Mercurial > repos > fubar > differential_count_models
changeset 146:474c08e747b6 draft
Uploaded
author | iuc |
---|---|
date | Tue, 28 Apr 2015 22:56:54 -0400 |
parents | a240760dff72 |
children | 34eedc5fe099 |
files | rgToolFactory.py rgedgeRpaired_nocamera.xml test-data/edgeRtest1out.html test-data/edgeRtest1out.xls test-data/gentestdata.sh test-data/test_bams2mx.xls tool_dependencies.xml |
diffstat | 7 files changed, 6787 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgToolFactory.py Tue Apr 28 22:56:54 2015 -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 + + <tool id="reverse" name="reverse" version="0.01"> + <description>a tabular file</description> + <command interpreter="python"> + reverse.py --script_path "$runMe" --interpreter "python" + --tool_name "reverse" --input_tab "$input1" --output_tab "$tab_file" + </command> + <inputs> + <param name="input1" type="data" format="tabular" label="Select a suitable input file from your history"/><param name="job_name" type="text" label="Supply a name for the outputs to remind you what they contain" value="reverse"/> + + </inputs> + <outputs> + <data format="tabular" name="tab_file" label="${job_name}"/> + + </outputs> + <help> + +**What it Does** + +Reverse the columns in a tabular file + + </help> + <configfiles> + <configfile name="runMe"> + +# reverse order of columns in a tabular file +import sys +inp = sys.argv[1] +outp = sys.argv[2] +i = open(inp,'r') +o = open(outp,'w') +for row in i: + rs = row.rstrip().split('\t') + rs.reverse() + o.write('\t'.join(rs)) + o.write('\n') +i.close() +o.close() + + + </configfile> + </configfiles> + </tool> + + """ + newXML="""<tool id="%(toolid)s" name="%(toolname)s" version="%(tool_version)s"> + %(tooldesc)s + %(command)s + <inputs> + %(inputs)s + </inputs> + <outputs> + %(outputs)s + </outputs> + <configfiles> + <configfile name="runMe"> + %(script)s + </configfile> + </configfiles> + %(tooltests)s + <help> + %(help)s + </help> + </tool>""" # needs a dict with toolname, toolid, interpreter, scriptname, command, inputs as a multi line string ready to write, outputs ditto, help ditto + + newCommand="""<command interpreter="python"> + %(toolname)s.py --script_path "$runMe" --interpreter "%(interpreter)s" + --tool_name "%(toolname)s" %(command_inputs)s %(command_outputs)s + </command>""" # may NOT be an input or htmlout + tooltestsTabOnly = """<tests><test> + <param name="input1" value="%(test1Input)s" ftype="tabular"/> + <param name="job_name" value="test1"/> + <param name="runMe" value="$runMe"/> + <output name="tab_file" file="%(test1Output)s" ftype="tabular"/> + </test></tests>""" + tooltestsHTMLOnly = """<tests><test> + <param name="input1" value="%(test1Input)s" ftype="tabular"/> + <param name="job_name" value="test1"/> + <param name="runMe" value="$runMe"/> + <output name="html_file" file="%(test1HTML)s" ftype="html" lines_diff="5"/> + </test></tests>""" + tooltestsBoth = """<tests><test> + <param name="input1" value="%(test1Input)s" ftype="tabular"/> + <param name="job_name" value="test1"/> + <param name="runMe" value="$runMe"/> + <output name="tab_file" file="%(test1Output)s" ftype="tabular" /> + <output name="html_file" file="%(test1HTML)s" ftype="html" lines_diff="10"/> + </test></tests>""" + xdict = {} + xdict['tool_version'] = self.opts.tool_version + xdict['test1Input'] = self.test1Input + xdict['test1HTML'] = self.test1HTML + xdict['test1Output'] = self.test1Output + if self.opts.make_HTML and self.opts.output_tab <> 'None': + xdict['tooltests'] = tooltestsBoth % xdict + elif self.opts.make_HTML: + xdict['tooltests'] = tooltestsHTMLOnly % xdict + else: + xdict['tooltests'] = tooltestsTabOnly % xdict + xdict['script'] = self.escapedScript + # configfile is least painful way to embed script to avoid external dependencies + # but requires escaping of <, > and $ to avoid Mako parsing + if self.opts.help_text: + xdict['help'] = open(self.opts.help_text,'r').read() + else: + xdict['help'] = 'Please ask the tool author for help as none was supplied at tool generation' + coda = ['**Script**','Pressing execute will run the following code over your input file and generate some outputs in your history::'] + 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'] = '<description>%s</description>' % self.opts.tool_desc + else: + xdict['tooldesc'] = '' + xdict['command_outputs'] = '' + xdict['outputs'] = '' + if self.opts.input_tab <> 'None': + xdict['command_inputs'] = '--input_tab "$input1" ' # the space may matter a lot if we append something + xdict['inputs'] = '<param name="input1" type="data" format="%s" label="Select a suitable input file from your history"/> \n' % self.inputFormats + else: + xdict['command_inputs'] = '' # assume no input - eg a random data generator + xdict['inputs'] = '' + xdict['inputs'] += '<param name="job_name" type="text" label="Supply a name for the outputs to remind you what they contain" value="%s"/> \n' % self.toolname + xdict['toolname'] = self.toolname + xdict['toolid'] = self.toolid + xdict['interpreter'] = self.opts.interpreter + xdict['scriptname'] = self.sfile + if self.opts.make_HTML: + xdict['command_outputs'] += ' --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" ' + xdict['outputs'] += ' <data format="html" name="html_file" label="${job_name}.html"/>\n' + if self.opts.output_tab <> 'None': + xdict['command_outputs'] += ' --output_tab "$tab_file"' + xdict['outputs'] += ' <data format="%s" name="tab_file" label="${job_name}"/>\n' % self.outFormats + xdict['command'] = newCommand % xdict + xmls = newXML % xdict + xf = open(self.xmlfile,'w') + xf.write(xmls) + xf.write('\n') + xf.close() + # ready for the tarball + + + def makeTooltar(self): + """ + a tool is a gz tarball with eg + /toolname/tool.xml /toolname/tool.py /toolname/test-data/test1_in.foo ... + """ + retval = self.run() + if retval: + print >> sys.stderr,'## Run failed. Cannot build yet. Please fix and retry' + sys.exit(1) + self.makeXML() + tdir = self.toolname + os.mkdir(tdir) + if self.opts.input_tab <> 'None': # no reproducible test otherwise? TODO: maybe.. + testdir = os.path.join(tdir,'test-data') + os.mkdir(testdir) # make tests directory + shutil.copyfile(self.opts.input_tab,os.path.join(testdir,self.test1Input)) + if self.opts.output_tab <> 'None': + shutil.copyfile(self.opts.output_tab,os.path.join(testdir,self.test1Output)) + if self.opts.make_HTML: + shutil.copyfile(self.opts.output_html,os.path.join(testdir,self.test1HTML)) + if self.opts.output_dir: + shutil.copyfile(self.tlog,os.path.join(testdir,'test1_out.log')) + op = '%s.py' % self.toolname # new name + outpiname = os.path.join(tdir,op) # path for the tool tarball + pyin = os.path.basename(self.pyfile) # our name - we rewrite ourselves (TM) + notes = ['# %s - a self annotated version of %s generated by running %s\n' % (op,pyin,pyin),] + notes.append('# to make a new Galaxy tool called %s\n' % self.toolname) + notes.append('# User %s at %s\n' % (self.opts.user_email,timenow())) + pi = open(self.pyfile,'r').readlines() # our code becomes new tool wrapper (!) - first Galaxy worm + notes += pi + outpi = open(outpiname,'w') + outpi.write(''.join(notes)) + outpi.write('\n') + outpi.close() + stname = os.path.join(tdir,self.sfile) + if not os.path.exists(stname): + shutil.copyfile(self.sfile, stname) + xtname = os.path.join(tdir,self.xmlfile) + if not os.path.exists(xtname): + shutil.copyfile(self.xmlfile,xtname) + tarpath = "%s.gz" % self.toolname + tar = tarfile.open(tarpath, "w:gz") + tar.add(tdir,arcname=self.toolname) + tar.close() + shutil.copyfile(tarpath,self.opts.new_tool) + shutil.rmtree(tdir) + ## TODO: replace with optional direct upload to local toolshed? + return retval + + + def compressPDF(self,inpdf=None,thumbformat='png'): + """need absolute path to pdf + 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 = """<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> + <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> + <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> + <meta name="generator" content="Galaxy %s tool output - see http://getgalaxy.org/" /> + <title></title> + <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> + </head> + <body> + <div class="toolFormBody"> + """ + galhtmlattr = """<hr/><div class="infomessage">This tool (%s) was generated by the <a href="https://bitbucket.org/fubar/galaxytoolfactory/overview">Galaxy Tool Factory</a></div><br/>""" + galhtmlpostfix = """</div></body></html>\n""" + + flist = os.listdir(self.opts.output_dir) + flist = [x for x in flist if x <> 'Rplots.pdf'] + flist.sort() + html = [] + html.append(galhtmlprefix % progname) + html.append('<div class="infomessage">Galaxy Tool "%s" run at %s</div><br/>' % (self.toolname,timenow())) + fhtml = [] + if len(flist) > 0: + 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('<tr class="odd_row"><td><a href="%s">%s</a></td><td>%s</td></tr>' % (fname,fname,sfsize)) + else: + fhtml.append('<tr><td><a href="%s">%s</a></td><td>%s</td></tr>' % (fname,fname,sfsize)) + 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('<div class="toolFormTitle">%s images and outputs</div>' % sectionname) + html.append('(Click on a