   2.921772 10.581277  77.26988 1.491231e-18 6.301832e-17 0.09125059    21886                                                2383.612353                                               2.512174e+03                                               5.321988e+03                                               2.361690e+03                                                   3.285136e+02                                                   3.477377e+02
+
+Mir429               Mir429  -5.341934  4.951103  76.38758 2.331128e-18 9.499346e-17 0.18324117      499                                                   1.783696                                               7.001600e-01                                               2.082155e+00                                               1.824168e+00                                                   9.173458e+01                                                   4.540524e+01
+
+Mir214               Mir214   3.125906  6.202347  74.28213 6.771387e-18 2.664191e-16 0.09532762     1048                                                 110.058161                                               5.642542e+01                                               1.982326e+02                                               9.371492e+01                                                   3.353949e+01                                                   1.545870e+01
+
+Mir200a             Mir200a  -7.592263  4.059943  73.71426 9.028450e-18 3.433821e-16 0.22417820      264                                                   0.000000                                               0.000000e+00                                               5.945653e-01                                               0.000000e+00                                                   9.022671e+01                                                   3.222698e+01
+
+Mir194-1           Mir194-1  -3.970219  5.393899  73.22380 1.157517e-17 4.260408e-16 0.13095886      635                                                   3.567392                                               9.102080e+00                                               5.552413e+00                                               3.648337e+00                                                   7.615701e+01                                                   8.859559e+01
+
+Mir378               Mir378   2.954320  8.053368  73.09647 1.234655e-17 4.402315e-16 0.09566655     4075                                                 425.697272                                               3.921392e+02                                               1.155915e+03                                               1.883155e+02                                                   7.641369e+01                                                   3.683119e+01
+
+Mir322               Mir322   3.058569  8.948242  72.83721 1.407969e-17 4.868160e-16 0.10365695     7074                                                1012.892580                                               6.718588e+02                                               1.784708e+03                                               8.550716e+02                                                   1.587027e+02                                                   7.892635e+01
+
+Mir215               Mir215   2.908934  6.381924  70.31105 5.065317e-17 1.699861e-15 0.09002036     1182                                                 123.075020                                               1.610368e+02                                               2.727623e+02                                               1.222193e+02                                                   1.938542e+01                                                   1.716540e+01
+
+Cyp3a25             Cyp3a25  -9.778602  3.913257  70.02642 5.851533e-17 1.907600e-15 0.26445237      226                                                   0.000000                                               0.000000e+00                                               0.000000e+00                                               0.000000e+00                                                   5.664447e+01                                                   1.783696e+01
+
+Mir183               Mir183  -4.214462  5.247929  66.31101 3.851010e-16 1.220556e-14 0.15955164      563                                                   8.401920                                               1.388103e+00                                               2.432225e+00                                               2.423177e+00                                                   8.638070e+01                                                   2.905572e+01
+
+Mir181a-2         Mir181a-2   2.837713  7.573798  65.52900 5.726500e-16 1.765929e-14 0.09834030     2817                                                 288.826664                                               1.377749e+02                                               6.395494e+02                                               1.821098e+02                                                   5.813511e+01                                                   4.102501e+01
+
+Mir155               Mir155   3.734310  4.975364  65.07600 7.206449e-16 2.163831e-14 0.13295455      463                                                  33.966543                                               5.888044e+01                                               1.141565e+02                                               5.251200e+01                                                   9.716723e+00                                                   1.824168e+00
+
+Snord91a           Snord91a  -2.818342  6.592251  64.29345 1.072014e-15 3.136329e-14 0.09422446     1437                                                  13.846728                                               1.605795e+01                                               3.314807e+01                                               1.863305e+01                                                   2.158272e+02                                                   1.792410e+02
+
+Mir125b-2         Mir125b-2   2.852233  8.081093  64.20822 1.119409e-15 3.193115e-14 0.10276578     3837                                                 315.520541                                               4.404853e+02                                               7.634219e+02                                               4.852109e+02                                                   1.082721e+02                                                   9.060036e+01
+
+Serpina4-ps1   Serpina4-ps1 -10.090564  4.224051  64.02094 1.231040e-15 3.425894e-14 0.34406452      295                                                   0.000000                                               0.000000e+00                                               0.000000e+00                                               0.000000e+00                                                   1.080717e+02                                                   1.129674e+01
+
+Mir133b             Mir133b   9.293392  3.404704  63.59576 1.527577e-15 4.149919e-14 0.26404392      159                                                  17.719118                                               1.064012e+01                                               5.738979e+01                                               1.426957e+01                                                   0.000000e+00                                                   0.000000e+00
+
+Gm10069             Gm10069  -7.475459  6.400326  63.15445 1.911172e-15 5.071271e-14 0.41541432     1309                                                   1.189131                                               1.400320e+00                                               6.940516e-01                                               6.080561e-01                                                   1.914310e+02                                                   6.644669e+00
+
+Mir871               Mir871  -5.633588  4.283233  62.67513 2.437746e-15 6.321517e-14 0.21907826      290                                                   1.227706                                               7.453220e-01                                               5.945653e-01                                               0.000000e+00                                                   5.691223e+01                                                   4.925255e+01
+
+Mir184               Mir184   5.002968  4.123691  61.97642 3.475940e-15 8.813439e-14 0.19289992      247                                                  33.443087                                               1.419290e+01                                               4.983502e+01                                               2.168948e+01                                                   2.981288e+00                                                   0.000000e+00
