Mercurial > repos > fubar > edger_test
changeset 0:82e0af566160 draft
Uploaded
| author | fubar |
|---|---|
| date | Wed, 12 Jun 2013 02:58:43 -0400 |
| parents | |
| children | 34cbb9e749da |
| files | rgedgeR/datatypes_conf_xml.sample rgedgeR/installBioC.R rgedgeR/rgGSEA.py rgedgeR/rgGSEAcolumns.xml rgedgeR/rgToolFactory.py rgedgeR/rgedgeR.xml rgedgeR/rgedgeRglm.xml rgedgeR/rgedgeRpaired.xml rgedgeR/rgedgeRpaired.xml.iaas1 rgedgeR/test-data/edgeRtest1out.html rgedgeR/test-data/edgeRtest1out.xls rgedgeR/test-data/gentestdata.sh rgedgeR/test-data/gentestdata.sh~ rgedgeR/test-data/test_bams2mx.xls rgedgeR/tool_dependencies.xml |
| diffstat | 15 files changed, 9466 insertions(+), 0 deletions(-) [+] |
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--- /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 @@ +<datatype extension="gseagmt" type="galaxy.datatypes.tabular:Tabular" subclass="True" display_in_upload="true" mimetype="application/vnd.ms-excel"/> +<datatype extension="gseachip" type="galaxy.datatypes.tabular:Tabular" subclass="True" display_in_upload="true" mimetype="application/vnd.ms-excel"/>
--- /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)
--- /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 ES</th><th class='richTable'>CORE ENRICHMENT</th><tr><td class='lessen'>1</td> + <td><a href='https://www.affymetrix.com/LinkServlet?probeset=LBR'>LBR</a></td><td></td><td></td><td>1113</td> + <td>0.194</td><td>-0.1065</td><td>No</td></tr><tr><td class='lessen'>2</td><td> + <a href='https://www.affymetrix.com/LinkServlet?probeset=GGPS1'>GGPS1</a></td><td></td><td></td><td>4309</td><td>0.014</td><td>-0.4328</td> + <td>No</td></tr> + """ + 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 = ['<pre>'] + s += self.comments + s.append('</pre>') + try: + i = html.index('<div id="footer">') + except: + i = len(html) - 7 # fudge + html = html[:i] + s + html[i:] + except: + html = [] + htmlhead = '<html><head></head><body>' + html.append('## Galaxy GSEA wrapper failure') + html.append('## Unable to find index.html in %s - listdir=%s' % (d,' '.join(os.listdir(self.opts.output_dir)))) + html.append('## Command line was %s' % (' '.join(self.cl))) + html.append('## commonly caused by mismatched ID/chip selection') + glog = open(os.path.join(self.opts.output_dir,'gsea_runner.log'),'r').readlines() + html.append('## gsea_runner.log=%s' % '\n'.join(glog)) + #tryme = self.grepIds() + retval = 1 + print >> sys.stderr,'\n'.join(html) + html = ['%s<br/>' % x for x in html] + html.insert(0,htmlhead) + html.append('</body></html>') + htmlf = file(self.opts.outhtml,'w') + htmlf.write('\n'.join(html)) + htmlf.write('\n') + htmlf.close() + os.unlink(self.fakeRanks) + os.unlink(self.fakeGMT) + if opts.outtab_neg: + tabs = glob.glob(os.path.join(opts.output_dir,"gsea_report_for_*.xls")) + if len(tabs) > 0: + for tabi,t in enumerate(tabs): + tkind = os.path.basename(t).split('_')[4].lower() + if tkind == 'neg': + outtab = opts.outtab_neg + elif tkind == 'pos': + outtab = opts.outtab_pos + else: + print >> sys.stderr, '## tab file matched %s which is not "neg" or "pos" in 4th segment %s' % (t,tkind) + sys.exit() + content = open(t).readlines() + tabf = open(outtab,'w') + tabf.write(''.join(content)) + tabf.close() + else: + print >> sys.stdout, 'Odd, maketab = %s but no matches - tabs = %s' % (makeTab,tabs) + return retval + + +if __name__ == "__main__": + """ + called as: + <command interpreter="python">rgGSEA.py --input_ranks "$input1" --outhtml "$html_file" + --setMax "$setMax" --setMin "$setMin" --nPerm "$nPerm" --plotTop "$plotTop" --gsea_jar "$GALAXY_DATA_INDEX_DIR/shared/jars/gsea2-2.07.jar" + --output_dir "$html_file.files_path" --use_gmt ""${use_gmt.fields.path}"" --chip "${use_chip.fields.path}" + </command> + """ + op = optparse.OptionParser() + a = op.add_option + a('--input_ranks',default=None) + a('--input_tab',default=None) + a('--input_name',default=None) + a('--use_gmt',default=None) + a('--history_gmt',default=None) + a('--builtin_gmt',default=None) + a('--history_gmt_name',default=None) + a('--setMax',default="500") + a('--setMin',default="15") + a('--nPerm',default="1000") + a('--title',default="GSEA report") + a('--chip',default='') + a('--plotTop',default='20') + a('--outhtml',default=None) + a('--makeTab',default=None) + a('--output_dir',default=None) + a('--outtab_neg',default=None) + a('--outtab_pos',default=None) + a('--adjpvalcol',default=None) + a('--signcol',default=None) + a('--idcol',default=None) + a('--mode',default='Max_probe') + a('-j','--gsea_jar',default='/usr/local/bin/gsea2-2.07.jar') + opts, args = op.parse_args() + assert os.path.isfile(opts.gsea_jar),'## GSEA runner unable to find supplied gsea java desktop executable file %s' % opts.gsea_jar + if opts.input_ranks: + inpf = opts.input_ranks + else: + inpf = opts.input_tab + assert opts.idcol <> None, '## GSEA runner needs an id column if a tabular file provided' + assert opts.signcol <> None, '## GSEA runner needs a sign column if a tabular file provided' + assert opts.adjpvalcol <> None, '## GSEA runner needs an adjusted p value column if a tabular file provided' + assert os.path.isfile(inpf),'## GSEA runner unable to open supplied input file %s' % inpf + if opts.chip > '': + assert os.path.isfile(opts.chip),'## GSEA runner unable to open supplied chip file %s' % opts.chip + some = None + if opts.history_gmt <> None: + some = 1 + assert os.path.isfile(opts.history_gmt),'## GSEA runner unable to open supplied history gene set matrix (.gmt) file %s' % opts.history_gmt + if opts.builtin_gmt <> None: + some = 1 + assert os.path.isfile(opts.builtin_gmt),'## GSEA runner unable to open supplied history gene set matrix (.gmt) file %s' % opts.builtin_gmt + assert some, '## GSEA runner needs a gene set matrix file - none chosen?' + opts.title = re.sub('[^a-zA-Z0-9_]+', '', opts.title) + myName=os.path.split(sys.argv[0])[-1] + gse = gsea_wrapper(myName, opts=opts) + retcode = gse.run() + if retcode <> 0: + sys.exit(retcode) # indicate failure to job runner + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/rgGSEAcolumns.xml Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,834 @@ +<tool id="rgGSEAcolumns" name="Gene set enrichment" version="0.05"> + <description>using a generic tabular file</description> + <requirements> + <requirement type="package" version="2.0.12">gsea_jar</requirement> + <requirement type="set_environment">GSEAJAR_PATH</requirement> + </requirements> + <command interpreter="python">rgGSEA.py --input_tab "$input1" --adjpvalcol "$adjpvalcol" --signcol "$signcol" + --idcol "$idcol" --outhtml "$html_file" --input_name "${input1.name}" + --setMax "$setMax" --setMin "$setMin" --nPerm "$nPerm" --plotTop "$plotTop" + --gsea_jar "\$GSEAJAR_PATH" + --output_dir "$html_file.files_path" --mode "$mode" --title "$title" +#if $makeTab.value=='Yes' + --outtab_pos "$outtab_pos" --outtab_neg "$outtab_neg" +#end if +#if $gmtSource.refgmtSource == "indexed" or $gmtSource.refgmtSource == "both": +--builtin_gmt "${gmtSource.builtinGMT.fields.path}" +#end if +#if $gmtSource.refgmtSource == "history" or $gmtSource.refgmtSource == "both": +--history_gmt "${gmtSource.ownGMT}" --history_gmt_name "${gmtSource.ownGMT.name}" +#end if +#if $chipSource.refchipSource=="builtin" + --chip "${chipSource.builtinChip.fields.path}" +#end if +#if $chipSource.refchipSource=="history" + --chip "${chipSource.ownChip}" +#end if +</command> + <inputs> + <param name="input1" type="data" format="tabular" label="Select a tab delimited file with a probe id, adjusted p value and a signed weight (eg t-test statistic) on each row" + help=""/> + <param name="adjpvalcol" label="Column containing a p value for the DEG statistical test" + help = "Use RAW p-values - not FDR adjusted as these have a non GSEA friendly non-uniform distribution" + type="data_column" data_ref="input1" numerical="True" + multiple="false" use_header_names="true" size="5"> + <validator type="no_options" message="Please select a p value column."/> + </param> + <param name="signcol" label="Column containing the DE sign - eg log fold change so positive/negative values = upregulated/downregulated in treatment" + type="data_column" data_ref="input1" numerical="True" + multiple="false" use_header_names="true" size="5"> + <validator type="no_options" message="Please select a sign column."/> + </param> + <param name="idcol" label="Column containing a gene id (refseq, symbol or Entrez" + type="data_column" data_ref="input1" numerical="False" + multiple="false" use_header_names="true" size="5"> + <validator type="no_options" message="Please select an id column."/> + </param> + + <param name="title" type="text" value="GSEA" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain"/> + <param name="setMin" type="integer" label="Minimum gene set size to prune (default=15)" size="5" value="15"/> + <param name="setMax" type="integer" label="Maximum gene set size to prune (default=500)" size="5" value="500"/> + <param name="nPerm" type="integer" label="Number of permutations (default=1000)" size="7" value="1000"/> + <param name="plotTop" type="integer" label="Number of top gene sets to plot and present in detailed reports(default=20)" size="10" value="20"/> + <param name="mode" type="select" label="Mode for dealing with duplicated gene ids" > + <option value="Max_probe" selected="true">Use the most extreme value</option> + <option value="Median_of_probes">Use the median of all supplied values</option> + </param> + <param name="makeTab" type="select" label="Create a tabular report containing ALL gene sets for downstream analysis" > + <option value="Yes">Yes</option> + <option value="No" selected="true">No</option> + </param> + <conditional name="chipSource" > + <param name="refchipSource" type="select" label="Translate the rank file IDs (first column) using a GSEA 'chip' lookup table"> + <option value="builtin">Use a builtin GSEA 'chip' file</option> + <option value="history">Use one of the GSEA 'chip' files in my history</option> + <option value="no" selected="true">IDs are already gene symbols - no translation needed </option> + </param> + <when value="no"> + </when> + <when value="builtin"> + <param name="builtinChip" type="select" label="Select a GSEA 'chip' to map each probe id in the input file to a gene symbol"> + <options from_data_table="gseaChip_3.1"> + <filter type="sort_by" column="2" /> + <validator type="no_options" message="No chip tables are available - please see the Galaxy GSEA tool documentation for instructions on installing them" /> + </options> + </param> + </when> + <when value="history"> + <param name="ownChip" type="data" format="gseachip" label="Select an existing gseachip filefrom your history. Probe_id matching ids in your input file and a gene_symbol must be the first 2 tabular columns. Header row required." + help='see docs at http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Data_formats#CHIP:_Chip_file_format_.28.2A.chip.29'> + <validator type="no_options" message="Need a gseachip datatype - tabular file, probe_id gene_symbol as first 2 columns. Header row required. Convert datatypes (pencil icon) of existing history tabular files if necessary"/> + </param> + </when> + </conditional> + <conditional name="gmtSource"> + <param name="refgmtSource" type="select" label="Use a gene set (.gmt) from your history or use a built-in (MSigDB etc) gene set"> + <option value="indexed" selected="true">Use a built-in gene set</option> + <option value="history">Use a gene set from my history</option> + <option value="both">Add a gene set from my history to a built in gene set</option> + </param> + <when value="indexed"> + <param name="builtinGMT" type="select" label="Select a gene set matrix (.gmt) file to use for the analysis"> + <options from_data_table="gseaGMT_3.1"> + <filter type="sort_by" column="2" /> + <validator type="no_options" message="No GMT v3.1 files are available - please install them"/> + </options> + </param> + </when> + <when value="history"> + <param name="ownGMT" type="data" format="gmt" label="Select a Gene Set from your history" /> + </when> + <when value="both"> + <param name="ownGMT" type="data" format="gseagmt" label="Select a Gene Set from your history" /> + <param name="builtinGMT" type="select" label="Select a gene set matrix (.gmt) file to use for the analysis"> + <options from_data_table="gseaGMT_3.1"> + <filter type="sort_by" column="2" /> + <validator type="no_options" message="No GMT v3.1 files are available - please install them and make sure they match [tool-data]/gseaGMT_3.1.loc, editing as needed"/> + </options> + </param> + </when> + </conditional> + </inputs> + <outputs> + <data format="html" name="html_file" label="${title}.html"/> + <data format="tabular" name="outtab_pos" label="${title}_pos_allsets.xls"> + <filter>makeTab=="Yes"</filter> + </data> + <data format="tabular" name="outtab_neg" label="${title}_neg_allsets.xls"> + <filter>makeTab=="Yes"</filter> + </data> + </outputs> + <tests> + <test> + <param name="input1" value="gsea_test_DGE.xls"/> + <param name="ownGMT" value="gsea_test_DGE.xls"/> + <param name="adjpvalcol" value="5"/> + <param name="signcol" value="2"/> + <param name="idcol" value="1"/> + <param name="plotTop" value="2"/> + <param name="makeTab" value="No"/> + <param name="refchipSource" value="no"/> + <param name="refgmtSource" value="history"/> + <param name="html_file" value="gseatestout.html"> + <assert_contents> + <has_text text="GSEA Report for Dataset" /> + <has_text text="enrichment results in html"/> + </assert_contents> + </param> + <param name="setMax" value="500"/> + <param name="setMin" value="15"/> + <param name="nPerm" value="100"/> + <param name="output_dir" value="gseatestout"/> + <param name="mode" value="Max_probe"/> + <param name="title" value="GSEA test"/> + <param name="builtin_gmt" value="gseatestdata.gmt"/> + </test> + </tests> +<help> +**What it does** + +Performs Gene set enrichment analysis using GSEA_ from the Broad as described in 2005Paper_ and v3.1 of msigdb + +Refer to http://www.pnas.org/cgi/content/abstract/0506580102v1 for details of the method. + +**Input** + +A tabular file with a header row and some columns containing probe ids or gene names; a p value or t-test statistic reflecting the +strength and a signed quantity such as log fold change or a t-test statistic reflecting the direction of differential gene expression from edgeR for counts +or some other valid analysis. +Probe names can be translated using the chip file option (see below) +A filter has been added to remove NA and null id rows as these can't be used. A summary will appear in stdout in the page linked from the "i" (info) button for the output + +**Gene sets** + +If you have your own gene sets as .gmt files (see GUIDE_ for details of format) upload them in your history and use them by nominating a history sourced gmt. + +Alternatively, use one of the built in MSigDB_ (molecular signatures database) sets. +There may be additional locally provided sets such as starBase + +Optionally, a local gmt can be merged with a built in one if you want to see how they perform competitively + +Select the most appropriate subset of MSigDB gene sets or use all by choosing msigdb.v3.0.symbols. +A non-cancer subset called c2_c3_c5_all.v3.0.symbols is recommended if you are working with non-cancer biology. + + +**Chip** + +If your input file contains HUGO gene symbols (TEK, SLC38A1...etc) then you do not need to specify a 'chip'. + +GSEA software allows you to specify a translation table from common microarray probe IDs to gene symbols - select the option to use a chip file and choose the one that matches your probe id's +Failure to match the translation correctly will generally result in failure of the job since none of your input data will be translated properly. + +**Output** + +A report as html - see the GUIDE_ for a guide on interpretation + + +**Attribution** + +This Galaxy tool wrapper was written by Ross Lazarus. +It executes the GSEA_ java application available using MSigDB and Chip files obtained from the GSEA_ web site + + +**Chip ID samples** + +You may be able to look for probe ids in similar form to the ones you have in the list below to find the correct GSEA 'chip' file if you do not have HUGO gene symbols + + :: + + AG.chip + Probe Set ID Gene Symbol Gene Title + 11986_at --- calcium-dependent lipid-binding protein, putative + 11987_at --- zinc finger (C3HC4-type RING