thumbnail image to download the corresponding original PDF image)<br/>') + ntogo = nacross # counter for table row padding with empty cells + html.append('<div><table class="simple" cellpadding="2" cellspacing="2">\n<tr>') + for i,paths in enumerate(ourpdfs): + fname,thumb = paths + s= """<td><a href="%s"><img src="%s" title="Click to download a PDF of %s" hspace="5" width="%d" + alt="Image called %s"/></a></td>\n""" % (fname,thumb,fname,width,fname) + if ((i+1) % nacross == 0): + s += '</tr>\n' + ntogo = 0 + if i < (npdf - 1): # more to come + s += '<tr>' + ntogo = nacross + else: + ntogo -= 1 + html.append(s) + if html[-1].strip().endswith('</tr>'): + html.append('</table></div>\n') + else: + if ntogo > 0: # pad + html.append('<td> </td>'*ntogo) + html.append('</tr></table></div>\n') + logt = open(logfname,'r').readlines() + logtext = [x for x in logt if x.strip() > ''] + html.append('<div class="toolFormTitle">%s log output</div>' % sectionname) + if len(logtext) > 1: + html.append('\n<pre>\n') + html += logtext + html.append('\n</pre>\n') + else: + html.append('%s is empty<br/>' % logfname) + if len(fhtml) > 0: + fhtml.insert(0,'<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr>\n') + fhtml.append('</table></div><br/>') + html.append('<div class="toolFormTitle">All output files available for downloading</div>\n') + html += fhtml # add all non-pdf files to the end of the display + else: + html.append('<div class="warningmessagelarge">### Error - %s returned no files - please confirm that parameters are sane</div>' % self.opts.interpreter) + 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').read() + 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: + <command interpreter="python">rgToolFactory.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript" + </command> + """ + op = optparse.OptionParser() + a = op.add_option + a('--script_path',default=None) + a('--tool_name',default=None) + a('--interpreter',default=None) + a('--output_dir',default=None) + a('--output_html',default=None) + a('--input_tab',default="None") + a('--output_tab',default="None") + a('--user_email',default='Unknown') + a('--bad_user',default=None) + a('--make_Tool',default=None) + a('--make_HTML',default=None) + a('--help_text',default=None) + a('--tool_desc',default=None) + a('--new_tool',default=None) + a('--tool_version',default=None) + opts, args = op.parse_args() + assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user) + assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq' + assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript' + assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R' + if opts.output_dir: + try: + os.makedirs(opts.output_dir) + except: + pass + r = ScriptRunner(opts) + if opts.make_Tool: + retcode = r.makeTooltar() + else: + retcode = r.run() + os.unlink(r.sfile) + if retcode: + sys.exit(retcode) # indicate failure to job runner + + +if __name__ == "__main__": + main() + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeRpaired_nocamera.xml Tue Apr 28 22:56:54 2015 -0400 @@ -0,0 +1,1049 @@ +<?xml version="1.0"?> +<tool id="rgdifferentialcount" name="Differential_Count" version="0.28"> + <description>models using BioConductor packages</description> + <requirements> + <requirement type="package" version="3.1.2">R</requirement> + <requirement type="package" version="1.3.18">graphicsmagick</requirement> + <requirement type="package" version="9.10">ghostscript</requirement> + <requirement type="package" version="2.14">biocbasics</requirement> + </requirements> + <command interpreter="python"> + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "Differential_Counts" + --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" + </command> + <inputs> + <param name="input1" type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample" help="Use the HTSeq based count matrix preparation tool to create these matrices from BAM/SAM files and a GTF file of genomic features"/> + <param name="title" type="text" value="Differential Counts" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain"> + <sanitizer invalid_char=""> + <valid initial="string.letters,string.digits"> + <add value="_"/> + </valid> + </sanitizer> + </param> + <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/> + <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True" multiple="true" use_header_names="true" size="120" display="checkboxes" force_select="True"> + <validator type="no_options" message="Please select at least one column."/> + </param> + <param name="control_name" type="text" value="Control" size="50" label="Control Name"/> + <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True" multiple="true" use_header_names="true" size="120" display="checkboxes" force_select="True"> + </param> + <param name="subjectids" type="text" optional="true" size="120" value="" label="IF SUBJECTS NOT ALL INDEPENDENT! Enter comma separated strings to indicate sample labels for (eg) pairing - must be one for every column in input" help="Leave blank if no pairing, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter 'A99,C21,A99,C21'"> + <sanitizer> + <valid initial="string.letters,string.digits"> + <add value=","/> + </valid> + </sanitizer> + </param> + <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs" help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/> + <param name="useNDF" type="boolean" truevalue="T" falsevalue="F" checked="false" size="1" label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples" help="May be a good or a bad idea depending on the biology and the question. This was the old default. Quantile based is available as an alternative"/> + <conditional name="edgeR"> + <param name="doedgeR" type="select" label="Run this model using edgeR" help="edgeR uses a negative binomial model and seems to be powerful, even with few replicates"> + <option value="F">Do not run edgeR</option> + <option value="T" selected="true">Run edgeR</option> + </param> + <when value="T"> + <param name="edgeR_priordf" type="integer" value="10" size="3" label="prior.df for tagwise dispersion - larger value = more squeezing of tag dispersions to common dispersion. Replaces prior.n and prior.df = prior.n * residual.df" help="10 = edgeR default. Use a larger value to 'smooth' small samples. See edgeR docs and note below"/> + <param name="edgeR_robust_method" type="select" value="20" size="3" label="Use robust dispersion method" help="Use ordinary, anscombe or deviance robust deviance estimates"> + <option value="ordinary" selected="true">Use ordinary deviance estimates</option> + <option value="deviance">Use robust deviance estimates</option> + <option value="anscombe">use Anscombe robust deviance estimates</option> + </param> + </when> + <when value="F"/> + </conditional> + <conditional name="DESeq2"> + <param name="doDESeq2" type="select" label="Run the same model with DESeq2 and compare findings" help="DESeq2 is an update to the DESeq package. It uses different assumptions and methods to edgeR"> + <option value="F" selected="true">Do not run DESeq2</option> + <option value="T">Run DESeq2</option> + </param> + <when value="T"> + <param name="DESeq_fitType" type="select"> + <option value="parametric" selected="true">Parametric (default) fit for dispersions</option> + <option value="local">Local fit - this will automagically be used if parametric fit fails</option> + <option value="mean">Mean dispersion fit- use this if you really understand what you're doing - read the fine manual linked below in the documentation</option> + </param> + </when> + <when value="F"> </when> + </conditional> + <param name="doVoom" type="select" label="Run the same model with Voom/limma and compare findings" help="Voom uses counts per million and a precise transformation of variance so count data can be analysed using limma"> + <option value="F" selected="true">Do not run VOOM</option> + <option value="T">Run VOOM</option> + </param> + <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control" help="Conventional default value of 0.05 recommended"/> + <param name="fdrtype" type="select" label="FDR (Type II error) control method" help="Use fdr or bh typically to control for the number of tests in a reliable way"> + <option value="fdr" selected="true">fdr</option> + <option value="BH">Benjamini Hochberg</option> + <option value="BY">Benjamini Yukateli</option> + <option value="bonferroni">Bonferroni</option> + <option value="hochberg">Hochberg</option> + <option value="holm">Holm</option> + <option value="hommel">Hommel</option> + <option value="none">no control for multiple tests</option> + </param> + </inputs> + <outputs> + <data format="tabular" name="out_edgeR" label="${title}_topTable_edgeR.xls"> + <filter>edgeR['doedgeR'] == "T"</filter> + </data> + <data format="tabular" name="out_DESeq2" label="${title}_topTable_DESeq2.xls"> + <filter>DESeq2['doDESeq2'] == "T"</filter> + </data> + <data format="tabular" name="out_VOOM" label="${title}_topTable_VOOM.xls"> + <filter>doVoom == "T"</filter> + </data> + <data format="html" name="html_file" label="${title}.html"/> + </outputs> + <stdio> + <exit_code range="4" level="fatal" description="Number