+
+Mir132               Mir132   2.702863  5.866134  58.80773 1.738573e-14 4.312416e-13 0.09148478      857                                                  82.695634                                               4.327103e+01                                               1.821747e+02                                               6.097608e+01                                                   2.832224e+01                                                   1.308044e+01
+
+Snord104           Snord104  -2.492063 11.108871  58.55547 1.976394e-14 4.798012e-13 0.09093023    33458                                                 614.929745                                               4.378004e+02                                               8.629974e+02                                               5.144081e+02                                                   3.826761e+03                                                   4.181256e+03
+
+Mir3074-2         Mir3074-2   2.635505  7.871042  57.72938 3.007773e-14 7.149726e-13 0.09900198     3470                                                 450.903031                                               3.206519e+02                                               8.354691e+02                                               1.716994e+02                                                   8.970303e+01                                                   5.197290e+01
+
+Mir24-2             Mir24-2   2.635505  7.871042  57.68174 3.081509e-14 7.175515e-13 0.09908656     3470                                                 479.987359                                               2.746892e+02                                               9.620198e+02                                               3.442496e+02                                                   9.850509e+01                                                   4.396336e+01
+
+5430416N02Rik 5430416N02Rik  -3.267228  5.202367  56.75137 4.945455e-14 1.128553e-12 0.12456687      564                                                   6.080561                                               1.730841e+00                                               1.273562e+01                                               4.910826e+00                                                   1.453378e+02                                                   5.886197e+01
+
+              control_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam case_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam control_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam
+
+Mir122a                                                         7.242557e+03                                                   8.468313e+03                                                         12                                                          7                                                         27                                                          8                                                          29767
+
+Mir192                                                          3.835916e+05                                                   1.822922e+05                                                       3214                                                       3163                                                       6029                                                       3252                                                         662133
+
+Mir208b                                                         0.000000e+00                                                   0.000000e+00                                                       2049                                                       1647                                                       8904                                                       2155                                                              1
+
+Mir208a                                                         0.000000e+00                                                   5.537224e-01                                                       1060                                                        956                                                       1693                                                        928                                                              0
+
+Mir499                                                          0.000000e+00                                                   0.000000e+00                                                        869                                                        730                                                       4147                                                        781                                                              0
+
+Mir149                                                          4.864449e+00                                                   5.538691e+00                                                       1399                                                       1123                                                       2295                                                       1289                                                             25
+
+Mir490                                                          0.000000e+00                                                   7.453220e-01                                                        353                                                        311                                                        750                                                        322                                                              3
+
+Mir802                                                          1.557723e+02                                                   2.092870e+02                                                          0                                                          0                                                          0                                                          0                                                            552
+
+Mir204                                                          5.537224e-01                                                   3.683119e+00                                                        571                                                        549                                                        923                                                        541                                                              5
+
+Mir1948                                                         1.813414e+02                                                   4.282299e+02                                                          2                                                          2                                                          3                                                          4                                                            869
+
+Mir3073                                                         7.253764e+01                                                   8.757639e+01                                                          0                                                          0                                                          0                                                          0                                                            341
+
+Mir194-2                                                        2.669366e+02                                                   3.046280e+02                                                         19                                                         15                                                         20                                                         16                                                           1316
+
+Mir10b                                                          6.583760e+02                                                   7.272114e+02                                                      34668                                                      54096                                                      51870                                                      49658                                                           3002
+
