Mercurial > repos > fubar > differential_count_models
changeset 0:2122e630b13a draft
Initial commit of replacement for edger_test
| author | fubar |
|---|---|
| date | Fri, 26 Jul 2013 23:50:59 -0400 |
| parents | |
| children | df4fea3be1b3 |
| files | rgedgeR/rgToolFactory.py rgedgeR/rgedgeRpaired.xml rgedgeR/test-data/edgeRtest1out.html rgedgeR/test-data/edgeRtest1out.xls rgedgeR/test-data/gentestdata.sh rgedgeR/test-data/test_bams2mx.xls rgedgeR/tool_dependencies.xml |
| diffstat | 7 files changed, 6701 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/rgToolFactory.py Fri Jul 26 23:50:59 2013 -0400 @@ -0,0 +1,599 @@ +# 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 +# +# 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 = 'V000.2 June 2012' +verbose = False +debug = False +toolFactoryURL = 'https://bitbucket.org/fubar/galaxytoolfactory' + +def timenow(): + """return current time as a string + """ + return time.strftime('%d/%m/%Y %H:%M:%S', time.localtime(time.time())) + +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.treatbashSpecial = treatbashSpecial + if opts.output_dir: # simplify for the tool tarball + os.chdir(opts.output_dir) + self.thumbformat = 'jpg' + self.opts = opts + self.toolname = re.sub('[^a-zA-Z0-9_]+', '', opts.tool_name) # a sanitizer now does this but.. + self.toolid = self.toolname + self.myname = sys.argv[0] # get our name because we write ourselves out as a tool later + self.pyfile = self.myname # crude but efficient - the cruft won't hurt much + self.xmlfile = '%s.xml' % self.toolname + s = open(self.opts.script_path,'r').readlines() + s = [x.rstrip() for x in s] # remove pesky dos line endings if needed + self.script = '\n'.join(s) + fhandle,self.sfile = tempfile.mkstemp(prefix=self.toolname,suffix=".%s" % (opts.interpreter)) + tscript = open(self.sfile,'w') # use self.sfile as script source for Popen + tscript.write(self.script) + tscript.close() + self.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]) + 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 + """ + assert os.path.isfile(inpdf), "## Input %s supplied to %s compressPDF not found" % (inpdf,self.myName) + hf,hlog = tempfile.mkstemp(suffix="%s.log" % self.toolname) + sto = open(hlog,'w') + outpdf = '%s_compressed' % inpdf + cl = ["gs", "-sDEVICE=pdfwrite", "-dNOPAUSE", "-dBATCH","-dPDFSETTINGS=/printer", "-sOutputFile=%s" % outpdf,inpdf] + x = subprocess.Popen(cl,stdout=sto,stderr=sto,cwd=self.opts.output_dir) + retval1 = x.wait() + if retval1 == 0: + os.unlink(inpdf) + shutil.move(outpdf,inpdf) + outpng = '%s.%s' % (os.path.splitext(inpdf)[0],thumbformat) + 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) + retval2 = x.wait() + sto.close() + retval = retval1 or retval2 + return retval + + + def getfSize(self,fpath,outpath): + """ + format a nice file size string + """ + size = '' + fp = os.path.join(outpath,fpath) + if os.path.isfile(fp): + size = '0 B' + n = float(os.path.getsize(fp)) + if n > 2**20: + size = '%1.1f MB' % (n/2**20) + elif n > 2**10: + size = '%1.1f KB' % (n/2**10) + elif n > 0: + size = '%d B' % (int(n)) + return size + + def makeHtml(self): + """ Create an HTML file content to list all the artifacts found in the output_dir + """ + + galhtmlprefix = """<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> + <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> + <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> + <meta name="generator" content="Galaxy %s tool output - see http://g2.trac.bx.psu.edu/" /> + <title></title> + <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> + </head> + <body> + <div class="toolFormBody"> + """ + galhtmlattr = """<hr/><div class="infomessage">This tool (%s) was generated by the <a href="https://bitbucket.org/fubar/galaxytoolfactory/overview">Galaxy Tool Factory</a></div><br/>""" + galhtmlpostfix = """</div></body></html>\n""" + + flist = os.listdir(self.opts.output_dir) + flist = [x for x in flist if x <> 'Rplots.pdf'] + flist.sort() + html = [] + html.append(galhtmlprefix % progname) + html.append('<div class="infomessage">Galaxy Tool "%s" run at %s</div><br/>' % (self.toolname,timenow())) + fhtml = [] + if len(flist) > 0: + 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)) + 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: + sto = open(self.tlog,'w') + sto.write('## Toolfactory generated command line = %s\n' % ' '.join(self.cl)) + sto.flush() + p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=sto,stdin=subprocess.PIPE,cwd=self.opts.output_dir) + else: + p = subprocess.Popen(self.cl,shell=False,stdin=subprocess.PIPE) + p.stdin.write(self.script) + p.stdin.close() + retval = p.wait() + if self.opts.output_dir: + sto.close() + if