of subject ids must match total number of samples in the input matrix"/> + </stdio> + <tests> + <test> + <param name="input1" value="test_bams2mx.xls" ftype="tabular"/> + <param name="treatment_name" value="liver"/> + <param name="title" value="edgeRtest"/> + <param name="useNDF" value=""/> + <param name="doedgeR" value="T"/> + <param name="doVoom" value="T"/> + <param name="doDESeq2" value="T"/> + <param name="fdrtype" value="fdr"/> + <param name="edgeR_priordf" value="8"/> + <param name="edgeR_robust" value="ordinary"/> + <param name="fdrthresh" value="0.05"/> + <param name="control_name" value="heart"/> + <param name="subjectids" value=""/> + <param name="Control_cols" value="3,4,5,9"/> + <param name="Treat_cols" value="2,6,7,8"/> + <output name="out_edgeR" file="edgeRtest1out.xls" compare="diff" lines_diff="20"/> + <output name="html_file" file="edgeRtest1out.html" compare="diff" lines_diff="20"/> + </test> + </tests> + <configfiles> + <configfile name="runme"><![CDATA[ +# +# edgeR.Rscript +# updated feb 2014 adding outlier-robust deviance estimate options by ross for R 3.0.2/bioc 2.13 +# updated npv 2011 for R 2.14.0 and edgeR 2.4.0 by ross +# Performs DGE on a count table containing n replicates of two conditions +# +# Parameters +# +# 1 - Output Dir + +# Original edgeR code by: S.Lunke and A.Kaspi +reallybig = log10(.Machine\$double.xmax) +reallysmall = log10(.Machine\$double.xmin) +library("gplots") +library("edgeR") +library('stringr') +hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here') +{ +# Perform clustering for significant pvalues after controlling FWER + samples = colnames(cmat) + gu = unique(group) + gn = rownames(cmat) + if (length(gu) == 2) { + col.map = function(g) {if (g==gu[1]) "#FF0000" else "#0000FF"} + pcols = unlist(lapply(group,col.map)) + } else { + colours = rainbow(length(gu),start=0,end=4/6) + pcols = colours[match(group,gu)] } + dm = cmat[(! is.na(gn)),] + # remove unlabelled hm rows + nprobes = nrow(dm) + # sub = paste('Showing',nprobes,'contigs ranked for evidence of differential abundance') + if (nprobes > nsamp) { + dm =dm[1:nsamp,] + #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total') + } + newcolnames = substr(colnames(dm),1,20) + colnames(dm) = newcolnames + pdf(outpdfname) + heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none', + Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5) + dev.off() +} + +hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here") +{ + # for 2 groups only was + #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"} + #pcols = unlist(lapply(group,col.map)) + gu = unique(group) + colours = rainbow(length(gu),start=0.3,end=0.6) + pcols = colours[match(group,gu)] + nrows = nrow(cmat) + mtitle = paste(myTitle,'Heatmap: n contigs =',nrows) + if (nrows > nsamp) { + cmat = cmat[c(1:nsamp),] + mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='') + } + newcolnames = substr(colnames(cmat),1,20) + colnames(cmat) = newcolnames + pdf(outpdfname) + heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols) + dev.off() +} + +qqPlot = function(descr='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(myDGEList,deTags, outSmear, outMain) + { + pdf(outSmear) + plotSmear(myDGEList,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('@@@ Library size 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='log2 raw counts') + 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('log2 counts 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 = "Differential_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("<a href=\'",u,"\'>",u,"</a>",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("<a href=\'",url,"\'>",url,"</a>",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(),TreatmentName="Rx",ControlName="Ctrl",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') +{ + +logf = file('Differential.log', open = "a") +sink(logf,type = c("output", "message")) + + +run_edgeR = function(workCM,pdata,subjects,group,priordf,robust_meth,mydesign,mt,cmrowsums,out_edgeR,nonzerod) +{ + logf = file('edgeR.log', open = "a") + sink(logf,type = c("output", "message")) + #### Setup myDGEList object + myDGEList = DGEList(counts=workCM, group = group) + myDGEList = calcNormFactors(myDGEList) + if (robust_meth == 'ordinary') { + myDGEList = estimateGLMCommonDisp(myDGEList,mydesign) + myDGEList = estimateGLMTrendedDisp(myDGEList,mydesign) + if (priordf > 0) { myDGEList = estimateGLMTagwiseDisp(myDGEList,mydesign,prior.df = priordf) + } else { myDGEList = estimateGLMTagwiseDisp(myDGEList,mydesign) } + comdisp = myDGEList\$common.dispersion + estpriorn = getPriorN(myDGEList) + print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F) + } else { + myDGEList = estimateGLMRobustDisp(myDGEList,design=mydesign, prior.df = priordf, maxit = 6, residual.type = robust_meth) + } + + + DGLM = glmFit(myDGEList,design=mydesign) + DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed + normData = cpm(myDGEList) + uoutput = cbind( + Name=as.character(rownames(myDGEList\$counts)), + DE\$table, + adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), + Dispersion=myDGEList\$tagwise.dispersion,totreads=cmrowsums,normData, + myDGEList\$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(paste("edgeR",mt,"GoodnessofFit.pdf",sep='_')) + qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") + abline(0,1,lwd=3) + points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon") + dev.off() + uniqueg = unique(group) + write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F) + tt = cbind( + Name=as.character(rownames(myDGEList)), + DE\$table, + adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), + Dispersion=myDGEList\$tagwise.dispersion,totreads=cmrowsums + ) + tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely + stt = tt[order(DE\$table\$PValue),] + print.noquote("@@ edgeR Top tags\n") + print.noquote(stt[1:50,]) + deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,]) + nsig = length(deTags) + print.noquote(paste('@@',nsig,'tags significant at adj p=',fdrthresh)) + deColours = ifelse(deTags,'red','black') + pdf(paste("edgeR",mt,"BCV_vs_abundance.pdf",sep="_")) + plotBCV(myDGEList, cex=0.3, main="Biological CV vs abundance") + dev.off() + dg = myDGEList[order(DE\$table\$PValue),] + outpdfname= paste("edgeR",mt,"top_100_heatmap.pdf",sep="_") + ocpm = normData[order(DE\$table\$PValue),] + ocpm = ocpm[c(1:100),] + hmap2(ocpm,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste(myTitle,'Heatmap')) + outSmear = paste("edgeR",mt,"smearplot.pdf",sep="_") + outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') + smearPlot(myDGEList=myDGEList,deTags=deTags, outSmear=outSmear, outMain = outMain) + qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf=paste('edgeR',mt,'qqplot.pdf',sep='_')) + 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() + return(list(myDGEList=myDGEList,edgeRcounts=edgeRcounts)) +} ### run_edgeR + + +run_DESeq2 = function(workCM,pdata,subjects,group,out_DESeq2,mt,DESeq_fitType) + + { + logf = file("DESeq2.log", open = "a") + sink(logf,type = c("output", "message")) + # 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)) + } + deSeqDatsizefac = estimateSizeFactors(deSEQds) + deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType) + resDESeq = nbinomWaldTest(deSeqDatdisp) + 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=paste('DESeq2',mt,'qqplot.pdf',sep="_")) + 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(paste("DESeq2",mt,"dispersion_estimates.pdf",sep='_')) + plotDispEsts(resDESeq) + dev.off() + ysmall = abs(min(rDESeq\$log2FoldChange)) + ybig = abs(max(rDESeq\$log2FoldChange)) + ylimit = min(4,ysmall,ybig) + pdf(paste("DESeq2",mt,"MA_plot.pdf",sep="_")) + 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(paste("DESeq2",mt,"sample_distance_plot.pdf",sep="_")) + heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"), + col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255)) + dev.off() + 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(paste("DESeq2",mt,"PCA_plot.pdf",sep="_")) + #### wtf - print? Seems needed to get this to work + print(ppca) + dev.off() + } + sink() + return(DESeqcounts) + } + + +run_Voom = function(workCM,pdata,subjects,group,mydesign,mt,out_Voom) + { + logf = file('VOOM.log', open = "a") + sink(logf,type = c("output", "message")) + if (doedgeR == F) { + #### Setup myDGEList object + myDGEList = DGEList(counts=workCM, group = group) + myDGEList = calcNormFactors(myDGEList) + myDGEList = estimateGLMCommonDisp(myDGEList,mydesign) + myDGEList = estimateGLMTrendedDisp(myDGEList,mydesign) + myDGEList = estimateGLMTagwiseDisp(myDGEList,mydesign) + } + pdf(paste("VOOM",mt,"mean_variance_plot.pdf",sep='_')) + dat.voomed <- voom(myDGEList, 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=paste('VOOM',mt,'qqplot.pdf',sep='_')) + rownames(rvoom) = rownames(workCM) + rvoom = cbind(Contig=rownames(workCM),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() + return(voomcounts) + } + + +#### data cleaning and analsis control starts here + + + # Error handling + nugroup = length(unique(group)) + if (nugroup!