# HG changeset patch # User fubar # Date 1404628607 14400 # Node ID 96ebf676f6c02e4763034359b7a5b52aafc83caf # Parent d6d45ba6f9d8fbec1aeadd1b2c1bbd5d68fa64ea consolidated from baker version. includes anscombe robust fit option. Heatmap is wrong still and the r packages won't export with all their dependencies so testing is painful diff -r d6d45ba6f9d8 -r 96ebf676f6c0 rgToolFactory.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgToolFactory.py Sun Jul 06 02:36:47 2014 -0400 @@ -0,0 +1,638 @@ +# 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 +# +# march 2014 +# added ghostscript and graphicsmagick as dependencies +# fixed a wierd problem where gs was trying to use the new_files_path from universe (database/tmp) as ./database/tmp +# errors ensued +# +# august 2013 +# found a problem with GS if $TMP or $TEMP missing - now inject /tmp and warn +# +# july 2013 +# added ability to combine images and individual log files into html output +# just make sure there's a log file foo.log and it will be output +# together with all images named like "foo_*.pdf +# otherwise old format for html +# +# January 2013 +# problem pointed out by Carlos Borroto +# added escaping for <>$ - thought I did that ages ago... +# +# August 11 2012 +# changed to use shell=False and cl as a sequence + +# This is a Galaxy tool factory for simple scripts in python, R or whatever ails ye. +# It also serves as the wrapper for the new tool. +# +# you paste and run your script +# Only works for simple scripts that read one input from the history. +# Optionally can write one new history dataset, +# and optionally collect any number of outputs into links on an autogenerated HTML page. + +# DO NOT install on a public or important site - please. + +# installed generated tools are fine if the script is safe. +# They just run normally and their user cannot do anything unusually insecure +# but please, practice safe toolshed. +# Read the fucking code before you install any tool +# especially this one + +# After you get the script working on some test data, you can +# optionally generate a toolshed compatible gzip file +# containing your script safely wrapped as an ordinary Galaxy script in your local toolshed for +# safe and largely automated installation in a production Galaxy. + +# If you opt for an HTML output, you get all the script outputs arranged +# as a single Html history item - all output files are linked, thumbnails for all the pdfs. +# Ugly but really inexpensive. +# +# Patches appreciated please. +# +# +# long route to June 2012 product +# Behold the awesome power of Galaxy and the toolshed with the tool factory to bind them +# derived from an integrated script model +# called rgBaseScriptWrapper.py +# Note to the unwary: +# This tool allows arbitrary scripting on your Galaxy as the Galaxy user +# There is nothing stopping a malicious user doing whatever they choose +# Extremely dangerous!! +# Totally insecure. So, trusted users only +# +# preferred model is a developer using their throw away workstation instance - ie a private site. +# no real risk. The universe_wsgi.ini admin_users string is checked - only admin users are permitted to run this tool. +# + +import sys +import shutil +import subprocess +import os +import time +import tempfile +import optparse +import tarfile +import re +import shutil +import math + +progname = os.path.split(sys.argv[0])[1] +myversion = 'V001.1 March 2014' +verbose = False +debug = False +toolFactoryURL = 'https://bitbucket.org/fubar/galaxytoolfactory' + +def timenow(): + """return current time as a string + """ + return time.strftime('%d/%m/%Y %H:%M:%S', time.localtime(time.time())) + +html_escape_table = { + "&": "&", + ">": ">", + "<": "<", + "$": "\$" + } + +def html_escape(text): + """Produce entities within text.""" + return "".join(html_escape_table.get(c,c) for c in text) + +def cmd_exists(cmd): + return subprocess.call("type " + cmd, shell=True, + stdout=subprocess.PIPE, stderr=subprocess.PIPE) == 0 + + +class ScriptRunner: + """class is a wrapper for an arbitrary script + """ + + def __init__(self,opts=None,treatbashSpecial=True): + """ + cleanup inputs, setup some outputs + + """ + self.useGM = cmd_exists('gm') + self.useIM = cmd_exists('convert') + self.useGS = cmd_exists('gs') + self.temp_warned = False # we want only one warning if $TMP not set + self.treatbashSpecial = treatbashSpecial + if opts.output_dir: # simplify for the tool tarball + os.chdir(opts.output_dir) + self.thumbformat = 'png' + self.opts = opts + self.toolname = re.sub('[^a-zA-Z0-9_]+', '', opts.tool_name) # a sanitizer now does this but.. + self.toolid = self.toolname + self.myname = sys.argv[0] # get our name because we write ourselves out as a tool later + self.pyfile = self.myname # crude but efficient - the cruft won't hurt much + self.xmlfile = '%s.xml' % self.toolname + s = open(self.opts.script_path,'r').readlines() + s = [x.rstrip() for x in s] # remove pesky dos line endings if needed + self.script = '\n'.join(s) + fhandle,self.sfile = tempfile.mkstemp(prefix=self.toolname,suffix=".%s" % (opts.interpreter)) + tscript = open(self.sfile,'w') # use self.sfile as script source for Popen + tscript.write(self.script) + tscript.close() + self.indentedScript = '\n'.join([' %s' % x for x in s]) # for restructured text in help + self.escapedScript = '\n'.join([html_escape(x) for x in s]) + self.elog = os.path.join(self.opts.output_dir,"%s_error.log" % self.toolname) + if opts.output_dir: # may not want these complexities + self.tlog = os.path.join(self.opts.output_dir,"%s_runner.log" % self.toolname) + art = '%s.%s' % (self.toolname,opts.interpreter) + artpath = os.path.join(self.opts.output_dir,art) # need full path + artifact = open(artpath,'w') # use self.sfile as script source for Popen + artifact.write(self.script) + artifact.close() + self.cl = [] + self.html = [] + 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 + + + a tabular file + + reverse.py --script_path "$runMe" --interpreter "python" + --tool_name "reverse" --input_tab "$input1" --output_tab "$tab_file" + + + + + + + + + + + +**What it Does** + +Reverse the columns in a tabular file + + + + + +# reverse order of columns in a tabular file +import sys +inp = sys.argv[1] +outp = sys.argv[2] +i = open(inp,'r') +o = open(outp,'w') +for row in i: + rs = row.rstrip().split('\t') + rs.reverse() + o.write('\t'.join(rs)) + o.write('\n') +i.close() +o.close() + + + + + + + """ + newXML=""" + %(tooldesc)s + %(command)s + + %(inputs)s + + + %(outputs)s + + + + %(script)s + + + %(tooltests)s + + %(help)s + + """ # needs a dict with toolname, toolid, interpreter, scriptname, command, inputs as a multi line string ready to write, outputs ditto, help ditto + + newCommand=""" + %(toolname)s.py --script_path "$runMe" --interpreter "%(interpreter)s" + --tool_name "%(toolname)s" %(command_inputs)s %(command_outputs)s + """ # may NOT be an input or htmlout + tooltestsTabOnly = """ + + + + + """ + tooltestsHTMLOnly = """ + + + + + """ + tooltestsBoth = """ + + + + + + """ + 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::'] + coda.append(self.indentedScript) + 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'] = '%s' % self.opts.tool_desc + else: + xdict['tooldesc'] = '' + xdict['command_outputs'] = '' + xdict['outputs'] = '' + if self.opts.input_tab <> 'None': + xdict['command_inputs'] = '--input_tab "$input1" ' # the space may matter a lot if we append something + xdict['inputs'] = ' \n' % self.inputFormats + else: + xdict['command_inputs'] = '' # assume no input - eg a random data generator + xdict['inputs'] = '' + xdict['inputs'] += ' \n' % self.toolname + xdict['toolname'] = self.toolname + xdict['toolid'] = self.toolid + xdict['interpreter'] = self.opts.interpreter + xdict['scriptname'] = self.sfile + if self.opts.make_HTML: + xdict['command_outputs'] += ' --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" ' + xdict['outputs'] += ' \n' + if self.opts.output_tab <> 'None': + xdict['command_outputs'] += ' --output_tab "$tab_file"' + xdict['outputs'] += ' \n' % self.outFormats + xdict['command'] = newCommand % xdict + xmls = newXML % xdict + xf = open(self.xmlfile,'w') + xf.write(xmls) + xf.write('\n') + xf.close() + # ready for the tarball + + + def makeTooltar(self): + """ + a tool is a gz tarball with eg + /toolname/tool.xml /toolname/tool.py /toolname/test-data/test1_in.foo ... + """ + retval = self.run() + if retval: + print >> sys.stderr,'## Run failed. Cannot build yet. Please fix and retry' + sys.exit(1) + 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 + note that GS gets confoozled if no $TMP or $TEMP + so we set it + """ + assert os.path.isfile(inpdf), "## Input %s supplied to %s compressPDF not found" % (inpdf,self.myName) + hlog = os.path.join(self.opts.output_dir,"compress_%s.txt" % os.path.basename(inpdf)) + sto = open(hlog,'a') + our_env = os.environ.copy() + our_tmp = our_env.get('TMP',None) + if not our_tmp: + our_tmp = our_env.get('TEMP',None) + if not (our_tmp and os.path.exists(our_tmp)): + newtmp = os.path.join(self.opts.output_dir,'tmp') + try: + os.mkdir(newtmp) + except: + sto.write('## WARNING - cannot make %s - it may exist or permissions need fixing\n' % newtmp) + our_env['TEMP'] = newtmp + if not self.temp_warned: + sto.write('## WARNING - no $TMP or $TEMP!!! Please fix - using %s temporarily\n' % newtmp) + self.temp_warned = True + outpdf = '%s_compressed' % inpdf + cl = ["gs", "-sDEVICE=pdfwrite", "-dNOPAUSE", "-dUseCIEColor", "-dBATCH","-dPDFSETTINGS=/printer", "-sOutputFile=%s" % outpdf,inpdf] + x = subprocess.Popen(cl,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env) + retval1 = x.wait() + sto.close() + if retval1 == 0: + os.unlink(inpdf) + shutil.move(outpdf,inpdf) + os.unlink(hlog) + hlog = os.path.join(self.opts.output_dir,"thumbnail_%s.txt" % os.path.basename(inpdf)) + sto = open(hlog,'w') + outpng = '%s.%s' % (os.path.splitext(inpdf)[0],thumbformat) + if self.useGM: + cl2 = ['gm', 'convert', inpdf, outpng] + else: # assume imagemagick + cl2 = ['convert', inpdf, outpng] + x = subprocess.Popen(cl2,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env) + retval2 = x.wait() + sto.close() + if retval2 == 0: + os.unlink(hlog) + retval = retval1 or retval2 + return retval + + + def getfSize(self,fpath,outpath): + """ + format a nice file size string + """ + size = '' + fp = os.path.join(outpath,fpath) + if os.path.isfile(fp): + size = '0 B' + n = float(os.path.getsize(fp)) + if n > 2**20: + size = '%1.1f MB' % (n/2**20) + elif n > 2**10: + size = '%1.1f KB' % (n/2**10) + elif n > 0: + size = '%d B' % (int(n)) + return size + + def makeHtml(self): + """ Create an HTML file content to list all the artifacts found in the output_dir + """ + + galhtmlprefix = """ + + + + + + + +
+ """ + galhtmlattr = """
This tool (%s) was generated by the Galaxy Tool Factory

""" + galhtmlpostfix = """
\n""" + + flist = os.listdir(self.opts.output_dir) + flist = [x for x in flist if x <> 'Rplots.pdf'] + flist.sort() + html = [] + html.append(galhtmlprefix % progname) + html.append('
Galaxy Tool "%s" run at %s

' % (self.toolname,timenow())) + fhtml = [] + if len(flist) > 0: + logfiles = [x for x in flist if x.lower().endswith('.log')] # log file names determine sections + logfiles.sort() + logfiles = [x for x in logfiles if os.path.abspath(x) <> os.path.abspath(self.tlog)] + logfiles.append(os.path.abspath(self.tlog)) # make it the last one + pdflist = [] + npdf = len([x for x in flist if os.path.splitext(x)[-1].lower() == '.pdf']) + for rownum,fname in enumerate(flist): + dname,e = os.path.splitext(fname) + sfsize = self.getfSize(fname,self.opts.output_dir) + if e.lower() == '.pdf' : # compress and make a thumbnail + thumb = '%s.%s' % (dname,self.thumbformat) + pdff = os.path.join(self.opts.output_dir,fname) + retval = self.compressPDF(inpdf=pdff,thumbformat=self.thumbformat) + if retval == 0: + pdflist.append((fname,thumb)) + else: + pdflist.append((fname,fname)) + if (rownum+1) % 2 == 0: + fhtml.append('%s%s' % (fname,fname,sfsize)) + else: + fhtml.append('%s%s' % (fname,fname,sfsize)) + for logfname in logfiles: # expect at least tlog - if more + if os.path.abspath(logfname) == os.path.abspath(self.tlog): # handled later + sectionname = 'All tool run' + if (len(logfiles) > 1): + sectionname = 'Other' + ourpdfs = pdflist + else: + realname = os.path.basename(logfname) + sectionname = os.path.splitext(realname)[0].split('_')[0] # break in case _ added to log + ourpdfs = [x for x in pdflist if os.path.basename(x[0]).split('_')[0] == sectionname] + pdflist = [x for x in pdflist if os.path.basename(x[0]).split('_')[0] <> sectionname] # remove + nacross = 1 + npdf = len(ourpdfs) + + if npdf > 0: + nacross = math.sqrt(npdf) ## int(round(math.log(npdf,2))) + if int(nacross)**2 != npdf: + nacross += 1 + nacross = int(nacross) + width = min(400,int(1200/nacross)) + html.append('
%s images and outputs
' % sectionname) + html.append('(Click on a thumbnail image to download the corresponding original PDF image)
') + ntogo = nacross # counter for table row padding with empty cells + html.append('
\n') + for i,paths in enumerate(ourpdfs): + fname,thumb = paths + s= """\n""" % (fname,thumb,fname,width,fname) + if ((i+1) % nacross == 0): + s += '\n' + ntogo = 0 + if i < (npdf - 1): # more to come + s += '' + ntogo = nacross + else: + ntogo -= 1 + html.append(s) + if html[-1].strip().endswith(''): + html.append('
Image called %s
\n') + else: + if ntogo > 0: # pad + html.append(' '*ntogo) + html.append('\n') + logt = open(logfname,'r').readlines() + logtext = [x for x in logt if x.strip() > ''] + html.append('
%s log output
' % sectionname) + if len(logtext) > 1: + html.append('\n
\n')
+                    html += logtext
+                    html.append('\n