+Mir101b                                                         1.128042e+04                                                   6.628420e+03                                                        635                                                        544                                                       1984                                                        573                                                          17063
+
+Mir182                                                          2.366022e+03                                                   6.536603e+02                                                         49                                                         26                                                         54                                                         42                                                           1655
+
+Mir200b                                                         4.621056e+01                                                   1.075780e+02                                                          4                                                          0                                                          3                                                          0                                                            496
+
+Mir181c                                                         2.283131e+02                                                   2.989683e+02                                                       3488                                                       3113                                                      11824                                                       3436                                                            567
+
+Mir181d                                                         2.547123e+01                                                   1.555095e+01                                                        416                                                        340                                                        771                                                        426                                                             60
+
+Mir21                                                           2.570722e+04                                                   3.537908e+04                                                       4621                                                       3603                                                      12563                                                       3631                                                          66353
+
+Mir199b                                                         0.000000e+00                                                   2.981288e+00                                                         73                                                         64                                                        143                                                         81                                                              4
+
+Mir193                                                          3.377707e+01                                                   3.396654e+01                                                          2                                                          2                                                          4                                                          1                                                            167
+
+Mir23a                                                          8.921333e+01                                                   2.437203e+02                                                       1934                                                       1611                                                       3561                                                       1803                                                            435
+
+Mir195                                                          4.560421e+01                                                   4.154019e+01                                                        775                                                        692                                                       1238                                                        792                                                            158
+
+Ahsg                                                            6.100714e+02                                                   2.432225e+01                                                          0                                                          1                                                          0                                                          0                                                            586
+
+Mir148a                                                         1.795497e+05                                                   1.342747e+05                                                      26191                                                      24636                                                      39859                                                      25878                                                         258578
+
+Dnm3os                                                          1.216112e+01                                                   1.384673e+01                                                        242                                                        213                                                        490                                                        307                                                             53
+
+Mir27a                                                          1.636942e+02                                                   5.664447e+02                                                       4009                                                       3588                                                       7668                                                       3884                                                            949
+
+Mir429                                                          1.677865e+01                                                   7.527752e+01                                                          3                                                          1                                                          3                                                          3                                                            265
+
+Mir214                                                          1.190272e+01                                                   2.012750e+01                                                        181                                                        163                                                        358                                                        229                                                             45
+
+Mir200a                                                         1.142355e+01                                                   2.602495e+01                                                          0                                                          0                                                          1                                                          0                                                            130
+
+Mir194-1                                                        3.314807e+01                                                   1.050904e+02                                                          6                                                         13                                                          8                                                          6                                                            220
+
+Mir378                                                          9.167460e+01                                                   6.302392e+01                                                        608                                                        565                                                       1901                                                        544                                                            138
+
+Mir322                                                          5.038874e+01                                                   9.166874e+01                                                       1359                                                       1130                                                       2549                                                       1232                                                            261