=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) # half samples + 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]+)" # ucsc chr:start-end regexp + genecards="<a href=\'http://www.genecards.org/index.php?path=/Search/keyword/" + ucsc = paste("<a href=\'http://genome.ucsc.edu/cgi-bin/hgTracks?db=",org,sep='') + testreg = str_match(allgenes,reg) + if (sum(!is.na(testreg[,1]))/length(testreg[,1]) > 0.8) # is ucsc style string + { + print("@@ using ucsc substitution for urls") + contigurls = paste0(ucsc,"&position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"</a>") + } else { + print("@@ using genecards substitution for urls") + contigurls = paste0(genecards,allgenes,"\'>",allgenes,"</a>") + } + print.noquote(paste("@@ Total low count contigs per sample = ",paste(table(lo),collapse=','))) + 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) + normData = cpm(workCM)*1e6 + colnames(normData) = paste( colnames(workCM),'N',sep="_") + print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=','))) + + if (doedgeR == T) { + eres = run_edgeR(workCM,pdata,subjects,group,priordf,robust_meth,mydesign,mt,cmrowsums,out_edgeR,nonzerod) + myDGEList = eres\$myDGEList + edgeRcounts = eres\$edgeRcounts + #### Plot MDS + sample_colors = match(group,levels(group)) + sampleTypes = levels(factor(group)) + print.noquote(sampleTypes) + pdf(paste("edgeR",mt,"MDSplot.pdf",sep='_')) + plotMDS.DGEList(myDGEList,main=paste("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() + scale <- myDGEList\$samples\$lib.size*myDGEList\$samples\$norm.factors + normCounts <- round(t(t(myDGEList\$counts)/scale)*mean(scale)) + try({boxPlot(rawrs=nzd,cleanrs=log2(normCounts+1),maint='Effects of TMM size normalisation',myTitle=myTitle,pdfname=paste("edgeR",mt,"raw_norm_counts_box.pdf",sep='_'))},T) + } + if (doDESeq2 == T) { DESeqcounts = run_DESeq2(workCM,pdata,subjects,group,out_DESeq2,mt,DESeq_fitType) } + if (doVoom == T) { voomcounts = run_Voom(workCM,pdata,subjects,group,mydesign,mt,out_Voom) } + + + if (doCamera) { + doGSEA(y=myDGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle, + outfname=paste("GSEA_Camera",mt,"table.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 = paste("Differential_venn",mt,"significant_genes_overlap.pdf",sep="_") + pdf(vennf) + vennDiagram(counts.venn,main=vennmain,col="maroon") + dev.off() + } + } #### doDESeq2 or doVoom +sink() +} +#### Done +]]> +builtin_gmt = "" +history_gmt = "" +history_gmt_name = "" +out_edgeR = F +out_DESeq2 = F +out_Voom = "$out_VOOM" +edgeR_robust_meth = "ordinary" +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') +<![CDATA[ +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') +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)) ) +group = factor(group, levels=c(ControlName,TreatmentName)) +colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_") +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,TreatmentName=TreatmentName,ControlName=ControlName, + 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() + +sink() +]]> +</configfile> + </configfiles> + <help> + +**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. +They must be unique and R names or they will be mangled - please read the fine R docs for the rules on identifiers. + +**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 +</help> + <citations> + <citation type="doi">doi: 10.1093/bioinformatics/btp616</citation> + </citations> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/edgeRtest1out.html Tue Apr 28 22:56:54 2015 -0400 @@ -0,0 +1,613 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> + <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> + <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> + <meta name="generator" content="Galaxy rgToolFactory.py tool output - see http://getgalaxy.org/" /> + <title></title> + <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> + </head> + <body> + <div class="toolFormBody"> + +<div class="infomessage">Galaxy Tool "Differential_Counts" run at 18/02/2015 14:14:12</div><br/> +<div class="toolFormTitle">DESeq2 images and outputs</div> +(Click on a thumbnail image to download the corresponding original PDF image)<br/> +<div><table class="simple" cellpadding="2" cellspacing="2"> +<tr> +<td><a href="DESeq2_edgeRtest_MA_plot.pdf"><img src="DESeq2_edgeRtest_MA_plot.png" title="Click to download a PDF of DESeq2_edgeRtest_MA_plot.pdf" hspace="5" width="400" + alt="Image called DESeq2_edgeRtest_MA_plot.pdf"/></a></td> + +<td><a href="DESeq2_edgeRtest_PCA_plot.pdf"><img src="DESeq2_edgeRtest_PCA_plot.png" title="Click to download a PDF of DESeq2_edgeRtest_PCA_plot.pdf" hspace="5" width="400" + alt="Image called DESeq2_edgeRtest_PCA_plot.pdf"/></a></td> + +<td><a href="DESeq2_edgeRtest_dispersion_estimates.pdf"><img src="DESeq2_edgeRtest_dispersion_estimates.png" title="Click to download a PDF of DESeq2_edgeRtest_dispersion_estimates.pdf" hspace="5" width="400" + alt="Image called DESeq2_edgeRtest_dispersion_estimates.pdf"/></a></td> +</tr> +<tr> +<td><a href="DESeq2_edgeRtest_qqplot.pdf"><img src="DESeq2_edgeRtest_qqplot.png" title="Click to download a PDF of DESeq2_edgeRtest_qqplot.pdf" hspace="5" width="400" + alt="Image called DESeq2_edgeRtest_qqplot.pdf"/></a></td> + +<td><a href="DESeq2_edgeRtest_sample_distance_plot.pdf"><img src="DESeq2_edgeRtest_sample_distance_plot.png" title="Click to download a PDF of DESeq2_edgeRtest_sample_distance_plot.pdf" hspace="5" width="400" + alt="Image called DESeq2_edgeRtest_sample_distance_plot.pdf"/></a></td> + +<td> </td> +</tr></table></div> + +<div class="toolFormTitle">DESeq2 log output</div> + +<pre> + +# DESeq top 50 + + Contig baseMean log2FoldChange lfcSE stat pvalue padj NReads URL + +Mir122a Mir122a 10112.31117 10.433687 0.3440661 30.324653 5.425198e-202 4.568017e-199 90428 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir122a'>Mir122a</a> + +Mir192 Mir192 271352.97636 6.994296 0.2346736 29.804353 3.430828e-195 1.444379e-192 2325567 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir192'>Mir192</a> + +Mir149 Mir149 810.35429 -6.947219 0.2752144 -25.242938 1.353995e-140 3.800212e-138 6164 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir149'>Mir149</a> + +Mir23a Mir23a 1289.18043 -3.104596 0.1650418 -18.810968 6.140634e-79 1.292603e-76 10118 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23a'>Mir23a</a> + +Mir204 Mir204 347.57397 -7.385478 0.4210055 -17.542475 6.790394e-69 1.143502e-66 2601 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir204'>Mir204</a> + +Mir10b Mir10b 27820.40551 -5.122625 0.3095281 -16.549791 1.606588e-61 2.254579e-59 197340 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir10b'>Mir10b</a> + +Mir194-2 Mir194-2 391.65678 5.272315 0.3261217 16.166709 8.660202e-59 1.041699e-56 3570 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-2'>Mir194-2</a> + +Mir181d Mir181d 275.22797 -3.578479 0.2232474 -16.029209 7.989327e-58 8.408767e-56 2139 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181d'>Mir181d</a> + +Mir27a Mir27a 2788.72629 -3.022597 0.1992370 -15.170866 5.514410e-52 5.159037e-50 21886 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27a'>Mir27a</a> + +Mir181c Mir181c 2765.96510 -3.678297 0.2459044 -14.958240 1.376168e-50 1.158734e-48 23605 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181c'>Mir181c</a> + +Mir195 Mir195 519.86200 -3.158968 0.2124717 -14.867713 5.340712e-50 4.088073e-48 3962 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir195'>Mir195</a> + +Mir208b Mir208b 1649.77924 -12.247146 0.8586111 -14.263905 3.673137e-46 2.577318e-44 14756 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208b'>Mir208b</a> + +Mir1948 Mir1948 263.89527 7.281854 0.5169425 14.086391 4.604994e-45 2.982619e-43 2404 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1948'>Mir1948</a> + +Mir490 Mir490 220.99790 -8.314250 0.5954353 -13.963315 2.610066e-44 1.569768e-42 1741 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir490'>Mir490</a> + +Mir23b Mir23b 2028.55377 -2.066561 0.1531073 -13.497466 1.618477e-41 9.085049e-40 16387 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23b'>Mir23b</a> + +Mir21 Mir21 26121.31011 2.974199 0.2230949 13.331544 1.517256e-40 7.984558e-39 229120 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir21'>Mir21</a> + +Mir101b Mir101b 6846.19683 3.823085 0.2899493 13.185354 1.065529e-39 5.277502e-38 59019 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir101b'>Mir101b</a> + +Mir208a Mir208a 616.76981 -11.025006 0.8475353 -13.008316 1.097341e-38 5.133115e-37 4638 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208a'>Mir208a</a> + +Dnm3os Dnm3os 179.61643 -3.289322 0.2559302 -12.852417 8.336403e-38 3.694343e-36 1401 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Dnm3os'>Dnm3os</a> + +Mir215 Mir215 152.78082 -3.012902 0.2351528 -12.812528 1.395132e-37 5.873506e-36 1182 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir215'>Mir215</a> + +Mir214 Mir214 134.69038 -3.229468 0.2571076 -12.560766 3.469589e-36 1.391140e-34 1048 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir214'>Mir214</a> + +Mir203 Mir203 772.92882 1.991755 0.1694325 11.755450 6.620692e-32 2.533919e-30 