\n') + else: + html.append('%s is empty
' % logfname) + if len(fhtml) > 0: + fhtml.insert(0,'
\n') + fhtml.append('
Output File Name (click to view)Size

') + html.append('
All output files available for downloading
\n') + html += fhtml # add all non-pdf files to the end of the display + else: + html.append('
### Error - %s returned no files - please confirm that parameters are sane
' % self.opts.interpreter) + html.append(galhtmlpostfix) + htmlf = file(self.opts.output_html,'w') + htmlf.write('\n'.join(html)) + htmlf.write('\n') + htmlf.close() + self.html = html + + + def run(self): + """ + scripts must be small enough not to fill the pipe! + """ + if self.treatbashSpecial and self.opts.interpreter in ['bash','sh']: + retval = self.runBash() + else: + if self.opts.output_dir: + ste = open(self.elog,'w') + sto = open(self.tlog,'w') + sto.write('## Toolfactory generated command line = %s\n' % ' '.join(self.cl)) + sto.flush() + p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=ste,stdin=subprocess.PIPE,cwd=self.opts.output_dir) + else: + p = subprocess.Popen(self.cl,shell=False,stdin=subprocess.PIPE) + p.stdin.write(self.script) + p.stdin.close() + retval = p.wait() + if self.opts.output_dir: + sto.close() + ste.close() + err = open(self.elog,'r').readlines() + if retval <> 0 and err: # problem + print >> sys.stderr,err + if self.opts.make_HTML: + self.makeHtml() + return retval + + def runBash(self): + """ + cannot use - for bash so use self.sfile + """ + if self.opts.output_dir: + s = '## Toolfactory generated command line = %s\n' % ' '.join(self.cl) + sto = open(self.tlog,'w') + sto.write(s) + sto.flush() + p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=sto,cwd=self.opts.output_dir) + else: + p = subprocess.Popen(self.cl,shell=False) + retval = p.wait() + if self.opts.output_dir: + sto.close() + if self.opts.make_HTML: + self.makeHtml() + return retval + + +def main(): + u = """ + This is a Galaxy wrapper. It expects to be called by a special purpose tool.xml as: + rgToolFactory.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript" + + """ + op = optparse.OptionParser() + a = op.add_option + a('--script_path',default=None) + a('--tool_name',default=None) + a('--interpreter',default=None) + a('--output_dir',default=None) + a('--output_html',default=None) + a('--input_tab',default="None") + a('--output_tab',default="None") + a('--user_email',default='Unknown') + a('--bad_user',default=None) + a('--make_Tool',default=None) + a('--make_HTML',default=None) + a('--help_text',default=None) + a('--tool_desc',default=None) + a('--new_tool',default=None) + a('--tool_version',default=None) + opts, args = op.parse_args() + assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user) + assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq' + assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript' + assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R' + if opts.output_dir: + try: + os.makedirs(opts.output_dir) + except: + pass + r = ScriptRunner(opts) + if opts.make_Tool: + retcode = r.makeTooltar() + else: + retcode = r.run() + os.unlink(r.sfile) + if retcode: + sys.exit(retcode) # indicate failure to job runner + + +if __name__ == "__main__": + main() + + diff -r d6d45ba6f9d8 -r 96ebf676f6c0 rgedgeRpaired.xml.camera --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeRpaired.xml.camera Sun Jul 06 02:36:47 2014 -0400 @@ -0,0 +1,1084 @@ + + models using BioConductor packages + + biocbasics + r302 + graphicsmagick + ghostscript + + + + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts" + --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + edgeR['doedgeR'] == "T" + + + DESeq2['doDESeq2'] == "T" + + + doVoom == "T" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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='qqplot',pvector, outpdf='qqplot.pdf',...) +# 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 + maint = descr + pdf(outpdf) + 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="lightgray", lty="dotted") + dev.off() + } + +boxPlot = function(rawrs,cleanrs,maint,myTitle,pdfname) +{ # + 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 + defpar = par(no.readonly=T) + print.noquote('raw contig counts by sample:') + print.noquote(summary(rawrs)) + print.noquote('normalised contig counts by sample:') + print.noquote(summary(cleanrs)) + pdf(pdfname) + par(mfrow=c(1,2)) + boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('Raw:',maint)) + grid(col="lightgray",lty="dotted") + boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('After ',maint)) + grid(col="lightgray",lty="dotted") + dev.off() + pdfname = "sample_counts_histogram.pdf" + 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) + ncols = length(fullnames) + if (ncols > 20) + { + scale = 7*ncols/20 + pdf(pdfname,width=scale,height=scale) + } else { + 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 = "Filtering_rowsum_bar_charts.pdf" + defpar = par(no.readonly=T) + lrs = log(rawrs,10) + lim = max(lrs) + pdf(pdfname) + par(mfrow=c(2,1)) + 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="lightgray", lty="dotted") + 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="lightgray", lty="dotted") + 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() +} + + + +doGSEAold = function(y=NULL,design=NULL,histgmt="", + bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") +{ + sink('Camera.log') + genesets = c() + if (bigmt > "") + { + bigenesets = readLines(bigmt) + genesets = bigenesets + } + if (histgmt > "") + { + hgenesets = readLines(histgmt) + if (bigmt > "") { + genesets = rbind(genesets,hgenesets) + } else { + genesets = hgenesets + } # use only history if no bi + } + print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) + genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n + outf = outfname + head=paste(myTitle,'edgeR GSEA') + write(head,file=outfname,append=F) + ntest=length(genesets) + urownames = toupper(rownames(y)) + upcam = c() + downcam = c() + for (i in 1:ntest) { + gs = unlist(genesets[i]) + g = gs[1] # geneset_id + u = gs[2] + if (u > "") { u = paste("",u,"",sep="") } + glist = gs[3:length(gs)] # member gene symbols + glist = toupper(glist) + inglist = urownames %in% glist + nin = sum(inglist) + if ((nin > minnin) && (nin < maxnin)) { + ### print(paste('@@found',sum(inglist),'genes in glist')) + camres = camera(y=y,index=inglist,design=design) + if (! is.null(camres)) { + rownames(camres) = g # gene set name + camres = cbind(GeneSet=g,URL=u,camres) + if (camres\$Direction == "Up") + { + upcam = rbind(upcam,camres) } else { + downcam = rbind(downcam,camres) + } + } + } + } + uscam = upcam[order(upcam\$PValue),] + unadjp = uscam\$PValue + uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + nup = max(10,sum((uscam\$adjPValue < fdrthresh))) + dscam = downcam[order(downcam\$PValue),] + unadjp = dscam\$PValue + dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) + write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) + write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) + write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) + write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) + sink() +} + + + + +doGSEA = function(y=NULL,design=NULL,histgmt="", + bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") +{ + sink('Camera.log') + genesets = c() + if (bigmt > "") + { + bigenesets = readLines(bigmt) + genesets = bigenesets + } + if (histgmt > "") + { + hgenesets = readLines(histgmt) + if (bigmt > "") { + genesets = rbind(genesets,hgenesets) + } else { + genesets = hgenesets + } # use only history if no bi + } + print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) + genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n + outf = outfname + head=paste(myTitle,'edgeR GSEA') + write(head,file=outfname,append=F) + ntest=length(genesets) + urownames = toupper(rownames(y)) + upcam = c() + downcam = c() + incam = c() + urls = c() + gsids = c() + for (i in 1:ntest) { + gs = unlist(genesets[i]) + gsid = gs[1] # geneset_id + url = gs[2] + if (url > "") { url = paste("",url,"",sep="") } + glist = gs[3:length(gs)] # member gene symbols + glist = toupper(glist) + inglist = urownames %in% glist + nin = sum(inglist) + if ((nin > minnin) && (nin < maxnin)) { + incam = c(incam,inglist) + gsids = c(gsids,gsid) + urls = c(urls,url) + } + } + incam = as.list(incam) + names(incam) = gsids + allcam = camera(y=y,index=incam,design=design) + allcamres = cbind(geneset=gsids,allcam,URL=urls) + for (i in 1:ntest) { + camres = allcamres[i] + res = try(test = (camres\$Direction == "Up")) + if ("try-error" %in% class(res)) { + cat("test failed, camres = :") + print.noquote(camres) + } else { if (camres\$Direction == "Up") + { upcam = rbind(upcam,camres) + } else { downcam = rbind(downcam,camres) + } + + } + } + uscam = upcam[order(upcam\$PValue),] + unadjp = uscam\$PValue + uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + nup = max(10,sum((uscam\$adjPValue < fdrthresh))) + dscam = downcam[order(downcam\$PValue),] + unadjp = dscam\$PValue + dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) + write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) + write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) + write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) + write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) + sink() + } + + +edgeIt = function (Count_Matrix=c(),group=c(),out_edgeR=F,out_VOOM=F,out_DESeq2=F,fdrtype='fdr',priordf=5, + fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F, + filterquantile=0.2, subjects=c(),mydesign=NULL, + doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19', + histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + doCook=F,DESeq_fitType="parameteric") +{ + # Error handling + if (length(unique(group))!=2){ + print("Number of conditions identified in experiment does not equal 2") + q() + } + require(edgeR) + options(width = 512) + mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ") + allN = nrow(Count_Matrix) + nscut = round(ncol(Count_Matrix)/2) + colTotmillionreads = colSums(Count_Matrix)/1e6 + counts.dataframe = as.data.frame(c()) + 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) + reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" + genecards=" 0.8) # is ucsc style string + { + print("@@ using ucsc substitution for urls") + contigurls = paste0(ucsc,"&position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"") + } else { + print("@@ using genecards substitution for urls") + contigurls = paste0(genecards,allgenes,"\'>",allgenes,"") + } + print.noquote("# urls") + print.noquote(head(contigurls)) + print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F) + cmrowsums = rowSums(workCM) + TName=unique(group)[1] + CName=unique(group)[2] + if (is.null(mydesign)) { + if (length(subjects) == 0) + { + mydesign = model.matrix(~group) + } + else { + subjf = factor(subjects) + mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it + } + } + print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=','))) + print.noquote('Using design matrix:') + print.noquote(mydesign) + if (doedgeR) { + sink('edgeR.log') + #### Setup DGEList object + DGEList = DGEList(counts=workCM, group = group) + DGEList = calcNormFactors(DGEList) + + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + comdisp = DGEList\$common.dispersion + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + if (edgeR_priordf > 0) { + print.noquote(paste("prior.df =",edgeR_priordf)) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = edgeR_priordf) + } else { + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + } + DGLM = glmFit(DGEList,design=mydesign) + DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed + efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors + normData = (1e+06*DGEList\$counts/efflib) + 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=cmrowsums,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)],collapse=','),quote=F) + } else { + print('No GLM fit outlier genes found\n') + } + z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) + pdf("edgeR_GoodnessofFit.pdf") + qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") + abline(0,1,lwd=3) + points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon") + dev.off() + 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)) + sampleTypes = levels(factor(group)) + print.noquote(sampleTypes) + pdf("edgeR_MDSplot.pdf") + plotMDS.DGEList(DGEList,main=paste("edgeR MDS 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)) + try( boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle,pdfname="edgeR_raw_norm_counts_box.pdf") ) + write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F) + tt = cbind( + Name=as.character(rownames(DGEList\$counts)), + DE\$table, + adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), + Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums + ) + print.noquote("# edgeR Top tags\n") + tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely + tt = tt[order(DE\$table\$PValue),] + print.noquote(tt[1:50,]) + deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,]) + nsig = length(deTags) + print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F) + deColours = ifelse(deTags,'red','black') + pdf("edgeR_BCV_vs_abundance.pdf") + plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance") + dev.off() + 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="edgeR_top_100_heatmap.pdf" + hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('edgeR Heatmap',myTitle)) + outSmear = "edgeR_smearplot.pdf" + outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') + smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain) + qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf='edgeR_qqplot.pdf') + norm.factor = DGEList\$samples\$norm.factors + topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ] + edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR)) + edgeRcounts = rep(0, length(allgenes)) + edgeRcounts[edgeRcountsindex] = 1 # Create venn diagram of hits + sink() + } ### doedgeR + if (doDESeq2 == T) + { + sink("DESeq2.log") + # DESeq2 + require('DESeq2') + library('RColorBrewer') + if (length(subjects) == 0) + { + pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM)) + deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ Rx)) + } else { + pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM)) + deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ subjects + Rx)) + } + #DESeq2 = DESeq(deSEQds,fitType='local',pAdjustMethod=fdrtype) + #rDESeq = results(DESeq2) + #newCountDataSet(workCM, group) + deSeqDatsizefac = estimateSizeFactors(deSEQds) + deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType) + resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype) + rDESeq = as.data.frame(results(resDESeq)) + rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls) + srDESeq = rDESeq[order(rDESeq\$pvalue),] + qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf') + cat("# DESeq top 50\n") + print.noquote(srDESeq[1:50,]) + write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F) + topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ] + DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq)) + DESeqcounts = rep(0, length(allgenes)) + DESeqcounts[DESeqcountsindex] = 1 + pdf("DESeq2_dispersion_estimates.pdf") + plotDispEsts(resDESeq) + dev.off() + ysmall = abs(min(rDESeq\$log2FoldChange)) + ybig = abs(max(rDESeq\$log2FoldChange)) + ylimit = min(4,ysmall,ybig) + pdf("DESeq2_MA_plot.pdf") + plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit)) + dev.off() + rlogres = rlogTransformation(resDESeq) + sampledists = dist( t( assay(rlogres) ) ) + sdmat = as.matrix(sampledists) + pdf("DESeq2_sample_distance_plot.pdf") + heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"), + col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255)) + dev.off() + ###outpdfname="DESeq2_top50_heatmap.pdf" + ###hmap2(sresDESeq,nsamp=50,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('DESeq2 vst rlog Heatmap',myTitle)) + sink() + result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) ) + if ("try-error" %in% class(result)) { + print.noquote('DESeq2 plotPCA failed.') + } else { + pdf("DESeq2_PCA_plot.pdf") + #### wtf - print? Seems needed to get this to work + print(ppca) + dev.off() + } + } + + if (doVoom == T) { + sink('VOOM.log') + if (doedgeR == F) { + #### Setup DGEList object + DGEList = DGEList(counts=workCM, group = group) + DGEList = calcNormFactors(DGEList) + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + norm.factor = DGEList\$samples\$norm.factors + } + pdf("VOOM_mean_variance_plot.pdf") + 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 = fdrtype, n = Inf, sort="none") + qqPlot(descr=paste(myTitle,'VOOM-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf='VOOM_qqplot.pdf') + rownames(rvoom) = rownames(workCM) + rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls) + srvoom = rvoom[order(rvoom\$P.Value),] + cat("# VOOM top 50\n") + print(srvoom[1:50,]) + write.table(srvoom,file=out_VOOM, 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), ] + voomcountsindex = which(allgenes %in% topresults.voom\$ID) + voomcounts = rep(0, length(allgenes)) + voomcounts[voomcountsindex] = 1 + sink() + } + + if (doCamera) { + doGSEA(y=DGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle, + outfname=paste(mt,"GSEA.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype) + } + + if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) { + if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) { + vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, + VOOM_limma = voomcounts, row.names = allgenes) + } else if ((doDESeq2==T) && (doedgeR==T)) { + vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes) + } else if ((doVoom==T) && (doedgeR==T)) { + vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes) + } + + if (nrow(counts.dataframe > 1)) { + counts.venn = vennCounts(counts.dataframe) + vennf = "Venn_significant_genes_overlap.pdf" + pdf(vennf) + vennDiagram(counts.venn,main=vennmain,col="maroon") + dev.off() + } + } #### doDESeq2 or doVoom + +} +#### Done + +###sink(stdout(),append=T,type="message") +builtin_gmt = "" +history_gmt = "" +history_gmt_name = "" +out_edgeR = F +out_DESeq2 = F +out_VOOM = "$out_VOOM" +doDESeq2 = $DESeq2.doDESeq2 # make these T or F +doVoom = $doVoom +doCamera = F +doedgeR = $edgeR.doedgeR +edgeR_priordf = 0 + + +#if $doVoom == "T": + out_VOOM = "$out_VOOM" +#end if + +#if $DESeq2.doDESeq2 == "T": + out_DESeq2 = "$out_DESeq2" + DESeq_fitType = "$DESeq2.DESeq_fitType" +#end if + +#if $edgeR.doedgeR == "T": + out_edgeR = "$out_edgeR" + edgeR_priordf = $edgeR.edgeR_priordf +#end if + + + +if (sum(c(doedgeR,doVoom,doDESeq2)) == 0) +{ +write("No methods chosen - nothing to do! Please try again after choosing one or more methods", stderr()) +quit(save="no",status=2) +} + +Out_Dir = "$html_file.files_path" +Input = "$input1" +TreatmentName = "$treatment_name" +TreatmentCols = "$Treat_cols" +ControlName = "$control_name" +ControlCols= "$Control_cols" +org = "$input1.dbkey" +if (org == "") { org = "hg19"} +fdrtype = "$fdrtype" +fdrthresh = $fdrthresh +useNDF = $useNDF +fQ = $fQ # non-differential centile cutoff +myTitle = "$title" +sids = strsplit("$subjectids",',') +subjects = unlist(sids) +nsubj = length(subjects) +TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1 +CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1 +cat('Got TCols=') +cat(TCols) +cat('; CCols=') +cat(CCols) +cat('\n') +useCols = c(TCols,CCols) +if (file.exists(Out_Dir) == F) dir.create(Out_Dir) +Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header +snames = colnames(Count_Matrix) +nsamples = length(snames) +if (nsubj > 0 & nsubj != nsamples) { +options("show.error.messages"=T) +mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','), + 'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=',')) +write(mess, stderr()) +quit(save="no",status=4) +} +if (length(subjects) != 0) {subjects = subjects[useCols]} +Count_Matrix = Count_Matrix[,useCols] ### reorder columns +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, out_edgeR=out_edgeR, out_VOOM=out_VOOM, out_DESeq2=out_DESeq2, + fdrtype='BH',mydesign=NULL,priordf=edgeR_priordf,fdrthresh=fdrthresh,outputdir='.', + myTitle=myTitle,useNDF=F,libSize=c(),filterquantile=fQ,subjects=subjects, + doDESeq2=doDESeq2,doVoom=doVoom,doCamera=doCamera,doedgeR=doedgeR,org=org, + histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fitType=DESeq_fitType) +sessionInfo() +]]> + + + + +**What it does** + +Allows short read sequence counts from controlled experiments to be analysed for differentially expressed genes. +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** + +Requires a count matrix as a tabular file. These are best made using the companion HTSeq_ based counter Galaxy wrapper +and your fave gene model to generate inputs. Each row is a genomic feature (gene or exon eg) and each column the +non-negative integer count of reads from one sample overlapping the feature. +The matrix must have a header row uniquely identifying the source samples, and unique row names in +the first column. Typically the row names are gene symbols or probe ids for downstream use in GSEA and other methods. + +**Specifying comparisons** + +This is basically dumbed down for two factors - case vs control. + +More complex interfaces are possible but painful at present. +Probably need to specify a phenotype file to do this better. +Work in progress. Send code. + +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 + +**Methods available** + +You can run 3 popular Bioconductor packages available for count data. + +edgeR - see edgeR_ for details + +VOOM/limma - see limma_VOOM_ for details + +DESeq2 - see DESeq2_ for details + +and optionally camera in edgeR which works better if MSigDB is installed. + +**Outputs** + +Some helpful plots and analysis results. Note that most of these are produced using R code +suggested by the excellent documentation and vignettes for the Bioconductor +packages invoked. The Tool Factory is used to automatically lay these out for you to enjoy. + +**Note on Voom** + +The voom from limma version 3.16.6 help in R includes this from the authors - but you should read the paper to interpret this method. + +This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma. + +voom is an acronym for mean-variance modelling at the observational level. +The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation. +Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend. +This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance. +The weights are then used in the linear modelling process to adjust for heteroscedasticity. + +In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess. +The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag. +The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag. +Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays. +Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation. +This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data. + + +Author(s) + +Charity Law and Gordon Smyth + +References + +Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia. + +Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts. +Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia. +http://www.statsci.org/smyth/pubs/VoomPreprint.pdf + +See Also + +A voom case study is given in the edgeR User's Guide. + +vooma is a similar function but for microarrays instead of RNA-seq. + + +***old rant on changes to Bioconductor package variable names between 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. + +---- + +**Attributions** + +edgeR - edgeR_ + +VOOM/limma - limma_VOOM_ + +DESeq2 - DESeq2_ for details + +See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package. + +Galaxy_ (that's what you are using right now!) for gluing everything together + +Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is +licensed to you under the LGPL_ like other rgenetics artefacts + +.. _LGPL: http://www.gnu.org/copyleft/lesser.html +.. _HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/index.html +.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html +.. _DESeq2: http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html +.. _limma_VOOM: http://www.bioconductor.org/packages/release/bioc/html/limma.html +.. _Galaxy: http://getgalaxy.org + + + + + diff -r d6d45ba6f9d8 -r 96ebf676f6c0 rgedgeRpaired_nocamera.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeRpaired_nocamera.xml Sun Jul 06 02:36:47 2014 -0400 @@ -0,0 +1,1086 @@ + + models using BioConductor packages + + biocbasics + r303 + graphicsmagick + ghostscript + + + + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts" + --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + edgeR['doedgeR'] == "T" + + + DESeq2['doDESeq2'] == "T" + + + doVoom == "T" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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='qqplot',pvector, outpdf='qqplot.pdf',...) +# 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 + maint = descr + pdf(outpdf) + 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="lightgray", lty="dotted") + dev.off() + } + +boxPlot = function(rawrs,cleanrs,maint,myTitle,pdfname) +{ # + 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 + defpar = par(no.readonly=T) + print.noquote('raw contig counts by sample:') + print.noquote(summary(rawrs)) + print.noquote('normalised contig counts by sample:') + print.noquote(summary(cleanrs)) + pdf(pdfname) + par(mfrow=c(1,2)) + boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('Raw:',maint)) + grid(col="lightgray",lty="dotted") + boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('After ',maint)) + grid(col="lightgray",lty="dotted") + dev.off() + pdfname = "sample_counts_histogram.pdf" + 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) + ncols = length(fullnames) + if (ncols > 20) + { + scale = 7*ncols/20 + pdf(pdfname,width=scale,height=scale) + } else { + 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 = "Filtering_rowsum_bar_charts.pdf" + defpar = par(no.readonly=T) + lrs = log(rawrs,10) + lim = max(lrs) + pdf(pdfname) + par(mfrow=c(2,1)) + 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="lightgray", lty="dotted") + 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="lightgray", lty="dotted") + 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() +} + + + +doGSEAold = function(y=NULL,design=NULL,histgmt="", + bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") +{ + sink('Camera.log') + genesets = c() + if (bigmt > "") + { + bigenesets = readLines(bigmt) + genesets = bigenesets + } + if (histgmt > "") + { + hgenesets = readLines(histgmt) + if (bigmt > "") { + genesets = rbind(genesets,hgenesets) + } else { + genesets = hgenesets + } # use only history if no bi + } + print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) + genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n + outf = outfname + head=paste(myTitle,'edgeR GSEA') + write(head,file=outfname,append=F) + ntest=length(genesets) + urownames = toupper(rownames(y)) + upcam = c() + downcam = c() + for (i in 1:ntest) { + gs = unlist(genesets[i]) + g = gs[1] # geneset_id + u = gs[2] + if (u > "") { u = paste("",u,"",sep="") } + glist = gs[3:length(gs)] # member gene symbols + glist = toupper(glist) + inglist = urownames %in% glist + nin = sum(inglist) + if ((nin > minnin) && (nin < maxnin)) { + ### print(paste('@@found',sum(inglist),'genes in glist')) + camres = camera(y=y,index=inglist,design=design) + if (! is.null(camres)) { + rownames(camres) = g # gene set name + camres = cbind(GeneSet=g,URL=u,camres) + if (camres\$Direction == "Up") + { + upcam = rbind(upcam,camres) } else { + downcam = rbind(downcam,camres) + } + } + } + } + uscam = upcam[order(upcam\$PValue),] + unadjp = uscam\$PValue + uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + nup = max(10,sum((uscam\$adjPValue < fdrthresh))) + dscam = downcam[order(downcam\$PValue),] + unadjp = dscam\$PValue + dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) + write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) + write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) + write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) + write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) + sink() +} + + + + +doGSEA = function(y=NULL,design=NULL,histgmt="", + bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") +{ + sink('Camera.log') + genesets = c() + if (bigmt > "") + { + bigenesets = readLines(bigmt) + genesets = bigenesets + } + if (histgmt > "") + { + hgenesets = readLines(histgmt) + if (bigmt > "") { + genesets = rbind(genesets,hgenesets) + } else { + genesets = hgenesets + } # use only history if no bi + } + print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) + genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n + outf = outfname + head=paste(myTitle,'edgeR GSEA') + write(head,file=outfname,append=F) + ntest=length(genesets) + urownames = toupper(rownames(y)) + upcam = c() + downcam = c() + incam = c() + urls = c() + gsids = c() + for (i in 1:ntest) { + gs = unlist(genesets[i]) + gsid = gs[1] # geneset_id + url = gs[2] + if (url > "") { url = paste("",url,"",sep="") } + glist = gs[3:length(gs)] # member gene symbols + glist = toupper(glist) + inglist = urownames %in% glist + nin = sum(inglist) + if ((nin > minnin) && (nin < maxnin)) { + incam = c(incam,inglist) + gsids = c(gsids,gsid) + urls = c(urls,url) + } + } + incam = as.list(incam) + names(incam) = gsids + allcam = camera(y=y,index=incam,design=design) + allcamres = cbind(geneset=gsids,allcam,URL=urls) + for (i in 1:ntest) { + camres = allcamres[i] + res = try(test = (camres\$Direction == "Up")) + if ("try-error" %in% class(res)) { + cat("test failed, camres = :") + print.noquote(camres) + } else { if (camres\$Direction == "Up") + { upcam = rbind(upcam,camres) + } else { downcam = rbind(downcam,camres) + } + + } + } + uscam = upcam[order(upcam\$PValue),] + unadjp = uscam\$PValue + uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + nup = max(10,sum((uscam\$adjPValue < fdrthresh))) + dscam = downcam[order(downcam\$PValue),] + unadjp = dscam\$PValue + dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) + write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) + write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) + write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) + write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) + sink() + } + + +edgeIt = function (Count_Matrix=c(),group=c(),out_edgeR=F,out_VOOM=F,out_DESeq2=F,fdrtype='fdr',priordf=5, + fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F, + filterquantile=0.2, subjects=c(),mydesign=NULL, + doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19', + histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + doCook=F,DESeq_fitType="parameteric",robust_meth='ordinary') +{ + # Error handling + if (length(unique(group))!=2){ + print("Number of conditions identified in experiment does not equal 2") + q() + } + require(edgeR) + options(width = 512) + mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ") + allN = nrow(Count_Matrix) + nscut = round(ncol(Count_Matrix)/2) + colTotmillionreads = colSums(Count_Matrix)/1e6 + counts.dataframe = as.data.frame(c()) + 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) + reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" + genecards=" 0.8) # is ucsc style string + { + print("@@ using ucsc substitution for urls") + contigurls = paste0(ucsc,"&position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"") + } else { + print("@@ using genecards substitution for urls") + contigurls = paste0(genecards,allgenes,"\'>",allgenes,"") + } + print.noquote("# urls") + print.noquote(head(contigurls)) + print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F) + cmrowsums = rowSums(workCM) + TName=unique(group)[1] + CName=unique(group)[2] + if (is.null(mydesign)) { + if (length(subjects) == 0) + { + mydesign = model.matrix(~group) + } + else { + subjf = factor(subjects) + mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it + } + } + print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=','))) + print.noquote('Using design matrix:') + print.noquote(mydesign) + if (doedgeR == T) { + sink('edgeR.log') + #### Setup DGEList object + DGEList = DGEList(counts=workCM, group = group) + DGEList = calcNormFactors(DGEList) + if (robust_meth == 'ordinary') { + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = edgeR_priordf) + + comdisp = DGEList\$common.dispersion + estpriorn = getPriorN(DGEList) + print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F) + } else { + DGEList = estimateGLMRobustDisp(DGEList,design=mydesign, prior.df = edgeR_priordf, maxit = 6, residual.type = robust_meth) + } + + + DGLM = glmFit(DGEList,design=mydesign) + DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed + efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors + normData = (1e+06*DGEList\$counts/efflib) + 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=cmrowsums,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)],collapse=','),quote=F) + } else { + print('No GLM fit outlier genes found\n') + } + z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) + pdf("edgeR_GoodnessofFit.pdf") + qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") + abline(0,1,lwd=3) + points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon") + dev.off() + 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)) + sampleTypes = levels(factor(group)) + print.noquote(sampleTypes) + pdf("edgeR_MDSplot.pdf") + plotMDS.DGEList(DGEList,main=paste("edgeR MDS 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)) + try( boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle,pdfname="edgeR_raw_norm_counts_box.pdf") ) + write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F) + tt = cbind( + Name=as.character(rownames(DGEList\$counts)), + DE\$table, + adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), + Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums + ) + print.noquote("# edgeR Top tags\n") + tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely + tt = tt[order(DE\$table\$PValue),] + print.noquote(tt[1:50,]) + deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,]) + nsig = length(deTags) + print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F) + deColours = ifelse(deTags,'red','black') + pdf("edgeR_BCV_vs_abundance.pdf") + plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance") + dev.off() + 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="edgeR_top_100_heatmap.pdf" + hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('edgeR Heatmap',myTitle)) + outSmear = "edgeR_smearplot.pdf" + outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') + smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain) + qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf='edgeR_qqplot.pdf') + norm.factor = DGEList\$samples\$norm.factors + topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ] + edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR)) + edgeRcounts = rep(0, length(allgenes)) + edgeRcounts[edgeRcountsindex] = 1 # Create venn diagram of hits + sink() + } ### doedgeR + if (doDESeq2 == T) + { + sink("DESeq2.log") + # DESeq2 + require('DESeq2') + library('RColorBrewer') + if (length(subjects) == 0) + { + pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM)) + deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ Rx)) + } else { + pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM)) + deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ subjects + Rx)) + } + #DESeq2 = DESeq(deSEQds,fitType='local',pAdjustMethod=fdrtype) + #rDESeq = results(DESeq2) + #newCountDataSet(workCM, group) + deSeqDatsizefac = estimateSizeFactors(deSEQds) + deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType) + resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype) + rDESeq = as.data.frame(results(resDESeq)) + rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls) + srDESeq = rDESeq[order(rDESeq\$pvalue),] + qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf') + cat("# DESeq top 50\n") + print.noquote(srDESeq[1:50,]) + write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F) + topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ] + DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq)) + DESeqcounts = rep(0, length(allgenes)) + DESeqcounts[DESeqcountsindex] = 1 + pdf("DESeq2_dispersion_estimates.pdf") + plotDispEsts(resDESeq) + dev.off() + ysmall = abs(min(rDESeq\$log2FoldChange)) + ybig = abs(max(rDESeq\$log2FoldChange)) + ylimit = min(4,ysmall,ybig) + pdf("DESeq2_MA_plot.pdf") + plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit)) + dev.off() + rlogres = rlogTransformation(resDESeq) + sampledists = dist( t( assay(rlogres) ) ) + sdmat = as.matrix(sampledists) + pdf("DESeq2_sample_distance_plot.pdf") + heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"), + col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255)) + dev.off() + ###outpdfname="DESeq2_top50_heatmap.pdf" + ###hmap2(sresDESeq,nsamp=50,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('DESeq2 vst rlog Heatmap',myTitle)) + sink() + result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) ) + if ("try-error" %in% class(result)) { + print.noquote('DESeq2 plotPCA failed.') + } else { + pdf("DESeq2_PCA_plot.pdf") + #### wtf - print? Seems needed to get this to work + print(ppca) + dev.off() + } + } + + if (doVoom == T) { + sink('VOOM.log') + if (doedgeR == F) { + #### Setup DGEList object + DGEList = DGEList(counts=workCM, group = group) + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + } + calcNormFactors(DGEList) + ls = colSums(DGEList\$counts) * DGEList\$samples\$norm.factors + pdf("VOOM_mean_variance_plot.pdf") + #dat.voomed = voom(DGEList, mydesign, plot = TRUE, lib.size = ls) + dat.voomed <- voom(DGEList, mydesign, plot = TRUE, normalize.method="quantil", lib.size = NULL) + dev.off() + # Use limma to fit data + fit = lmFit(dat.voomed, mydesign) + fit = eBayes(fit) + rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none") + qqPlot(descr=paste(myTitle,'VOOM-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf='VOOM_qqplot.pdf') + rownames(rvoom) = rownames(workCM) + rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls) + srvoom = rvoom[order(rvoom\$P.Value),] + cat("# VOOM top 50\n") + print(srvoom[1:50,]) + write.table(srvoom,file=out_VOOM, 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), ] + voomcountsindex <- which(allgenes %in% rownames(topresults.voom)) + voomcounts = rep(0, length(allgenes)) + voomcounts[voomcountsindex] = 1 + sink() + } + + if (doCamera) { + doGSEA(y=DGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle, + outfname=paste(mt,"GSEA.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype) + } + counts.dataframe = c() + vennmain = 'no venn' + if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) { + if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) { + vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, + VOOM_limma = voomcounts, row.names = allgenes) + } else if ((doDESeq2==T) && (doedgeR==T)) { + vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes) + } else if ((doVoom==T) && (doedgeR==T)) { + vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes) + } + + if (nrow(counts.dataframe > 1)) { + counts.venn = vennCounts(counts.dataframe) + vennf = "Venn_significant_genes_overlap.pdf" + pdf(vennf) + vennDiagram(counts.venn,main=vennmain,col="maroon") + dev.off() + } + } #### doDESeq2 or doVoom + +} +#### Done + +###sink(stdout(),append=T,type="message") +builtin_gmt = "" +history_gmt = "" +history_gmt_name = "" +out_edgeR = F +out_DESeq2 = F +out_VOOM = "$out_VOOM" +edgeR_robust_meth = "ordinary" # control robust deviance options +doDESeq2 = $DESeq2.doDESeq2 +doVoom = $doVoom +doCamera = F +doedgeR = $edgeR.doedgeR +edgeR_priordf = 10 + + +#if $doVoom == "T": + out_VOOM = "$out_VOOM" +#end if + +#if $DESeq2.doDESeq2 == "T": + out_DESeq2 = "$out_DESeq2" + doDESeq2 = T + DESeq_fitType = "$DESeq2.DESeq_fitType" +#end if + +#if $edgeR.doedgeR == "T": + out_edgeR = "$out_edgeR" + edgeR_priordf = $edgeR.edgeR_priordf + edgeR_robust_meth = "$edgeR.edgeR_robust_method" +#end if + + +if (sum(c(doedgeR,doVoom,doDESeq2)) == 0) +{ +write("No methods chosen - nothing to do! Please try again after choosing one or more methods", stderr()) +quit(save="no",status=2) +} + +Out_Dir = "$html_file.files_path" +Input = "$input1" +TreatmentName = "$treatment_name" +TreatmentCols = "$Treat_cols" +ControlName = "$control_name" +ControlCols= "$Control_cols" +org = "$input1.dbkey" +if (org == "") { org = "hg19"} +fdrtype = "$fdrtype" +fdrthresh = $fdrthresh +useNDF = $useNDF +fQ = $fQ # non-differential centile cutoff +myTitle = "$title" +sids = strsplit("$subjectids",',') +subjects = unlist(sids) +nsubj = length(subjects) +TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1 +CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1 +cat('Got TCols=') +cat(TCols) +cat('; CCols=') +cat(CCols) +cat('\n') +useCols = c(TCols,CCols) +if (file.exists(Out_Dir) == F) dir.create(Out_Dir) +Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header +snames = colnames(Count_Matrix) +nsamples = length(snames) +if (nsubj > 0 & nsubj != nsamples) { +options("show.error.messages"=T) +mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','), + 'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=',')) +write(mess, stderr()) +quit(save="no",status=4) +} +if (length(subjects) != 0) {subjects = subjects[useCols]} +Count_Matrix = Count_Matrix[,useCols] ### reorder columns +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, out_edgeR=out_edgeR, out_VOOM=out_VOOM, out_DESeq2=out_DESeq2, + fdrtype='BH',mydesign=NULL,priordf=edgeR_priordf,fdrthresh=fdrthresh,outputdir='.', + myTitle=myTitle,useNDF=F,libSize=c(),filterquantile=fQ,subjects=subjects, + doDESeq2=doDESeq2,doVoom=doVoom,doCamera=doCamera,doedgeR=doedgeR,org=org, + histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fitType=DESeq_fitType,robust_meth=edgeR_robust_meth) +sessionInfo() +]]> + + + + +**What it does** + +Allows short read sequence counts from controlled experiments to be analysed for differentially expressed genes. +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** + +Requires a count matrix as a tabular file. These are best made using the companion HTSeq_ based counter Galaxy wrapper +and your fave gene model to generate inputs. Each row is a genomic feature (gene or exon eg) and each column the +non-negative integer count of reads from one sample overlapping the feature. +The matrix must have a header row uniquely identifying the source samples, and unique row names in +the first column. Typically the row names are gene symbols or probe ids for downstream use in GSEA and other methods. + +**Specifying comparisons** + +This is basically dumbed down for two factors - case vs control. + +More complex interfaces are possible but painful at present. +Probably need to specify a phenotype file to do this better. +Work in progress. Send code. + +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 + +**Methods available** + +You can run 3 popular Bioconductor packages available for count data. + +edgeR - see edgeR_ for details + +VOOM/limma - see limma_VOOM_ for details + +DESeq2 - see DESeq2_ for details + +and optionally camera in edgeR which works better if MSigDB is installed. + +**Outputs** + +Some helpful plots and analysis results. Note that most of these are produced using R code +suggested by the excellent documentation and vignettes for the Bioconductor +packages invoked. The Tool Factory is used to automatically lay these out for you to enjoy. + +**Note on Voom** + +The voom from limma version 3.16.6 help in R includes this from the authors - but you should read the paper to interpret this method. + +This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma. + +voom is an acronym for mean-variance modelling at the observational level. +The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation. +Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend. +This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance. +The weights are then used in the linear modelling process to adjust for heteroscedasticity. + +In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess. +The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag. +The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag. +Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays. +Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation. +This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data. + + +Author(s) + +Charity Law and Gordon Smyth + +References + +Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia. + +Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts. +Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia. +http://www.statsci.org/smyth/pubs/VoomPreprint.pdf + +See Also + +A voom case study is given in the edgeR User's Guide. + +vooma is a similar function but for microarrays instead of RNA-seq. + + +***old rant on changes to Bioconductor package variable names between 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. + +---- + +**Attributions** + +edgeR - edgeR_ + +VOOM/limma - limma_VOOM_ + +DESeq2 - DESeq2_ for details + +See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package. + +Galaxy_ (that's what you are using right now!) for gluing everything together + +Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is +licensed to you under the LGPL_ like other rgenetics artefacts + +.. _LGPL: http://www.gnu.org/copyleft/lesser.html +.. _HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/index.html +.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html +.. _DESeq2: http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html +.. _limma_VOOM: http://www.bioconductor.org/packages/release/bioc/html/limma.html +.. _Galaxy: http://getgalaxy.org + + + + + diff -r d6d45ba6f9d8 -r 96ebf676f6c0 runme.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runme.R Sun Jul 06 02:36:47 2014 -0400 @@ -0,0 +1,7 @@ +bioclite = "http://bioconductor.org/biocLite.R" +install.packages(c("stringr","gplots"),dependencies=T,repos=http://cran.us.r-project.org) +source(bioclite) +installme=c(edgeR,limma,DESeq,DESeq2) +biocLite() +biocLite(installme) +quit(save="no") diff -r d6d45ba6f9d8 -r 96ebf676f6c0 test-data/edgeRtest1out.html --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/edgeRtest1out.html Sun Jul 06 02:36:47 2014 -0400 @@ -0,0 +1,733 @@ + + + + + + + + +
+ +
Galaxy Tool "DifferentialCounts" run at 07/08/2013 15:46:55

+
DESeq2 images and outputs
+(Click on a thumbnail image to download the corresponding original PDF image)
+
+ + + + + + + + + + + + + +
Image called DESeq2_MA_plot.pdfImage called DESeq2_PCA_plot.pdfImage called DESeq2_dispersion_estimates.pdf
Image called DESeq2_qqplot.pdfImage called DESeq2_sample_distance_plot.pdf 
+ +
DESeq2 log output
+ +
+
+# DESeq top 50
+
+                     Contig     baseMean log2FoldChange     lfcSE        pvalue          padj  NReads                                                                                               URL
+
+Mir192               Mir192 271352.97636       6.965264 0.2150593 4.096936e-230 4.576278e-227 2325567               Mir192
+
+Mir122a             Mir122a  10112.31117      10.312083 0.3292695 2.649323e-215 1.479647e-212   90428             Mir122a
+
+Mir149               Mir149    810.35429      -6.911118 0.2341392 1.735537e-191 6.461982e-189    6164               Mir149
+
+Mir23a               Mir23a   1289.18043      -3.104086 0.1191688 1.424246e-149 3.977206e-147   10118               Mir23a
+
+Mir181d             Mir181d    275.22797      -3.581172 0.1778187  3.329371e-90  7.437816e-88    2139             Mir181d
+
+Mir204               Mir204    347.57397      -7.284200 0.3771119  3.959336e-83  7.370965e-81    2601               Mir204
+
+Mir23b               Mir23b   2028.55377      -2.065110 0.1085802  1.182361e-80  1.886711e-78   16387               Mir23b
+
+Mir27a               Mir27a   2788.72629      -3.016676 0.1688167  2.036708e-71  2.843754e-69   21886               Mir27a
+
+Mir195               Mir195    519.86200      -3.152795 0.1784796  7.838123e-70  9.727982e-68    3962               Mir195
+
+Mir194-2           Mir194-2    391.65678       5.222911 0.3099275  1.013490e-63  1.132068e-61    3570           Mir194-2
+
+Mir208b             Mir208b   1649.77924     -11.396172 0.6771238  1.464479e-63  1.487112e-61   14756             Mir208b
+
+Mir10b               Mir10b  27820.40551      -5.071453 0.3044889  2.754493e-62  2.563974e-60  197340               Mir10b
+
+Mir181c             Mir181c   2765.96510      -3.660964 0.2275711  3.141153e-58  2.698975e-56   23605             Mir181c
+
+Mir208a             Mir208a    616.76981     -10.356524 0.6559217  3.688385e-56  2.942804e-54    4638             Mir208a
+
+Mir490               Mir490    220.99790      -8.059660 0.5142876  2.369067e-55  1.764165e-53    1741               Mir490
+
+Mir203               Mir203    772.92882       1.990849 0.1274099  4.877239e-55  3.404923e-53    6739               Mir203
+
+Mir215               Mir215    152.78082      -3.004380 0.1939090  3.822339e-54  2.511502e-52    1182               Mir215
+
+Dnm3os               Dnm3os    179.61643      -3.278392 0.2166491  9.922020e-52  6.157165e-50    1401               Dnm3os
+
+Mir214               Mir214    134.69038      -3.216444 0.2154916  2.230148e-50  1.311093e-48    1048               Mir214
+
+Mir21                 Mir21  26121.31011       2.963903 0.2008617  2.817434e-49  1.573537e-47  229120                 Mir21
+
+Mir1948             Mir1948    263.89527       7.074045 0.4867225  7.374030e-48  3.922282e-46    2404             Mir1948
+
+Mir27b               Mir27b  76478.05753      -1.904653 0.1312889  1.088626e-47  5.527251e-46  625308               Mir27b
+
+Rabggtb             Rabggtb   2257.19195       1.988368 0.1401741  1.134862e-45  5.511484e-44   19535             Rabggtb
+
+Mir499               Mir499    712.45950     -10.577061 0.7528467  7.766408e-45  3.614616e-43    6527               Mir499
+
+Mir101b             Mir101b   6846.19683       3.791681 0.2809666  1.670548e-41  7.464007e-40   59019             Mir101b
+
+Mir132               Mir132    106.46062      -2.797928 0.2083376  4.046163e-41  1.738294e-39     857               Mir132
+
+Mir143hg           Mir143hg 180217.77425      -2.169143 0.1685614  6.764675e-38  2.798571e-36 1407364           Mir143hg
+
+Mir143               Mir143 179219.35960      -2.170303 0.1696199  1.746403e-37  6.966899e-36 1399819               Mir143
+
+Mir155               Mir155     57.66182      -3.788079 0.3056585  2.845488e-35  1.096004e-33     463               Mir155
+
+Mir322               Mir322    899.53469      -3.126011 0.2622595  9.363374e-33  3.486296e-31    7074               Mir322
+
+Mir378               Mir378    483.21548      -2.994300 0.2577321  3.343457e-31  1.204723e-29    4075               Mir378
+
+Mir24-2             Mir24-2    424.48288      -2.712674 0.2361028  1.491830e-30  5.049617e-29    3470             Mir24-2
+
+Mir3074-2         Mir3074-2    424.48288      -2.712674 0.2361028  1.491830e-30  5.049617e-29    3470         Mir3074-2
+
+Mir199b             Mir199b     47.84725      -5.294373 0.4644474  4.215162e-30  1.384805e-28     370             Mir199b
+
+Mir802               Mir802    166.83414       8.816580 0.7782636  9.478527e-30  3.025004e-28    1514               Mir802
+
+Mir125b-2         Mir125b-2    493.08516      -2.919341 0.2631193  1.324797e-28  4.110551e-27    3837         Mir125b-2
+
+Mir301               Mir301    260.53406      -1.676984 0.1526772  4.570133e-28  1.379686e-26    2119               Mir301
+
+Snord104           Snord104   3851.90119       2.386573 0.2173857  4.847914e-28  1.425032e-26   33458           Snord104
+
+Mir150               Mir150    553.20599      -2.836881 0.2595088  8.127991e-28  2.327940e-26    4229               Mir150
+
+Mir148a             Mir148a 118994.46955       2.678852 0.2481801  3.675045e-27  1.026256e-25 1002397             Mir148a
+
+5430416N02Rik 5430416N02Rik     62.15966       3.089960 0.2941123  8.101331e-26  2.207119e-24     564 5430416N02Rik
+
+Mir193               Mir193     45.70861       4.991530 0.4814098  3.446492e-25  9.166027e-24     421               Mir193
+
+Mir3073             Mir3073     98.93199       8.208709 0.7944742  5.036320e-25  1.308272e-23     904             Mir3073
+
+Mir125b-1         Mir125b-1     79.01988      -3.020660 0.2937360  8.355633e-25  2.121191e-23     609         Mir125b-1
+
+2610203C20Rik 2610203C20Rik     79.17666      -3.023491 0.2948614  1.136165e-24  2.820214e-23     610 2610203C20Rik
+
+Mir181a-1         Mir181a-1     59.53826      -3.151487 0.3211628  9.923707e-23  2.409735e-21     506         Mir181a-1
+
+Mir184               Mir184     32.23796      -4.865023 0.4962776  1.092606e-22  2.596683e-21     247               Mir184
+
+Mir199a-2         Mir199a-2     44.84878      -3.422216 0.3545647  4.826269e-22  1.123113e-20     352         Mir199a-2
+
+Snord91a           Snord91a    168.95251       2.700421 0.2835464  1.670595e-21  3.808275e-20    1437           Snord91a
+
+Mir200b             Mir200b     87.13638       5.940702 0.6338554  7.094881e-21  1.584996e-19     888             Mir200b
+
+
+
+ +
DifferentialCounts log output
+ +
+
+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 is masked from ‘package:stats’:
+
+    nobs
+
+The following object is 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 is masked from ‘package:stats’:
+
+    lowess
+
+Loading required package: methods
+
+Loading required package: limma
+
+Loading required package: splines
+
+Loading required package: DESeq2
+
+Loading required package: GenomicRanges
+
+Loading required package: BiocGenerics
+
+Loading required package: parallel
+
+Attaching package: ‘BiocGenerics’
+
+The following object is masked from ‘package:parallel’:
+
+    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB
+
+The following object is masked from ‘package:gdata’:
+
+    combine
+
+The following object is masked from ‘package:stats’:
+
+    xtabs
+
+The following object is masked from ‘package:base’:
+
+    anyDuplicated, as.data.frame, cbind, colnames, duplicated, eval, Filter, Find, get, intersect, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rep.int, rownames, sapply, setdiff, sort, table, tapply, union, unique, unlist
+
+Loading required package: IRanges
+
+Attaching package: ‘IRanges’
+
+The following object is masked from ‘package:gplots’:
+
+    space
+
+The following object is masked from ‘package:caTools’:
+
+    runmean
+
+The following object is masked from ‘package:gdata’:
+
+    trim
+
+Loading required package: Biobase
+
+Welcome to Bioconductor
+
+    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'.