+
+Mir215                                                          1.145859e+01                                                   2.683159e+01                                                        207                                                        230                                                        393                                                        201                                                             56
+
+Cyp3a25                                                         6.511488e+01                                                   1.873939e+01                                                          0                                                          0                                                          0                                                          0                                                             76
+
+Mir183                                                          1.349033e+02                                                   7.729349e+01                                                         12                                                          2                                                          4                                                          7                                                            156
+
+Mir181a-2                                                       8.051840e+01                                                   5.691223e+01                                                        475                                                        398                                                       1155                                                        445                                                             78
+
+Mir155                                                          2.769346e+00                                                   4.983502e+00                                                         83                                                         79                                                        192                                                         75                                                             14
+
+Snord91a                                                        2.484705e+02                                                   1.732960e+02                                                         40                                                         29                                                         81                                                         25                                                            363
+
+Mir125b-2                                                       2.077009e+01                                                   7.364508e+01                                                        771                                                        591                                                       1284                                                        693                                                            156
+
+Serpina4-ps1                                                    8.191872e+01                                                   9.716723e+00                                                          0                                                          0                                                          0                                                          0                                                            145
+
+Mir133b                                                         0.000000e+00                                                   0.000000e+00                                                         32                                                         26                                                         77                                                         24                                                              0
+
+Gm10069                                                         2.868741e+02                                                   2.757691e+01                                                          2                                                          2                                                          1                                                          1                                                            553
+
+Mir871                                                          1.003888e+01                                                   5.149619e+01                                                          3                                                          1                                                          1                                                          0                                                             82
+
+Mir184                                                          1.400320e+00                                                   1.388103e+00                                                         55                                                         41                                                         90                                                         53                                                              4
+
+Mir132                                                          2.100480e+01                                                   1.943345e+01                                                        136                                                        125                                                        329                                                        149                                                             38
+
+Snord104                                                        4.430701e+03                                                   4.212863e+03                                                        886                                                        720                                                       2493                                                        929                                                           9351
+
+Mir3074-2                                                       4.844593e+01                                                   8.323914e+01                                                        644                                                        462                                                       1374                                                        496                                                            162
+
+Mir24-2                                                         3.599196e+01                                                   5.729296e+01                                                        644                                                        462                                                       1374                                                        496                                                            162
+
+5430416N02Rik                                                   5.531264e+01                                                   9.786128e+01                                                         10                                                          5                                                         23                                                         12                                                            195
+
+              control_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam ntotreads                                                                                                   URL
+
+Mir122a                                                                24233                                                          11911                                                          24463     90428             Mir122a
+
+Mir192                                                                490327                                                         630849                                                         526600   2325567               Mir192
+
+Mir208b                                                                    0                                                              0                                                              0     14756             Mir208b
+
+Mir208a                                                                    0                                                              0                                                              1      4638             Mir208a