finger) family protein + 11988_at --- WD-40 repeat family protein + + Agilent_Human1A.chip + Probe Set ID Gene Symbol Gene Title + A_23_P100001 LOC400451 hypothetical gene supported by AK075564; BC060873 + A_23_P100011 AP3S2 adaptor-related protein complex 3, sigma 2 subunit + A_23_P10002 CEP63 centrosome protein Cep63 + + Agilent_Human1Av2.chip + Probe Set ID Gene Symbol Gene Title + A_23_P100001 LOC400451 hypothetical gene supported by AK075564; BC060873 + A_23_P100011 AP3S2 adaptor-related protein complex 3, sigma 2 subunit + A_23_P10002 CEP63 centrosome protein Cep63 + + Agilent_Human1B.chip + Probe Set ID Gene Symbol Gene Title + A_32_P10002 TUBA2 tubulin, alpha 2 + A_32_P100076 RPL19 ribosomal protein L19 + A_32_P100100 THOC3 THO complex 3 + + Agilent_Human1_cDNA.chip + Probe Set ID Gene Symbol Gene Title + 1 RNASEH2A ribonuclease H2, large subunit + 10 SF3B1 splicing factor 3b, subunit 1, 155kDa + 100 S100A16 S100 calcium binding protein A16 + + Agilent_HumanGenome.chip + Probe Set ID Gene Symbol Gene Title + A_23_P100001 LOC400451 hypothetical gene supported by AK075564; BC060873 + A_23_P100011 NULL NULL + A_23_P100022 SV2B synaptic vesicle glycoprotein 2B + + Agilent_Mouse_cDNA.chip + Probe Set ID Gene Symbol Gene Title + 1000982 SLC6A9 solute carrier family 6 (neurotransmitter transporter, glycine), member 9 + 1001007 IGF1 insulin-like growth factor 1 + 1001011 FCNA ficolin A + + Agilent_MouseDev.chip + Probe Set ID Gene Symbol Gene Title + A_65_P00523 NULL NULL + A_65_P00524 NULL NULL + A_65_P00525 SFTPD surfactant associated protein D + + Agilent_MouseGenome.chip + Probe Set ID Gene Symbol Gene Title + A_51_P100021 NULL NULL + A_51_P100034 2310075G12RIK RIKEN cDNA 2310075G12 gene + A_51_P100052 SLITRK2 SLIT and NTRK-like family, member 2 + + Agilent_MouseOligo.chip + Probe Set ID Gene Symbol Gene Title + A_51_P100021 NULL NULL + A_51_P100034 2310075G12RIK RIKEN cDNA 2310075G12 gene + A_51_P100052 SLITRK2 SLIT and NTRK-like family, member 2 + + Agilent_RatGenome_G4131A.chip + Probe Set ID Gene Symbol Gene Title + A_42_P453055 LU Lutheran blood group (Auberger b antigen included) + A_42_P453737 NULL NULL + A_42_P453894 RGD1306682_PREDICTED similar to RIKEN cDNA 1810046J19 (predicted) + + Agilent_RatOligo.chip + Probe Set ID Gene Symbol Gene Title + A_42_P453131 CNTN1 contactin 1 + A_42_P453151 TTC8_PREDICTED tetratricopeptide repeat domain 8 (predicted) + A_42_P453162 NULL NULL + + APPLERA_ABI1700.chip + Probe Set ID Gene Symbol Gene Title + 100002 NULL NULL + 100003 NULL NULL + 100027 ZNF192 zinc finger protein 192 + + ATH1_121501.chip + Probe Set ID Gene Symbol Gene Title + 244901_at --- --- + 244902_at --- --- + 244903_at --- --- + + AtlasMouse1.2.chip + Probe Set ID Gene Symbol Gene Title + MA099 HTR1A 5-hydroxytryptamine (serotonin) receptor 1A + MA015 JARID2 jumonji, AT rich interactive domain 2 + M443 CDKN2C cyclin-dependent kinase inhibitor 2C (p18, inhibits CDK4) + + AtlasRat1.2.chip + Probe Set ID Gene Symbol Gene Title + R484 TPP2 tripeptidylpeptidase II + RG348 MOS v-mos moloney murine sarcoma viral oncogene homolog + RG119 KCNJ1 potassium inwardly-rectifying channel, subfamily J, member 1 + + Bovine.chip + Probe Set ID Gene Symbol Gene Title + AFFX-BioB-3_at --- --- + AFFX-BioB-5_at --- --- + AFFX-BioB-M_at --- --- + + Caltech16KMouse.chip + Probe Set ID Gene Symbol Gene Title + 6712 CCL6 chemokine (C-C motif) ligand 6 + 8563 ACTL7B actin-like 7b + 8434 1700049M11RIK RIKEN cDNA 1700049M11 gene + + Caltech16KOligoMouse.chip + Probe Set ID Gene Symbol Gene Title + 6712 GCA grancalcin, EF-hand calcium binding protein + 8563 MARVELD2 MARVEL (membrane-associating) domain containing 2 + 8434 BC048825 NULL + + Canine_2.chip + Probe Set ID Gene Symbol Gene Title + AFFX-BioB-3_at --- --- + AFFX-BioB-5_at --- --- + AFFX-BioB-M_at --- --- + + Clontech_Atlas_13K.chip + Probe Set ID Gene Symbol Gene Title + 1-A01 NULL NULL + 1-A02 A2M alpha-2-macroglobulin + 1-A03 AADAC arylacetamide deacetylase (esterase) + + Clontech_BD_Atlas.chip + Probe Set ID Gene Symbol Gene Title + A01a PCP4 Purkinje cell protein 4 + A01b NULL NULL + A01c PMP22 peripheral myelin protein + + CNMCMuscleChip.chip + Probe Set ID Gene Symbol Gene Title + HSPD4570_at MBP myelin basic protein + HSPD7328_at MAPK12 mitogen-activated protein kinase 12 + HSPD17028_i_at DIA1 diaphorase (NADH) (cytochrome b-5 reductase) + + CodeLink_Human_Whole_Genome.chip + Probe Set ID Gene Symbol Gene Title + GE469530 NULL NULL + GE469548 TMEM1 transmembrane protein 1 + GE469549 NULL NULL + + CodeLink_UniSet_Human_20K_I_Bioarray.chip + Probe Set ID Gene Symbol Gene Title + 000106CB1_PROBE1 NDUFA4 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4, 9kDa + 000136CB1_PROBE1 MRPS33 mitochondrial ribosomal protein S33 + 000278CB1_PROBE1 FDFT1 farnesyl-diphosphate farnesyltransferase 1 + + CodeLink_UniSet_Human_I_Bioarray.chip + Probe Set ID Gene Symbol Gene Title + AA001334_PROBE1 ZHX2 zinc fingers and homeoboxes 2 + AA002191_PROBE1 PITPNA phosphatidylinositol transfer protein, alpha + AA004381_PROBE1 NULL NULL + + CodeLink_UniSet_Human_II_Bioarray.chip + Probe Set ID Gene Symbol Gene Title + 000106CB1_PROBE1 NDUFA4 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4, 9kDa + 000136CB1_PROBE1 MRPS33 mitochondrial ribosomal protein S33 + 000278CB1_PROBE1 FDFT1 farnesyl-diphosphate farnesyltransferase 1 + + CodeLink_UniSet_Rat_I_Bioarray.chip + Probe Set ID Gene Symbol Gene Title + AA799301_PROBE1 NULL NULL + AA799313_PROBE1 RGD:1303279 sialyltransferase 10 (alpha-2,3-sialyltransferase VI) + AA799329_PROBE1 NULL NULL + + DrosGenome1.chip + Probe Set ID Gene Symbol Gene Title + 141200_at CG9418 CG9418 + 141201_at CG17840 CG17840 + 141202_at QKR58E-3 quaking related 58E-3 + + Drosophila_2.chip + Probe Set ID Gene Symbol Gene Title + 1616608_a_at GPDH Glycerol 3 phosphate dehydrogenase + 1622892_s_at CG33057 /// MKG-P CG33057 /// monkey king protein + 1622893_at IM3 /// RPS10B Immune induced molecule 3 /// Ribosomal protein S10b + + G4110A.chip + Probe Set ID Gene Symbol Gene Title + A_23_P7033 MAGEF1 melanoma antigen, family F, 1 + A_23_P170713 A_23_P170713 ***description missing*** + A_23_P107801 FLJ21742 hypothetical protein FLJ21742 + + G4110Av2.chip + Probe Set ID Gene Symbol Gene Title + A_23_P7033 MAGEF1 melanoma antigen, family F, 1 + A_23_P170713 A_23_P170713 No description available + A_23_P107801 FLJ21742 hypothetical protein FLJ21742 + + GENE_SYMBOL.chip + Probe Set ID Gene Symbol Gene Title Aliases + A1BG A1BG alpha-1-B glycoprotein GABDKFZP686F0970ABGHYST2477A1B + A2M A2M alpha-2-macroglobulin DKFZP779B086S863-7ALPHA 2MFWP007CPAMD5 + A2ML1 A2ML1 alpha-2-macroglobulin-like 1 FLJ39129DKFZP686O1010FLJ41598DKFZP686G1812FLJ25179CPAMD9DKFZP686D2011FLJ16045FLJ41607DKFZP686C1729FLJ41597DKFZP686L1821 + + GenosysCytokineV2.chip + Probe Set ID Gene Symbol Gene Title + M05.2 GDF11 growth differentiation factor 11 + M22.2 AE000170 No description available + E06.3 IL5 interleukin 5 (colony-stimulating factor, eosinophil) + + HC_G110.chip + Probe Set ID Gene Symbol Gene Title + 1000_at MAPK3 mitogen-activated protein kinase 3 + 1001_at TIE1 tyrosine kinase with immunoglobulin-like and EGF-like domains 1 + 1002_f_at CYP2C19 cytochrome P450, family 2, subfamily C, polypeptide 19 + + HG_Focus.chip + Probe Set ID Gene Symbol Gene Title + 1007_s_at DDR1 discoidin domain receptor family, member 1 + 1053_at RFC2 replication factor C (activator 1) 2, 40kDa + 117_at HSPA6 /// LOC652878 heat shock 70kDa protein 6 (HSP70B') /// similar to heat shock 70kDa protein 6 (HSP70B) + + HG_U133A_2.chip + Probe Set ID Gene Symbol Gene Title + 1007_s_at DDR1 discoidin domain receptor family, member 1 + 1053_at RFC2 replication factor C (activator 1) 2, 40kDa + 117_at HSPA6 /// LOC652878 heat shock 70kDa protein 6 (HSP70B') /// similar to heat shock 70kDa protein 6 (HSP70B) + + HG_U133AAofAv2.chip + Probe Set ID Gene Symbol Gene Title + 1007_s_at DDR1 discoidin domain receptor family, member 1 + 1053_at RFC2 replication factor C (activator 1) 2, 40kDa + 117_at HSPA6 /// LOC652878 heat shock 70kDa protein 6 (HSP70B') /// similar to heat shock 70kDa protein 6 (HSP70B) + + HG_U133A.chip + Probe Set ID Gene Symbol Gene Title + 1007_s_at DDR1 discoidin domain receptor family, member 1 + 1053_at RFC2 replication factor C (activator 1) 2, 40kDa + 117_at HSPA6 /// LOC652878 heat shock 70kDa protein 6 (HSP70B') /// similar to heat shock 70kDa protein 6 (HSP70B) + + HG_U133B.chip + Probe Set ID Gene Symbol Gene Title + 200000_s_at PRPF8 PRP8 pre-mRNA processing factor 8 homolog (S. cerevisiae) /// PRP8 pre-mRNA processing factor 8 homolog (S. cerevisiae) + 200001_at CAPNS1 calpain, small subunit 1 /// calpain, small subunit 1 + 200002_at RPL35 ribosomal protein L35 /// ribosomal protein L35 + + HG_U133_Plus_2.chip + Probe Set ID Gene Symbol Gene Title + 1007_s_at DDR1 discoidin domain receptor family, member 1 + 1053_at RFC2 replication factor C (activator 1) 2, 40kDa + 117_at HSPA6 /// LOC652878 heat shock 70kDa protein 6 (HSP70B') /// similar to heat shock 70kDa protein 6 (HSP70B) + + HG_U95Av2.chip + Probe Set ID Gene Symbol Gene Title + 1000_at MAPK3 mitogen-activated protein kinase 3 + 1001_at TIE1 tyrosine kinase with immunoglobulin-like and EGF-like domains 1 + 1002_f_at CYP2C19 cytochrome P450, family 2, subfamily C, polypeptide 19 + + HG_U95B.chip + Probe Set ID Gene Symbol Gene Title + 41880_at --- Transcribed locus + 41881_at FLJ40142 FLJ40142 protein + 41882_at FBXO42 F-box protein 42 + + HG_U95C.chip + Probe Set ID Gene Symbol Gene Title + 48609_r_at KIAA1333 KIAA1333 + 48610_at C20ORF102 chromosome 20 open reading frame 102 + 48612_at N4BP1 /// LOC653213 Nedd4 binding protein 1 /// similar to Nedd4 binding protein 1 + + HG_U95D.chip + Probe Set ID Gene Symbol Gene Title + 67020_at --- Transcribed locus + 67021_at FAM44B Family with sequence similarity 44, member B + 67023_at --- Transcribed locus + + HG_U95E.chip + Probe Set ID Gene Symbol Gene Title + 67022_at PIP5K2B Phosphatidylinositol-4-phosphate 5-kinase, type II, beta + 67024_at CASD1 CAS1 domain containing 1 + 67026_at NFASC neurofascin homolog (chicken) + + HPCGGCompugenAnnotations.chip + Probe Set ID Gene Symbol Gene Title + MGI:1913066 SH3MD2 Sh3md2, SH3 multiple domains 2 + MGI:1913099 5430413I02RIK 5430413I02Rik, RIKEN cDNA 5430413I02 gene + MGI:98848 TSHB Tshb, thyroid stimulating hormone, beta subunit + + HT_HG_U133A.chip + Probe Set ID Gene Symbol Gene Title + 1007_s_at DDR1 discoidin domain receptor family, member 1 + 1053_at RFC2 replication factor C (activator 1) 2, 40kDa + 117_at HSPA6 /// LOC652878 heat shock 70kDa protein 6 (HSP70B') /// similar to heat shock 70kDa protein 6 (HSP70B) + + Hu35KsubA.chip + Probe Set ID Gene Symbol Gene Title + AA000993_at PRDM8 PR domain containing 8 + AA001296_s_at PHF23 PHD finger protein 23 + AA002245_at ZC3H11A zinc finger CCCH-type containing 11A + + Hu35KsubB.chip + Probe Set ID Gene Symbol Gene Title + AFFX-BioB-3_at --- --- + AFFX-BioB-3_st --- --- + AFFX-BioB-5_at --- --- + + Hu35KsubC.chip + Probe Set ID Gene Symbol Gene Title + AFFX-BioB-3_at --- --- + AFFX-BioB-3_st --- --- + AFFX-BioB-5_at --- --- + + Hu35KsubD.chip + Probe Set ID Gene Symbol Gene Title + AFFX-BioB-3_at --- --- + AFFX-BioB-3_st --- --- + AFFX-BioB-5_at --- --- + + Hu6800.chip + Probe Set ID Gene Symbol Gene Title + A28102_at GABRA3 gamma-aminobutyric acid (GABA) A receptor, alpha 3 + AB000114_at OMD osteomodulin + AB000115_at IFI44L interferon-induced protein 44-like + + Illumina_Human.chip + Probe Set ID Gene Symbol Gene Title + 1-A-1 BCAP31 B-cell receptor-associated protein 31 + 1-A-10 NSFL1C NSFL1 (p97) cofactor (p47) + 1-A-11 C20ORF24 chromosome 20 open reading frame 24 + + ilmn_HumanHT_12_V4_0_R1_15002873_B.chip + Probe Set ID Gene Symbol Gene Title Entrez Gene ID + ILMN_1725881 LOC23117 PREDICTED: Homo sapiens KIAA0220-like protein, transcript variant 11 (LOC23117), mRNA. 23117 + ILMN_1910180 Homo sapiens cDNA: FLJ21027 fis, clone CAE07110 -999 + ILMN_1804174 FCGR2B PREDICTED: Homo sapiens Fc fragment of IgG, low affinity IIb, receptor (CD32) (FCGR2B), mRNA. 2213 + + ilmn_MouseRef_8_V2_0_R3_11278551_A.chip + Probe Set ID Gene Symbol Gene Title Entrez Gene ID + ILMN_2607609 YIF1B Mus musculus Yip1 interacting factor homolog B (S. cerevisiae) (Yif1b), mRNA. 77254 + ILMN_1238674 2700007P21RIK Mus musculus RIKEN cDNA 2700007P21 gene (2700007P21Rik), transcript variant 2, mRNA. 212772 + ILMN_3062534 2700007P21RIK Mus musculus RIKEN cDNA 2700007P21 gene (2700007P21Rik), transcript variant 2, mRNA. 212772 + + ilmn_MouseWG_6_V2_0_R3_11278593_A.chip + Probe Set ID Gene Symbol Gene Title Entrez Gene ID + ILMN_1243094 THRSP NULL -999 + ILMN_1238674 2700007P21RIK Mus musculus RIKEN cDNA 2700007P21 gene (2700007P21Rik), transcript variant 2, mRNA. 212772 + ILMN_2454720 2700007P21RIK Mus musculus RIKEN cDNA 2700007P21 gene (2700007P21Rik), transcript variant 2, mRNA. 212772 + + labonweb_human.chip + Probe Set ID Gene Symbol Gene Title + 1-A1 GAPD glyceraldehyde-3-phosphate dehydrogenase + 1-A10 BMP5 bone morphogenetic protein 5 + 1-A11 C9ORF78 chromosome 9 open reading frame 78 + + lymphochip.chip + Probe Set ID Gene Symbol Gene Title + 1317148 TNF tumor necrosis factor (TNF superfamily, member 2) + 814251 SLAMF1 signaling lymphocytic activation molecule family member 1 + 712899___1 AA282233 zt12d02.s1 NCI_CGAP_GCB1 Homo sapiens cDNA clone + + MG_U74Av2.chip + Probe Set ID Gene Symbol Gene Title + 100001_at CD3G CD3 antigen, gamma polypeptide + 100002_at ITIH3 inter-alpha trypsin inhibitor, heavy chain 3 + 100003_at RYR1 ryanodine receptor 1, skeletal muscle + + MG_U74Bv2.chip + Probe Set ID Gene Symbol Gene Title + 104769_at LIMA1 LIM domain and actin binding 1 + 104770_at --- 16 days neonate heart cDNA, RIKEN full-length enriched library, clone:D830033B01 product:unclassifiable, full insert sequence + 104771_at RAC2 RAS-related C3 botulinum substrate 2 + + MG_U74Cv2.chip + Probe Set ID Gene Symbol Gene Title + 128520_at CCDC97 coiled-coil domain containing 97 + 128523_at A730063M14RIK RIKEN cDNA A730063M14 gene + 128529_at A930009G19RIK RIKEN cDNA A930009G19 gene + + MOE430A.chip + Probe Set ID Gene Symbol Gene Title + 1415670_at COPG coatomer protein complex, subunit gamma + 1415671_at ATP6V0D1 ATPase, H+ transporting, lysosomal V0 subunit D1 + 1415672_at GOLGA7 golgi autoantigen, golgin subfamily a, 7 + + MOE430B.chip + Probe Set ID Gene Symbol Gene Title + 1415670_at COPG coatomer protein complex, subunit gamma + 1415671_at ATP6V0D1 ATPase, H+ transporting, lysosomal V0 subunit D1 + 1415672_at GOLGA7 golgi autoantigen, golgin subfamily a, 7 + + Mouse430_2.chip + Probe Set ID Gene Symbol Gene Title + 1415670_at COPG coatomer protein complex, subunit gamma + 1415671_at ATP6V0D1 ATPase, H+ transporting, lysosomal V0 subunit D1 + 1415672_at GOLGA7 golgi autoantigen, golgin subfamily a, 7 + + Mouse430A_2.chip + Probe Set ID Gene Symbol Gene Title + 1415670_at COPG coatomer protein complex, subunit gamma + 1415671_at ATP6V0D1 ATPase, H+ transporting, lysosomal V0 subunit D1 + 1415672_at GOLGA7 golgi autoantigen, golgin subfamily a, 7 + + Mu11KsubA.chip + Probe Set ID Gene Symbol Gene Title + aa000148_s_at NIP7 nuclear import 7 homolog (S. cerevisiae) + AA000151_at 1200011I18RIK RIKEN cDNA 1200011I18 gene + aa000380_s_at TARDBP TAR DNA binding protein + + Mu11KsubB.chip + Probe Set ID Gene Symbol Gene Title + AFFX-18SRNAMur/X00686_3_at --- --- + AFFX-18SRNAMur/X00686_5_at --- --- + AFFX-18SRNAMur/X00686_M_at --- --- + + Mu19KsubA.chip + Probe Set ID Gene Symbol Gene Title + AFFX-18SRNAMur/X00686_3_at --- --- + AFFX-18SRNAMur/X00686_5_at --- --- + AFFX-18SRNAMur/X00686_M_at --- --- + + Mu19KsubB.chip + Probe Set ID Gene Symbol Gene Title + AFFX-18SRNAMur/X00686_3_at --- --- + AFFX-18SRNAMur/X00686_5_at --- --- + AFFX-18SRNAMur/X00686_M_at --- --- + + Mu19KsubC.chip + Probe Set ID Gene Symbol Gene Title + AFFX-18SRNAMur/X00686_3_at --- --- + AFFX-18SRNAMur/X00686_5_at --- --- + AFFX-18SRNAMur/X00686_M_at --- --- + + MWG_Human_30K_A.chip + Probe Set ID Gene Symbol Gene Title + 1-A1 IFNA8 interferon, alpha 8 + 1-A10 AGL amylo-1, 6-glucosidase, 4-alpha-glucanotransferase (glycogen debranching enzyme, glycogen storage disease type III) + 1-A11 NULL NULL + + MWG_Human_30K_B.chip + Probe Set ID Gene Symbol Gene Title + 1-A10 ADAM11 a disintegrin and metalloproteinase domain 11 + 1-A11 NULL NULL + 1-A12 MAST3 microtubule associated serine/threonine kinase 3 + + Netherland_cancer_institute_operon_human_35k.chip + Probe Set ID Gene Symbol Gene Title + H200001046 ZZZ3 zinc finger, ZZ domain containing 3 [Homo sapiens]. [Source:RefSeq;Acc:NM_015534] + H200011098 ZZEF1 zinc finger, ZZ-type with EF hand domain 1 [Homo sapiens]. [Source:RefSeq;Acc:NM_015113] + H200006223 ZYX Zyxin (Zyxin 2). [Source:Uniprot/SWISSPROT;Acc:Q15942] + + Netherland_cancer_institute_operon_mouse_FOOk.chip + Probe Set ID Gene Symbol Gene Title + M300012631 ZZEF1 NULL + M300012632 ZZEF1 NULL + M200002019 ZYX Zyxin. [Source:Uniprot/SWISSPROT;Acc:Q62523] + + NIA15k.chip + Probe Set ID Gene Symbol Gene Title + H3051B03 BG067146 H3051B03-3 NIA Mouse 15K cDNA Clone Set Mus musculus cDNA + H3067H02 BG068648 H3067H02-3 NIA Mouse 15K cDNA Clone Set Mus musculus cDNA + H3121E03 AK029878 NULL + + OPERON_HUMANv2.chip + Probe Set ID Gene Symbol Gene Title + 1-A01 NULL NULL + 1-A02 NULL NULL + 1-A03 NAT2 N-acetyltransferase 2 (arylamine N-acetyltransferase) + + OPERON_HUMANv3.chip + Probe Set ID Gene Symbol Gene Title + 1-A03 TSPAN6 tetraspanin 6 + 1-A04 AK2 adenylate kinase 2 + 1-A05 NULL NULL + + RAE230A.chip + Probe Set ID Gene Symbol Gene Title + 1367452_at SUMO2 SMT3 suppressor of mif two 3 homolog 2 (yeast) + 1367453_at CDC37 cell division cycle 37 homolog (S. cerevisiae) + 1367454_at COPB2 coatomer protein complex, subunit beta 2 (beta prime) + + RAE230B.chip + Probe Set ID Gene Symbol Gene Title + 1367452_at SUMO2 SMT3 suppressor of mif two 3 homolog 2 (yeast) + 1367453_at CDC37 cell division cycle 37 homolog (S. cerevisiae) + 1367454_at COPB2 coatomer protein complex, subunit beta 2 (beta prime) + + Rat230_2.chip + Probe Set ID Gene Symbol Gene Title + 1367452_at SUMO2 SMT3 suppressor of mif two 3 homolog 2 (yeast) + 1367453_at CDC37 cell division cycle 37 homolog (S. cerevisiae) + 1367454_at COPB2 coatomer protein complex, subunit beta 2 (beta prime) + + RefSeq_human.chip + Probe Set ID Gene Symbol Gene Title + NM_030625 CXXC6 CXXC finger 6 + NM_181776 SLC36A2 solute carrier family 36 (proton/amino acid symporter), member 2 + NM_152616 TRIM42 tripartite motif-containing 42 + + RefSeq_NP_Human.chip + Probe Set ID Gene Symbol Gene Title + NP_000005 A2M alpha-2-macroglobulin + NP_000006 NAT2 N-acetyltransferase 2 (arylamine N-acetyltransferase) + NP_000007 ACADM acyl-Coenzyme A dehydrogenase, C-4 to C-12 straight chain + + RefSeq_NP_Mouse.chip + Probe Set ID Gene Symbol Gene Title + AAF62769 NULL NULL + AAF62770 NULL NULL + AAF62771 NULL NULL + + RefSeq_NP_Rat.chip + Probe Set ID Gene Symbol Gene Title + NP_007225 NULL NULL + NP_007226 NULL NULL + NP_007227 NULL NULL + + Research_Genetics.chip + Probe Set ID Gene Symbol Gene Title + 1-a-1 NULL NULL + 1-a-10 WNT2 wingless-type MMTV integration site family member 2 + 1-a-11 VHL von Hippel-Lindau tumor suppressor + + RG_U34A.chip + Probe Set ID Gene Symbol Gene Title + A01157cds_s_at LIPF lipase, gastric + A03913cds_s_at SERPINE2 serine (or cysteine) proteinase inhibitor, clade E, member 2 + A04674cds_s_at UCP1 uncoupling protein 1 (mitochondrial, proton carrier) + + RG_U34B.chip + Probe Set ID Gene Symbol Gene Title + AFFX-BioB-3_at --- --- + AFFX-BioB-3_st --- --- + AFFX-BioB-5_at --- --- + + RG_U34C.chip + Probe Set ID Gene Symbol Gene Title + AA012646_i_at --- Transcribed locus + AA012646_r_at --- Transcribed locus + AA012654_at --- Transcribed locus + + RN_U34.chip + Probe Set ID Gene Symbol Gene Title + A03913cds_s_at SERPINE2 serine (or cysteine) proteinase inhibitor, clade E, member 2 + A17753cds_s_at DRD3 dopamine receptor D3 + AA848563_s_at HSPA1A /// HSPA1B_MAPPED heat shock 70kD protein 1A /// heat shock 70kD protein 1B (mapped) + + Rosetta50K.chip + Probe Set ID Gene Symbol Gene Title + 1360465 RANBP9 RAN binding protein 9 + 1176829 LOC375449 similar to microtubule associated testis specific serine/threonine protein kinase + 1351074 NEDD4L neural precursor cell expressed, developmentally down-regulated 4-like + + Rosetta.chip + Probe Set ID Gene Symbol Gene Title + NM_000504 F10 coagulation factor X + Contig32955_RC ARL6IP6 ADP-ribosylation-like factor 6 interacting protein 6 + AK000455 MGC16733 hypothetical gene MGC16733 similar to CG12113 + + RT_U34.chip + Probe Set ID Gene Symbol Gene Title + AA108277_at HSPH1 heat shock 105kDa/110kDa protein 1 + AA108308_i_at MDM2_PREDICTED Transformed mouse 3T3 cell double minute 2 homolog (mouse) (predicted) + AA108308_s_at --- --- + + RZPD_Human_Ensembl1.1.chip + Probe Set ID Gene Symbol Gene Title + RZPDp203A011001D SARDH sarcosine dehydrogenase + RZPDp203A011002D ARHGAP22 Rho GTPase activating protein 22 + RZPDp203A011003D CNGA3 cyclic nucleotide gated channel alpha 3 + + RZPD_Human_ORF_Clones_Gateway.chip + Probe Set ID Gene Symbol Gene Title + RZPDo834A0110D - Gateway (closed) PTD015 PTD015 protein + RZPDo834A0114D - Gateway (closed) TRIB3 tribbles homolog 3 (Drosophila) + RZPDo834A0116D - Gateway (closed) ORM2 orosomucoid 2 + + RZPD_Human_Unigene3.1.chip + Probe Set ID Gene Symbol Gene Title + HU3_p983A011001D SARDH sarcosine dehydrogenase + HU3_p983A011002D ARHGAP22 Rho GTPase activating protein 22 + HU3_p983A011003D CNGA3 cyclic nucleotide gated channel alpha 3 + + Seq_Accession.chip + Probe Set ID Gene Symbol + AA017197 C21ORF36 + AA191116 MTVR2 + AA280701 CXYORF7 + + Stanford.chip + Probe Set ID Gene Symbol Gene Title + IMAGE:703849 DDB2 damage-specific DNA binding protein 2, 48kDa + IMAGE:1301778 ZFY zinc finger protein, Y-linked + IMAGE:795810 HS.99503HS.520681 Homo sapiens transcribed sequenceHomo sapiens, clone IMAGE:4823270, mRNA + + Stanford_Source_Accessions.chip + Probe Set ID Gene Symbol Gene Title + AI848107 0610010K14RIK RIKEN cDNA 0610010K14 gene + AK002491 0610010K14RIK RIKEN cDNA 0610010K14 gene + AK003842 0610010K14RIK RIKEN cDNA 0610010K14 gene + + TIGR_31K_Human_Set.chip + Probe Set ID Gene Symbol Gene Title + 1-1 NULL NULL + 1-10 WNT2 wingless-type MMTV integration site family member 2 + 1-11 VHL von Hippel-Lindau tumor suppressor + + TIGR_40K_Human_Set.chip + Probe Set ID Gene Symbol Gene Title + 10 NULL NULL + 100 TEX27 testis expressed sequence 27 + 1000 HOXA1 homeo box A1 + + U133_X3P.chip + Probe Set ID Gene Symbol Gene Title + 1053_3p_at RFC2 replication factor C (activator 1) 2, 40kDa + 117_3p_at HSPA6 /// LOC652878 heat shock 70kDa protein 6 (HSP70B') /// similar to heat shock 70kDa protein 6 (HSP70B) + 1494_3p_f_at CYP2A6 cytochrome P450, family 2, subfamily A, polypeptide 6 + + UCLA_NIH_33K.chip + Probe Set ID Gene Symbol Gene Title + 1020181 NULL NULL + 1020315 VAV2 vav 2 oncogene + 1020478 AP1GBP1 AP1 gamma subunit binding protein 1 + + Zebrafish.chip + Probe Set ID Gene Symbol Gene Title + AFFX-BioB-3_at --- --- + AFFX-BioB-5_at --- --- + AFFX-BioB-M_at --- --- + + + .. _LGPL: http://www.gnu.org/copyleft/lesser.html + .. _GSEA: http://www.broadinstitute.org/gsea + .. _GUIDE: http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html?_Interpreting_GSEA_Results + .. _MSigDB: http://www.broadinstitute.org/gsea/msigdb/index.jsp + .. _2005Paper: http://www.pnas.org/content/102/43/15545.full + +</help> + +</tool> + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/rgToolFactory.py Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,575 @@ +# rgToolFactory.py +# see https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home +# +# copyright ross lazarus (ross stop lazarus at gmail stop com) May 2012 +# +# all rights reserved +# Licensed under the LGPL +# suggestions for improvement and bug fixes welcome at https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home +# +# January 2013 +# problem pointed out by Carlos Borroto +# added escaping for <>$ - thought I did that ages ago... +# +# August 11 2012 +# changed to use shell=False and cl as a sequence + +# This is a Galaxy tool factory for simple scripts in python, R or whatever ails ye. +# It also serves as the wrapper for the new tool. +# +# you paste and run your script +# Only works for simple scripts that read one input from the history. +# Optionally can write one new history dataset, +# and optionally collect any number of outputs into links on an autogenerated HTML page. + +# DO NOT install on a public or important site - please. + +# installed generated tools are fine if the script is safe. +# They just run normally and their user cannot do anything unusually insecure +# but please, practice safe toolshed. +# Read the fucking code before you install any tool +# especially this one + +# After you get the script working on some test data, you can +# optionally generate a toolshed compatible gzip file +# containing your script safely wrapped as an ordinary Galaxy script in your local toolshed for +# safe and largely automated installation in a production Galaxy. + +# If you opt for an HTML output, you get all the script outputs arranged +# as a single Html history item - all output files are linked, thumbnails for all the pdfs. +# Ugly but really inexpensive. +# +# Patches appreciated please. +# +# +# long route to June 2012 product +# Behold the awesome power of Galaxy and the toolshed with the tool factory to bind them +# derived from an integrated script model +# called rgBaseScriptWrapper.py +# Note to the unwary: +# This tool allows arbitrary scripting on your Galaxy as the Galaxy user +# There is nothing stopping a malicious user doing whatever they choose +# Extremely dangerous!! +# Totally insecure. So, trusted users only +# +# preferred model is a developer using their throw away workstation instance - ie a private site. +# no real risk. The universe_wsgi.ini admin_users string is checked - only admin users are permitted to run this tool. +# + +import sys +import shutil +import subprocess +import os +import time +import tempfile +import optparse +import tarfile +import re +import shutil +import math + +progname = os.path.split(sys.argv[0])[1] +myversion = 'V000.2 June 2012' +verbose = False +debug = False +toolFactoryURL = 'https://bitbucket.org/fubar/galaxytoolfactory' + +def timenow(): + """return current time as a string + """ + return time.strftime('%d/%m/%Y %H:%M:%S', time.localtime(time.time())) + +cheetah_escape_table = { + "$": "\$" + } + +cheetah_unescape_table = { + "\$": "$" + } + +def html_escape(t): + """Unescape \$ first in case already done + cheetah barfs if any $ without \ + xml parsing is controlled with <![CDATA[...]]> + """ + text = t + for k in cheetah_unescape_table.keys(): + text = text.replace(k,cheetah_unescape_table[k]) + for k in cheetah_escape_table.keys(): + text = text.replace(k,cheetah_escape_table[k]) + return text + + +class ScriptRunner: + """class is a wrapper for an arbitrary script + """ + + def __init__(self,opts=None,treatbashSpecial=True): + """ + cleanup inputs, setup some outputs + + """ + self.treatbashSpecial = treatbashSpecial + if opts.output_dir: # simplify for the tool tarball + os.chdir(opts.output_dir) + self.thumbformat = 'jpg' + self.opts = opts + self.toolname = re.sub('[^a-zA-Z0-9_]+', '', opts.tool_name) # a sanitizer now does this but.. + self.toolid = self.toolname + self.myname = sys.argv[0] # get our name because we write ourselves out as a tool later + self.pyfile = self.myname # crude but efficient - the cruft won't hurt much + self.xmlfile = '%s.xml' % self.toolname + s = open(self.opts.script_path,'r').readlines() + s = [x.rstrip() for x in s] # remove pesky dos line endings if needed + self.script = '\n'.join(s) + fhandle,self.sfile = tempfile.mkstemp(prefix=self.toolname,suffix=".%s" % (opts.interpreter)) + tscript = open(self.sfile,'w') # use self.sfile as script source for Popen + tscript.write(self.script) + tscript.close() + self.escapedS = [html_escape(x) for x in s] # for restructured text in help + self.escapedScript = '\n'.join(self.escapedS) + self.indentedScript = '\n'.join([' %s' % x for x in s]) # for restructured text in help + if opts.output_dir: # may not want these complexities + self.tlog = os.path.join(opts.output_dir,"%s_runner.log" % self.toolname) + art = '%s.%s' % (self.toolname,opts.interpreter) + artpath = os.path.join(self.opts.output_dir,art) # need full path + artifact = open(artpath,'w') # use self.sfile as script source for Popen + artifact.write(self.script) + artifact.close() + self.cl = [] + self.html = [] + a = self.cl.append + a(opts.interpreter) + if self.treatbashSpecial and opts.interpreter in ['bash','sh']: + a(self.sfile) + else: + a('-') # stdin + a(opts.input_tab) + a(opts.output_tab) + self.outFormats = 'tabular' # TODO make this an option at tool generation time + self.inputFormats = 'tabular' # TODO make this an option at tool generation time + self.test1Input = '%s_test1_input.xls' % self.toolname + self.test1Output = '%s_test1_output.xls' % self.toolname + self.test1HTML = '%s_test1_output.html' % self.toolname + + def makeXML(self): + """ + Create a Galaxy xml tool wrapper for the new script as a string to write out + fixme - use templating or something less fugly than this example of what we produce + + <tool id="reverse" name="reverse" version="0.01"> + <description>a tabular file</description> + <command interpreter="python"> + reverse.py --script_path "$runMe" --interpreter "python" + --tool_name "reverse" --input_tab "$input1" --output_tab "$tab_file" + </command> + <inputs> + <param name="input1" type="data" format="tabular" label="Select a suitable input file from your history"/><param name="job_name" type="text" label="Supply a name for the outputs to remind you what they contain" value="reverse"/> + + </inputs> + <outputs> + <data format="tabular" name="tab_file" label="${job_name}"/> + + </outputs> + <help> + +**What it Does** + +Reverse the columns in a tabular file + + </help> + <configfiles> + <configfile name="runMe"> + +# reverse order of columns in a tabular file +import sys +inp = sys.argv[1] +outp = sys.argv[2] +i = open(inp,'r') +o = open(outp,'w') +for row in i: + rs = row.rstrip().split('\t') + rs.reverse() + o.write('\t'.join(rs)) + o.write('\n') +i.close() +o.close() + + + </configfile> + </configfiles> + </tool> + + """ + newXML="""<tool id="%(toolid)s" name="%(toolname)s" version="%(tool_version)s"> +%(tooldesc)s +%(command)s +<inputs> +%(inputs)s +</inputs> +<outputs> +%(outputs)s +</outputs> +<configfiles> +<configfile name="runMe"> +<![CDATA[ +%(script)s +]]> +</configfile> +</configfiles> +%(tooltests)s +<help> +<![CDATA[ +%(help)s +]]> +</help> +</tool>""" +# needs a dict with toolname, toolid, interpreter, scriptname, command, inputs as a multi line string ready to write, outputs ditto, help ditto + + newCommand="""<command interpreter="python"> + %(toolname)s.py --script_path "$runMe" --interpreter "%(interpreter)s" + --tool_name "%(toolname)s" %(command_inputs)s %(command_outputs)s + </command>""" # may NOT be an input or htmlout + tooltestsTabOnly = """<tests><test> + <param name="input1" value="%(test1Input)s" ftype="tabular"/> + <param name="job_name" value="test1"/> + <param name="runMe" value="$runMe"/> + <output name="tab_file" file="%(test1Output)s" ftype="tabular"/> + </test></tests>""" + tooltestsHTMLOnly = """<tests><test> + <param name="input1" value="%(test1Input)s" ftype="tabular"/> + <param name="job_name" value="test1"/> + <param name="runMe" value="$runMe"/> + <output name="html_file" file="%(test1HTML)s" ftype="html" lines_diff="5"/> + </test></tests>""" + tooltestsBoth = """<tests><test> + <param name="input1" value="%(test1Input)s" ftype="tabular"/> + <param name="job_name" value="test1"/> + <param name="runMe" value="$runMe"/> + <output name="tab_file" file="%(test1Output)s" ftype="tabular" /> + <output name="html_file" file="%(test1HTML)s" ftype="html" lines_diff="10"/> + </test></tests>""" + xdict = {} + xdict['tool_version'] = self.opts.tool_version + xdict['test1Input'] = self.test1Input + xdict['test1HTML'] = self.test1HTML + xdict['test1Output'] = self.test1Output + if self.opts.make_HTML and self.opts.output_tab <> 'None': + xdict['tooltests'] = tooltestsBoth % xdict + elif self.opts.make_HTML: + xdict['tooltests'] = tooltestsHTMLOnly % xdict + else: + xdict['tooltests'] = tooltestsTabOnly % xdict + xdict['script'] = self.escapedScript + # configfile is least painful way to embed script to avoid external dependencies + # but requires escaping of <, > and $ to avoid Mako parsing + if self.opts.help_text: + xdict['help'] = open(self.opts.help_text,'r').read() + else: + xdict['help'] = 'Please ask the tool author for help as none was supplied at tool generation' + coda = ['**Script**','Pressing execute will run the following code over your input file and generate some outputs in your history::\n'] + coda.append(self.indentedScript) + coda.append('\n\n') + coda.append('**Attribution** This Galaxy tool was created by %s at %s\nusing the Galaxy Tool Factory.' % (self.opts.user_email,timenow())) + coda.append('See %s for details of that project' % (toolFactoryURL)) + coda.append('Please cite: Creating re-usable tools from scripts: The Galaxy Tool Factory. Ross Lazarus; Antony Kaspi; Mark Ziemann; The Galaxy Team. ') + coda.append('Bioinformatics 2012; doi: 10.1093/bioinformatics/bts573') + xdict['help'] = '%s\n%s' % (xdict['help'],'\n'.join(coda)) + if self.opts.tool_desc: + xdict['tooldesc'] = '<description>%s</description>' % self.opts.tool_desc + else: + xdict['tooldesc'] = '' + xdict['command_outputs'] = '' + xdict['outputs'] = '' + if self.opts.input_tab <> 'None': + xdict['command_inputs'] = '--input_tab "$input1" ' # the space may matter a lot if we append something + xdict['inputs'] = '<param name="input1" type="data" format="%s" label="Select a suitable input file from your history"/> \n' % self.inputFormats + else: + xdict['command_inputs'] = '' # assume no input - eg a random data generator + xdict['inputs'] = '' + xdict['inputs'] += '<param name="job_name" type="text" label="Supply a name for the outputs to remind you what they contain" value="%s"/> \n' % self.toolname + xdict['toolname'] = self.toolname + xdict['toolid'] = self.toolid + xdict['interpreter'] = self.opts.interpreter + xdict['scriptname'] = self.sfile + if self.opts.make_HTML: + xdict['command_outputs'] += ' --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" ' + xdict['outputs'] += ' <data format="html" name="html_file" label="${job_name}.html"/>\n' + if self.opts.output_tab <> 'None': + xdict['command_outputs'] += ' --output_tab "$tab_file"' + xdict['outputs'] += ' <data format="%s" name="tab_file" label="${job_name}"/>\n' % self.outFormats + xdict['command'] = newCommand % xdict + xmls = newXML % xdict + xf = open(self.xmlfile,'w') + xf.write(xmls) + xf.write('\n') + xf.close() + # ready for the tarball + + + def makeTooltar(self): + """ + a tool is a gz tarball with eg + /toolname/tool.xml /toolname/tool.py /toolname/test-data/test1_in.foo ... + """ + retval = self.run() + if retval: + print >> sys.stderr,'## Run failed. Cannot build yet. Please fix and retry' + sys.exit(1) + self.makeXML() + tdir = self.toolname + os.mkdir(tdir) + if self.opts.input_tab <> 'None': # no reproducible test otherwise? TODO: maybe.. + testdir = os.path.join(tdir,'test-data') + os.mkdir(testdir) # make tests directory + shutil.copyfile(self.opts.input_tab,os.path.join(testdir,self.test1Input)) + if self.opts.output_tab <> 'None': + shutil.copyfile(self.opts.output_tab,os.path.join(testdir,self.test1Output)) + if self.opts.make_HTML: + shutil.copyfile(self.opts.output_html,os.path.join(testdir,self.test1HTML)) + if self.opts.output_dir: + shutil.copyfile(self.tlog,os.path.join(testdir,'test1_out.log')) + op = '%s.py' % self.toolname # new name + outpiname = os.path.join(tdir,op) # path for the tool tarball + pyin = os.path.basename(self.pyfile) # our name - we rewrite ourselves (TM) + notes = ['# %s - a self annotated version of %s generated by running %s\n' % (op,pyin,pyin),] + notes.append('# to make a new Galaxy tool called %s\n' % self.toolname) + notes.append('# User %s at %s\n' % (self.opts.user_email,timenow())) + pi = open(self.pyfile,'r').readlines() # our code becomes new tool wrapper (!) - first Galaxy worm + notes += pi + outpi = open(outpiname,'w') + outpi.write(''.join(notes)) + outpi.write('\n') + outpi.close() + stname = os.path.join(tdir,self.sfile) + if not os.path.exists(stname): + shutil.copyfile(self.sfile, stname) + xtname = os.path.join(tdir,self.xmlfile) + if not os.path.exists(xtname): + shutil.copyfile(self.xmlfile,xtname) + tarpath = "%s.gz" % self.toolname + tar = tarfile.open(tarpath, "w:gz") + tar.add(tdir,arcname=self.toolname) + tar.close() + shutil.copyfile(tarpath,self.opts.new_tool) + shutil.rmtree(tdir) + ## TODO: replace with optional direct upload to local toolshed? + return retval + + def compressPDF(self,inpdf=None,thumbformat='png'): + """need absolute path to pdf + """ + assert os.path.isfile(inpdf), "## Input %s supplied to %s compressPDF not found" % (inpdf,self.myName) + hf,hlog = tempfile.mkstemp(suffix="%s.log" % self.toolname) + sto = open(hlog,'w') + outpdf = '%s_compressed' % inpdf + cl = ["gs", "-sDEVICE=pdfwrite", "-dNOPAUSE", "-dBATCH", "-sOutputFile=%s" % outpdf,inpdf] + x = subprocess.Popen(cl,stdout=sto,stderr=sto,cwd=self.opts.output_dir) + retval1 = x.wait() + if retval1 == 0: + os.unlink(inpdf) + shutil.move(outpdf,inpdf) + outpng = '%s.%s' % (os.path.splitext(inpdf)[0],thumbformat) + cl2 = ['convert', inpdf, outpng] + x = subprocess.Popen(cl2,stdout=sto,stderr=sto,cwd=self.opts.output_dir) + retval2 = x.wait() + sto.close() + retval = retval1 or retval2 + return retval + + + def getfSize(self,fpath,outpath): + """ + format a nice file size string + """ + size = '' + fp = os.path.join(outpath,fpath) + if os.path.isfile(fp): + size = '0 B' + n = float(os.path.getsize(fp)) + if n > 2**20: + size = '%1.1f MB' % (n/2**20) + elif n > 2**10: + size = '%1.1f KB)' % (n/2**10) + elif n > 0: + size = '%d B' % (int(n)) + return size + + def makeHtml(self): + """ Create an HTML file content to list all the artifacts found in the output_dir + """ + + galhtmlprefix = """<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> + <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> + <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> + <meta name="generator" content="Galaxy %s tool output - see http://g2.trac.bx.psu.edu/" /> + <title></title> + <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> + </head> + <body> + <div class="toolFormBody"> + """ + galhtmlattr = """<hr/><div class="infomessage">This tool (%s) was generated by the <a href="https://bitbucket.org/fubar/galaxytoolfactory/overview">Galaxy Tool Factory</a></div><br/>""" + galhtmlpostfix = """</div></body></html>\n""" + + flist = os.listdir(self.opts.output_dir) + flist = [x for x in flist if x <> 'Rplots.pdf'] + flist.sort() + html = [] + html.append(galhtmlprefix % progname) + html.append('<div class="infomessage">Galaxy Tool "%s" run at %s</div><br/>' % (self.toolname,timenow())) + fhtml = [] + if len(flist) > 0: + pdflist = [] + npdf = len([x for x in flist if os.path.splitext(x)[-1].lower() == '.pdf']) + nacross = 1 + if npdf > 0: + nacross = int(round(math.log(npdf,2))) + nacross = max(1,nacross) + width = min(400,int(1200/nacross)) + for rownum,fname in enumerate(flist): + dname,e = os.path.splitext(fname) + sfsize = self.getfSize(fname,self.opts.output_dir) + if e.lower() == '.pdf' : # compress and make a thumbnail + thumb = '%s.%s' % (dname,self.thumbformat) + pdff = os.path.join(self.opts.output_dir,fname) + retval = self.compressPDF(inpdf=pdff,thumbformat=self.thumbformat) + if retval == 0: + pdflist.append((fname,thumb)) + if (rownum+1) % 2 == 0: + fhtml.append('<tr class="odd_row"><td><a href="%s">%s</a></td><td>%s</td></tr>' % (fname,fname,sfsize)) + else: + fhtml.append('<tr><td><a href="%s">%s</a></td><td>%s</td></tr>' % (fname,fname,sfsize)) + ntogo = nacross # counter for table row padding with empty cells + if len(pdflist) > 0: + html.append('<div><table class="simple" cellpadding="2" cellspacing="2">\n<tr>') + for i,paths in enumerate(pdflist): + fname,thumb = paths + s= """<td><a href="%s"><img src="%s" title="Click to download a PDF of %s" hspace="5" width="%d" + alt="Image called %s"/></a></td>\n""" % (fname,thumb,fname,width,fname) + if ((i+1) % nacross == 0): + s += '</tr>\n' + ntogo = 0 + if i < (npdf - 1): # more to come + s += '<tr>' + ntogo = nacross + else: + ntogo -= 1 + html.append(s) + if html[-1].strip().endswith('</tr>'): + html.append('</table></div>\n') + else: + if ntogo > 0: # pad + html.append('<td> </td>'*ntogo) + html.append('</tr></table></div>\n') + if len(fhtml) > 0: + fhtml.insert(0,'<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr>\n') + fhtml.append('</table></div><br/>') + html += fhtml # add all non-pdf files to the end of the display + else: + html.append('<div class="warningmessagelarge">### Error - %s returned no files - please confirm that parameters are sane</div>' % self.opts.interpreter) + rlog = open(self.tlog,'r').readlines() + rlog = [x for x in rlog if x.strip() > ''] + if len(rlog) > 1: + html.append('<div class="toolFormTitle">%s log</div><pre>\n' % self.opts.interpreter) + html += rlog + html.append('</pre>\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: + <command interpreter="python">rgBaseScriptWrapper.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript" + </command> + """ + op = optparse.OptionParser() + a = op.add_option + a('--script_path',default=None) + a('--tool_name',default=None) + a('--interpreter',default=None) + a('--output_dir',default=None) + a('--output_html',default=None) + a('--input_tab',default="None") + a('--output_tab',default="None") + a('--user_email',default='Unknown') + a('--bad_user',default=None) + a('--make_Tool',default=None) + a('--make_HTML',default=None) + a('--help_text',default=None) + a('--tool_desc',default=None) + a('--new_tool',default=None) + a('--tool_version',default=None) + opts, args = op.parse_args() + assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user) + assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq' + assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript' + assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R' + if opts.output_dir: + try: + os.makedirs(opts.output_dir) + except: + pass + r = ScriptRunner(opts) + if opts.make_Tool: + retcode = r.makeTooltar() + else: + retcode = r.run() + os.unlink(r.sfile) + if retcode: + sys.exit(retcode) # indicate failure to job runner + + +if __name__ == "__main__": + main() + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/rgedgeR.xml Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,504 @@ +<tool id="rgedgeR" name="edgeR" version="0.18"> + <description>digital DGE between two groups of replicates</description> + <command interpreter="python"> + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "edgeR" + --output_dir "$html_file.files_path" --output_html "$html_file" --output_tab "$outtab" --make_HTML "yes" + </command> + <inputs> + <param name="input1" type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample" + help="Use the HTSeq based count matrix preparation tool to create these count matrices from BAM files and a GTF file"/> + <param name="title" type="text" value="DGE" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain"> + <sanitizer invalid_char=""> + <valid initial="string.letters,string.digits"><add value="_" /> </valid> + </sanitizer> + </param> + <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/> + <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True" + multiple="true" use_header_names="true" size="120" display="checkboxes"> + <validator type="no_options" message="Please select at least one column."/> + </param> + <param name="control_name" type="text" value="Control" size="50" label="Control Name"/> + <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True" + multiple="true" use_header_names="true" size="120" display="checkboxes" optional="true"> + </param> + <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs" + help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/> + <param name="useQuantile" type="boolean" truevalue="T" checked='false' falsevalue="" size="1" label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples" + help="May be a good or a bad idea depending on the biology and the question. This was the old default. Quantile based is available as an alternative"/> + <param name="priorn" type="integer" value="4" size="3" label="prior.df for tagwise dispersion - lower value = more emphasis on each tag's variance - note this used to be prior.n" + help="Zero = auto-estimate. 1 to force high variance tags out. Use a small value to 'smooth' small samples. See edgeR docs and note below"/> + <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control" + help="Conventional default value of 0.05 recommended"/> + <param name="fdrtype" type="select" label="FDR (Type II error) control method" + help="Use fdr or bh typically to control for the number of tests in a reliable way"> + <option value="fdr" selected="true">fdr</option> + <option value="BH">Benjamini Hochberg</option> + <option value="BY">Benjamini Yukateli</option> + <option value="bonferroni">Bonferroni</option> + <option value="hochberg">Hochberg</option> + <option value="holm">Holm</option> + <option value="hommel">Hommel</option> + <option value="none">no control for multiple tests</option> + </param> + </inputs> + <outputs> + <data format="tabular" name="outtab" label="${title}.xls"/> + <data format="html" name="html_file" label="${title}.html"/> + <data format="gsearank" name="outgsea" label="${title}.gsearank"> + <filter> makeRank == 'Yes' </filter> + </data> + </outputs> +<configfiles> +<configfile name="runme"> + +# 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() + + +</configfile> +</configfiles> +<tests> +<test> +<param name='input1' value='DGEtest.xls' ftype='tabular' /> + <param name='treatment_name' value='case' /> + <param name='title' value='DGEtest' /> + <param name='fdrtype' value='fdr' /> + <param name='priorn' value="5" /> + <param name='fdrthresh' value="0.05" /> + <param name='control_name' value='control' /> + <param name='Treat_cols' value='c3,c6,c9' /> + <param name='Control_cols' value='c2,c5,c8' /> + <output name='outtab' file='DGEtest1out.xls' ftype='tabular' compare='diff' /> + <output name='html_file' file='DGEtest1out.html' ftype='html' compare='diff' lines_diff='20' /> +</test> +</tests> +<help> +**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 +</help> + +</tool> + +