+
+Loading required package: lattice
+
+Loading required package: Rcpp
+
+Loading required package: RcppArmadillo
+
+Attaching package: ‘DESeq2’
+
+The following object is masked from ‘package:limma’:
+
+    plotMA
+
+gene-wise dispersion estimates
+
+mean-dispersion relationship
+
+final dispersion estimates
+
+you had estimated dispersions, replacing these
+
+gene-wise dispersion estimates
+
+mean-dispersion relationship
+
+final dispersion estimates
+
+you had estimated dispersions, replacing these
+
+gene-wise dispersion estimates
+
+mean-dispersion relationship
+
+final dispersion estimates
+
+Warning messages:
+
+1: In bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group ==  :
+
+  Outlier (-Inf) in boxplot 1 is not drawn
+
+2: In bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group ==  :
+
+  Outlier (-Inf) in boxplot 3 is not drawn
+
+3: In bxp(list(stats = c(-0.430723026286372, -0.127900608036896, 0.474159383291067,  :
+
+  some notches went outside hinges ('box'): maybe set notch=FALSE
+
+4: In par(defpar) : calling par(new=TRUE) with no plot
+
+
+
+ +
VOOM images and outputs
+(Click on a thumbnail image to download the corresponding original PDF image)
+
+ + + + + + +
Image called VOOM_mean_variance_plot.pdfImage called VOOM_qqplot.pdf
+ +
VOOM log output
+ +
+
+# VOOM top 50
+
+                         ID      logFC    AveExpr          t      P.Value    adj.P.Val         B  NReads                                                                                               URL
+
+Mir192               Mir192   6.948883 14.6763803  42.722954 2.301199e-16 2.625668e-13 27.266471 2325567               Mir192
+
+Mir208a             Mir208a -11.015018  3.9395538 -23.252407 1.118938e-12 6.383542e-10 17.208662    4638             Mir208a
+
+Mir122a             Mir122a  10.426125  8.1698641  21.722912 2.859682e-12 1.087633e-09 17.760171   90428             Mir122a
+
+Mir149               Mir149  -7.030463  6.3160807 -20.883835 4.915491e-12 1.402144e-09 17.277609    6164               Mir149
+
+Mir208b             Mir208b -12.433228  4.6076218 -19.592458 1.179199e-11 2.690931e-09 15.683666   14756             Mir208b
+
+Mir10b               Mir10b  -5.130915 12.2628672 -18.242023 3.124991e-11 4.963978e-09 16.221503  197340               Mir10b
+
+Mir143hg           Mir143hg  -2.249221 16.2444825 -18.082481 3.521739e-11 4.963978e-09 16.026695 1407364           Mir143hg
+
+Mir143               Mir143  -2.251067 16.2358599 -18.081481 3.524391e-11 4.963978e-09 16.026084 1399819               Mir143
+
+Mir499               Mir499 -11.567529  3.7874598 -17.942086 3.915496e-11 4.963978e-09 14.821741    6527               Mir499
+
+Mir802               Mir802   9.158434  2.9157675  17.316522 6.338616e-11 7.232360e-09 14.381577    1514               Mir802
+
+Mir3073             Mir3073   8.420542  2.5457189  16.702657 1.033066e-10 1.036045e-08 13.985845     904             Mir3073
+
+Mir148a             Mir148a   2.638213 15.4435820  16.548188 1.171186e-10 1.036045e-08 14.814792 1002397             Mir148a
+
+Mir101b             Mir101b   3.765722 10.8508440  16.538566 1.180419e-10 1.036045e-08 14.900027   59019             Mir101b
+
+Mir490               Mir490  -8.474378  3.7506957 -16.259650 1.484816e-10 1.210125e-08 13.424617    1741               Mir490
+
+Mir21                 Mir21   2.938537 13.1642917  15.375404 3.148335e-10 2.394833e-08 13.867698  229120                 Mir21
+
+Mir181c             Mir181c  -3.742560  9.6295577 -15.242361 3.537063e-10 2.522368e-08 13.810405   23605             Mir181c
+
+Mir204               Mir204  -7.684425  4.7751735 -15.033484 4.254268e-10 2.855364e-08 12.822427    2601               Mir204
+
+Mir23a               Mir23a  -3.165768  8.7896592 -14.631179 6.110682e-10 3.873493e-08 13.269174   10118               Mir23a
+
+Mir181d             Mir181d  -3.636211  6.3713218 -14.317073 8.157508e-10 4.898798e-08 12.956333    2139             Mir181d
+
+Mir133b             Mir133b  -6.493549  1.2544862 -13.969968 1.129934e-09 6.446275e-08 11.982684     159             Mir133b
+
+Mir27a               Mir27a  -3.106935  9.9255796 -13.838251 1.281011e-09 6.960160e-08 12.513086   21886               Mir27a
+
+Mir194-2           Mir194-2   5.264136  6.0897615  13.044012 2.792884e-09 1.448491e-07 11.715753    3570           Mir194-2
+
+Mir195               Mir195  -3.216595  7.4509350 -12.869478 3.332788e-09 1.653353e-07 11.587523    3962               Mir195
+
+Mir27b               Mir27b  -1.976376 15.0957731 -11.756036 1.082197e-08 5.144946e-07 10.127719  625308               Mir27b
+
+Mir378               Mir378  -3.097393  7.3832049 -11.684163 1.171371e-08 5.346138e-07 10.329692    4075               Mir378
+
+Snord104           Snord104   2.337374 10.6109024  11.495675 1.444482e-08 6.339052e-07 10.023395   33458           Snord104
+
+Mir1983             Mir1983  -5.895500  0.9931851 -11.445812 1.527548e-08 6.441605e-07  9.749260     101             Mir1983
+
+Mir322               Mir322  -3.296618  8.2153415 -11.415362 1.580762e-08 6.441605e-07 10.008472    7074               Mir322
+
+Mir200a             Mir200a   6.191561  1.7981309  11.322172 1.756229e-08 6.909853e-07  9.662295     264             Mir200a
+
+Mir215               Mir215  -3.045873  5.7544234 -11.148134 2.141822e-08 8.082459e-07  9.753268    1182               Mir215
+
+Dnm3os               Dnm3os  -3.363344  5.8607432 -11.092261 2.283960e-08 8.082459e-07  9.689496    1401               Dnm3os
+
+Mir182               Mir182   4.903995  7.1511683  11.074468 2.331304e-08 8.082459e-07  9.658842    7189               Mir182
+
+Mir181a-2         Mir181a-2  -3.048298  6.9414651 -11.072128 2.337609e-08 8.082459e-07  9.644017    2817         Mir181a-2
+
+Mir1948             Mir1948   7.195525  4.5513493  11.005492 2.524936e-08 8.473388e-07  9.341794    2404             Mir1948
+
+Mir214               Mir214  -3.280874  5.4784451 -10.768257 3.332555e-08 1.086413e-06  9.318504    1048               Mir214
+
+Mir153               Mir153  -5.963803  1.4386315 -10.727082 3.498742e-08 1.093990e-06  9.035569     140               Mir153
+
+Cyp3a25             Cyp3a25   6.318200  1.4888933  10.698226 3.620443e-08 1.093990e-06  9.024973     226             Cyp3a25
+
+Gm5441               Gm5441  -5.982176  1.4484953 -10.692891 3.643436e-08 1.093990e-06  9.000362     142               Gm5441
+
+Mir125b-2         Mir125b-2  -3.077678  7.4316058 -10.446668 4.893073e-08 1.431538e-06  8.884250    3837         Mir125b-2
+
+Mir133a-1         Mir133a-1  -5.144671  0.5903264 -10.358205 5.447229e-08 1.553822e-06  8.575535      60         Mir133a-1
+
+1110038B12Rik 1110038B12Rik   2.226702 10.8487089  10.194609 6.655312e-08 1.852125e-06  8.439308   37066 1110038B12Rik
+
+Mir132               Mir132  -2.847559  5.3211839 -10.110952 7.380297e-08 2.004981e-06  8.531491     857               Mir132
+
+Rabggtb             Rabggtb   1.935779  9.9874171   9.928995 9.262821e-08 2.457879e-06  8.133384   19535             Rabggtb
+
+Mir504               Mir504  -5.256127  0.6221088  -9.892894 9.693595e-08 2.513725e-06  8.068853      69               Mir504
+
+Mir150               Mir150  -2.938531  7.6297870  -9.842102 1.033602e-07 2.620755e-06  8.116464    4229               Mir150
+
+Mir199b             Mir199b  -5.752816  2.8805143  -9.823920 1.057683e-07 2.623514e-06  7.979387     370             Mir199b
+
+Mir23b               Mir23b  -2.124129  9.8141190  -9.806316 1.081569e-07 2.625681e-06  7.979464   16387               Mir23b
+
+Mir24-2             Mir24-2  -2.833979  7.3083691  -9.767192 1.136724e-07 2.646944e-06  8.030550    3470             Mir24-2
+
+Mir3074-2         Mir3074-2  -2.833979  7.3083691  -9.767192 1.136724e-07 2.646944e-06  8.030550    3470         Mir3074-2
+
+Mir155               Mir155  -3.906600  3.9899000  -9.732173 1.188627e-07 2.712448e-06  8.046518     463               Mir155
+
+
+
+ +
edgeR images and outputs
+(Click on a thumbnail image to download the corresponding original PDF image)
+
+ + + + + + + + + + + + + + + + + + +
Image called edgeR_BCV_vs_abundance.pdfImage called edgeR_GoodnessofFit.pdfImage called edgeR_MDSplot.pdf
Image called edgeR_qqplot.pdfImage called edgeR_raw_norm_counts_box.pdfImage called edgeR_smearplot.pdf
Image called edgeR_top_100_heatmap.pdf  
+ +
edgeR log output
+ +
+
+[1] prior.df = 8
+
+[1] "No GLM fit outlier genes found\n"
+
+[1] Common Dispersion = 0.228651460998105 CV =  0.478175136323613 getPriorN =  3.33333333333333
+
+[1] heart liver
+
+[1] "Raw sample read totals 2443751,1644652,1682104,1806045,1440960,1341813,2888924,1428365"
+
+[1] raw contig counts by sample:
+
+ liver_X11706Liv_CAAA liver_X11700Liv_ATTC liver_X11698Liv_ACTG liver_X11699Liv_ATGA heart_X11706He_AGTTC heart_X11699He_GGCTA heart_X11698He_TAGCT heart_X11700He_CTTGT
+
+ 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.:0.3032       1st Qu.:0.0043       1st Qu.:0.3032       1st Qu.:0.0043       1st Qu.:0.0043       1st Qu.:0.0043       1st Qu.:0.0043       1st Qu.:0.0043      
+
+ Median :0.7789       Median :0.6031       Median :0.6998       Median :0.6031       Median :0.4786       Median :0.4786       Median :0.4786       Median :0.4786      
+
+ Mean   :1.0519       Mean   :1.0343       Mean   :0.9855       Mean   :0.9966       Mean   :0.9210       Mean   :0.9428       Mean   :1.0205       Mean   :0.9753      
+
+ 3rd Qu.:1.5410       3rd Qu.:1.6335       3rd Qu.:1.4473       3rd Qu.:1.5534       3rd Qu.:1.5770       3rd Qu.:1.5855       3rd Qu.:1.7161       3rd Qu.:1.6022      
+
+ Max.   :5.8209       Max.   :5.6905       Max.   :5.7999       Max.   :5.7215       Max.   :5.3609       Max.   :5.3589       Max.   :5.6967       Max.   :5.3702      
+
+ NA's   :650          NA's   :969          NA's   :664          NA's   :886          NA's   :902          NA's   :957          NA's   :821          NA's   :950         
+
+[1] normalised contig counts by sample:
+
+ liver_X11706Liv_CAAA liver_X11700Liv_ATTC liver_X11698Liv_ACTG liver_X11699Liv_ATGA heart_X11706He_AGTTC heart_X11699He_GGCTA heart_X11698He_TAGCT heart_X11700He_CTTGT
+
+ Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf        
+
+ 1st Qu.:   0         1st Qu.:-Inf         1st Qu.:   0         1st Qu.:-Inf         1st Qu.:-Inf         1st Qu.:-Inf         1st Qu.:-Inf         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.   :   6         Max.   :   6         Max.   :   6         Max.   :   5         Max.   :   5         Max.   :   5         Max.   :   6         Max.   :   5        
+
+[1] Using ncol rawrs= 8
+
+[1] # edgeR Top tags\n
+
+                       Name      logFC    logCPM        LR        PValue   adj.p.value Dispersion totreads                                                                                               URL