6739 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir203'>Mir203</a> + +Mir499 Mir499 712.45950 -11.716229 1.0177724 -11.511639 1.152660e-30 4.219739e-29 6527 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir499'>Mir499</a> + +Mir27b Mir27b 76478.05753 -1.906131 0.1672784 -11.394959 4.430372e-30 1.554322e-28 625308 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27b'>Mir27b</a> + +Mir322 Mir322 899.53469 -3.147196 0.2773005 -11.349407 7.466726e-30 2.514793e-28 7074 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir322'>Mir322</a> + +Rabggtb Rabggtb 2257.19195 1.991117 0.1756753 11.334078 8.896442e-30 2.881079e-28 19535 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Rabggtb'>Rabggtb</a> + +Mir143hg Mir143hg 180217.77425 -2.173582 0.1966899 -11.050806 2.172555e-28 6.775153e-27 1407364 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir143hg'>Mir143hg</a> + +Mir143 Mir143 179219.35960 -2.174835 0.1975414 -11.009513 3.438555e-28 1.034023e-26 1399819 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir143'>Mir143</a> + +Mir378 Mir378 483.21548 -3.012592 0.2747646 -10.964266 5.675913e-28 1.647972e-26 4075 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir378'>Mir378</a> + +Mir132 Mir132 106.46062 -2.793901 0.2568175 -10.878935 1.452498e-27 4.076678e-26 857 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir132'>Mir132</a> + +Mir155 Mir155 57.66182 -3.815683 0.3605552 -10.582799 3.580985e-26 9.726416e-25 463 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir155'>Mir155</a> + +Mir24-2 Mir24-2 424.48288 -2.725743 0.2589560 -10.525891 6.563718e-26 1.674743e-24 3470 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir24-2'>Mir24-2</a> + +Mir3074-2 Mir3074-2 424.48288 -2.725743 0.2589560 -10.525891 6.563718e-26 1.674743e-24 3470 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3074-2'>Mir3074-2</a> + +Mir125b-2 Mir125b-2 493.08516 -2.938770 0.2803125 -10.483910 1.024200e-25 2.536401e-24 3837 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-2'>Mir125b-2</a> + +Mir150 Mir150 553.20599 -2.856250 0.2760016 -10.348671 4.243363e-25 1.020832e-23 4229 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir150'>Mir150</a> + +Mir182 Mir182 886.79583 5.089394 0.4937848 10.306907 6.557770e-25 1.533789e-23 7189 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir182'>Mir182</a> + +Mir148a Mir148a 118994.46955 2.695116 0.2621094 10.282410 8.458605e-25 1.924904e-23 1002397 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir148a'>Mir148a</a> + +Mir199b Mir199b 47.84725 -5.417036 0.5304738 -10.211694 1.757697e-24 3.894687e-23 370 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199b'>Mir199b</a> + +Snord104 Snord104 3851.90119 2.396960 0.2371478 10.107451 5.119918e-24 1.105377e-22 33458 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord104'>Snord104</a> + +Mir193 Mir193 45.70861 5.113887 0.5386870 9.493243 2.239557e-21 4.714267e-20 421 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir193'>Mir193</a> + +Mir200b Mir200b 87.13638 6.279428 0.6679043 9.401688 5.369454e-21 1.102703e-19 888 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200b'>Mir200b</a> + +5430416N02Rik 5430416N02Rik 62.15966 3.116558 0.3399987 9.166382 4.891610e-20 9.806514e-19 564 <a href='http://www.genecards.org/index.php?path=/Search/keyword/5430416N02Rik'>5430416N02Rik</a> + +Mir802 Mir802 166.83414 9.771028 1.0750153 9.089199 9.977106e-20 1.953657e-18 1514 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir802'>Mir802</a> + +Mir125b-1 Mir125b-1 79.01988 -3.046196 0.3356809 -9.074680 1.140124e-19 2.181783e-18 609 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-1'>Mir125b-1</a> + +2610203C20Rik 2610203C20Rik 79.17666 -3.049266 0.3363186 -9.066600 1.227890e-19 2.297519e-18 610 <a href='http://www.genecards.org/index.php?path=/Search/keyword/2610203C20Rik'>2610203C20Rik</a> + +Snord91a Snord91a 168.95251 2.722976 0.3035329 8.970941 2.939927e-19 5.381346e-18 1437 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord91a'>Snord91a</a> + +Mir194-1 Mir194-1 71.10636 3.788463 0.4230333 8.955471 3.382877e-19 6.060389e-18 635 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-1'>Mir194-1</a> + +Mir181a-1 Mir181a-1 59.53826 -3.173584 0.3580582 -8.863320 7.766622e-19 1.362395e-17 506 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181a-1'>Mir181a-1</a> + +Mir181a-2 Mir181a-2 347.82506 -2.863247 0.3233431 -8.855136 8.358205e-19 1.436247e-17 2817 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181a-2'>Mir181a-2</a> + +Mir184 Mir184 32.23796 -4.996780 0.5668564 -8.814896 1.197967e-18 2.017377e-17 247 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir184'>Mir184</a> + + +</pre> + +<div class="toolFormTitle">Differential images and outputs</div> +(Click on a thumbnail image to download the corresponding original PDF image)<br/> +<div><table class="simple" cellpadding="2" cellspacing="2"> +<tr> +<td><a href="Differential_rowsum_bar_charts.pdf"><img src="Differential_rowsum_bar_charts.png" title="Click to download a PDF of Differential_rowsum_bar_charts.pdf" hspace="5" width="400" + alt="Image called Differential_rowsum_bar_charts.pdf"/></a></td> + +<td><a href="Differential_venn_edgeRtest_significant_genes_overlap.pdf"><img src="Differential_venn_edgeRtest_significant_genes_overlap.png" title="Click to download a PDF of Differential_venn_edgeRtest_significant_genes_overlap.pdf" hspace="5" width="400" + alt="Image called Differential_venn_edgeRtest_significant_genes_overlap.pdf"/></a></td> +</tr> + +</table></div> + +<div class="toolFormTitle">Differential log output</div> + +<pre> + +[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] @@ Total low count contigs per sample = 1,1,1,1,1,1,1,1 + +[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 + +[1] "Raw sample read totals 2443751,1644652,1682104,1806045,1440960,1341813,2888924,1428365" + +[1] heart liver + + +</pre> + +<div class="toolFormTitle">Differential log output</div> + +<pre> + +Attaching package: ‘gplots’ + +The following object is masked from ‘package:stats’: + + lowess + +Loading required package: limma + +Loading required package: methods + +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 objects are masked from ‘package:parallel’: + + clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB + +The following object is masked from ‘package:limma’: + + plotMA + +The following object is masked from ‘package:stats’: + + xtabs + +The following objects are masked from ‘package:base’: + + anyDuplicated, append, as.data.frame, as.vector, cbind, colnames, do.call, duplicated, eval, evalq, Filter, Find, get, intersect, is.unsorted, 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 + +Loading required package: GenomeInfoDb + +Loading required package: Rcpp + +Loading required package: RcppArmadillo + +gene-wise dispersion estimates + +mean-dispersion relationship + +final dispersion estimates + +Warning message: + +closing unused connection 4 (edgeR.log) + +Warning message: + +In sink() : no sink to remove + + +</pre> + +<div class="toolFormTitle">VOOM images and outputs</div> +(Click on a thumbnail image to download the corresponding original PDF image)<br/> +<div><table class="simple" cellpadding="2" cellspacing="2"> +<tr> +<td><a href="VOOM_edgeRtest_mean_variance_plot.pdf"><img src="VOOM_edgeRtest_mean_variance_plot.png" title="Click to download a PDF of VOOM_edgeRtest_mean_variance_plot.pdf" hspace="5" width="400" + alt="Image called VOOM_edgeRtest_mean_variance_plot.pdf"/></a></td> + +<td><a href="VOOM_edgeRtest_qqplot.pdf"><img src="VOOM_edgeRtest_qqplot.png" title="Click to download a PDF of VOOM_edgeRtest_qqplot.pdf" hspace="5" width="400" + alt="Image called VOOM_edgeRtest_qqplot.pdf"/></a></td> +</tr> + +</table></div> + +<div class="toolFormTitle">VOOM log output</div> + +<pre> + +# VOOM top 50 + + Contig logFC AveExpr t P.Value adj.P.Val B NReads URL + +Mir192 Mir192 6.689950 14.4417888 50.335160 1.802287e-16 2.056409e-13 27.414844 2325567 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir192'>Mir192</a> + +Mir208a Mir208a -10.458438 3.8918506 -29.183545 2.249812e-13 1.283518e-10 19.141041 4638 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208a'>Mir208a</a> + +Mir3073 Mir3073 8.318578 2.6485638 25.821264 1.102217e-12 4.192097e-10 18.063600 904 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3073'>Mir3073</a> + +Mir802 Mir802 8.992449 2.9857711 25.195575 1.514327e-12 4.319618e-10 17.906674 1514 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir802'>Mir802</a> + +Mir208b Mir208b -12.256447 4.4678897 -22.360114 7.074494e-12 1.614400e-09 16.920424 14756 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208b'>Mir208b</a> + +Mir499 Mir499 -11.104485 3.8066799 -21.990054 8.769804e-12 1.667724e-09 16.728874 6527 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir499'>Mir499</a> + +Mir10b Mir10b -4.775768 12.4173688 -21.487387 1.180685e-11 1.924516e-09 17.249348 197340 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir10b'>Mir10b</a> + +Mir148a Mir148a 2.751538 15.4237642 20.289553 2.464883e-11 3.515539e-09 16.455471 1002397 