+
+Mir499                                                                     0                                                              0                                                              0      6527               Mir499
+
+Mir149                                                                     9                                                              8                                                             16      6164               Mir149
+
+Mir490                                                                     1                                                              0                                                              1      1741               Mir490
+
+Mir802                                                                   401                                                            209                                                            352      1514               Mir802
+
+Mir204                                                                     2                                                              1                                                              9      2601               Mir204
+
+Mir1948                                                                  648                                                            259                                                            617      2404             Mir1948
+
+Mir3073                                                                  218                                                            131                                                            214       904             Mir3073
+
+Mir194-2                                                                 865                                                            439                                                            880      3570           Mir194-2
+
+Mir10b                                                                  1080                                                           1189                                                           1777    197340               Mir10b
+
+Mir101b                                                                11066                                                          16253                                                          10901     59019             Mir101b
+
+Mir182                                                                   879                                                           3409                                                           1075      7189               Mir182
+
+Mir200b                                                                  164                                                             66                                                            155       888             Mir200b
+
+Mir181c                                                                  366                                                            384                                                            427     23605             Mir181c
+
+Mir181d                                                                   42                                                             46                                                             38      2139             Mir181d
+
+Mir21                                                                  44582                                                          43237                                                          50530    229120                 Mir21
+
+Mir199b                                                                    1                                                              0                                                              4       370             Mir199b
+
+Mir193                                                                   101                                                             61                                                             83       421               Mir193
+
+Mir23a                                                                   229                                                            218                                                            327     10118               Mir23a
+
+Mir195                                                                   112                                                             75                                                            120      3962               Mir195
+
+Ahsg                                                                      18                                                            879                                                             40      1524                   Ahsg
+
+Mir148a                                                               177349                                                         256441                                                         193465   1002397             Mir148a
+
+Dnm3os                                                                    36                                                             20                                                             40      1401               Dnm3os
+
+Mir27a                                                                   628                                                            400                                                            760     21886               Mir27a
+
+Mir429                                                                    82                                                             41                                                            101       499               Mir429
+
+Mir214                                                                    26                                                             17                                                             29      1048               Mir214
+
+Mir200a                                                                   53                                                             33                                                             47       264             Mir200a
+
+Mir194-1                                                                 160                                                             81                                                            141       635           Mir194-1
+
+Mir378                                                                    90                                                            123                                                            106      4075               Mir378
+
+Mir322                                                                   228                                                             91                                                            224      7074               Mir322
+
+Mir215                                                                    31                                                             28                                                             36      1182               Mir215
+
+Cyp3a25                                                                   30                                                             93                                                             27       226             Cyp3a25
+
+Mir183                                                                    71                                                            181                                                            130       563               Mir183
+
+Mir181a-2                                                                 69                                                            115                                                             82      2817         Mir181a-2
+
+Mir155                                                                     3                                                              8                                                              9       463               Mir155
+
+Snord91a                                                                 256                                                            358                                                            285      1437           Snord91a
+
+Mir125b-2                                                                149                                                             60                                                            133      3837         Mir125b-2
+
+Serpina4-ps1                                                              19                                                            117                                                             14       295   Serpina4-ps1