self.opts.make_HTML: + self.makeHtml() + return retval + + def runBash(self): + """ + cannot use - for bash so use self.sfile + """ + if self.opts.output_dir: + s = '## Toolfactory generated command line = %s\n' % ' '.join(self.cl) + sto = open(self.tlog,'w') + sto.write(s) + sto.flush() + p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=sto,cwd=self.opts.output_dir) + else: + p = subprocess.Popen(self.cl,shell=False) + retval = p.wait() + if self.opts.output_dir: + sto.close() + if self.opts.make_HTML: + self.makeHtml() + return retval + + +def main(): + u = """ + This is a Galaxy wrapper. It expects to be called by a special purpose tool.xml as: + <command interpreter="python">rgBaseScriptWrapper.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript" + </command> + """ + op = optparse.OptionParser() + a = op.add_option + a('--script_path',default=None) + a('--tool_name',default=None) + a('--interpreter',default=None) + a('--output_dir',default=None) + a('--output_html',default=None) + a('--input_tab',default="None") + a('--output_tab',default="None") + a('--user_email',default='Unknown') + a('--bad_user',default=None) + a('--make_Tool',default=None) + a('--make_HTML',default=None) + a('--help_text',default=None) + a('--tool_desc',default=None) + a('--new_tool',default=None) + a('--tool_version',default=None) + opts, args = op.parse_args() + assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user) + assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq' + assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript' + assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R' + if opts.output_dir: + try: + os.makedirs(opts.output_dir) + except: + pass + r = ScriptRunner(opts) + if opts.make_Tool: + retcode = r.makeTooltar() + else: + retcode = r.run() + os.unlink(r.sfile) + if retcode: + sys.exit(retcode) # indicate failure to job runner + + +if __name__ == "__main__": + main() + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/rgedgeRpaired.xml Fri Jul 26 23:50:59 2013 -0400 @@ -0,0 +1,867 @@ +<tool id="rgDifferentialCount" name="Differential_Count" version="0.20"> + <description>models using BioConductor packages</description> + <requirements> + <requirement type="package" version="2.12">biocbasics</requirement> + <requirement type="package" version="3.0.1">r3</requirement> + </requirements> + + <command interpreter="python"> + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts" + --output_dir "$html_file.files_path" --output_html "$html_file" --output_tab "$outtab" --make_HTML "yes" + </command> + <inputs> + <param name="input1" type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample" + help="Use the HTSeq based count matrix preparation tool to create these matrices from BAM/SAM files and a GTF file of genomic features"/> + <param name="title" type="text" value="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"> + <validator type="no_options" message="Please select at least one column."/> + </param> + <param name="control_name" type="text" value="Control" size="50" label="Control Name"/> + <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True" + multiple="true" use_header_names="true" size="120" display="checkboxes" optional="true"> + </param> + <param name="subjectids" type="text" optional="true" size="120" value = "" + label="IF SUBJECTS NOT ALL INDEPENDENT! Enter integers to indicate sample pairing for every column in input" + help="Leave blank if no pairing, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter '1,2,1,2'"> + <sanitizer> + <valid initial="string.digits"><add value="," /> </valid> + </sanitizer> + </param> + <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs" + help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/> + <param name="useNDF" type="boolean" truevalue="T" 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="20" size="3" + label="prior.df for tagwise dispersion - lower value = more emphasis on each tag's variance. Replaces prior.n and prior.df = prior.n * residual.df" + help="0 = Use edgeR default. Use a small value to 'smooth' small samples. See edgeR docs and note below"/> + </when> + <when value="F"> </when> + </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 (only works if NO second GLM factor supplied at present)</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 - use this if parametric fails</option> + <option value="mean">Mean dispersion fit- use this if you really understand what you're doing - read the fine manual</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="The VOOM transformation allows analysis of count data 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="outtab" label="${title}.xls"/> + <data