--- /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 @@ + +<tool id="rgedgeRglm" name="edgeRglm" version="0.18"> + <description>digital DGE glm</description> + <command interpreter="python"> + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "edgeRglm" + --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" + </command> + <inputs> + <param name="input1" type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample" + help="Use the DGE matrix preparation tool to create these matrices from BAM files and a BED file of contigs"/> + <param name="title" type="text" value="Factorial DGE" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain"> + <sanitizer invalid_char=""> + <valid initial="string.letters,string.digits"><add value="_" /> </valid> + </sanitizer> + </param> + <param name="factor1name" type="text" value="Factor 1" size="80" label="Factor 1 name" help="Supply a meaningful name here to remind you when looking at the results"> + <sanitizer invalid_char=""> + <valid initial="string.letters,string.digits"><add value="_" /> </valid> + </sanitizer> + </param> + <param name="factor1" type="text" optional="false" size="120" + label="Enter comma separated values to indicate the factor level for all input file count columns" + help="EG if there are 4 columns of counts from 2 treatment replicates (2,4) and 2 control replicates (3,5) then enter 'treat,control,treat,control'"> + <sanitizer> + <valid initial="string.digits,string.letters"><add value="," /> </valid> + </sanitizer> + </param> + <param name="factor2name" type="text" value="Factor 2" size="80" label="Factor 2 name" help="Supply a meaningful name here to remind you when looking at the results"> + <sanitizer invalid_char=""> + <valid initial="string.letters,string.digits"><add value="_" /> </valid> + </sanitizer> + </param> + <param name="factor2" type="text" optional="false" size="120" + label="Enter comma separated values to indicate factor 2 level for all input file count columns" + help="Leave blank if no factor 2, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter '1,2,1,2'"> + <sanitizer> + <valid initial="string.digits,string.letters"><add value="," /> </valid> + </sanitizer> + </param> + <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs" + help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/> + <param name="useNDfilt" type="boolean" truevalue="T" checked='false' falsevalue="" size="1" label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples" + help="May be a good or a bad idea depending on the biology and the question. This was the old default. Quantile based is available as an alternative"/> + <param name="priorn" type="integer" value="4" size="3" label="prior.df for tagwise dispersion - higher value = more emphasis on each tag's variance - note this used to be prior.n" + help="Zero = auto-estimate. 1 to force high variance tags out. Use a small value to 'smooth' small samples. See edgeR docs and note below"/> + <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for family wise error rate control" + help="Conventional default value of 0.05 recommended"/> + <param name="fdrtype" type="select" label="FDR (Type II error) control method" + help="Use fdr or bh typically to control for the number of tests in a reliable way"> + <option value="fdr" selected="true">fdr</option> + <option value="BH">Benjamini Hochberg</option> + <option value="BY">Benjamini Yukateli</option> + <option value="bonferroni">Bonferroni</option> + <option value="hochberg">Hochberg</option> + <option value="holm">Holm</option> + <option value="hommel">Hommel</option> + <option value="none">no control for multiple tests</option> + </param> + </inputs> + <outputs> + <data format="tabular" name="outtab1" label="${treatment1_name}-${control1_name}_topTable.xls"/> + <data format="tabular" name="outtab2" label="${treatment2_name}-${control2_name}_topTable.xls"/> + <data format="tabular" name="outtab3" label="${treatment1_name}-${control1_name}-${treatment2_name}-${control2_name}_topTable.xls"/> + <data format="html" name="html_file" label="${input1.name}_${title}.html"/> + </outputs> +<configfiles> +<configfile name="runme"> +# 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() + +</configfile> +</configfiles> +<tests> +<test> +<param name='input1' value='DGEtest.xls' ftype='tabular' /> + <param name='treatment_name' value='case' /> + <param name='title' value='DGEtest' /> + <param name='fdrtype' value='fdr' /> + <param name='priorn' value="5" /> + <param name='fdrthresh' value="0.05" /> + <param name='control_name' value='control' /> + <param name='Treat_cols' value='c3,c6,c9' /> + <param name='Control_cols' value='c2,c5,c8' /> + <output name='outtab' file='DGEtest1out.xls' ftype='tabular' compare='diff' /> + <output name='html_file' file='DGEtest1out.html' ftype='html' compare='diff' lines_diff='20' /> +</test> +</tests> +<help> +**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 + +</help> + +</tool> + +
--- /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 @@ +<tool id="rgedgeRpaired" name="edgeR paired" version="0.18"> + <description>2 level Anova for counts</description> + <command interpreter="python"> + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "edgeR" + --output_dir "$html_file.files_path" --output_html "$html_file" --output_tab "$outtab" --make_HTML "yes" + </command> + <inputs> + <param name="input1" type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample" + help="Use the HTSeq based count matrix preparation tool to create these matrices from BAM/SAM files and a GTF file of genomic features"/> + <param name="title" type="text" value="edgeR" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain"> + <sanitizer invalid_char=""> + <valid initial="string.letters,string.digits"><add value="_" /> </valid> + </sanitizer> + </param> + <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/> + <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True" + multiple="true" use_header_names="true" size="120" display="checkboxes"> + <validator type="no_options" message="Please select at least one column."/> + </param> + <param name="control_name" type="text" value="Control" size="50" label="Control Name"/> + <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True" + multiple="true" use_header_names="true" size="120" display="checkboxes" optional="true"> + </param> + <param name="subjectids" type="text" optional="true" size="120" + label="IF SUBJECTS NOT ALL INDEPENDENT! Enter integers to indicate sample pairing for every column in input" + help="Leave blank if no pairing, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter '1,2,1,2'"> + <sanitizer> + <valid initial="string.digits"><add value="," /> </valid> + </sanitizer> + </param> + <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs" + help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/> + <param name="useNDF" type="boolean" truevalue="T" checked='false' falsevalue="" size="1" label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples" + help="May be a good or a bad idea depending on the biology and the question. This was the old default. Quantile based is available as an alternative"/> + <param name="priordf" type="integer" value="20" size="3" label="prior.df for tagwise dispersion - lower value = more emphasis on each tag's variance. Replaces prior.n and prior.df = prior.n * residual.df" + help="Zero = Use edgeR default. Use a small value to 'smooth' small samples. See edgeR docs and note below"/> + <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control" + help="Conventional default value of 0.05 recommended"/> + <param name="fdrtype" type="select" label="FDR (Type II error) control method" + help="Use fdr or bh typically to control for the number of tests in a reliable way"> + <option value="fdr" selected="true">fdr</option> + <option value="BH">Benjamini Hochberg</option> + <option value="BY">Benjamini Yukateli</option> + <option value="bonferroni">Bonferroni</option> + <option value="hochberg">Hochberg</option> + <option value="holm">Holm</option> + <option value="hommel">Hommel</option> + <option value="none">no control for multiple tests</option> + </param> + </inputs> + <outputs> + <data format="tabular" name="outtab" label="${title}.xls"/> + <data format="html" name="html_file" label="${title}.html"/> + </outputs> + <stdio> + <exit_code range="4" level="fatal" description="Number of subject ids must match total number of samples in the input matrix" /> + </stdio> + <tests> +<test> +<param name='input1' value='test_bams2mx.xls' ftype='tabular' /> + <param name='treatment_name' value='case' /> + <param name='title' value='edgeRtest' /> + <param name='fdrtype' value='fdr' /> + <param name='priordf' value="0" /> + <param name='fdrthresh' value="0.05" /> + <param name='control_name' value='control' /> + <param name='Treat_cols' value='3,4,5,9' /> + <param name='Control_cols' value='2,6,7,8' /> + <output name='outtab' file='edgeRtest1out.xls' ftype='tabular' compare='diff' /> + <output name='html_file' file='edgeRtest1out.html' ftype='html' compare='diff' lines_diff='20' /> +</test> +</tests> + +<configfiles> +<configfile name="runme"> +<![CDATA[ +# +# edgeR.Rscript +# updated npv 2011 for R 2.14.0 and edgeR 2.4.0 by ross +# Performs DGE on a count table containing n replicates of two conditions +# +# Parameters +# +# 1 - Output Dir + +# Original edgeR code by: S.Lunke and A.Kaspi +reallybig = log10(.Machine\$double.xmax) +reallysmall = log10(.Machine\$double.xmin) +library('stringr') +library('gplots') +library('DESeq') +library('edgeR') +hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here') +{ +# Perform clustering for significant pvalues after controlling FWER + samples = colnames(cmat) + gu = unique(group) + if (length(gu) == 2) { + col.map = function(g) {if (g==gu[1]) "#FF0000" else "#0000FF"} + pcols = unlist(lapply(group,col.map)) + } else { + colours = rainbow(length(gu),start=0,end=4/6) + pcols = colours[match(group,gu)] } + gn = rownames(cmat) + dm = cmat[(! is.na(gn)),] + # remove unlabelled hm rows + nprobes = nrow(dm) + # sub = paste('Showing',nprobes,'contigs ranked for evidence of differential abundance') + if (nprobes > nsamp) { + dm =dm[1:nsamp,] + #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total') + } + newcolnames = substr(colnames(dm),1,20) + colnames(dm) = newcolnames + pdf(outpdfname) + heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none', + Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5) + dev.off() +} + +hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here") +{ + # for 2 groups only was + #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"} + #pcols = unlist(lapply(group,col.map)) + gu = unique(group) + colours = rainbow(length(gu),start=0.3,end=0.6) + pcols = colours[match(group,gu)] + nrows = nrow(cmat) + mtitle = paste(myTitle,'Heatmap: n contigs =',nrows) + if (nrows > nsamp) { + cmat = cmat[c(1:nsamp),] + mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='') + } + newcolnames = substr(colnames(cmat),1,20) + colnames(cmat) = newcolnames + pdf(outpdfname) + heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols) + dev.off() +} + +qqPlot = function(descr='Title',pvector, ...) +# stolen from https://gist.github.com/703512 +{ + o = -log10(sort(pvector,decreasing=F)) + e = -log10( 1:length(o)/length(o) ) + o[o==-Inf] = reallysmall + o[o==Inf] = reallybig + pdfname = paste(gsub(" ","", descr , fixed=TRUE),'pval_qq.pdf',sep='_') + maint = paste(descr,'QQ Plot') + pdf(pdfname) + plot(e,o,pch=19,cex=1, main=maint, ..., + xlab=expression(Expected~~-log[10](italic(p))), + ylab=expression(Observed~~-log[10](italic(p))), + xlim=c(0,max(e)), ylim=c(0,max(o))) + lines(e,e,col="red") + grid(col = "lightgray", lty = "dotted") + dev.off() +} + +smearPlot = function(DGEList,deTags, outSmear, outMain) + { + pdf(outSmear) + plotSmear(DGEList,de.tags=deTags,main=outMain) + grid(col="blue") + dev.off() + } + +boxPlot = function(rawrs,cleanrs,maint,myTitle) +{ # + nc = ncol(rawrs) + for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA} + fullnames = colnames(rawrs) + newcolnames = substr(colnames(rawrs),1,20) + colnames(rawrs) = newcolnames + newcolnames = substr(colnames(cleanrs),1,20) + colnames(cleanrs) = newcolnames + pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"sampleBoxplot.pdf",sep='_') + defpar = par(no.readonly=T) + pdf(pdfname,height=6,width=8) + #par(mfrow=c(1,2)) # 1 rows 2 col + l = layout(matrix(c(1,2),1,2,byrow=T)) + print.noquote('raw contig counts by sample:') + print.noquote(summary(rawrs)) + print.noquote('normalised contig counts by sample:') + print.noquote(summary(cleanrs)) + boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('Raw:',maint)) + grid(col="blue") + boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('After ',maint)) + grid(col="blue") + dev.off() + pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"samplehistplot.pdf",sep='_') + nc = ncol(rawrs) + print.noquote(paste('Using ncol rawrs=',nc)) + ncroot = round(sqrt(nc)) + if (ncroot*ncroot < nc) { ncroot = ncroot + 1 } + m = c() + for (i in c(1:nc)) { + rhist = hist(rawrs[,i],breaks=100,plot=F) + m = append(m,max(rhist\$counts)) + } + ymax = max(m) + pdf(pdfname) + par(mfrow=c(ncroot,ncroot)) + for (i in c(1:nc)) { + hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon", + breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax)) + } + dev.off() + par(defpar) + +} + +cumPlot = function(rawrs,cleanrs,maint,myTitle) +{ # updated to use ecdf + pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_') + defpar = par(no.readonly=T) + pdf(pdfname) + par(mfrow=c(2,1)) + lrs = log(rawrs,10) + lim = max(lrs) + hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)", + ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1) + grid(col="blue") + lrs = log(cleanrs,10) + hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)", + ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1) + grid(col="blue") + dev.off() + par(defpar) +} + +cumPlot1 = function(rawrs,cleanrs,maint,myTitle) +{ # updated to use ecdf + pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_') + pdf(pdfname) + par(mfrow=c(2,1)) + lastx = max(rawrs) + rawe = knots(ecdf(rawrs)) + cleane = knots(ecdf(cleanrs)) + cy = 1:length(cleane)/length(cleane) + ry = 1:length(rawe)/length(rawe) + plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads", + ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) + grid(col="blue") + plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads", + ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) + grid(col="blue") + dev.off() +} + + + +edgeIt = function (Count_Matrix,group,outputfilename,fdrtype='fdr',priordf=5,fdrthresh=0.05,outputdir='.', + myTitle='edgeR',libSize=c(),useNDF="T",filterquantile=0.2,subjects=c()) { + + # Error handling + if (length(unique(group))!=2){ + print("Number of conditions identified in experiment does not equal 2") + q() + } + require(edgeR) + mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ") + allN = nrow(Count_Matrix) + nscut = round(ncol(Count_Matrix)/2) + colTotmillionreads = colSums(Count_Matrix)/1e6 + rawrs = rowSums(Count_Matrix) + nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes + nzN = nrow(nonzerod) + nzrs = rowSums(nonzerod) + zN = allN - nzN + print('# Quantiles for non-zero row counts:',quote=F) + print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F) + if (useNDF == "T") + { + gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut + lo = colSums(Count_Matrix[!gt1rpin3,]) + workCM = Count_Matrix[gt1rpin3,] + cleanrs = rowSums(workCM) + cleanN = length(cleanrs) + meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="") + print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F) + maint = paste('Filter >=1/million reads in >=',nscut,'samples') + } + else { + useme = (nzrs > quantile(nzrs,filterquantile)) + workCM = nonzerod[useme,] + lo = colSums(nonzerod[!useme,]) + cleanrs = rowSums(workCM) + cleanN = length(cleanrs) + meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="") + print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F) + maint = paste('Filter below',filterquantile,'quantile') + } + cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle) + allgenes <- rownames(workCM) + print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F) + rsums = rowSums(workCM) + TName=unique(group)[1] + CName=unique(group)[2] + # Setup DGEList object + DGEList = DGEList(counts=workCM, group = group) + if (length(subjects) == 0) + { + doDESEQ = T + mydesign = model.matrix(~group) + } + else { + doDESEQ = F + subjf = factor(subjects) + mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it + } + print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=','))) + print.noquote('Using design matrix:') + print.noquote(mydesign) + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + comdisp = DGEList\$common.dispersion + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + if (priordf > 0) { + print.noquote(paste("prior.df =",priordf)) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = priordf) + } else { + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + } + DGLM = glmFit(DGEList,design=mydesign) + efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors + normData = (1e+06*DGEList\$counts/efflib) + co = length(colnames(mydesign)) + DE = glmLRT(DGLM,coef=co) # always last one - subject is first if needed + uoutput = cbind( + Name=as.character(rownames(DGEList\$counts)), + DE\$table, + adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), + Dispersion=DGEList\$tagwise.dispersion,totreads=rsums,normData, + DGEList\$counts + ) + soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable + goodness = gof(DGLM, pcutoff=fdrthresh) + if (sum(goodness\$outlier) > 0) { + print.noquote('GLM outliers:') + print(paste(rownames(DGLM)[(goodness\$outlier != 0)],collapse=','),quote=F) + z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) + pdf(paste(mt,"GoodnessofFit.pdf",sep='_')) + qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") + abline(0,1,lwd=3) + points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="dodgerblue") + dev.off() + } else { print('No GLM fit outlier genes found\n')} + estpriorn = getPriorN(DGEList) + print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F) + efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors + normData = (1e+06*DGEList\$counts/efflib) + uniqueg = unique(group) + # Plot MDS + sample_colors = match(group,levels(group)) + pdf(paste(mt,"MDSplot.pdf",sep='_')) + sampleTypes = levels(group) + plotMDS.DGEList(DGEList,main=paste("MDS Plot for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors) + legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19) + grid(col="blue") + dev.off() + colnames(normData) = paste( colnames(normData),'N',sep="_") + print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=','))) + nzd = data.frame(log(nonzerod + 1e-2,10)) + boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle) + if (doDESEQ) + { + # DESeq + deSeqDatcount <- newCountDataSet(workCM, group) + deSeqDatsizefac <- estimateSizeFactors(deSeqDatcount) + deSeqDatdisp <- estimateDispersions(deSeqDatsizefac) + rDESeq <- nbinomTest(deSeqDatdisp, levels(group)[1], levels(group)[2]) + rDESeq <- rDESeq[order(rDESeq\$pval), ] + write.table(rDESeq,paste(mt,'DESeq_TopTable.xls',sep='_'), quote=FALSE, sep="\t",row.names=F) + topresults.DESeq <- rDESeq[which(rDESeq\$padj < fdrthresh), ] + DESeqcountsindex <- which(allgenes %in% topresults.DESeq\$id) + DESeqcounts <- rep(0, length(allgenes)) + DESeqcounts[DESeqcountsindex] <- 1 + } + DGEList = calcNormFactors(DGEList) + norm.factor = DGEList\$samples\$norm.factors + pdf(paste(mt,"voomplot.pdf",sep='_')) + dat.voomed <- voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor) + dev.off() + # Use limma to fit data + fit <- lmFit(dat.voomed, mydesign) + fit <- eBayes(fit) + rvoom <- topTable(fit, coef = length(colnames(mydesign)), adj = "BH", n = Inf) + write.table(rvoom,paste(mt,'VOOM_topTable.xls',sep='_'), quote=FALSE, sep="\t",row.names=F) + # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma + topresults.voom <- rvoom[which(rvoom\$adj.P.Val < fdrthresh), ] + topresults.edgeR <- soutput[which(soutput\$adj.p.value < fdrthresh), ] + # Create venn diagram of hits + edgeRcountsindex <- which(allgenes %in% rownames(topresults.edgeR)) + voomcountsindex <- which(allgenes %in% topresults.voom\$ID) + edgeRcounts <- rep(0, length(allgenes)) + edgeRcounts[edgeRcountsindex] <- 1 + voomcounts <- rep(0, length(allgenes)) + voomcounts[voomcountsindex] <- 1 + if (doDESEQ) { + vennmain = paste(mt,'Voom,edgeR and DESeq overlap at FDR=',fdrthresh) + counts.dataframe <- data.frame(edgeRcounts = edgeRcounts, DESeqcounts = DESeqcounts, + voomcounts = voomcounts, row.names = allgenes) + } else { + vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh) + counts.dataframe <- data.frame(edgeRcounts = edgeRcounts, voomcounts = voomcounts, row.names = allgenes) + } + counts.venn <- vennCounts(counts.dataframe) + vennf = paste(mt,'venn.pdf',sep='_') + pdf(vennf) + vennDiagram(counts.venn,main=vennmain,col="maroon") + dev.off() + #Prepare our output file + nreads = soutput\$totreads # ordered same way + print('# writing output',quote=F) + write.table(soutput,outputfilename, quote=FALSE, sep="\t",row.names=F) + rn = uoutput\$Name + reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" + org="hg19" + genecards="<a href='http://www.genecards.org/index.php?path=/Search/keyword/" + ucsc = paste("<a href='http://genome.ucsc.edu/cgi-bin/hgTracks?db=",org,sep='') + testreg = str_match(rn,reg) + nreads = uoutput\$totreads # ordered same way + if (sum(!is.na(testreg[,1]))/length(testreg[,1]) > 0.8) # is ucsc style string + { + urls = paste(ucsc,'&position=chr',testreg[,2],':',testreg[,3],"-",testreg[,4],"'>",rn,'</a>',sep='') + } else { + urls = paste(genecards,rn,"'></a>",rn,'</a>',sep="") + } + print.noquote('# urls') + cat(head(urls)) + tt = uoutput + cat("# Top tags\n") + tt = cbind(tt,ntotreads=nreads,URL=urls) # add to end so table isn't laid out strangely + tt = tt[order(DE\$table\$PValue),] + options(width = 500) + print.noquote(tt[1:50,]) + pdf(paste(mt,"BCV_vs_abundance.pdf",sep='_')) + plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance") + dev.off() + # Plot MAplot + deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,]) + nsig = length(deTags) + print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F) + print('# deTags',quote=F) + print(head(deTags)) + dg = DGEList[order(DE\$table\$PValue),] + #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg))) + efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors + normData = (1e+06*dg\$counts/efflib) + outpdfname=paste(mt,"heatmap.pdf",sep='_') + hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=myTitle) + outSmear = paste(mt,"Smearplot.pdf",sep='_') + outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') + smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain) + qqPlot(descr=myTitle,pvector=DE\$table\$PValue) + if (doDESEQ) { + cat("# DESeq top 50\n") + print(rDESeq[1:50,]) + } + cat("# VOOM top 50\n") + print(rvoom[1:50,]) + # need a design matrix and glm to use this + goodness = gof(DGLM, pcutoff=fdrthresh) + nout = sum(goodness\$outlier) + if (nout > 0) { + print.noquote(paste('Found',nout,'Goodness of fit outliers')) + rownames(DGLM)[goodness\$outlier] + z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) + pdf(paste(mt,"GoodnessofFit.pdf",sep='_')) + qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") + abline(0,1,lwd=3) + points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="dodgerblue") + dev.off() + } + #Return our main table + uoutput + +} #Done +sink(stdout(),append=T,type="message") +options(width=512) +Out_Dir = "$html_file.files_path" +Input = "$input1" +TreatmentName = "$treatment_name" +TreatmentCols = "$Treat_cols" +ControlName = "$control_name" +ControlCols= "$Control_cols" +outputfilename = "$outtab" +fdrtype = "$fdrtype" +priordf = $priordf +fdrthresh = $fdrthresh +useNDF = "$useNDF" +fQ = $fQ # non-differential centile cutoff +myTitle = "$title" +subjects = c($subjectids) +nsubj = length(subjects) +#Set our columns +TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1 +CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1 +cat('# got TCols=') +cat(TCols) +cat('; CCols=') +cat(CCols) +cat('\n') +useCols = c(TCols,CCols) +# Create output dir if non existent + if (file.exists(Out_Dir) == F) dir.create(Out_Dir) + +Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header +snames = colnames(Count_Matrix) +nsamples = length(snames) +if (nsubj > 0 & nsubj != nsamples) { +options("show.error.messages"=T) +mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','),'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=',')) +write(mess, stderr()) +#print(mess) +quit(save="no",status=4) +} + +Count_Matrix = Count_Matrix[,useCols] # reorder columns +if (length(subjects) != 0) {subjects = subjects[useCols]} +rn = rownames(Count_Matrix) +islib = rn %in% c('librarySize','NotInBedRegions') +LibSizes = Count_Matrix[subset(rn,islib),][1] # take first +Count_Matrix = Count_Matrix[subset(rn,! islib),] +group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) ) #Build a group descriptor +group = factor(group, levels=c(ControlName,TreatmentName)) +colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_") #Relable columns +results = edgeIt(Count_Matrix=Count_Matrix,group=group,outputfilename=outputfilename,fdrtype=fdrtype,priordf=priordf,fdrthresh=fdrthresh, + outputdir=Out_Dir,myTitle=myTitle,libSize=c(),useNDF=useNDF,filterquantile=fQ,subjects=subjects) +#Run the main function +# for the log +sessionInfo() +]]> +</configfile> +</configfiles> +<help> +**What it does** + +Performs digital gene expression analysis between a treatment and control on a count matrix. +Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design. + +**Input** + +A matrix consisting of non-negative integers. The matrix must have a unique header row identifiying the samples, and a unique set of row names +as the first column. Typically the row names are gene symbols or probe id's for downstream use in GSEA and other methods. + +If you have (eg) paired samples and wish to include a term in the GLM to account for some other factor (subject in the case of paired samples), +put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or +A list of integers, one for each subject or an empty string if samples are all independent. +If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix. +Integers for samples that are not in the analysis *must* be present in the string as filler even if not used. + +So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones +eg if you had 6 samples with the first two independent but the second and third pairs each being from independent subjects. you might use +8,9,1,1,2,2 +as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6 + +**Output** + +A matrix which consists the original data and relative expression levels and some helpful plots + +**Note on edgeR versions** + +The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue) +breaking this and all other code that assumed the old name for this variable, +between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). +This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing +to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly +when their old scripts break. This tool currently now works with 2.4.6. + +**Note on prior.N** + +http://seqanswers.com/forums/showthread.php?t=5591 says: + +*prior.n* + +The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. +You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood +in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your +tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the +common likelihood the weight of one observation. + +In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, +or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that +you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation +(squeezing) of the tagwise dispersions. How many samples do you have in your experiment? +What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10. +If you have more samples, then the tagwise dispersion estimates will be more reliable, +so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. + + +From Bioconductor Digest, Vol 118, Issue 5, Gordon writes: + +Dear Dorota, + +The important settings are prior.df and trend. + +prior.n and prior.df are related through prior.df = prior.n * residual.df, +and your experiment has residual.df = 36 - 12 = 24. So the old setting of +prior.n=10 is equivalent for your data to prior.df = 240, a very large +value. Going the other way, the new setting of prior.df=10 is equivalent +to prior.n=10/24. + +To recover old results with the current software you would use + + estimateTagwiseDisp(object, prior.df=240, trend="none") + +To get the new default from old software you would use + + estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE) + +Actually the old trend method is equivalent to trend="loess" in the new +software. You should use plotBCV(object) to see whether a trend is +required. + +Note you could also use + + prior.n = getPriorN(object, prior.df=10) + +to map between prior.df and prior.n. + +</help> + +</tool> + +
--- /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 @@ +<tool id="rgedgeRpaired" name="edgeR paired" version="0.18"> + <description>2 level Anova for counts</description> + <command interpreter="python"> + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "edgeR" + --output_dir "$html_file.files_path" --output_html "$html_file" --output_tab "$outtab" --make_HTML "yes" + </command> + <inputs> + <param name="input1" type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample" + help="Use the HTSeq based count matrix preparation tool to create these matrices from BAM/SAM files and a GTF file of genomic features"/> + <param name="title" type="text" value="edgeR" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain"> + <sanitizer invalid_char=""> + <valid initial="string.letters,string.digits"><add value="_" /> </valid> + </sanitizer> + </param> + <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/> + <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True" + multiple="true" use_header_names="true" size="120" display="checkboxes"> + <validator type="no_options" message="Please select at least one column."/> + </param> + <param name="control_name" type="text" value="Control" size="50" label="Control Name"/> + <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True" + multiple="true" use_header_names="true" size="120" display="checkboxes" optional="true"> + </param> + <param name="subjectids" type="text" optional="true" size="120" + label="IF SUBJECTS NOT ALL INDEPENDENT! Enter integers to indicate sample pairing for every column in input" + help="Leave blank if no pairing, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter '1,2,1,2'"> + <sanitizer> + <valid initial="string.digits"><add value="," /> </valid> + </sanitizer> + </param> + <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs" + help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/> + <param name="useNDF" type="boolean" truevalue="T" checked='false' falsevalue="" size="1" label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples" + help="May be a good or a bad idea depending on the biology and the question. This was the old default. Quantile based is available as an alternative"/> + <param name="priordf" type="integer" value="20" size="3" label="prior.df for tagwise dispersion - lower value = more emphasis on each tag's variance. Replaces prior.n and prior.df = prior.n * residual.df" + help="Zero = Use edgeR default. Use a small value to 'smooth' small samples. See edgeR docs and note below"/> + <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control" + help="Conventional default value of 0.05 recommended"/> + <param name="fdrtype" type="select" label="FDR (Type II error) control method" + help="Use fdr or bh typically to control for the number of tests in a reliable way"> + <option value="fdr" selected="true">fdr</option> + <option value="BH">Benjamini Hochberg</option> + <option value="BY">Benjamini Yukateli</option> + <option value="bonferroni">Bonferroni</option> + <option value="hochberg">Hochberg</option> + <option value="holm">Holm</option> + <option value="hommel">Hommel</option> + <option value="none">no control for multiple tests</option> + </param> + </inputs> + <outputs> + <data format="tabular" name="outtab" label="${title}.xls"/> + <data format="html" name="html_file" label="${title}.html"/> + </outputs> + <stdio> + <exit_code range="4" level="fatal" description="Number of subject ids must match total number of samples in the input matrix" /> + </stdio> + <tests> +<test> +<param name='input1' value='test_bams2mx.xls' ftype='tabular' /> + <param name='treatment_name' value='case' /> + <param name='title' value='edgeRtest' /> + <param name='fdrtype' value='fdr' /> + <param name='priordf' value="0" /> + <param name='fdrthresh' value="0.05" /> + <param name='control_name' value='control' /> + <param name='Treat_cols' value='c3,c4,c5,c9' /> + <param name='Control_cols' value='c2,c6,c7,c8' /> + <output name='outtab' file='edgeRtest1out.xls' ftype='tabular' compare='diff' /> + <output name='html_file' file='edgeRtest1out.html' ftype='html' compare='diff' lines_diff='20' /> +</test> +</tests> + +<configfiles> +<configfile name="runme"> +<![CDATA[ +# +# edgeR.Rscript +# updated npv 2011 for R 2.14.0 and edgeR 2.4.0 by ross +# Performs DGE on a count table containing n replicates of two conditions +# +# Parameters +# +# 1 - Output Dir + +# Original edgeR code by: S.Lunke and A.Kaspi +reallybig = log10(.Machine\$double.xmax) +reallysmall = log10(.Machine\$double.xmin) +library('stringr') +library('gplots') +library('DESeq') +library('edgeR') +hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here') +{ +# Perform clustering for significant pvalues after controlling FWER + samples = colnames(cmat) + gu = unique(group) + if (length(gu) == 2) { + col.map = function(g) {if (g==gu[1]) "#FF0000" else "#0000FF"} + pcols = unlist(lapply(group,col.map)) + } else { + colours = rainbow(length(gu),start=0,end=4/6) + pcols = colours[match(group,gu)] } + gn = rownames(cmat) + dm = cmat[(! is.na(gn)),] + # remove unlabelled hm rows + nprobes = nrow(dm) + # sub = paste('Showing',nprobes,'contigs ranked for evidence of differential abundance') + if (nprobes > nsamp) { + dm =dm[1:nsamp,] + #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total') + } + newcolnames = substr(colnames(dm),1,20) + colnames(dm) = newcolnames + pdf(outpdfname) + heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none', + Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5) + dev.off() +} + +hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here") +{ + # for 2 groups only was + #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"} + #pcols = unlist(lapply(group,col.map)) + gu = unique(group) + colours = rainbow(length(gu),start=0.3,end=0.6) + pcols = colours[match(group,gu)] + nrows = nrow(cmat) + mtitle = paste(myTitle,'Heatmap: n contigs =',nrows) + if (nrows > nsamp) { + cmat = cmat[c(1:nsamp),] + mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='') + } + newcolnames = substr(colnames(cmat),1,20) + colnames(cmat) = newcolnames + pdf(outpdfname) + heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols) + dev.off() +} + +qqPlot = function(descr='Title',pvector, ...) +# stolen from https://gist.github.com/703512 +{ + o = -log10(sort(pvector,decreasing=F)) + e = -log10( 1:length(o)/length(o) ) + o[o==-Inf] = reallysmall + o[o==Inf] = reallybig + pdfname = paste(gsub(" ","", descr , fixed=TRUE),'pval_qq.pdf',sep='_') + maint = paste(descr,'QQ Plot') + pdf(pdfname) + plot(e,o,pch=19,cex=1, main=maint, ..., + xlab=expression(Expected~~-log[10](italic(p))), + ylab=expression(Observed~~-log[10](italic(p))), + xlim=c(0,max(e)), ylim=c(0,max(o))) + lines(e,e,col="red") + grid(col = "lightgray", lty = "dotted") + dev.off() +} + +smearPlot = function(DGEList,deTags, outSmear, outMain) + { + pdf(outSmear) + plotSmear(DGEList,de.tags=deTags,main=outMain) + grid(col="blue") + dev.off() + } + +boxPlot = function(rawrs,cleanrs,maint,myTitle) +{ # + nc = ncol(rawrs) + for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA} + fullnames = colnames(rawrs) + newcolnames = substr(colnames(rawrs),1,20) + colnames(rawrs) = newcolnames + newcolnames = substr(colnames(cleanrs),1,20) + colnames(cleanrs) = newcolnames + pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"sampleBoxplot.pdf",sep='_') + defpar = par(no.readonly=T) + pdf(pdfname,height=6,width=8) + #par(mfrow=c(1,2)) # 1 rows 2 col + l = layout(matrix(c(1,2),1,2,byrow=T)) + print.noquote('raw contig counts by sample:') + print.noquote(summary(rawrs)) + print.noquote('normalised contig counts by sample:') + print.noquote(summary(cleanrs)) + boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('Raw:',maint)) + grid(col="blue") + boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('After ',maint)) + grid(col="blue") + dev.off() + pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"samplehistplot.pdf",sep='_') + nc = ncol(rawrs) + print.noquote(paste('Using ncol rawrs=',nc)) + ncroot = round(sqrt(nc)) + if (ncroot*ncroot < nc) { ncroot = ncroot + 1 } + m = c() + for (i in c(1:nc)) { + rhist = hist(rawrs[,i],breaks=100,plot=F) + m = append(m,max(rhist\$counts)) + } + ymax = max(m) + pdf(pdfname) + par(mfrow=c(ncroot,ncroot)) + for (i in c(1:nc)) { + hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon", + breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax)) + } + dev.off() + par(defpar) + +} + +cumPlot = function(rawrs,cleanrs,maint,myTitle) +{ # updated to use ecdf + pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_') + defpar = par(no.readonly=T) + pdf(pdfname) + par(mfrow=c(2,1)) + lrs = log(rawrs,10) + lim = max(lrs) + hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)", + ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1) + grid(col="blue") + lrs = log(cleanrs,10) + hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)", + ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1) + grid(col="blue") + dev.off() + par(defpar) +} + +cumPlot1 = function(rawrs,cleanrs,maint,myTitle) +{ # updated to use ecdf + pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_') + pdf(pdfname) + par(mfrow=c(2,1)) + lastx = max(rawrs) + rawe = knots(ecdf(rawrs)) + cleane = knots(ecdf(cleanrs)) + cy = 1:length(cleane)/length(cleane) + ry = 1:length(rawe)/length(rawe) + plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads", + ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) + grid(col="blue") + plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads", + ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) + grid(col="blue") + dev.off() +} + + + +edgeIt = function (Count_Matrix,group,outputfilename,fdrtype='fdr',priordf=5,fdrthresh=0.05,outputdir='.', + myTitle='edgeR',libSize=c(),useNDF="T",filterquantile=0.2,subjects=c()) { + + # Error handling + if (length(unique(group))!=2){ + print("Number of conditions identified in experiment does not equal 2") + q() + } + require(edgeR) + mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ") + allN = nrow(Count_Matrix) + nscut = round(ncol(Count_Matrix)/2) + colTotmillionreads = colSums(Count_Matrix)/1e6 + rawrs = rowSums(Count_Matrix) + nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes + nzN = nrow(nonzerod) + nzrs = rowSums(nonzerod) + zN = allN - nzN + print('# Quantiles for non-zero row counts:',quote=F) + print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F) + if (useNDF == "T") + { + gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut + lo = colSums(Count_Matrix[!gt1rpin3,]) + workCM = Count_Matrix[gt1rpin3,] + cleanrs = rowSums(workCM) + cleanN = length(cleanrs) + meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="") + print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F) + maint = paste('Filter >=1/million reads in >=',nscut,'samples') + } + else { + useme = (nzrs > quantile(nzrs,filterquantile)) + workCM = nonzerod[useme,] + lo = colSums(nonzerod[!useme,]) + cleanrs = rowSums(workCM) + cleanN = length(cleanrs) + meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="") + print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F) + maint = paste('Filter below',filterquantile,'quantile') + } + cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle) + allgenes <- rownames(workCM) + print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F) + rsums = rowSums(workCM) + TName=unique(group)[1] + CName=unique(group)[2] + # Setup DGEList object + DGEList = DGEList(counts=workCM, group = group) + if (length(subjects) == 0) + { + doDESEQ = T + mydesign = model.matrix(~group) + } + else { + doDESEQ = F + subjf = factor(subjects) + mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it + } + print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=','))) + print.noquote('Using design matrix:') + print.noquote(mydesign) + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + comdisp = DGEList\$common.dispersion + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + if (priordf > 0) { + print.noquote(paste("prior.df =",priordf)) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = priordf) + } else { + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + } + DGLM = glmFit(DGEList,design=mydesign) + efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors + normData = (1e+06*DGEList\$counts/efflib) + co = length(colnames(mydesign)) + DE = glmLRT(DGLM,coef=co) # always last one - subject is first if needed + uoutput = cbind( + Name=as.character(rownames(DGEList\$counts)), + DE\$table, + adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), + Dispersion=DGEList\$tagwise.dispersion,totreads=rsums,normData, + DGEList\$counts + ) + soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable + goodness = gof(DGLM, pcutoff=fdrthresh) + if (sum(goodness\$outlier) > 0) { + print.noquote('GLM outliers:') + print(paste(rownames(DGLM)[(goodness\$outlier != 0)],collapse=','),quote=F) + z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) + pdf(paste(mt,"GoodnessofFit.pdf",sep='_')) + qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") + abline(0,1,lwd=3) + points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="dodgerblue") + dev.off() + } else { print('No GLM fit outlier genes found\n')} + estpriorn = getPriorN(DGEList) + print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F) + efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors + normData = (1e+06*DGEList\$counts/efflib) + uniqueg = unique(group) + # Plot MDS + sample_colors = match(group,levels(group)) + pdf(paste(mt,"MDSplot.pdf",sep='_')) + sampleTypes = levels(group) + plotMDS.DGEList(DGEList,main=paste("MDS Plot for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors) + legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19) + grid(col="blue") + dev.off() + colnames(normData) = paste( colnames(normData),'N',sep="_") + print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=','))) + nzd = data.frame(log(nonzerod + 1e-2,10)) + boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle) + if (doDESEQ) + { + # DESeq + deSeqDatcount <- newCountDataSet(workCM, group) + deSeqDatsizefac <- estimateSizeFactors(deSeqDatcount) + deSeqDatdisp <- estimateDispersions(deSeqDatsizefac) + rDESeq <- nbinomTest(deSeqDatdisp, levels(group)[1], levels(group)[2]) + rDESeq <- rDESeq[order(rDESeq\$pval), ] + write.table(rDESeq,paste(mt,'DESeq_TopTable.xls',sep='_'), quote=FALSE, sep="\t",row.names=F) + topresults.DESeq <- rDESeq[which(rDESeq\$padj < fdrthresh), ] + DESeqcountsindex <- which(allgenes %in% topresults.DESeq\$id) + DESeqcounts <- rep(0, length(allgenes)) + DESeqcounts[DESeqcountsindex] <- 1 + } + DGEList = calcNormFactors(DGEList) + norm.factor = DGEList\$samples\$norm.factors + pdf(paste(mt,"voomplot.pdf",sep='_')) + dat.voomed <- voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor) + dev.off() + # Use limma to fit data + fit <- lmFit(dat.voomed, mydesign) + fit <- eBayes(fit) + rvoom <- topTable(fit, coef = length(colnames(mydesign)), adj = "BH", n = Inf) + write.table(rvoom,paste(mt,'VOOM_topTable.xls',sep='_'), quote=FALSE, sep="\t",row.names=F) + # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma + topresults.voom <- rvoom[which(rvoom\$adj.P.Val < fdrthresh), ] + topresults.edgeR <- soutput[which(soutput\$adj.p.value < fdrthresh), ] + # Create venn diagram of hits + edgeRcountsindex <- which(allgenes %in% rownames(topresults.edgeR)) + voomcountsindex <- which(allgenes %in% topresults.voom\$ID) + edgeRcounts <- rep(0, length(allgenes)) + edgeRcounts[edgeRcountsindex] <- 1 + voomcounts <- rep(0, length(allgenes)) + voomcounts[voomcountsindex] <- 1 + if (doDESEQ) { + vennmain = paste(mt,'Voom,edgeR and DESeq overlap at FDR=',fdrthresh) + counts.dataframe <- data.frame(edgeRcounts = edgeRcounts, DESeqcounts = DESeqcounts, + voomcounts = voomcounts, row.names = allgenes) + } else { + vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh) + counts.dataframe <- data.frame(edgeRcounts = edgeRcounts, voomcounts = voomcounts, row.names = allgenes) + } + counts.venn <- vennCounts(counts.dataframe) + vennf = paste(mt,'venn.pdf',sep='_') + pdf(vennf) + vennDiagram(counts.venn,main=vennmain,col="maroon") + dev.off() + #Prepare our output file + nreads = soutput\$totreads # ordered same way + print('# writing output',quote=F) + write.table(soutput,outputfilename, quote=FALSE, sep="\t",row.names=F) + rn = uoutput\$Name + reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" + org="hg19" + genecards="<a href='http://www.genecards.org/index.php?path=/Search/keyword/" + ucsc = paste("<a href='http://genome.ucsc.edu/cgi-bin/hgTracks?db=",org,sep='') + testreg = str_match(rn,reg) + nreads = uoutput\$totreads # ordered same way + if (sum(!is.na(testreg[,1]))/length(testreg[,1]) > 0.8) # is ucsc style string + { + urls = paste(ucsc,'&position=chr',testreg[,2],':',testreg[,3],"-",testreg[,4],"'>",rn,'</a>',sep='') + } else { + urls = paste(genecards,rn,"'></a>",rn,'</a>',sep="") + } + print.noquote('# urls') + cat(head(urls)) + tt = uoutput + cat("# Top tags\n") + tt = cbind(tt,ntotreads=nreads,URL=urls) # add to end so table isn't laid out strangely + tt = tt[order(DE\$table\$PValue),] + options(width = 500) + print.noquote(tt[1:50,]) + pdf(paste(mt,"BCV_vs_abundance.pdf",sep='_')) + plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance") + dev.off() + # Plot MAplot + deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,]) + nsig = length(deTags) + print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F) + print('# deTags',quote=F) + print(head(deTags)) + dg = DGEList[order(DE\$table\$PValue),] + #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg))) + efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors + normData = (1e+06*dg\$counts/efflib) + outpdfname=paste(mt,"heatmap.pdf",sep='_') + hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=myTitle) + outSmear = paste(mt,"Smearplot.pdf",sep='_') + outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') + smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain) + qqPlot(descr=myTitle,pvector=DE\$table\$PValue) + if (doDESEQ) { + cat("# DESeq top 50\n") + print(rDESeq[1:50,]) + } + cat("# VOOM top 50\n") + print(rvoom[1:50,]) + # need a design matrix and glm to use this + goodness = gof(DGLM, pcutoff=fdrthresh) + nout = sum(goodness\$outlier) + if (nout > 0) { + print.noquote(paste('Found',nout,'Goodness of fit outliers')) + rownames(DGLM)[goodness\$outlier] + z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) + pdf(paste(mt,"GoodnessofFit.pdf",sep='_')) + qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") + abline(0,1,lwd=3) + points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="dodgerblue") + dev.off() + } + #Return our main table + uoutput + +} #Done +sink(stdout(),append=T,type="message") +options(width=512) +Out_Dir = "$html_file.files_path" +Input = "$input1" +TreatmentName = "$treatment_name" +TreatmentCols = "$Treat_cols" +ControlName = "$control_name" +ControlCols= "$Control_cols" +outputfilename = "$outtab" +fdrtype = "$fdrtype" +priordf = $priordf +fdrthresh = $fdrthresh +useNDF = "$useNDF" +fQ = $fQ # non-differential centile cutoff +myTitle = "$title" +subjects = c($subjectids) +nsubj = length(subjects) +#Set our columns +TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1 +CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1 +cat('# got TCols=') +cat(TCols) +cat('; CCols=') +cat(CCols) +cat('\n') +useCols = c(TCols,CCols) +# Create output dir if non existent + if (file.exists(Out_Dir) == F) dir.create(Out_Dir) + +Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header +snames = colnames(Count_Matrix) +nsamples = length(snames) +if (nsubj > 0 & nsubj != nsamples) { +options("show.error.messages"=T) +mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','),'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=',')) +write(mess, stderr()) +#print(mess) +quit(save="no",status=4) +} + +Count_Matrix = Count_Matrix[,useCols] # reorder columns +if (length(subjects) != 0) {subjects = subjects[useCols]} +rn = rownames(Count_Matrix) +islib = rn %in% c('librarySize','NotInBedRegions') +LibSizes = Count_Matrix[subset(rn,islib),][1] # take first +Count_Matrix = Count_Matrix[subset(rn,! islib),] +group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) ) #Build a group descriptor +group = factor(group, levels=c(ControlName,TreatmentName)) +colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_") #Relable columns +results = edgeIt(Count_Matrix=Count_Matrix,group=group,outputfilename=outputfilename,fdrtype=fdrtype,priordf=priordf,fdrthresh=fdrthresh, + outputdir=Out_Dir,myTitle=myTitle,libSize=c(),useNDF=useNDF,filterquantile=fQ,subjects=subjects) +#Run the main function +# for the log +sessionInfo() +]]> +</configfile> +</configfiles> +<help> +**What it does** + +Performs digital gene expression analysis between a treatment and control on a count matrix. +Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design. + +**Input** + +A matrix consisting of non-negative integers. The matrix must have a unique header row identifiying the samples, and a unique set of row names +as the first column. Typically the row names are gene symbols or probe id's for downstream use in GSEA and other methods. + +If you have (eg) paired samples and wish to include a term in the GLM to account for some other factor (subject in the case of paired samples), +put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or +A list of integers, one for each subject or an empty string if samples are all independent. +If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix. +Integers for samples that are not in the analysis *must* be present in the string as filler even if not used. + +So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones +eg if you had 6 samples with the first two independent but the second and third pairs each being from independent subjects. you might use +8,9,1,1,2,2 +as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6 + +**Output** + +A matrix which consists the original data and relative expression levels and some helpful plots + +**Note on edgeR versions** + +The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue) +breaking this and all other code that assumed the old name for this variable, +between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). +This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing +to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly +when their old scripts break. This tool currently now works with 2.4.6. + +**Note on prior.N** + +http://seqanswers.com/forums/showthread.php?t=5591 says: + +*prior.n* + +The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. +You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood +in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your +tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the +common likelihood the weight of one observation. + +In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, +or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that +you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation +(squeezing) of the tagwise dispersions. How many samples do you have in your experiment? +What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10. +If you have more samples, then the tagwise dispersion estimates will be more reliable, +so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. + + +From Bioconductor Digest, Vol 118, Issue 5, Gordon writes: + +Dear Dorota, + +The important settings are prior.df and trend. + +prior.n and prior.df are related through prior.df = prior.n * residual.df, +and your experiment has residual.df = 36 - 12 = 24. So the old setting of +prior.n=10 is equivalent for your data to prior.df = 240, a very large +value. Going the other way, the new setting of prior.df=10 is equivalent +to prior.n=10/24. + +To recover old results with the current software you would use + + estimateTagwiseDisp(object, prior.df=240, trend="none") + +To get the new default from old software you would use + + estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE) + +Actually the old trend method is equivalent to trend="loess" in the new +software. You should use plotBCV(object) to see whether a trend is +required. + +Note you could also use + + prior.n = getPriorN(object, prior.df=10) + +to map between prior.df and prior.n. + +</help> + +</tool> + +