+
+Mir133b                                                                    0                                                              0                                                              0       159             Mir133b
+
+Gm10069                                                                   12                                                            701                                                             37      1309             Gm10069
+
+Mir871                                                                    81                                                             29                                                             93       290               Mir871
+
+Mir184                                                                     0                                                              2                                                              2       247               Mir184
+
+Mir132                                                                    22                                                             30                                                             28       857               Mir132
+
+Snord104                                                                5610                                                           7452                                                           6017     33458           Snord104
+
+Mir3074-2                                                                127                                                             65                                                            140      3470         Mir3074-2
+
+Mir24-2                                                                  127                                                             65                                                            140      3470             Mir24-2
+
+5430416N02Rik                                                             99                                                             79                                                            141       564 5430416N02Rik
+
+[1] # 445 tags significant at adj p= 0.05
+
+[1] # deTags
+
+[1] "0610005C13Rik" "0610007N19Rik" "0610008F07Rik" "0610012G03Rik" "0610031O16Rik" "1110038B12Rik"
+
+# DESeq top 50
+
+               id     baseMean    baseMeanA    baseMeanB   foldChange log2FoldChange         pval         padj
+
+536       Mir122a  10112.31117 2.021162e+04 1.300527e+01 6.434550e-04     -10.601873 5.488369e-57 6.262229e-54
+
+645       Mir208a    616.76981 2.430915e-01 1.233297e+03 5.073385e+03      12.308733 6.046036e-53 3.449263e-50
+
+600        Mir192 271352.97636 5.385927e+05 4.113233e+03 7.637001e-03      -7.032778 1.311798e-38 4.989204e-36
+
+568        Mir149    810.35429 1.224267e+01 1.608466e+03 1.313819e+02       7.037623 7.073079e-37 2.017596e-34
+
+791        Mir490    220.99790 9.450198e-01 4.410508e+02 4.667107e+02       8.866385 1.354865e-36 3.091803e-34
+
+642        Mir204    347.57397 3.737736e+00 6.914102e+02 1.849810e+02       7.531234 8.058436e-36 1.532446e-33
+
+844        Mir802    166.83414 3.336683e+02 0.000000e+00 0.000000e+00           -Inf 3.255469e-35 5.306414e-33
+
+698       Mir3073     98.93199 1.978640e+02 0.000000e+00 0.000000e+00           -Inf 1.111351e-28 1.585065e-26
+
+646       Mir208b   1649.77924 1.445675e-01 3.299414e+03 2.282265e+04      14.478178 7.505934e-28 9.515857e-26
+
+614       Mir1948    263.89527 5.246983e+02 3.092210e+00 5.893310e-03      -7.406706 1.534188e-26 1.750509e-24
+
+608      Mir194-2    391.65678 7.637703e+02 1.954329e+01 2.558791e-02      -5.288394 1.622682e-24 1.683163e-22
+
+796        Mir499    712.45950 0.000000e+00 1.424919e+03          Inf            Inf 2.577318e-19 2.450600e-17
+
+585       Mir181c   2765.96510 3.955399e+02 5.136390e+03 1.298577e+01       3.698860 1.942959e-17 1.705320e-15
+
+631       Mir199b     47.84725 1.818862e+00 9.387565e+01 5.161231e+01       5.689643 4.012622e-15 3.270287e-13
+
+530        Mir10b  27820.40551 1.501200e+03 5.413961e+04 3.606421e+01       5.172496 3.069912e-14 2.335180e-12
+
+586       Mir181d    275.22797 4.254569e+01 5.079103e+02 1.193800e+01       3.577489 5.204519e-14 3.711473e-12
+
+602        Mir193     45.70861 8.913819e+01 2.279021e+00 2.556728e-02      -5.289558 1.657854e-13 1.112713e-11
+
+637       Mir200a     27.09009 5.402561e+01 1.545578e-01 2.860824e-03      -8.449354 2.466964e-13 1.563781e-11
+
+722        Mir322    899.53469 1.797868e+02 1.619283e+03 9.006682e+00       3.170996 1.359804e-12 8.165978e-11
+
+756        Mir378    483.21548 1.056050e+02 8.608260e+02 8.151374e+00       3.027043 2.046396e-12 1.160965e-10
+
+523       Mir101b   6846.19683 1.280838e+04 8.840090e+02 6.901800e-02      -3.856884 2.136745e-12 1.160965e-10
+
+552       Mir133b     19.46050 0.000000e+00 3.892101e+01          Inf            Inf 2.388227e-12 1.238621e-10
+
+663        Mir23a   1289.18043 2.671574e+02 2.311203e+03 8.651093e+00       3.112882 3.093049e-12 1.534421e-10
+
+671        Mir27a   2788.72629 6.066392e+02 4.970813e+03 8.194019e+00       3.034571 5.970859e-12 2.838646e-10
+
+616        Mir195    519.86200 1.038507e+02 9.358733e+02 9.011721e+00       3.171803 6.245320e-12 2.850364e-10
+
+607      Mir194-1     71.10636 1.325374e+02 9.675354e+00 7.300095e-02      -3.775941 1.405195e-11 6.166645e-10
+
+344        Dnm3os    179.61643 3.285433e+01 3.263785e+02 9.934110e+00       3.312391 1.503324e-11 6.352936e-10
+
+590        Mir184     32.23796 1.645700e+00 6.283021e+01 3.817841e+01       5.254685 1.644760e-11 6.702396e-10
+
+582     Mir181a-2    347.82506 8.313904e+01 6.125111e+02 7.367310e+00       2.881138 2.735424e-11 1.076248e-09
+
+541     Mir125b-2    493.08516 1.122867e+02 8.738836e+02 7.782608e+00       2.960254 5.662328e-11 2.153572e-09
+
+573        Mir155     57.66182 7.341434e+00 1.079822e+02 1.470860e+01       3.878588 6.173765e-11 2.272344e-09
+
+464        Gm5441     20.24317 2.430915e-01 4.024325e+01 1.655478e+02       7.371104 6.503611e-11 2.318944e-09
+
+571        Mir153     19.90411 2.430915e-01 3.956513e+01 1.627582e+02       7.346586 8.675688e-11 2.999685e-09
+
+649         Mir21  26121.31011 4.639963e+04 5.842995e+03 1.259276e-01      -2.989333 1.048681e-10 3.519252e-09
+
+654        Mir214    134.69038 2.546966e+01 2.439111e+02 9.576536e+00       3.259504 1.338023e-10 4.361954e-09
+
+638       Mir200b     87.13638 1.725548e+02 1.717931e+00 9.955853e-03      -6.650239 2.397950e-10 7.600169e-09
+
+666       Mir24-2    424.48288 1.104079e+02 7.385578e+02 6.689355e+00       2.741867 4.542021e-10 1.363802e-08
+
+700     Mir3074-2    424.48288 1.104079e+02 7.385578e+02 6.689355e+00       2.741867 4.542021e-10 1.363802e-08
+
+845        Mir871     33.03360 6.461640e+01 1.450790e+00 2.245235e-02      -5.476990 5.839129e-10 1.708320e-08
+
+767        Mir429     49.75018 9.677270e+01 2.727653e+00 2.818618e-02      -5.148868 6.903556e-10 1.969239e-08
+
+655        Mir215    152.78082 3.329953e+01 2.722621e+02 8.176156e+00       3.031423 8.994273e-10 2.503040e-08
+
+566       Mir148a 118994.46955 2.065090e+05 3.147993e+04 1.524386e-01      -2.713700 1.022762e-09 2.772155e-08
+
+569        Mir150    553.20599 1.318948e+02 9.745171e+02 7.388592e+00       2.885299 1.044721e-09 2.772155e-08
+
+628       Mir1983     14.04997 0.000000e+00 2.809994e+01          Inf            Inf 2.859722e-09 7.415778e-08
+
+581     Mir181a-1     59.53826 1.179305e+01 1.072835e+02 9.097179e+00       3.185419 3.537731e-09 8.970115e-08
+
+784        Mir470     18.37716 3.608521e+01 6.691037e-01 1.854232e-02      -5.753034 7.467127e-09 1.852172e-07
+
+630     Mir199a-2     44.84878 7.384673e+00 8.231288e+01 1.114645e+01       3.478512 8.433815e-09 2.047443e-07
+
+550        Mir132    106.46062 2.691980e+01 1.860014e+02 6.909467e+00       2.788574 1.329950e-08 3.161402e-07
+
+76  2610203C20Rik     79.17666 1.649993e+01 1.418534e+02 8.597210e+00       3.103869 1.594438e-08 3.712764e-07
+
+540     Mir125b-1     79.01988 1.649993e+01 1.415398e+02 8.578206e+00       3.100676 1.650666e-08 3.766821e-07
+
+# VOOM top 50
+
+                ID      logFC    AveExpr          t      P.Value    adj.P.Val         B
+
+600         Mir192  -6.948883 14.6763803 -42.722954 2.301199e-16 2.625668e-13 27.266471
+
+536        Mir122a -10.426125  8.1698641 -21.722912 2.859682e-12 1.087633e-09 17.760171
+
+568         Mir149   7.030463  6.3160807  20.883835 4.915491e-12 1.402144e-09 17.277609
+
+645        Mir208a  11.015018  3.9395538  23.252407 1.118938e-12 6.383542e-10 17.208662