+
+Mir208a             Mir208a -11.840751  8.465017 594.16946 3.104543e-131 3.542284e-128 0.05171220     4638             Mir208a
+
+Mir149               Mir149  -7.008984  8.861767 484.30321 2.473909e-107 1.411365e-104 0.04959937     6164               Mir149
+
+Mir208b             Mir208b -13.291635  9.905945 417.69758  7.737463e-93  2.942815e-90 0.10508096    14756             Mir208b
+
+Mir122a             Mir122a  10.514683 12.478088 415.17429  2.740525e-92  7.817349e-90 0.10803882    90428             Mir122a
+
+Mir204               Mir204  -7.498162  7.634507 341.30678  3.313430e-76  7.561247e-74 0.06907958     2601               Mir204
+
+Mir499               Mir499 -13.577454  8.700078 325.79199  7.930755e-73  1.508165e-70 0.12042284     6527               Mir499
+
+Mir490               Mir490  -8.534394  6.991023 303.17184  6.710366e-68  1.093790e-65 0.07949711     1741               Mir490
+
+Mir192               Mir192   6.953853 17.169364 217.22867  3.638307e-49  5.189135e-47 0.12700995  2325567               Mir192
+
+Mir802               Mir802  11.440805  6.593380 212.88059  3.231644e-48  4.097007e-46 0.12273671     1514               Mir802
+
+Mir1948             Mir1948   7.418142  7.252734 195.66958  1.840248e-44  2.099723e-42 0.12060221     2404             Mir1948
+
+Mir194-2           Mir194-2   5.298950  7.811522 191.85588  1.250960e-43  1.297587e-41 0.08670751     3570           Mir194-2
+
+Mir23a               Mir23a  -3.153807  9.529402 177.53185  1.676248e-40  1.593833e-38 0.04442763    10118               Mir23a
+
+Mir181c             Mir181c  -3.767686 10.639598 169.87390  7.883295e-39  6.919107e-37 0.06368883    23605             Mir181c
+
+Mir3073             Mir3073  10.686337  5.859950 164.86740  9.778593e-38  7.969554e-36 0.14069249      904             Mir3073
+
+Mir181d             Mir181d  -3.643963  7.300371 162.18591  3.767663e-37  2.865936e-35 0.05729574     2139             Mir181d
+
+Mir195               Mir195  -3.203683  8.215089 150.20548  1.563314e-34  1.114838e-32 0.05235020     3962               Mir195
+
+Mir10b               Mir10b  -5.182616 13.946466 147.24793  6.926819e-34  4.649118e-32 0.12268790   197340               Mir10b
+
+Mir101b             Mir101b   3.759962 11.863187 136.31359  1.703812e-31  1.080028e-29 0.07961343    59019             Mir101b
+
+Mir378               Mir378  -3.115599  8.119617 126.76408  2.092233e-29  1.256441e-27 0.05942391     4075               Mir378
+
+Mir27a               Mir27a  -3.064687 10.642480 124.98911  5.117477e-29  2.919520e-27 0.06113852    21886               Mir27a
+
+Mir182               Mir182   5.057509  8.846381 123.17765  1.275060e-28  6.927826e-27 0.13653707     7189               Mir182
+
+Mir322               Mir322  -3.194159  9.012888 107.34926  3.732413e-25  1.935765e-23 0.07536483     7074               Mir322
+
+Mir199b             Mir199b  -5.520119  4.792610 102.10724  5.259607e-24  2.609223e-22 0.13417024      370             Mir199b
+
+Mir181a-2         Mir181a-2  -3.000177  7.637692 101.38361  7.578821e-24  3.603098e-22 0.06896654     2817         Mir181a-2
+
+Mir125b-2         Mir125b-2  -2.987759  8.144514  91.72544  9.957640e-22  4.488356e-20 0.07737381     3837         Mir125b-2
+
+Dnm3os               Dnm3os  -3.331215  6.686950  91.67250  1.022763e-21  4.488356e-20 0.08810497     1401               Dnm3os
+
+Mir184               Mir184  -5.111350  4.234160  84.35542  4.133639e-20  1.686711e-18 0.13502324      247               Mir184
+
+Mir215               Mir215  -3.058208  6.447966  84.35278  4.139167e-20  1.686711e-18 0.08138517     1182               Mir215
+
+Mir133b             Mir133b  -8.383611  3.584760  83.96681  5.031517e-20  1.960318e-18 0.17482280      159             Mir133b
+
+Mir150               Mir150  -2.883446  8.307765  83.91918  5.154210e-20  1.960318e-18 0.08008123     4229               Mir150
+
+Mir3074-2         Mir3074-2  -2.778308  7.935651  83.74839  5.619282e-20  2.040616e-18 0.07424646     3470         Mir3074-2
+
+Mir24-2             Mir24-2  -2.778307  7.935651  83.71222  5.723024e-20  2.040616e-18 0.07427992     3470             Mir24-2
+
+Mir193               Mir193   5.176579  4.801090  83.19222  7.445011e-20  2.574169e-18 0.14794861      421               Mir193
+
+Scarna17           Scarna17   2.182159  9.244479  81.91330  1.421894e-19  4.771710e-18 0.04982909     9224           Scarna17
+
+Mir214               Mir214  -3.271172  6.271755  80.43948  2.997458e-19  9.771712e-18 0.09566584     1048               Mir214
+
+Snord104           Snord104   2.330488 11.053611  79.50529  4.809369e-19  1.524303e-17 0.05915990    33458           Snord104
+
+Mir200a             Mir200a   7.201555  4.139422  77.35503  1.428304e-18  4.365755e-17 0.19287764      264             Mir200a
+
+Mir200b             Mir200b   6.525423  5.752604  77.31985  1.453976e-18  4.365755e-17 0.26237966      888             Mir200b
+
+Mir21                 Mir21   2.923147 13.825255  75.51798  3.620938e-18  1.059357e-16 0.09395834   229120                 Mir21
+
+Mir203               Mir203   1.956427  8.767610  75.17870  4.299815e-18  1.226522e-16 0.04381710     6739               Mir203
+
+Mir155               Mir155  -3.886731  5.068563  73.81316  8.587210e-18  2.389758e-16 0.12522673      463               Mir155
+
+Cyp3a25             Cyp3a25   8.681501  3.972085  72.29680  1.851471e-17  5.029829e-16 0.23125383      226             Cyp3a25
+
+Rabggtb             Rabggtb   1.934093 10.298211  72.02043  2.129809e-17  5.651422e-16 0.04596646    19535             Rabggtb
+
+Mir23b               Mir23b  -2.100584 10.184110  71.44225  2.854935e-17  7.403367e-16 0.05416378    16387               Mir23b
+
+Snord52             Snord52   2.207491 10.217554  71.27974  3.100027e-17  7.860292e-16 0.05941483    18059             Snord52
+
+Gm5441               Gm5441  -6.881248  3.538457  70.05615  5.764004e-17  1.429724e-15 0.20097284      142               Gm5441
+
+Mir153               Mir153  -6.857671  3.517446  69.37600  8.137282e-17  1.975455e-15 0.20158808      140               Mir153
+
+Mir132               Mir132  -2.858294  5.938312  64.52507  9.531204e-16  2.265647e-14 0.09274248      857               Mir132
+
+1110038B12Rik 1110038B12Rik   2.195962 11.253090  62.92015  2.152583e-15  5.012443e-14 0.06712174    37066 1110038B12Rik
+
+Snord91a           Snord91a   2.654072  6.557504  62.40549  2.795431e-15  6.379174e-14 0.08637410     1437           Snord91a
+
+[1] # 416 tags significant at adj p= 0.05
+
+
+
+ +
Other images and outputs
+(Click on a thumbnail image to download the corresponding original PDF image)
+
+ + + + + + + + + +
Image called Filtering_rowsum_bar_charts.pdfImage called Venn_significant_genes_overlap.pdf
Image called sample_counts_histogram.pdf 
+ +
Other log output
+ +
+
+## Toolfactory generated command line = Rscript - None None
+
+Got TCols=1 5 6 7; CCols=2 3 4 8
+
+[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] "@@ using genecards substitution for urls"
+
+[1] # urls
+
+[1] 0610005C13Rik 0610007N19Rik 0610008F07Rik 0610009L18Rik 0610012G03Rik
+
+[6] 0610031O16Rik
+
+[1] # Total low count contigs per sample =  170,67,203,86,145,111,155,120
+
+[1] Using samples: liver_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam
+
+[1] Using design matrix:
+
+  (Intercept) groupliver
+
+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
+
+R version 3.0.1 (2013-05-16)
+
+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] parallel  splines   methods   grid      stats     graphics  grDevices utils     datasets  base     
+
+other attached packages:
+
+ [1] RColorBrewer_1.0-5      DESeq2_1.0.18           RcppArmadillo_0.3.900.7 Rcpp_0.10.4             lattice_0.20-15         Biobase_2.20.1          GenomicRanges_1.12.4    IRanges_1.18.2          BiocGenerics_0.6.0      edgeR_3.2.4             limma_3.16.7            gplots_2.11.3           MASS_7.3-28             KernSmooth_2.23-10      caTools_1.14            gdata_2.13.2            gtools_3.0.0            stringr_0.6.2          
+
+loaded via a namespace (and not attached):
+
+ [1] annotate_1.38.0      AnnotationDbi_1.22.6 bitops_1.0-5         DBI_0.2-7            genefilter_1.42.0    locfit_1.5-9.1       RSQLite_0.11.4       stats4_3.0.1         survival_2.37-4      XML_3.98-1.1         xtable_1.7-1        
+
+
+
+ +
All output files available for downloading
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + +
Output File Name (click to view)Size
DESeq2.log10.0 KB
DESeq2_MA_plot.pdf14.7 KB
DESeq2_PCA_plot.pdf4.9 KB
DESeq2_dispersion_estimates.pdf188.4 KB
DESeq2_qqplot.pdf14.4 KB
DESeq2_sample_distance_plot.pdf9.5 KB
DifferentialCounts.Rscript27.1 KB
DifferentialCounts_error.log3.1 KB
DifferentialCounts_runner.log3.2 KB
Filtering_rowsum_bar_charts.pdf6.3 KB
VOOM.log10.1 KB
VOOM_mean_variance_plot.pdf16.7 KB
VOOM_qqplot.pdf17.6 KB
Venn_significant_genes_overlap.pdf9.7 KB
edgeR.log13.1 KB
edgeR_BCV_vs_abundance.pdf17.4 KB
edgeR_GoodnessofFit.pdf13.1 KB
edgeR_MDSplot.pdf4.9 KB
edgeR_qqplot.pdf15.2 KB
edgeR_raw_norm_counts_box.pdf7.8 KB
edgeR_smearplot.pdf16.6 KB
edgeR_top_100_heatmap.pdf11.3 KB
sample_counts_histogram.pdf11.0 KB

+