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir148a'>Mir148a</a> + +Mir490 Mir490 -8.497742 3.6613221 -18.336110 8.980482e-11 1.138526e-08 13.923237 1741 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir490'>Mir490</a> + +Mir122a Mir122a 10.197963 8.1512374 17.467826 1.663427e-10 1.897970e-08 14.215445 90428 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir122a'>Mir122a</a> + +Mir133b Mir133b -6.172367 1.3497975 -17.274094 1.916064e-10 1.987481e-08 13.840201 159 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir133b'>Mir133b</a> + +Mir149 Mir149 -7.041176 6.0886889 -16.861286 2.602547e-10 2.474589e-08 13.714880 6164 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir149'>Mir149</a> + +Mir101b Mir101b 3.837883 10.6216725 15.443054 7.873164e-10 6.910215e-08 13.054350 59019 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir101b'>Mir101b</a> + +Mir143 Mir143 -1.912927 16.0353646 -14.922755 1.209475e-09 9.857220e-08 12.374988 1399819 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir143'>Mir143</a> + +Mir194-2 Mir194-2 5.534694 6.2627211 14.703097 1.455682e-09 1.107289e-07 12.316769 3570 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-2'>Mir194-2</a> + +Mir23a Mir23a -2.905961 8.6431895 -14.558394 1.646894e-09 1.174441e-07 12.339306 10118 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23a'>Mir23a</a> + +Mir1983 Mir1983 -5.612359 1.1061384 -14.266537 2.119488e-09 1.422551e-07 11.743589 101 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1983'>Mir1983</a> + +Mir27a Mir27a -2.849084 10.0939084 -14.158498 2.329669e-09 1.476752e-07 11.960195 21886 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27a'>Mir27a</a> + +Cyp3a25 Cyp3a25 6.312461 1.6425308 13.845627 3.074630e-09 1.846396e-07 11.502713 226 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Cyp3a25'>Cyp3a25</a> + +Mir200a Mir200a 6.129125 1.8320913 13.226966 5.410979e-09 3.086963e-07 10.979834 264 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200a'>Mir200a</a> + +Mir181d Mir181d -3.405544 6.3702152 -13.064584 6.300369e-09 3.423201e-07 11.006301 2139 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181d'>Mir181d</a> + +Mir153 Mir153 -5.698257 1.5328802 -12.832092 7.856705e-09 3.829623e-07 10.583835 140 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir153'>Mir153</a> + +Mir204 Mir204 -7.718081 4.5031856 -12.808496 8.036265e-09 3.829623e-07 10.353229 2601 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir204'>Mir204</a> + +Gm5441 Gm5441 -5.716851 1.5430406 -12.806028 8.055298e-09 3.829623e-07 10.562881 142 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Gm5441'>Gm5441</a> + +Rabggtb Rabggtb 2.327908 9.9369857 12.760291 8.416902e-09 3.841474e-07 10.654006 19535 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Rabggtb'>Rabggtb</a> + +Mir504 Mir504 -5.122304 0.8161671 -12.391521 1.205304e-08 5.289430e-07 10.144865 69 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir504'>Mir504</a> + +Mir133a-1 Mir133a-1 -4.912497 0.7297882 -12.335045 1.274466e-08 5.385801e-07 10.076395 60 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir133a-1'>Mir133a-1</a> + +Mir195 Mir195 -2.954216 7.3530970 -12.081859 1.641098e-08 6.603731e-07 10.055879 3962 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir195'>Mir195</a> + +Mir27b Mir27b -1.496991 14.9464877 -12.059553 1.678424e-08 6.603731e-07 9.674850 625308 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27b'>Mir27b</a> + +Snord52 Snord52 2.631712 9.7652181 11.922618 1.928407e-08 7.334374e-07 9.811774 18059 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord52'>Snord52</a> + +Mir322 Mir322 -3.029558 8.1188344 -11.736839 2.333148e-08 8.587488e-07 9.679815 7074 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir322'>Mir322</a> + +Mir181c Mir181c -3.676262 9.6244506 -11.575598 2.758358e-08 9.835271e-07 9.453057 23605 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181c'>Mir181c</a> + +Mir1948 Mir1948 7.101780 4.7821564 11.471202 3.077353e-08 1.033009e-06 9.233632 2404 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1948'>Mir1948</a> + +0610031O16Rik 0610031O16Rik 4.519875 0.7388871 11.470939 3.078205e-08 1.033009e-06 9.284014 78 <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610031O16Rik'>0610031O16Rik</a> + +Mir201 Mir201 -4.964105 0.7490919 -11.289794 3.729283e-08 1.210650e-06 9.114028 63 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir201'>Mir201</a> + +Mir21 Mir21 2.746616 13.2835800 11.267331 3.819755e-08 1.210650e-06 8.925217 229120 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir21'>Mir21</a> + +Mir184 Mir184 -5.569565 2.3521173 -11.190343 4.148052e-08 1.279170e-06 8.854599 247 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir184'>Mir184</a> + +1810019D21Rik 1810019D21Rik 5.164581 1.0784751 11.082009 4.662057e-08 1.399844e-06 8.951908 117 <a href='http://www.genecards.org/index.php?path=/Search/keyword/1810019D21Rik'>1810019D21Rik</a> + +Mir203 Mir203 2.216791 8.5426169 10.866758 5.896538e-08 1.725115e-06 8.715639 6739 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir203'>Mir203</a> + +1110038B12Rik 1110038B12Rik 2.383720 10.6847245 10.832157 6.125623e-08 1.744094e-06 8.573067 37066 <a href='http://www.genecards.org/index.php?path=/Search/keyword/1110038B12Rik'>1110038B12Rik</a> + +Snord104 Snord104 2.571210 10.4798167 10.811468 6.267120e-08 1.744094e-06 8.561110 33458 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord104'>Snord104</a> + +Mir182 Mir182 5.196800 7.2088299 10.640454 7.579543e-08 2.021320e-06 8.545839 7189 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir182'>Mir182</a> + +Mir547 Mir547 -4.542934 0.5799793 -10.635980 7.617593e-08 2.021320e-06 8.435473 42 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir547'>Mir547</a> + +Mir143hg Mir143hg -2.291921 16.3789153 -10.597275 7.955395e-08 2.062978e-06 7.952584 1407364 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir143hg'>Mir143hg</a> + +Scnn1b Scnn1b -4.541403 0.5700621 -10.243065 1.190487e-07 3.018546e-06 8.023327 45 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Scnn1b'>Scnn1b</a> + +Mir125b-2 Mir125b-2 -2.896115 7.2737925 -10.091091 1.420082e-07 3.522420e-06 7.876068 3837 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-2'>Mir125b-2</a> + +Mir1a-1 Mir1a-1 -4.402568 0.4498447 -9.950346 1.675164e-07 4.066729e-06 7.692790 42 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1a-1'>Mir1a-1</a> + +Mir378 Mir378 -2.733247 7.2964165 -9.922980 1.730212e-07 4.112858e-06 7.672216 4075 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir378'>Mir378</a> + +Mir199b Mir199b -5.651345 2.8029895 -9.883978 1.812024e-07 4.219426e-06 7.548022 370 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199b'>Mir199b</a> + +Mir155 Mir155 -4.158272 3.8002361 -9.845490 1.896814e-07 4.328530e-06 7.604112 463 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir155'>Mir155</a> + + +</pre> + +<div class="toolFormTitle">edgeR images and outputs</div> +(Click on a thumbnail image to download the corresponding original PDF image)<br/> +<div><table class="simple" cellpadding="2" cellspacing="2"> +<tr> +<td><a href="edgeR_edgeRtest_BCV_vs_abundance.pdf"><img src="edgeR_edgeRtest_BCV_vs_abundance.png" title="Click to download a PDF of edgeR_edgeRtest_BCV_vs_abundance.pdf" hspace="5" width="400" + alt="Image called edgeR_edgeRtest_BCV_vs_abundance.pdf"/></a></td> + +<td><a href="edgeR_edgeRtest_GoodnessofFit.pdf"><img src="edgeR_edgeRtest_GoodnessofFit.png" title="Click to download a PDF of edgeR_edgeRtest_GoodnessofFit.pdf" hspace="5" width="400" + alt="Image called edgeR_edgeRtest_GoodnessofFit.pdf"/></a></td> + +<td><a href="edgeR_edgeRtest_MDSplot.pdf"><img src="edgeR_edgeRtest_MDSplot.png" title="Click to download a PDF of edgeR_edgeRtest_MDSplot.pdf" hspace="5" width="400" + alt="Image called edgeR_edgeRtest_MDSplot.pdf"/></a></td> +</tr> +<tr> +<td><a href="edgeR_edgeRtest_qqplot.pdf"><img src="edgeR_edgeRtest_qqplot.png" title="Click to download a PDF of edgeR_edgeRtest_qqplot.pdf" hspace="5" width="400" + alt="Image called edgeR_edgeRtest_qqplot.pdf"/></a></td> + +<td><a href="edgeR_edgeRtest_smearplot.pdf"><img src="edgeR_edgeRtest_smearplot.png" title="Click to download a PDF of edgeR_edgeRtest_smearplot.pdf" hspace="5" width="400" + alt="Image called edgeR_edgeRtest_smearplot.pdf"/></a></td> + +<td><a href="edgeR_edgeRtest_top_100_heatmap.pdf"><img src="edgeR_edgeRtest_top_100_heatmap.png" title="Click to download a PDF of edgeR_edgeRtest_top_100_heatmap.pdf" hspace="5" width="400" + alt="Image called edgeR_edgeRtest_top_100_heatmap.pdf"/></a></td> +</tr> + +</table></div> + +<div class="toolFormTitle">edgeR log output</div> + +<pre> + +[1] Common Dispersion = 0.228651459675839 CV = 0.478175134940995 getPriorN = 3.33333333333333 + +[1] "No GLM fit outlier genes found\n" + +[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 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208a'>Mir208a</a> + +Mir149 Mir149 -7.008984 8.861767 484.30321 2.473909e-107 1.411365e-104 0.04959937 6164 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir149'>Mir149</a> + +Mir208b Mir208b -13.291635 9.905945 417.69758 7.737466e-93 2.942816e-90 