+
+530         Mir10b   5.130915 12.2628672  18.242023 3.124991e-11 4.963978e-09 16.221503
+
+561       Mir143hg   2.249221 16.2444825  18.082481 3.521739e-11 4.963978e-09 16.026695
+
+560         Mir143   2.251067 16.2358599  18.081481 3.524391e-11 4.963978e-09 16.026084
+
+646        Mir208b  12.433228  4.6076218  19.592458 1.179199e-11 2.690931e-09 15.683666
+
+523        Mir101b  -3.765722 10.8508440 -16.538566 1.180419e-10 1.036045e-08 14.900027
+
+796         Mir499  11.567529  3.7874598  17.942086 3.915496e-11 4.963978e-09 14.821741
+
+566        Mir148a  -2.638213 15.4435820 -16.548188 1.171186e-10 1.036045e-08 14.814792
+
+844         Mir802  -9.158434  2.9157675 -17.316522 6.338616e-11 7.232360e-09 14.381577
+
+698        Mir3073  -8.420542  2.5457189 -16.702657 1.033066e-10 1.036045e-08 13.985845
+
+649          Mir21  -2.938537 13.1642917 -15.375404 3.148335e-10 2.394833e-08 13.867698
+
+585        Mir181c   3.742560  9.6295577  15.242361 3.537063e-10 2.522368e-08 13.810405
+
+791         Mir490   8.474378  3.7506957  16.259650 1.484816e-10 1.210125e-08 13.424617
+
+663         Mir23a   3.165768  8.7896592  14.631179 6.110682e-10 3.873493e-08 13.269174
+
+586        Mir181d   3.636211  6.3713218  14.317073 8.157508e-10 4.898798e-08 12.956333
+
+642         Mir204   7.684425  4.7751735  15.033484 4.254268e-10 2.855364e-08 12.822427
+
+671         Mir27a   3.106935  9.9255796  13.838251 1.281011e-09 6.960160e-08 12.513086
+
+552        Mir133b   6.493549  1.2544862  13.969968 1.129934e-09 6.446275e-08 11.982684
+
+608       Mir194-2  -5.264136  6.0897615 -13.044012 2.792884e-09 1.448491e-07 11.715753
+
+616         Mir195   3.216595  7.4509350  12.869478 3.332788e-09 1.653353e-07 11.587523
+
+756         Mir378   3.097393  7.3832049  11.684163 1.171371e-08 5.346138e-07 10.329692
+
+672         Mir27b   1.976376 15.0957731  11.756036 1.082197e-08 5.144946e-07 10.127719
+
+1017      Snord104  -2.337374 10.6109024 -11.495675 1.444482e-08 6.339052e-07 10.023395
+
+722         Mir322   3.296618  8.2153415  11.415362 1.580762e-08 6.441605e-07 10.008472
+
+655         Mir215   3.045873  5.7544234  11.148134 2.141822e-08 8.082459e-07  9.753268
+
+628        Mir1983   5.895500  0.9931851  11.445812 1.527548e-08 6.441605e-07  9.749260
+
+344         Dnm3os   3.363344  5.8607432  11.092261 2.283960e-08 8.082459e-07  9.689496
+
+637        Mir200a  -6.191561  1.7981309 -11.322172 1.756229e-08 6.909853e-07  9.662295
+
+587         Mir182  -4.903995  7.1511683 -11.074468 2.331304e-08 8.082459e-07  9.658842
+
+582      Mir181a-2   3.048298  6.9414651  11.072128 2.337609e-08 8.082459e-07  9.644017
+
+614        Mir1948  -7.195525  4.5513493 -11.005492 2.524936e-08 8.473388e-07  9.341794
+
+654         Mir214   3.280874  5.4784451  10.768257 3.332555e-08 1.086413e-06  9.318504
+
+571         Mir153   5.963803  1.4386315  10.727082 3.498742e-08 1.093990e-06  9.035569
+
+318        Cyp3a25  -6.318200  1.4888933 -10.698226 3.620443e-08 1.093990e-06  9.024973
+
+464         Gm5441   5.982176  1.4484953  10.692891 3.643436e-08 1.093990e-06  9.000362
+
+541      Mir125b-2   3.077678  7.4316058  10.446668 4.893073e-08 1.431538e-06  8.884250
+
+551      Mir133a-1   5.144671  0.5903264  10.358205 5.447229e-08 1.553822e-06  8.575535
+
+550         Mir132   2.847559  5.3211839  10.110952 7.380297e-08 2.004981e-06  8.531491
+
+13   1110038B12Rik  -2.226702 10.8487089 -10.194609 6.655312e-08 1.852125e-06  8.439308
+
+922        Rabggtb  -1.935779  9.9874171  -9.928995 9.262821e-08 2.457879e-06  8.133384
+
+569         Mir150   2.938531  7.6297870   9.842102 1.033602e-07 2.620755e-06  8.116464
+
+800         Mir504   5.256127  0.6221088   9.892894 9.693595e-08 2.513725e-06  8.068853
+
+573         Mir155   3.906600  3.9899000   9.732173 1.188627e-07 2.712448e-06  8.046518
+
+666        Mir24-2   2.833979  7.3083691   9.767192 1.136724e-07 2.646944e-06  8.030550
+
+700      Mir3074-2   2.833979  7.3083691   9.767192 1.136724e-07 2.646944e-06  8.030550
+
+664         Mir23b   2.124129  9.8141190   9.806316 1.081569e-07 2.625681e-06  7.979464
+
+631        Mir199b   5.752816  2.8805143   9.823920 1.057683e-07 2.623514e-06  7.979387
+
+Warning messages:
+
+1: In bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group ==  :
+
+  Outlier (-Inf) in boxplot 5 is not drawn
+
+2: In bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group ==  :
+
+  Outlier (-Inf) in boxplot 7 is not drawn
+
+3: In bxp(list(stats = c(-Inf, -Inf, -0.158608221176912, 0.826586442285183,  :
+
+  some notches went outside hinges ('box'): maybe set notch=FALSE
+
+4: In par(defpar) : calling par(new=TRUE) with no plot
+
+R version 2.15.1 (2012-06-22)
+
+Platform: x86_64-unknown-linux-gnu (64-bit)
+
+locale:
+
+ [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C               LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8     LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8    LC_PAPER=C                 LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       
+
+attached base packages:
+
+[1] methods   grid      stats     graphics  grDevices utils     datasets  base     
+
+other attached packages:
+
+ [1] edgeR_3.0.8        limma_3.14.4       DESeq_1.10.1       lattice_0.20-15    locfit_1.5-9.1     Biobase_2.18.0     BiocGenerics_0.4.0 gplots_2.11.0      MASS_7.3-23        KernSmooth_2.23-10 caTools_1.14       gdata_2.12.0.2     gtools_2.7.1       stringr_0.6.2     
+
+loaded via a namespace (and not attached):
+
+ [1] annotate_1.36.0      AnnotationDbi_1.20.7 bitops_1.0-5         DBI_0.2-7            genefilter_1.40.0    geneplotter_1.36.0   IRanges_1.16.6       parallel_2.15.1      RColorBrewer_1.0-5   RSQLite_0.11.3       splines_2.15.1       stats4_2.15.1        survival_2.37-4      XML_3.96-1.1         xtable_1.7-1        
+
+
+ +
This tool (edgeR) was generated by the Galaxy Tool Factory

+
+ diff -r 000000000000 -r 82e0af566160 rgedgeR/test-data/edgeRtest1out.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/test-data/edgeRtest1out.xls Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,1142 @@ +Name logFC logCPM LR PValue adj.p.value Dispersion totreads case_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam control_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam case_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam control_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam +Mir122a -10.6613482198735 12.5379487184339 445.912028851371 5.59474310450852e-99 6.38360188224422e-96 0.102055096028295 90428 6.64466912039531 2.86464823551992 20.1236936368881 4.75652253016081 20841.6623198401 16818.9531619257 7242.55663282834 8468.31297344994 12 7 27 8 29767 24233 11911 24463 +Mir192 -7.10901851688288 17.2474145508888 366.131659138235 1.3015583263717e-81 7.42539025195057e-79 0.0776548700185199 2325567 1779.66387941254 1294.41176699279 4493.54625691845 1933.52640851037 463599.032378899 340312.253828562 383591.60517699 182292.180510107 3214 3163 6029 3252 662133 490327 630849 526600 +Mir208b 13.4237202575152 9.82233816515324 355.417815392181 2.80129611458365e-79 1.06542628891331e-76 0.124440587141633 14756 1245.90702213628 570.139045385768 4930.34448733332 881.902421077918 0.74532198655141 0 0 0 2049 1647 8904 2155 1 0 0 0 +Mir208a 11.9775863683342 8.38828977985358 348.815401742963 7.675450754893e-78 2.18942232783323e-75 0.0993679816515115 4638 433.789589950159 712.527819143148 1006.59908044528 649.748467524829 0 0 0 0.553722426699609 1060 956 1693 928 0 0 0 1 +Mir499 14.4882160574998 8.61277040381698 291.127944921827 2.82350748745294e-65 6.4432440863676e-63 0.134816198797062 6527 608.439028317971 506.657690265579 2521.60879492395 270.357373677161 0 0 0 0 869 730 4147 781 0 0 0 0 +Mir149 6.86973108218652 8.78566474384905 253.019861795716 5.70293016120625e-57 1.08450721898939e-54 0.0983468375157501 6164 774.657674952754 459.571424069838 1710.51395913549 766.394692672161 17.5039996639232 6.24646467450714 4.86444908593961 5.5386913941544 1399 1123 2295 1289 25 9 8 16 +Mir490 8.4539298899245 6.90959613094456 251.056095815646 1.52828085290668e-56 2.4910977902379e-54 0.0974500488770791 1741 209.881556643346 217.749755819205 520.538722875595 195.794075709069 1.03850463640395 0.553722426699609 0 0.74532198655141 353 311 750 322 3 1 0 1 +Mir802 -12.4996776940066 6.62939088571757 237.561606597444 1.33781241656995e-53 1.90805495913289e-51 0.10939980863872 1514 0 0 0 0 305.654779538184 164.10342034907 155.772295189245 209.286991327076 0 0 0 0 552 401 209 352 +Mir204 7.37117920479641 7.55899555710097 237.12302218121 1.66736458270285e-53 2.11384776540439e-51 0.10292005004102 2601 425.578854320855 