+ diff -r d6d45ba6f9d8 -r 96ebf676f6c0 test-data/edgeRtest1out.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/edgeRtest1out.xls Sun Jul 06 02:36:47 2014 -0400 @@ -0,0 +1,1142 @@ +ID logFC AveExpr t P.Value adj.P.Val B NReads URL +Mir192 6.94888256843679 14.6763802609023 42.7229535356942 2.30119906424271e-16 2.62566813230094e-13 27.2664713266936 2325567 Mir192 +Mir208a -11.0150177152075 3.93955375669227 -23.2524066836307 1.11893807599952e-12 6.38354172357727e-10 17.2086622097974 4638 Mir208a +Mir122a 10.4261254701779 8.16986409392255 21.7229119192922 2.85968233611017e-12 1.08763251516723e-09 17.760171141852 90428 Mir122a +Mir149 -7.03046258655617 6.31608073609863 -20.8838348040628 4.91549082404237e-12 1.40214375755809e-09 17.2776088871455 6164 Mir149 +Mir208b -12.4332279840446 4.60762179736006 -19.5924575126382 1.17919871718875e-11 2.69093147262473e-09 15.6836663826186 14756 Mir208b +Mir10b -5.1309149063532 12.2628671946242 -18.2420234752943 3.12499057505143e-11 4.96397841614262e-09 16.2215027882858 197340 Mir10b +Mir143hg -2.24922058313374 16.2444825488726 -18.0824813146443 3.52173903971276e-11 4.96397841614262e-09 16.0266951625541 1407364 Mir143hg +Mir143 -2.25106712131643 16.235859869169 -18.0814805993441 3.524391092512e-11 4.96397841614262e-09 16.0260836456534 1399819 Mir143 +Mir499 -11.5675289490546 3.78745976580796 -17.9420857279689 3.91549568319751e-11 4.96397841614262e-09 14.8217405828874 6527 Mir499 +Mir802 9.15843445824816 2.91576747878654 17.3165224121399 6.33861560587965e-11 7.23236040630868e-09 14.381577240531 1514 Mir802 +Mir3073 8.42054159318439 2.54571889776166 16.7026571721381 1.03306635740721e-10 1.03604453339228e-08 13.9858447292853 904 Mir3073 +Mir148a 2.63821345578617 15.4435819751152 16.5481882215215 1.17118649515038e-10 1.03604453339228e-08 14.8147917664862 1002397 Mir148a +Mir101b 3.76572195114225 10.8508440499081 16.5385659719288 1.1804188373444e-10 1.03604453339228e-08 14.9000274171241 59019 Mir101b +Mir490 -8.47437764634465 3.75069567634692 -16.2596504905533 1.48481644820999e-10 1.21012540529114e-08 13.4246171016517 1741 Mir490 +Mir21 2.93853744034991 13.1642916950886 15.3754036511693 3.14833456057776e-10 2.39483315574615e-08 13.8676979022068 229120 Mir21 +Mir181c -3.74256009957124 9.62955774646065 -15.2423608550805 3.53706264458683e-10 2.52236779842098e-08 13.8104046176901 23605 Mir181c +Mir204 -7.68442507149438 4.77517348536933 -15.0334839919296 4.2542677795722e-10 2.85536443323052e-08 12.8224274879526 2601 Mir204 +Mir23a -3.16576837850821 8.78965917558611 -14.6311785109623 6.11068192724496e-10 3.87349337721472e-08 13.2691736804205 10118 Mir23a +Mir181d -3.63621106402109 6.37132182424908 -14.3170733565449 8.15750840848868e-10 4.89879847057136e-08 12.9563328312209 2139 Mir181d +Mir133b -6.49354876170712 1.25448620431148 -13.969968060601 1.12993427319653e-09 6.44627502858619e-08 11.9826837063041 159 Mir133b +Mir27a -3.10693537246128 9.92557960348829 -13.8382510839158 1.28101104196848e-09 6.96015999469543e-08 12.5130856443239 21886 Mir27a +Mir194-2 5.26413595786074 6.08976151689046 13.0440120203829 2.79288399641768e-09 1.44849119996026e-07 11.7157527118771 3570 Mir194-2 +Mir195 -3.21659545049586 7.4509349905835 -12.869478368273 3.33278798407795e-09 1.65335264775345e-07 11.5875227405737 3962 Mir195 +Mir27b -1.97637614533106 15.0957731023791 -11.75603589654 1.08219717999805e-08 5.14494575990741e-07 10.1277185662145 625308 Mir27b +Mir378 -3.09739319841142 7.38320489393809 -11.6841625470748 1.17137125863851e-08 5.34613842442616e-07 10.3296922348831 4075 Mir378 +Snord104 2.33737428989677 10.6109023861403 11.4956750870273 1.44448164322638e-08 6.33905213431269e-07 10.0233949189609 33458 Snord104 +Mir1983 -5.89550024150745 0.993185099223749 -11.4458119994178 1.52754786535047e-08 6.44160462853232e-07 9.74926029381244 101 Mir1983 +Mir322 -3.29661750880005 8.21534154356388 -11.4153616003567 1.58076187203247e-08 6.44160462853232e-07 10.0084716002011 7074 Mir322 +Mir200a 6.19156065085543 1.79813092499896 11.3221723123067 1.75622912046568e-08 6.9098531946598e-07 9.66229453831667 264 Mir200a +Mir215 -3.04587333807051 5.75442336214621 -11.1481336257529 2.14182153707674e-08 8.08245865886245e-07 9.75326755116029 1182 Mir215 +Dnm3os -3.36334357719079 5.86074322417943 -11.0922610835813 2.28395969947309e-08 8.08245865886245e-07 9.68949616901383 1401 Dnm3os +Mir182 4.90399541739044 7.1511683493624 11.0744681203078 2.33130367310143e-08 8.08245865886245e-07 9.65884218207857 7189 Mir182 +Mir181a-2 -3.04829832099813 6.94146510070354 -11.0721276255975 2.33760855164295e-08 8.08245865886245e-07 9.64401697815694 2817 Mir181a-2 +Mir1948 7.19552540631629 4.5513492833967 11.0054920626234 2.52493600829575e-08 8.47338819254543e-07 9.34179361673467 2404 Mir1948 +Mir214 -3.28087400431203 5.47844506177362 -10.7682572190636 3.3325545851092e-08 1.0864127947456e-06 9.3185039394887 1048 Mir214 +Mir153 -5.9638030672045 1.43863148956561 -10.7270821099311 3.49874201497232e-08 1.09398957489501e-06 9.03556928822473 140 Mir153 +Cyp3a25 6.3181999278218 1.48889330889732 10.698226339624 3.62044305804168e-08 1.09398957489501e-06 9.02497280953977 226 Cyp3a25 +Gm5441 -5.98217559924296 1.44849534030207 -10.6928905219357 3.64343591989574e-08 1.09398957489501e-06 9.00036161342169 142 Gm5441 +Mir125b-2 -3.07767777869034 7.43160584496732 -10.4466676018996 4.89307304174606e-08 1.43153752323904e-06 8.88425047860251 3837 Mir125b-2 +Mir133a-1 -5.1446707114636 0.590326422018506 -10.3582048674166 5.44722862183524e-08 1.5538219643785e-06 8.57553468686339 60 Mir133a-1 +1110038B12Rik 2.2267024913641 10.8487089345135 10.1946092089644 6.6553123680318e-08 1.85212473461568e-06 8.43930767784171 37066 1110038B12Rik +Mir132 -2.84755882904953 5.32118389129247 -10.1109518209472 7.38029688527407e-08 2.00498065383279e-06 8.53149092529729 857 Mir132 +Rabggtb 1.93577875106629 9.98741711657765 9.92899544482161 9.26282059254969e-08 2.45787867351144e-06 8.13338404178509 19535 Rabggtb +Mir504 -5.25612736011945 0.622108812393712 -9.89289391617729 9.69359452558378e-08 2.51372530765707e-06 8.06885345567596 69 Mir504 +Mir150 -2.93853062743745 7.62978695478889 -9.84210172881295 1.0336018288322e-07 2.62075485932786e-06 8.11646444295523 4229 Mir150 +Mir199b -5.75281586680568 2.88051428778536 -9.82392040215114 1.05768314120536e-07 2.62351405242461e-06 7.97938664656761 370 Mir199b +Mir23b -2.12412853458823 9.81411903779331 -9.80631552439508 1.08156895246613e-07 2.62568122290182e-06 7.97946421769546 16387 Mir23b +Mir24-2 -2.83397931671342 7.30836912335631 -9.76719188905173 1.13672435494913e-07 2.64694385509584e-06 8.03055035937719 3470 Mir24-2 +Mir3074-2 -2.83397931671342 7.30836912335631 -9.76719188905173 1.13672435494913e-07 2.64694385509584e-06 8.03055035937719 3470 Mir3074-2 +Mir155 -3.90660012470502 3.98990000150695 -9.73217278591154 1.18862734970705e-07 2.71244761203149e-06 8.04651834419983 463 Mir155 +Snord52 2.24297808473649 9.80695706696893 9.65508926768283 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-6.25776602096988 61 Chpt1 +Mir101a 0.0244432527205966 10.3082648606422 0.106757481268119 0.916479919716901 0.948054023931989 -7.4177003681481 19692 Mir101a +E130102H24Rik 0.0240283211557467 10.3090843068977 0.10496799166705 0.917874358796149 0.94863645234276 -7.4179565369203 19702 E130102H24Rik +Mir425 0.033427373456698 6.66465089052376 0.101272505699857 0.920754905156141 0.950345151621533 -7.15835044873542 1583 Mir425 +Mir362 -0.0544522631314578 3.35231486548691 -0.100710009948844 0.921193459854001 0.950345151621533 -6.73276860148804 169 Mir362 +Mir17 0.0294271489414047 5.12380283689553 0.0952058023213346 0.925486243109504 0.953911294840058 -7.01165582330098 534 Mir17 +E230016M11Rik -0.0696354231248524 -0.941052160679803 -0.0918911504840257 0.928072541539687 0.955713691242584 -6.26870805660384 5 E230016M11Rik +Chkb 0.0776691859878007 0.62903919856468 0.0899505655256887 0.929587100348321 0.956253428651282 -6.22872128912026 28 Chkb +Arhgap1 0.0966234721391633 -0.198705450261483 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-0.0415019911233814 -0.880373807283523 -0.0480992443304892 0.962309326654761 0.975480839012736 -6.26535636348749 6 H19 +Ppifos -0.045813233148843 -0.325698143245111 -0.047655434486338 0.962656813959983 0.975480839012736 -6.21600758126971 15 Ppifos +Mirlet7a-2 -0.0146726577509692 3.37472927372453 -0.0386745178686727 0.969690141624003 0.981735981892624 -6.74139737131634 158 Mirlet7a-2 +9530082P21Rik -0.027891459987058 -1.27661443246381 -0.0346374807689275 0.972852600944678 0.983192929741255 -6.29990631175915 4 9530082P21Rik +Mir1196 -0.027891459987058 -1.27661443246381 -0.0346374807689275 0.972852600944678 0.983192929741255 -6.29990631175915 4 Mir1196 +Kcnk2 -0.0253403588431765 -0.835052547360434 -0.031369255395874 0.975413152136616 0.984908324414052 -6.26109829686273 7 Kcnk2 +A930011G23Rik -0.0241809579465951 -0.00965077170076215 -0.0291709235913898 0.977135635463562 0.985775207837245 -6.2074206456237 12 A930011G23Rik +Mir3103 0.0180714470554271 -1.13917247326995 0.0259355368025229 0.979670906552206 0.987459809519494 -6.2920042186438 4 Mir3103 +Snora75 -0.0172327656019052 -0.590132795422603 -0.021476287065286 0.983165572405378 0.989793060148976 -6.23767380603265 10 Snora75 +4930414L22Rik -0.0152743869054217 -0.484133182103234 -0.0207679670021376 0.983720710086713 0.989793060148976 -6.22874423282067 8 4930414L22Rik +Mipep 0.0164012673799433 -0.280376883322022 0.0179445082376201 0.985933647271923 0.991145631310365 -6.21618933242714 14 Mipep +Ippk -0.0101933209515463 0.0857377294202925 -0.0128542290662295 0.989923536727642 0.99288598158411 -6.20831119150974 15 Ippk +Mir598 -0.00787301950188535 1.52736379718861 -0.0127956250729132 0.989969473858512 0.99288598158411 -6.3747989571524 44 Mir598 +1500017E21Rik 0.0125149432592357 0.202348454937975 0.0124053255048405 0.990275413709656 0.99288598158411 -6.21004071500431 20 1500017E21Rik +Map1lc3a 0.00968300434227123 0.00717248334519302 0.0097742078067436 0.992337878036726 0.994080350166729 -6.20806203375241 21 Map1lc3a +Trappc13 -0.00465914834988075 -0.880373807283523 -0.00622703745231414 0.995118499154886 0.995991410119056 -6.26653097741332 6 Trappc13 +Mir1966 -0.00246570556544304 -0.590132795422603 -0.00337044327478241 0.997357828291427 0.997357828291427 -6.23790561933129 8 Mir1966 diff -r d6d45ba6f9d8 -r 96ebf676f6c0 test-data/gentestdata.sh --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/gentestdata.sh Sun Jul 06 02:36:47 2014 -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 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+Zfp57 0 0 0 0 0 0 0 0 +Zfp572 0 0 0 0 0 0 0 0 +Zfp672 0 0 0 0 0 2 0 0 +Zfp783 0 0 0 0 0 0 0 0 +Zfp809 7 1 0 0 0 4 2 0 +Zfp821 0 0 0 2 2 0 0 0 +Zfp862 4 0 0 0 0 10 2 0 +Zim3 0 0 0 0 0 0 0 0 +Zmynd8 3 5 4 4 3 8 2 1 +Znf41-ps 0 0 0 0 0 0 0 0 +Zp4-ps 0 0 0 0 0 0 0 0 +Zscan4a 0 0 0 0 0 0 0 0 +Zxda 0 0 0 0 0 0 0 0 diff -r d6d45ba6f9d8 -r 96ebf676f6c0 tool_dependencies.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_dependencies.xml Sun Jul 06 02:36:47 2014 -0400 @@ -0,0 +1,40 @@ + + + + + + + + + + + + + + + + + + + + $INSTALL_DIR + echo "bioclite=\"http://bioconductor.org/biocLite.R\"" > $INSTALL_DIR/runme.R + echo "source(bioclite)" >> $INSTALL_DIR/runme.R + echo "installme=c(\"edgeR\",\"limma\",\"DESeq\",\"DESeq2\")" >> $INSTALL_DIR/runme.R + echo "biocLite()" >> $INSTALL_DIR/runme.R + echo "biocLite(installme)" >> $INSTALL_DIR/runme.R + echo "install.packages(c(\"stringr\",\"gplots\"),dependencies=T,repos=\"http://cran.us.r-project.org\")" >> $INSTALL_DIR/runme.R + echo "quit(save=\"no\")" >> $INSTALL_DIR/runme.R + echo "PATH=$PATH 1>&2" + echo "R_PATH=$R_PATH 1>&2" + export PATH=$PATH && $R_PATH/R CMD BATCH $INSTALL_DIR/runme.R + + + Installs some basic bioc packages for the edgeR wrapper and dependencies graphicsmagick (replaces imagemagick) and ghostscript for compressing R's bloated pdfs + It's clunky but this is the most convenient way I could get anything installed into the package_r3 + Note we use cran at fred hutch since no fastest mirror thingy + If atlas will not compile - eg: it thinks autoscaling is on or something, + you need (eg for ubuntu) libopenblas-base installed on all nodes after commenting or removing all atlas references + + +