0.10508096 14756 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208b'>Mir208b</a> + +Mir122a Mir122a 10.514683 12.478088 415.17429 2.740528e-92 7.817355e-90 0.10803882 90428 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir122a'>Mir122a</a> + +Mir204 Mir204 -7.498162 7.634507 341.30678 3.313429e-76 7.561245e-74 0.06907958 2601 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir204'>Mir204</a> + +Mir499 Mir499 -13.577454 8.700078 325.79199 7.930762e-73 1.508166e-70 0.12042284 6527 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir499'>Mir499</a> + +Mir490 Mir490 -8.534394 6.991023 303.17184 6.710365e-68 1.093790e-65 0.07949711 1741 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir490'>Mir490</a> + +Mir192 Mir192 6.953853 17.169364 217.22866 3.638312e-49 5.189142e-47 0.12700995 2325567 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir192'>Mir192</a> + +Mir802 Mir802 11.440805 6.593380 212.88058 3.231647e-48 4.097010e-46 0.12273671 1514 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir802'>Mir802</a> + +Mir1948 Mir1948 7.418142 7.252734 195.66958 1.840248e-44 2.099723e-42 0.12060221 2404 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1948'>Mir1948</a> + +Mir194-2 Mir194-2 5.298950 7.811522 191.85588 1.250960e-43 1.297587e-41 0.08670751 3570 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-2'>Mir194-2</a> + +Mir23a Mir23a -3.153807 9.529402 177.53185 1.676248e-40 1.593833e-38 0.04442763 10118 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23a'>Mir23a</a> + +Mir181c Mir181c -3.767686 10.639598 169.87390 7.883296e-39 6.919108e-37 0.06368883 23605 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181c'>Mir181c</a> + +Mir3073 Mir3073 10.686337 5.859950 164.86740 9.778601e-38 7.969560e-36 0.14069249 904 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3073'>Mir3073</a> + +Mir181d Mir181d -3.643963 7.300371 162.18591 3.767663e-37 2.865936e-35 0.05729574 2139 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181d'>Mir181d</a> + +Mir195 Mir195 -3.203683 8.215089 150.20548 1.563314e-34 1.114838e-32 0.05235020 3962 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir195'>Mir195</a> + +Mir10b Mir10b -5.182616 13.946466 147.24792 6.926822e-34 4.649120e-32 0.12268790 197340 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir10b'>Mir10b</a> + +Mir101b Mir101b 3.759962 11.863187 136.31359 1.703813e-31 1.080028e-29 0.07961343 59019 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir101b'>Mir101b</a> + +Mir378 Mir378 -3.115599 8.119617 126.76408 2.092233e-29 1.256441e-27 0.05942391 4075 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir378'>Mir378</a> + +Mir27a Mir27a -3.064687 10.642480 124.98911 5.117477e-29 2.919521e-27 0.06113852 21886 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27a'>Mir27a</a> + +Mir182 Mir182 5.057509 8.846381 123.17765 1.275060e-28 6.927826e-27 0.13653707 7189 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir182'>Mir182</a> + +Mir322 Mir322 -3.194159 9.012888 107.34926 3.732413e-25 1.935765e-23 0.07536483 7074 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir322'>Mir322</a> + +Mir199b Mir199b -5.520119 4.792610 102.10724 5.259607e-24 2.609223e-22 0.13417024 370 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199b'>Mir199b</a> + +Mir181a-2 Mir181a-2 -3.000177 7.637692 101.38361 7.578821e-24 3.603098e-22 0.06896653 2817 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181a-2'>Mir181a-2</a> + +Mir125b-2 Mir125b-2 -2.987759 8.144514 91.72544 9.957640e-22 4.488356e-20 0.07737381 3837 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-2'>Mir125b-2</a> + +Dnm3os Dnm3os -3.331215 6.686950 91.67250 1.022763e-21 4.488356e-20 0.08810497 1401 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Dnm3os'>Dnm3os</a> + +Mir184 Mir184 -5.111350 4.234160 84.35542 4.133639e-20 1.686711e-18 0.13502324 247 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir184'>Mir184</a> + +Mir215 Mir215 -3.058208 6.447966 84.35278 4.139167e-20 1.686711e-18 0.08138517 1182 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir215'>Mir215</a> + +Mir133b Mir133b -8.383611 3.584760 83.96681 5.031521e-20 1.960318e-18 0.17482280 159 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir133b'>Mir133b</a> + +Mir150 Mir150 -2.883446 8.307765 83.91918 5.154209e-20 1.960318e-18 0.08008123 4229 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir150'>Mir150</a> + +Mir3074-2 Mir3074-2 -2.778308 7.935651 83.74839 5.619282e-20 2.040616e-18 0.07424646 3470 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3074-2'>Mir3074-2</a> + +Mir24-2 Mir24-2 -2.778307 7.935651 83.71222 5.723023e-20 2.040616e-18 0.07427992 3470 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir24-2'>Mir24-2</a> + +Mir193 Mir193 5.176579 4.801090 83.19222 7.445011e-20 2.574169e-18 0.14794861 421 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir193'>Mir193</a> + +Scarna17 Scarna17 2.182159 9.244479 81.91330 1.421894e-19 4.771710e-18 0.04982909 9224 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Scarna17'>Scarna17</a> + +Mir214 Mir214 -3.271172 6.271755 80.43948 2.997457e-19 9.771711e-18 0.09566584 1048 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir214'>Mir214</a> + +Snord104 Snord104 2.330488 11.053611 79.50529 4.809370e-19 1.524303e-17 0.05915990 33458 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord104'>Snord104</a> + +Mir200a Mir200a 7.201555 4.139422 77.35503 1.428304e-18 4.365755e-17 0.19287764 264 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200a'>Mir200a</a> + +Mir200b Mir200b 6.525423 5.752604 77.31985 1.453976e-18 4.365755e-17 0.26237966 888 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200b'>Mir200b</a> + +Mir21 Mir21 2.923147 13.825255 75.51798 3.620939e-18 1.059357e-16 0.09395834 229120 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir21'>Mir21</a> + +Mir203 Mir203 1.956427 8.767610 75.17870 4.299815e-18 1.226522e-16 0.04381710 6739 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir203'>Mir203</a> + +Mir155 Mir155 -3.886731 5.068563 73.81316 8.587210e-18 2.389758e-16 0.12522673 463 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir155'>Mir155</a> + +Cyp3a25 Cyp3a25 8.681501 3.972085 72.29680 1.851472e-17 5.029834e-16 0.23125383 226 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Cyp3a25'>Cyp3a25</a> + +Rabggtb Rabggtb 1.934093 10.298211 72.02043 2.129809e-17 5.651423e-16 0.04596646 19535 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Rabggtb'>Rabggtb</a> + +Mir23b Mir23b -2.100584 10.184110 71.44225 2.854936e-17 7.403367e-16 0.05416378 16387 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23b'>Mir23b</a> + +Snord52 Snord52 2.207491 10.217554 71.27974 3.100028e-17 7.860292e-16 0.05941483 18059 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord52'>Snord52</a> + +Gm5441 Gm5441 -6.881248 3.538457 70.05615 5.764005e-17 1.429724e-15 0.20097284 142 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Gm5441'>Gm5441</a> + +Mir153 Mir153 -6.857671 3.517446 69.37600 8.137283e-17 1.975455e-15 0.20158808 140 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir153'>Mir153</a> + +Mir132 Mir132 -2.858294 5.938312 64.52507 9.531204e-16 2.265647e-14 0.09274248 857 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir132'>Mir132</a> + +1110038B12Rik 1110038B12Rik 2.195962 11.253090 62.92015 2.152583e-15 5.012443e-14 0.06712174 37066 <a href='http://www.genecards.org/index.php?path=/Search/keyword/1110038B12Rik'>1110038B12Rik</a> + +Snord91a Snord91a 2.654072 6.557504 62.40549 2.795431e-15 6.379174e-14 0.08637410 1437 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord91a'>Snord91a</a> + +[1] @@ 416 tags significant at adj p= 0.05 + + +</pre> + +<div class="toolFormTitle">Other log output</div> + +<pre> + +## Toolfactory generated command line = Rscript - None None + +Got TCols=1 5 6 7; CCols=2 3 4 8 + +R version 3.1.0 (2014-04-10) + +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=en_AU.UTF-8 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 stats graphics grDevices utils datasets base + +other attached packages: + + [1] RColorBrewer_1.1-2 DESeq2_1.4.5 RcppArmadillo_0.4.600.4.0 Rcpp_0.11.4 GenomicRanges_1.16.4 GenomeInfoDb_1.0.2 IRanges_1.22.10 BiocGenerics_0.10.0 stringr_0.6.2 edgeR_3.6.8 limma_3.20.9 gplots_2.16.0 + +loaded via a namespace (and not attached): + + [1] annotate_1.42.1 AnnotationDbi_1.26.1 Biobase_2.24.0 bitops_1.0-6 caTools_1.17.1 DBI_0.3.1 gdata_2.13.3 genefilter_1.46.1 geneplotter_1.42.0 grid_3.1.0 gtools_3.4.1 KernSmooth_2.23-13 lattice_0.20-29 locfit_1.5-9.1 RSQLite_1.0.0 stats4_3.1.0 survival_2.37-7 XML_3.98-1.1 xtable_1.7-4 XVector_0.4.0 + + +</pre> + +<div class="toolFormTitle">All output files available for downloading</div> + +<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr> + +<tr><td><a href="DESeq2.log">DESeq2.log</a></td><td>10.5 KB</td></tr> +<tr class="odd_row"><td><a href="DESeq2_edgeRtest_MA_plot.pdf">DESeq2_edgeRtest_MA_plot.pdf</a></td><td>14.9 KB</td></tr> +<tr><td><a href="DESeq2_edgeRtest_PCA_plot.pdf">DESeq2_edgeRtest_PCA_plot.pdf</a></td><td>4.9 KB</td></tr> +<tr class="odd_row"><td><a