--- /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 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> + <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> + <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> + <meta name="generator" content="Galaxy rgToolFactory.py tool output - see http://g2.trac.bx.psu.edu/" /> + <title></title> + <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> + </head> + <body> + <div class="toolFormBody"> + +<div class="infomessage">Galaxy Tool "edgeR" run at 12/06/2013 16:42:51</div><br/> +<div><table class="simple" cellpadding="2" cellspacing="2"> +<tr> +<td><a href="edgeRtest_BCV_vs_abundance.pdf"><img src="edgeRtest_BCV_vs_abundance.jpg" title="Click to download a PDF of edgeRtest_BCV_vs_abundance.pdf" hspace="5" width="400" + alt="Image called edgeRtest_BCV_vs_abundance.pdf"/></a></td> + +<td><a href="edgeRtest_MDSplot.pdf"><img src="edgeRtest_MDSplot.jpg" title="Click to download a PDF of edgeRtest_MDSplot.pdf" hspace="5" width="400" + alt="Image called edgeRtest_MDSplot.pdf"/></a></td> + +<td><a href="edgeRtest_RowsumCum.pdf"><img src="edgeRtest_RowsumCum.jpg" title="Click to download a PDF of edgeRtest_RowsumCum.pdf" hspace="5" width="400" + alt="Image called edgeRtest_RowsumCum.pdf"/></a></td> +</tr> +<tr> +<td><a href="edgeRtest_Smearplot.pdf"><img src="edgeRtest_Smearplot.jpg" title="Click to download a PDF of edgeRtest_Smearplot.pdf" hspace="5" width="400" + alt="Image called edgeRtest_Smearplot.pdf"/></a></td> + +<td><a href="edgeRtest_heatmap.pdf"><img src="edgeRtest_heatmap.jpg" title="Click to download a PDF of edgeRtest_heatmap.pdf" hspace="5" width="400" + alt="Image called edgeRtest_heatmap.pdf"/></a></td> + +<td><a href="edgeRtest_pval_qq.pdf"><img src="edgeRtest_pval_qq.jpg" title="Click to download a PDF of edgeRtest_pval_qq.pdf" hspace="5" width="400" + alt="Image called edgeRtest_pval_qq.pdf"/></a></td> +</tr> +<tr> +<td><a href="edgeRtest_sampleBoxplot.pdf"><img src="edgeRtest_sampleBoxplot.jpg" title="Click to download a PDF of edgeRtest_sampleBoxplot.pdf" hspace="5" width="400" + alt="Image called edgeRtest_sampleBoxplot.pdf"/></a></td> + +<td><a href="edgeRtest_samplehistplot.pdf"><img src="edgeRtest_samplehistplot.jpg" title="Click to download a PDF of edgeRtest_samplehistplot.pdf" hspace="5" width="400" + alt="Image called edgeRtest_samplehistplot.pdf"/></a></td> + +<td><a href="edgeRtest_venn.pdf"><img src="edgeRtest_venn.jpg" title="Click to download a PDF of edgeRtest_venn.pdf" hspace="5" width="400" + alt="Image called edgeRtest_venn.pdf"/></a></td> +</tr> +<tr> +<td><a href="edgeRtest_voomplot.pdf"><img src="edgeRtest_voomplot.jpg" title="Click to download a PDF of edgeRtest_voomplot.pdf" hspace="5" width="400" + alt="Image called edgeRtest_voomplot.pdf"/></a></td> + +<td> </td><td> </td> +</tr></table></div> + +<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr> + +<tr><td><a href="edgeR.Rscript">edgeR.Rscript</a></td><td>19.7 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeR_runner.log">edgeR_runner.log</a></td><td>79.2 KB)</td></tr> +<tr><td><a href="edgeRtest_BCV_vs_abundance.pdf">edgeRtest_BCV_vs_abundance.pdf</a></td><td>16.4 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeRtest_DESeq_TopTable.xls">edgeRtest_DESeq_TopTable.xls</a></td><td>135.1 KB)</td></tr> +<tr><td><a href="edgeRtest_MDSplot.pdf">edgeRtest_MDSplot.pdf</a></td><td>4.9 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeRtest_RowsumCum.pdf">edgeRtest_RowsumCum.pdf</a></td><td>6.3 KB)</td></tr> +<tr><td><a href="edgeRtest_Smearplot.pdf">edgeRtest_Smearplot.pdf</a></td><td>16.7 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeRtest_VOOM_topTable.xls">edgeRtest_VOOM_topTable.xls</a></td><td>131.4 KB)</td></tr> +<tr><td><a href="edgeRtest_heatmap.pdf">edgeRtest_heatmap.pdf</a></td><td>11.3 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeRtest_pval_qq.pdf">edgeRtest_pval_qq.pdf</a></td><td>15.9 KB)</td></tr> +<tr><td><a href="edgeRtest_sampleBoxplot.pdf">edgeRtest_sampleBoxplot.pdf</a></td><td>7.8 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeRtest_samplehistplot.pdf">edgeRtest_samplehistplot.pdf</a></td><td>10.9 KB)</td></tr> +<tr><td><a href="edgeRtest_venn.pdf">edgeRtest_venn.pdf</a></td><td>9.7 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeRtest_voomplot.pdf">edgeRtest_voomplot.pdf</a></td><td>16.7 KB)</td></tr> +</table></div><br/> +<div class="toolFormTitle">Rscript log</div><pre> + +## 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 + +<a href='http://www.genecards.org/index.php?path=/Search/keyword/0610005C13Rik'></a>0610005C13Rik</a> <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610007N19Rik'></a>0610007N19Rik</a> <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610008F07Rik'></a>0610008F07Rik</a> <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610009L18Rik'></a>0610009L18Rik</a> <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610012G03Rik'></a>0610012G03Rik</a> <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610031O16Rik'></a>0610031O16Rik</a># 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 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir122a'></a>Mir122a</a> + +Mir192 490327 630849 526600 2325567 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir192'></a>Mir192</a> + +Mir208b 0 0 0 14756 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208b'></a>Mir208b</a> + +Mir208a 0 0 1 4638 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208a'></a>Mir208a</a> + +Mir499 0 0 0 6527 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir499'></a>Mir499</a> + +Mir149 9 8 16 6164 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir149'></a>Mir149</a> + +Mir490 1 0 1 1741 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir490'></a>Mir490</a> + +Mir802 401 209 352 1514 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir802'></a>Mir802</a> + +Mir204 2 1 9 2601 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir204'></a>Mir204</a> + +Mir1948 648 259 617 2404 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1948'></a>Mir1948</a> + +Mir3073 218 131 214 904 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3073'></a>Mir3073</a> + +Mir194-2 865 439 880 3570 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-2'></a>Mir194-2</a> + +Mir10b 1080 1189 1777 197340 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir10b'></a>Mir10b</a> + +Mir101b 11066 16253 10901 59019 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir101b'></a>Mir101b</a> + +Mir182 879 3409 1075 7189 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir182'></a>Mir182</a> + +Mir200b 164 66 155 888 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200b'></a>Mir200b</a> + +Mir181c 366 384 427 23605 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181c'></a>Mir181c</a> + +Mir181d 42 46 38 2139 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181d'></a>Mir181d</a> + +Mir21 44582 43237 50530 229120 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir21'></a>Mir21</a> + +Mir199b 1 0 4 370 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199b'></a>Mir199b</a> + +Mir193 101 61 83 421 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir193'></a>Mir193</a> + +Mir23a 229 218 327 10118 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23a'></a>Mir23a</a> + +Mir195 112 75 120 3962 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir195'></a>Mir195</a> + +Ahsg 18 879 40 1524 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Ahsg'></a>Ahsg</a> + +Mir148a 177349 256441 193465 1002397 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir148a'></a>Mir148a</a> + +Dnm3os 36 20 40 1401 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Dnm3os'></a>Dnm3os</a> + +Mir27a 628 400 760 21886 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27a'></a>Mir27a</a> + +Mir429 82 41 101 499 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir429'></a>Mir429</a> + +Mir214 26 17 29 1048 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir214'></a>Mir214</a> + +Mir200a 53 33 47 264 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200a'></a>Mir200a</a> + +Mir194-1 160 81 141 635 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-1'></a>Mir194-1</a> + +Mir378 90 123 106 4075 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir378'></a>Mir378</a> + +Mir322 228 91 224 7074 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir322'></a>Mir322</a> + +Mir215 31 28 36 1182 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir215'></a>Mir215</a> + +Cyp3a25 30 93 27 226 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Cyp3a25'></a>Cyp3a25</a> + +Mir183 71 181 130 563 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir183'></a>Mir183</a> + +Mir181a-2 69 115 82 2817 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181a-2'></a>Mir181a-2</a> + +Mir155 3 8 9 463 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir155'></a>Mir155</a> + +Snord91a 256 358 285 1437 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord91a'></a>Snord91a</a> + +Mir125b-2 149 60 133 3837 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-2'></a>Mir125b-2</a> + +Serpina4-ps1 19 117 14 295 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Serpina4-ps1'></a>Serpina4-ps1</a> + +Mir133b 0 0 0 159 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir133b'></a>Mir133b</a> + +Gm10069 12 701 37 1309 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Gm10069'></a>Gm10069</a> + +Mir871 81 29 93 290 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir871'></a>Mir871</a> + +Mir184 0 2 2 247 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir184'></a>Mir184</a> + +Mir132 22 30 28 857 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir132'></a>Mir132</a> + +Snord104 5610 7452 6017 33458 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord104'></a>Snord104</a> + +Mir3074-2 127 65 140 3470 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3074-2'></a>Mir3074-2</a> + +Mir24-2 127 65 140 3470 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir24-2'></a>Mir24-2</a> + +5430416N02Rik 99 79 141 564 <a href='http://www.genecards.org/index.php?path=/Search/keyword/5430416N02Rik'></a>5430416N02Rik</a> + +[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 + +</pre> + +<hr/><div class="infomessage">This tool (edgeR) was generated by the <a href="https://bitbucket.org/fubar/galaxytoolfactory/overview">Galaxy Tool Factory</a></div><br/> +</div></body></html> +
--- /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 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--- /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 "$GSEAJAR_PATH" --output_dir "gseatestout" --mode "Max_probe" +--title "GSEA test" --builtin_gmt "gseatestdata.gmt" + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/test-data/gentestdata.sh~ Wed Jun 12 02:58:43 2013 -0400 @@ -0,0 +1,8 @@ +#!/bin/bash +# generate test data for rgGSEA +# ross lazarus June 2013 +# adjust gseajar_path ! +GSEAJAR_PATH=/home/rlazarus/galaxy-central/tool_dependency_dir/gsea_jar/2.0.12/fubar/rg_gsea_test/8e291f464aa0/jars/gsea2-2.0.12.jar +python ../rgGSEA.py --input_tab "gsea_test_DGE.xls" --adjpvalcol "5" --signcol "2" --idcol "1" --outhtml "gseatestout.html" --input_name "gsea_test" --setMax "500" --setMin "15" --nPerm "10" --plotTop "20" --gsea_jar "$GSEAJAR_PATH" --output_dir "gseatestout" --mode "Max_probe" --title "GSEA test" --builtin_gmt "gseatestdata.gmt" + +
--- /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 11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam 11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam 11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam 11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam 11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam 11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam 11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam 11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam +0610005C13Rik 40 0 2 0 6 70 6 2 +0610007N19Rik 10 17 11 42 2 6 6 10 +0610008F07Rik 16 0 0 0 8 5 4 1 +0610009B14Rik 0 0 0 1 0 0 0 0 +0610009L18Rik 3 2 2 11 0 1 1 1 +0610012G03Rik 6 0 0 0 4 5 2 0 +0610031O16Rik 33 0 0 0 10 25 10 0 +0610038B21Rik 0 0 0 2 0 0 0 0 +0610038L08Rik 0 0 0 0 0 3 0 0 +0610039K10Rik 9 0 0 1 3 1 4 3 +0610040B10Rik 0 0 0 0 0 0 2 0 +0610040F04Rik 2 1 0 1 2 5 2 0 +0610043K17Rik 9 2 0 4 4 12 0 1 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--- /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 @@ + +<?xml version="1.0"?> +<!-- +blame Boris - Ross stole it from his old 2.15 one +--> +<tool_dependency> + <package name="readline" version="6.2"> + <repository name="package_readline_6_2" owner="boris" prior_installation_required="True" toolshed="http://testtoolshed.g2.bx.psu.edu/" /> + </package> + <package name="package_R" version="3.0.1"> + <install version="1.0"> + <actions> + <action type="download_by_url">http://cran.ms.unimelb.edu.au/src/base/R-3/R-3.0.1.tar.gz</action> + <action type="set_environment_for_install"> + <repository name="package_readline_6_2" owner="boris" toolshed="http://testtoolshed.g2.bx.psu.edu/"> + <package name="readline" version="6.2" /> + </repository> + </action> + <action type="make_directory">$INSTALL_DIR</action> + <action type="shell_command">./configure --with-blas --with-lapack --enable-R-shlib --with-readline=no --with-x=no --prefix=$INSTALL_DIR && make && make install</action> + <action type="set_environment"> + <environment_variable action="set_to" name="R_HOME">$INSTALL_DIR/lib/R</environment_variable> + <environment_variable action="set_to" name="R_LIBS">$INSTALL_DIR/lib/R/library</environment_variable> + <environment_variable action="prepend_to" name="PATH">$INSTALL_DIR/lib/R/bin</environment_variable> + + </action> + </actions> + </install> + <readme>R is a free software environment for statistical computing and graphics + WARNING: See custom compilation options above + Modified from an older version of R by Boris by Ross Lazarus for R 3.0 + Added Bioc basics too + </readme> + </package> + <package name="package_BioCBasics" version="2.12"> + <install version="1.0"> + <actions> + <action type="shell_command">$INSTALL_DIR/lib/R/bin/R CMD BATCH installBioC.R </action> + </actions> + </install> + <readme>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 + </readme> + </package> +</tool_dependency>