326.416358632286 646.247667592045 375.481932100929 3.04028067871226 0.6923364242693 0.553722426699609 3.68311915995418 571 549 923 541 5 2 1 9 +Mir1948 -7.58332571104735 7.29345576009458 204.796406017043 1.87589722530299e-46 2.14039873407072e-44 0.118339985035871 2404 1.2161122714849 0.6923364242693 1.66116728009883 1.63694184886853 647.684806313175 385.278324943026 181.341436518244 428.229856018989 2 2 3 4 869 648 259 617 +Mir3073 -11.7485555893732 5.88146234349958 200.033500954155 2.05362547312175e-45 2.13016969530174e-43 0.110685621278186 904 0 0 0 0 207.347142288176 75.4646702453536 72.5376378976488 87.5763889144661 0 0 0 0 341 218 131 214 +Mir194-2 -5.43982669183809 7.86004832737241 165.572392495399 6.85919857312405e-38 6.52195464327879e-36 0.106710199302315 3570 10.5207261072926 6.13853193325697 14.9064397310282 9.51304506032163 921.410542308918 600.354660383186 266.936643590936 304.628026678492 19 15 20 16 1316 865 439 880 +Mir10b 5.0328982107069 13.880769769219 160.065802570007 1.0946399286548e-36 9.60757045073175e-35 0.107954343937396 197340 25838.8226297643 32163.6053489474 36317.2985027079 34465.2158674084 1825.38451949884 373.861669105422 658.375965345836 727.211416359842 34668 54096 51870 49658 3002 1080 1189 1777 +Mir101b -3.92528713393055 11.9360901455378 120.624266541894 4.61814317471517e-28 3.76378668739286e-26 0.0966307527814339 59019 219.816814705503 301.225000124588 811.923157038789 427.069498293958 10145.0679915167 7747.97041123897 11280.4211505294 6628.41993572847 635 544 1984 573 17063 11066 16253 10901 +Mir182 -5.24438373094747 8.94270765623571 118.928149724545 1.08592583416118e-27 8.26027584518606e-26 0.149934428671101 7189 16.9622423945978 14.3967830941898 22.0987149597251 31.3035234351592 984.005598427018 615.44062818354 2366.0220083772 653.660345923136 49 26 54 42 1655 879 3409 1075 +Mir200b -6.72534784619757 5.76518685294719 115.731891447532 5.44098879544178e-27 3.88010513474942e-25 0.167124904530932 888 2.43222454296981 0 1.66116728009883 0 369.679705329499 97.5087118682966 46.2105591127573 107.578002727623 4 0 3 0 496 164 66 155 +Mir181c 3.6118671281253 10.5698150360209 107.820107113202 2.94315295097335e-25 1.97537501003564e-23 0.0938527006387662 23605 2420.85208718677 1892.87875056625 4093.0929402801 1902.59025813986 232.036507077113 272.787847077816 228.313081447719 298.968314259808 3488 3113 11824 3436 567 366 384 427 +Mir181d 3.48997562250607 7.2320639569436 102.186614268585 5.05301947727886e-24 3.20305290198621e-22 0.087384984044046 2139 310.053946405387 202.152207531835 539.823349635392 295.665994593338 36.4833681445471 14.5390649096553 25.471231628182 15.550947564251 416 340 771 426 60 42 46 38 +Mir21 -3.07777147203033 13.8815003666918 91.3732407445455 1.1897452302054e-21 7.14473319823348e-20 0.0849427206355669 229120 3207.21258454416 2190.82625708005 4348.9112490476 2010.56613134628 27154.0006244933 33227.944804435 25707.2205795704 35379.0841207216 4621 3603 12563 3631 66353 44582 43237 50530 +Mir199b 5.41147631803522 4.69586911909022 86.7037178851385 1.26059609329233e-20 7.19170071223273e-19 0.156561826531982 370 43.4032680877174 44.8102391396434 99.2493831616134 49.2525469951386 1.3846728485386 0.553722426699609 0 2.98128794620564 73 64 143 81 4 1 0 4 +Mir193 -5.36015662788668 4.77547692611212 84.6612650026128 3.5412053458764e-20 1.92405490459285e-18 0.155464968416992 421 1.49064397310282 1.1891306325402 2.80063994622771 0.694051630500793 101.545374668989 34.9629894255996 33.7770680286762 33.9665433640219 2 2 4 1 167 101 61 83 +Mir23a 3.00307076424737 9.46741956688047 82.28684490401 1.17702908493873e-19 6.10450084506861e-18 0.0892034490333833 10118 1149.88932166638 1127.95773834321 2471.51785621332 1096.32521274364 150.583172278573 126.802435714211 89.2133307633346 243.720289602311 1934 1611 3561 1803 435 229 218 327 +Mir195 3.06054621394571 8.15279745103936 81.3972715690713 1.84612638492429e-19 9.15839219651573e-18 0.0913301572784805 3962 429.134880692197 283.190939854255 922.708619350646 470.89573048592 110.625277875995 77.7337826160888 45.6042101806839 41.540185456158 775 692 1238 792 158 112 75 120 +Ahsg -10.1013431702234 6.6385887851201 79.821328032349 4.09844702296257e-19 1.92375926690416e-17 0.42092199405061 1524 0 0.553722426699609 0 0 348.415275334279 12.6028797580247 610.071383210197 24.3222454296981 0 1 0 0 586 18 879 40 +Mir148a -2.78937502090353 16.047168680842 79.7658888859378 4.21507288979878e-19 1.92375926690416e-17 0.0819605364404836 1002397 15925.5982512306 8528.20007414923 22070.8222058197 10590.1952912549 192723.868638491 105445.564275186 179549.727112645 134274.698694836 26191 24636 39859 25878 258578 177349 256441 193465 +Dnm3os 3.18944316253823 6.61883431628908 79.1961422287326 5.62397892398994e-19 2.46806152010482e-17 0.0949653148790185 1401 134.000827261305 87.167153452249 365.207773410191 182.531552094921 37.1084792875172 24.9858586980285 12.161122714849 13.846728485386 242 213 490 307 53 36 20 40 +Mir27a 2.92177170736328 10.5812768795259 77.2698776994316 1.49123094631961e-18 6.30183151759511e-17 0.0912505912483571 21886 2383.61235292684 2512.17403176626 5321.98790268008 2361.69003122368 328.513633315783 347.737683967355 163.694184886853 566.444709779072 4009 3588 7668 3884 949 628 400 760 +Mir429 -5.34193377949947 4.95110312707712 76.3875799779135 2.33112791050646e-18 9.49934623531384e-17 0.183241171201345 499 1.7836959488103 0.700159986556928 2.08215489150238 1.82416840722736 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0.998669131632702 0.592374698826143 42 1.49064397310282 2.37826126508041 7.70175985212621 2.08215489150238 9.72889817187923 1.73084106067325 0.553722426699609 0 2 4 11 3 16 5 1 0 diff -r 000000000000 -r 82e0af566160 rgedgeR/test-data/gentestdata.sh --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/test-data/gentestdata.sh Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,9 @@ +#!/bin/bash +# generate test data for rgGSEA +# ross lazarus June 2013 +# adjust gseajar_path ! +GSEAJAR_PATH=/home/rlazarus/galaxy-central/tool_dependency_dir/gsea_jar/2.0.12/fubar/rg_gsea_test/8e291f464aa0/jars/gsea2-2.0.12.jar +python ../rgGSEA.py --input_tab "gsea_test_DGE.xls" --adjpvalcol "5" --signcol "2" --idcol "1" --outhtml "gseatestout.html" --input_name "gsea_test" --setMax "500" --setMin "15" --nPerm "10" --plotTop "20" --gsea_jar "$GSEAJAR_PATH" --output_dir "gseatestout" --mode "Max_probe" +--title "GSEA test" --builtin_gmt "gseatestdata.gmt" + + diff -r 000000000000 -r 82e0af566160 rgedgeR/test-data/gentestdata.sh~ --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/test-data/gentestdata.sh~ Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,8 @@ +#!/bin/bash +# generate test data for rgGSEA +# ross lazarus June 2013 +# adjust gseajar_path ! +GSEAJAR_PATH=/home/rlazarus/galaxy-central/tool_dependency_dir/gsea_jar/2.0.12/fubar/rg_gsea_test/8e291f464aa0/jars/gsea2-2.0.12.jar +python ../rgGSEA.py --input_tab "gsea_test_DGE.xls" --adjpvalcol "5" --signcol "2" --idcol "1" --outhtml "gseatestout.html" --input_name "gsea_test" --setMax "500" --setMin "15" --nPerm "10" --plotTop "20" --gsea_jar "$GSEAJAR_PATH" --output_dir "gseatestout" --mode "Max_probe" --title "GSEA test" --builtin_gmt "gseatestdata.gmt" + + diff -r 000000000000 -r 82e0af566160 rgedgeR/test-data/test_bams2mx.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/test-data/test_bams2mx.xls Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,3243 @@ +Contigname 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+Znf41-ps 0 0 0 0 0 0 0 0 +Zp4-ps 0 0 0 0 0 0 0 0 +Zscan4a 0 0 0 0 0 0 0 0 +Zxda 0 0 0 0 0 0 0 0 diff -r 000000000000 -r 82e0af566160 rgedgeR/tool_dependencies.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/tool_dependencies.xml Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,47 @@ + + + + + + + + + + + http://cran.ms.unimelb.edu.au/src/base/R-3/R-3.0.1.tar.gz + + + + + + $INSTALL_DIR + ./configure --with-blas --with-lapack --enable-R-shlib --with-readline=no --with-x=no --prefix=$INSTALL_DIR && make && make install + + $INSTALL_DIR/lib/R + $INSTALL_DIR/lib/R/library + $INSTALL_DIR/lib/R/bin + + + + + R is a free software environment for statistical computing and graphics + WARNING: See custom compilation options above + Modified from an older version of R by Boris by Ross Lazarus for R 3.0 + Added Bioc basics too + + + + + + $INSTALL_DIR/lib/R/bin/R CMD BATCH installBioC.R + + + R is a free software environment for statistical computing and graphics + WARNING: See custom compilation options above + Modified from an older version of R by Boris by Ross Lazarus for R 3.0 + Added Bioc basics via this package installBioC.R script + + +