href="DESeq2_edgeRtest_dispersion_estimates.pdf">DESeq2_edgeRtest_dispersion_estimates.pdf</a></td><td>188.4 KB</td></tr> +<tr><td><a href="DESeq2_edgeRtest_qqplot.pdf">DESeq2_edgeRtest_qqplot.pdf</a></td><td>12.8 KB</td></tr> +<tr class="odd_row"><td><a href="DESeq2_edgeRtest_sample_distance_plot.pdf">DESeq2_edgeRtest_sample_distance_plot.pdf</a></td><td>9.4 KB</td></tr> +<tr><td><a href="Differential.log">Differential.log</a></td><td>1.4 KB</td></tr> +<tr class="odd_row"><td><a href="Differential_Counts.Rscript">Differential_Counts.Rscript</a></td><td>28.3 KB</td></tr> +<tr><td><a href="Differential_Counts_error.log">Differential_Counts_error.log</a></td><td>1.6 KB</td></tr> +<tr class="odd_row"><td><a href="Differential_Counts_runner.log">Differential_Counts_runner.log</a></td><td>1.4 KB</td></tr> +<tr><td><a href="Differential_rowsum_bar_charts.pdf">Differential_rowsum_bar_charts.pdf</a></td><td>6.3 KB</td></tr> +<tr class="odd_row"><td><a href="Differential_venn_edgeRtest_significant_genes_overlap.pdf">Differential_venn_edgeRtest_significant_genes_overlap.pdf</a></td><td>9.7 KB</td></tr> +<tr><td><a href="VOOM.log">VOOM.log</a></td><td>10.1 KB</td></tr> +<tr class="odd_row"><td><a href="VOOM_edgeRtest_mean_variance_plot.pdf">VOOM_edgeRtest_mean_variance_plot.pdf</a></td><td>18.3 KB</td></tr> +<tr><td><a href="VOOM_edgeRtest_qqplot.pdf">VOOM_edgeRtest_qqplot.pdf</a></td><td>17.5 KB</td></tr> +<tr class="odd_row"><td><a href="edgeR.log">edgeR.log</a></td><td>10.4 KB</td></tr> +<tr><td><a href="edgeR_edgeRtest_BCV_vs_abundance.pdf">edgeR_edgeRtest_BCV_vs_abundance.pdf</a></td><td>17.4 KB</td></tr> +<tr class="odd_row"><td><a href="edgeR_edgeRtest_GoodnessofFit.pdf">edgeR_edgeRtest_GoodnessofFit.pdf</a></td><td>13.1 KB</td></tr> +<tr><td><a href="edgeR_edgeRtest_MDSplot.pdf">edgeR_edgeRtest_MDSplot.pdf</a></td><td>4.9 KB</td></tr> +<tr class="odd_row"><td><a href="edgeR_edgeRtest_qqplot.pdf">edgeR_edgeRtest_qqplot.pdf</a></td><td>15.2 KB</td></tr> +<tr><td><a href="edgeR_edgeRtest_smearplot.pdf">edgeR_edgeRtest_smearplot.pdf</a></td><td>16.6 KB</td></tr> +<tr class="odd_row"><td><a href="edgeR_edgeRtest_top_100_heatmap.pdf">edgeR_edgeRtest_top_100_heatmap.pdf</a></td><td>11.2 KB</td></tr> +</table></div><br/> +</div></body></html> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/edgeRtest1out.xls Tue Apr 28 22:56:54 2015 -0400 @@ -0,0 +1,1142 @@ +Name logFC logCPM LR PValue adj.p.value Dispersion totreads 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 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 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/gentestdata.sh Tue Apr 28 22:56:54 2015 -0400 @@ -0,0 +1,9 @@ +#!/bin/bash +# generate test data for rgGSEA +# ross lazarus June 2013 +# adjust gseajar_path ! +GSEAJAR_PATH=/home/rlazarus/galaxy-central/tool_dependency_dir/gsea_jar/2.0.12/fubar/rg_gsea_test/8e291f464aa0/jars/gsea2-2.0.12.jar +python ../rgGSEA.py --input_tab "gsea_test_DGE.xls" --adjpvalcol "5" --signcol "2" --idcol "1" --outhtml "gseatestout.html" --input_name "gsea_test" --setMax "500" --setMin "15" --nPerm "10" --plotTop "20" --gsea_jar "$GSEAJAR_PATH" --output_dir "gseatestout" --mode "Max_probe" +--title "GSEA test" --builtin_gmt "gseatestdata.gmt" + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/test_bams2mx.xls Tue Apr 28 22:56:54 2015 -0400 @@ -0,0 +1,3243 @@ +Contigname 11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam 11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam 11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam 11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam 11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam 11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam 11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam 11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam +0610005C13Rik 40 0 2 0 6 70 6 2 +0610007N19Rik 10 17 11 42 2 6 6 10 +0610008F07Rik 16 0 0 0 8 5 4 1 +0610009B14Rik 0 0 0 1 0 0 0 0 +0610009L18Rik 3 2 2 11 0 1 1 1 +0610012G03Rik 6 0 0 0 4 5 2 0 +0610031O16Rik 33 0 0 0 10 25 10 0 +0610038B21Rik 0 0 0 2 0 0 0 0 +0610038L08Rik 0 0 0 0 0 3 0 0 +0610039K10Rik 9 0 0 1 3 1 4 3 +0610040B10Rik 0 0 0 0 0 0 2 0 +0610040F04Rik 2 1 0 1 2 5 2 0 +0610043K17Rik 9 2 0 4 4 12 0 1 +1110002L01Rik 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_dependencies.xml Tue Apr 28 22:56:54 2015 -0400 @@ -0,0 +1,93 @@ +<?xml version="1.0"?> +<tool_dependency> + <package name="R" version="3.1.2"> + <repository changeset_revision="41f43a2064ba" name="package_r_3_1_2" owner="iuc" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + </package> + <package name="graphicsmagick" version="1.3.18"> + <repository changeset_revision="bff3f66adff2" name="package_graphicsmagick_1_3" owner="iuc" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + </package> + <package name="ghostscript" version="9.10"> + <repository changeset_revision="9345d2740f0c" name="package_ghostscript_9_10" owner="devteam" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + </package> + + <package name="biocbasics" version="2.14"> + <install version="1.0"> + <actions> + <action type="setup_r_environment"> + <repository changeset_revision="41f43a2064ba" name="package_r_3_1_2" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu"> + <package name="R" version="3.1.2" /> + </repository> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/RColorBrewer_1.1-2.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/stringr_0.6.2.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/gtools_3.4.1.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/gdata_2.13.3.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/bitops_1.0-6.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/caTools_1.17.1.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/KernSmooth_2.23-13.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/gplots_2.15.0.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/limma_3.22.1.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/edgeR_3.8.5.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/BiocGenerics_0.12.1.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/S4Vectors_0.4.0.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/IRanges_2.0.1.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/GenomeInfoDb_1.2.4.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/XVector_0.6.0.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/GenomicRanges_1.18.3.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/Rcpp_0.11.3.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/RcppArmadillo_0.4.600.0.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/Biobase_2.26.0.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/codetools_0.2-9.tar.gz?raw=true</package> + 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<package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/fail_1.2.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/RSQLite_1.0.0.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/base64enc_0.1-2.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/sendmailR_1.2-1.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/BatchJobs_1.5.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/BiocParallel_1.0.0.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/AnnotationDbi_1.28.1.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/XML_3.98-1.1.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/xtable_1.7-4.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/annotate_1.44.0.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/survival_2.37-7.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/genefilter_1.48.1.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/lattice_0.20-29.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/latticeExtra_0.6-26.tar.gz?raw=true</package> + 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<package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/munsell_0.4.2.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/labeling_0.3.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/scales_0.2.4.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/proto_0.3-10.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/MASS_7.3-35.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/ggplot2_1.0.0.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/Formula_1.1-2.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/cluster_1.15.3.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/rpart_4.1-8.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/nnet_7.3-8.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/acepack_1.3-3.3.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/foreign_0.8-61.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/Hmisc_3.14-6.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/diffcount/DESeq2_1.6.3.tar.gz?raw=true</package> + + + </action> + </actions> + </install> + <readme> + Differential gene expression analysis + You may need libxml2-dev for XML to compile + Ubuntu has a bug with libgfortran. To fix that create a symlink like: sudo ln -s /usr/lib/x86_64-linux-gnu/libgfortran.so.3 /usr/lib/x86_64-linux-gnu/libgfortran.so + </readme> + </package> +</tool_dependency>