format="html" name="html_file" label="${title}.html"/> + </outputs> + <stdio> + <exit_code range="4" level="fatal" description="Number of subject ids must match total number of samples in the input matrix" /> + </stdio> + <tests> +<test> +<param name='input1' value='test_bams2mx.xls' ftype='tabular' /> + <param name='treatment_name' value='case' /> + <param name='title' value='edgeRtest' /> + <param name='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='fdrthresh' value="0.05" /> + <param name='control_name' value='control' /> + <param name='subjectids' value='' /> + <param name='Treat_cols' value='3,4,5,9' /> + <param name='Control_cols' value='2,6,7,8' /> + <output name='outtab' file='edgeRtest1out.xls' compare='diff' /> + <output name='html_file' file='edgeRtest1out.html' compare='diff' lines_diff='20' /> +</test> +</tests> + +<configfiles> +<configfile name="runme"> +<![CDATA[ +# +# edgeR.Rscript +# updated npv 2011 for R 2.14.0 and edgeR 2.4.0 by ross +# Performs DGE on a count table containing n replicates of two conditions +# +# Parameters +# +# 1 - Output Dir + +# Original edgeR code by: S.Lunke and A.Kaspi +reallybig = log10(.Machine\$double.xmax) +reallysmall = log10(.Machine\$double.xmin) +library('stringr') +library('gplots') +library('edgeR') +hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here') +{ +# Perform clustering for significant pvalues after controlling FWER + samples = colnames(cmat) + gu = unique(group) + if (length(gu) == 2) { + col.map = function(g) {if (g==gu[1]) "#FF0000" else "#0000FF"} + pcols = unlist(lapply(group,col.map)) + } else { + colours = rainbow(length(gu),start=0,end=4/6) + pcols = colours[match(group,gu)] } + gn = rownames(cmat) + dm = cmat[(! is.na(gn)),] + # remove unlabelled hm rows + nprobes = nrow(dm) + # sub = paste('Showing',nprobes,'contigs ranked for evidence of differential abundance') + if (nprobes > nsamp) { + dm =dm[1:nsamp,] + #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total') + } + newcolnames = substr(colnames(dm),1,20) + colnames(dm) = newcolnames + pdf(outpdfname) + heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none', + Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5) + dev.off() +} + +hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here") +{ + # for 2 groups only was + #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"} + #pcols = unlist(lapply(group,col.map)) + gu = unique(group) + colours = rainbow(length(gu),start=0.3,end=0.6) + pcols = colours[match(group,gu)] + nrows = nrow(cmat) + mtitle = paste(myTitle,'Heatmap: n contigs =',nrows) + if (nrows > nsamp) { + cmat = cmat[c(1:nsamp),] + mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='') + } + newcolnames = substr(colnames(cmat),1,20) + colnames(cmat) = newcolnames + pdf(outpdfname) + heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols) + dev.off() +} + +qqPlot = function(descr='qqplot',pvector, outpdf='qqplot.pdf',...) +# stolen from https://gist.github.com/703512 +{ + o = -log10(sort(pvector,decreasing=F)) + e = -log10( 1:length(o)/length(o) ) + o[o==-Inf] = reallysmall + o[o==Inf] = reallybig + maint = descr + pdf(outpdf) + plot(e,o,pch=19,cex=1, main=maint, ..., + xlab=expression(Expected~~-log[10](italic(p))), + ylab=expression(Observed~~-log[10](italic(p))), + xlim=c(0,max(e)), ylim=c(0,max(o))) + lines(e,e,col="red") + grid(col = "lightgray", lty = "dotted") + dev.off() +} + +smearPlot = function(DGEList,deTags, outSmear, outMain) + { + pdf(outSmear) + plotSmear(DGEList,de.tags=deTags,main=outMain) + grid(col="lightgray", lty="dotted") + dev.off() + } + +boxPlot = function(rawrs,cleanrs,maint,myTitle,pdfname) +{ # + nc = ncol(rawrs) + for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA} + fullnames = colnames(rawrs) + newcolnames = substr(colnames(rawrs),1,20) + colnames(rawrs) = newcolnames + newcolnames = substr(colnames(cleanrs),1,20) + colnames(cleanrs) = newcolnames + defpar = par(no.readonly=T) + print.noquote('raw contig counts by sample:') + print.noquote(summary(rawrs)) + print.noquote('normalised contig counts by sample:') + print.noquote(summary(cleanrs)) + pdf(pdfname) + par(mfrow=c(1,2)) + boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('Raw:',maint)) + grid(col="lightgray",lty="dotted") + boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('After ',maint)) + grid(col="lightgray",lty="dotted") + dev.off() + pdfname = "sample_counts_histogram.pdf" + nc = ncol(rawrs) + print.noquote(paste('Using ncol rawrs=',nc)) + ncroot = round(sqrt(nc)) + if (ncroot*ncroot < nc) { ncroot = ncroot + 1 } + m = c() + for (i in c(1:nc)) { + rhist = hist(rawrs[,i],breaks=100,plot=F) + m = append(m,max(rhist\$counts)) + } + ymax = max(m) + pdf(pdfname) + par(mfrow=c(ncroot,ncroot)) + for (i in c(1:nc)) { + hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon", + breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax)) + } + dev.off() + par(defpar) + +} + +cumPlot = function(rawrs,cleanrs,maint,myTitle) +{ # updated to use ecdf + pdfname = "Filtering_rowsum_bar_charts.pdf" + defpar = par(no.readonly=T) + lrs = log(rawrs,10) + lim = max(lrs) + pdf(pdfname) + par(mfrow=c(2,1)) + hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)", + ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1) + grid(col="lightgray", lty="dotted") + lrs = log(cleanrs,10) + hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)", + ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1) + grid(col="lightgray", lty="dotted") + dev.off() + par(defpar) +} + +cumPlot1 = function(rawrs,cleanrs,maint,myTitle) +{ # updated to use ecdf + pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_') + pdf(pdfname) + par(mfrow=c(2,1)) + lastx = max(rawrs) + rawe = knots(ecdf(rawrs)) + cleane = knots(ecdf(cleanrs)) + cy = 1:length(cleane)/length(cleane) + ry = 1:length(rawe)/length(rawe) + plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads", + ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) + grid(col="blue") + plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads", + ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) + grid(col="blue") + dev.off() +} + + + +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() + 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() +} + + + +edgeIt = function (Count_Matrix,group,outputfilename,fdrtype='fdr',priordf=5, + fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F, + filterquantile=0.2, subjects=c(),mydesign=NULL, + doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19', + histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + doCook=F,DESeq_fitType="parameteric") +{ + # Error handling + if (length(unique(group))!=2){ + print("Number of conditions identified in experiment does not equal 2") + q() + } + require(edgeR) + options(width = 512) + mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ") + allN = nrow(Count_Matrix) + nscut = round(ncol(Count_Matrix)/2) + colTotmillionreads = colSums(Count_Matrix)/1e6 + counts.dataframe = as.data.frame(c()) + rawrs = rowSums(Count_Matrix) + nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes + nzN = nrow(nonzerod) + nzrs = rowSums(nonzerod) + zN = allN - nzN + print('# Quantiles for non-zero row counts:',quote=F) + print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F) + if (useNDF == "T") + { + gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut + lo = colSums(Count_Matrix[!gt1rpin3,]) + workCM = Count_Matrix[gt1rpin3,] + cleanrs = rowSums(workCM) + cleanN = length(cleanrs) + meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="") + print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F) + maint = paste('Filter >=1/million reads in >=',nscut,'samples') + } else { + useme = (nzrs > quantile(nzrs,filterquantile)) + workCM = nonzerod[useme,] + lo = colSums(nonzerod[!useme,]) + cleanrs = rowSums(workCM) + cleanN = length(cleanrs) + meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="") + print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F) + maint = paste('Filter below',filterquantile,'quantile') + } + cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle) + allgenes = rownames(workCM) + reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" + genecards="<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("# urls") + print.noquote(head(contigurls)) + print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F) + cmrowsums = rowSums(workCM) + TName=unique(group)[1] + CName=unique(group)[2] + if (is.null(mydesign)) { + if (length(subjects) == 0) + { + mydesign = model.matrix(~group) + } + else { + subjf = factor(subjects) + mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it + } + } + print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=','))) + print.noquote('Using design matrix:') + print.noquote(mydesign) + if (doedgeR) { + sink('edgeR.log') + #### Setup DGEList object + DGEList = DGEList(counts=workCM, group = group) + DGEList = calcNormFactors(DGEList) + + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + comdisp = DGEList\$common.dispersion + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + if (edgeR_priordf > 0) { + print.noquote(paste("prior.df =",edgeR_priordf)) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = edgeR_priordf) + } else { + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + } + DGLM = glmFit(DGEList,design=mydesign) + DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed + efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors + normData = (1e+06*DGEList\$counts/efflib) + uoutput = cbind( + Name=as.character(rownames(DGEList\$counts)), + DE\$table, + adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), + Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums,normData, + DGEList\$counts + ) + soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable + goodness = gof(DGLM, pcutoff=fdrthresh) + if (sum(goodness\$outlier) > 0) { + print.noquote('GLM outliers:') + print(paste(rownames(DGLM)[(goodness\$outlier)],collapse=','),quote=F) + } else { + print('No GLM fit outlier genes found\n') + } + z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) + pdf("edgeR_GoodnessofFit.pdf") + qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") + abline(0,1,lwd=3) + points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon") + dev.off() + estpriorn = getPriorN(DGEList) + print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F) + efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors + normData = (1e+06*DGEList\$counts/efflib) + uniqueg = unique(group) + #### Plot MDS + sample_colors = match(group,levels(group)) + sampleTypes = levels(factor(group)) + print.noquote(sampleTypes) + pdf("edgeR_MDSplot.pdf") + plotMDS.DGEList(DGEList,main=paste("edgeR MDS for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors) + legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19) + grid(col="blue") + dev.off() + colnames(normData) = paste( colnames(normData),'N',sep="_") + print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=','))) + nzd = data.frame(log(nonzerod + 1e-2,10)) + boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle,pdfname="edgeR_raw_norm_counts_box.pdf") + write.table(soutput,outputfilename, quote=FALSE, sep="\t",row.names=F) + tt = cbind( + Name=as.character(rownames(DGEList\$counts)), + DE\$table, + adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), + Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums + ) + print.noquote("# edgeR Top tags\n") + tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely + tt = tt[order(DE\$table\$PValue),] + print.noquote(tt[1:50,]) + deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,]) + nsig = length(deTags) + print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F) + deColours = ifelse(deTags,'red','black') + pdf("edgeR_BCV_vs_abundance.pdf") + plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance") + dev.off() + dg = DGEList[order(DE\$table\$PValue),] + #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg))) + efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors + normData = (1e+06*dg\$counts/efflib) + outpdfname="edgeR_heatmap.pdf" + hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=myTitle) + outSmear = "edgeR_smearplot.pdf" + outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') + smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain) + qqPlot(descr=paste(myTitle,'edgeR QQ plot'),pvector=DE\$table\$PValue,outpdf='edgeR_qqplot.pdf') + norm.factor = DGEList\$samples\$norm.factors + topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ] + edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR)) + edgeRcounts = rep(0, length(allgenes)) + edgeRcounts[edgeRcountsindex] = 1 # Create venn diagram of hits + sink() + } ### doedgeR + if (doDESeq2 == T) + { + sink("DESeq2.log") + # DESeq2 + require('DESeq2') + library('RColorBrewer') + pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM)) + deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ Rx)) + #DESeq2 = DESeq(deSEQds,fitType='local',pAdjustMethod=fdrtype) + #rDESeq = results(DESeq2) + #newCountDataSet(workCM, group) + deSeqDatsizefac = estimateSizeFactors(deSEQds) + deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType) + resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype) + rDESeq = as.data.frame(results(resDESeq)) + rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls) + srDESeq = rDESeq[order(rDESeq\$pvalue),] + qqPlot(descr=paste(myTitle,'DESeq2 qqplot'),pvector=rDESeq\$pvalue,outpdf='DESeq2_qqplot.pdf') + cat("# DESeq top 50\n") + print.noquote(srDESeq[1:50,]) + write.table(srDESeq,paste(mt,'DESeq2_TopTable.xls',sep='_'), quote=FALSE, sep="\t",row.names=F) + topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ] + DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq)) + DESeqcounts = rep(0, length(allgenes)) + DESeqcounts[DESeqcountsindex] = 1 + pdf("DESeq2_dispersion_estimates.pdf") + plotDispEsts(resDESeq) + dev.off() + ysmall = abs(min(rDESeq\$log2FoldChange)) + ybig = abs(max(rDESeq\$log2FoldChange)) + ylimit = min(4,ysmall,ybig) + pdf("DESeq2_MA_plot.pdf") + plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit)) + dev.off() + rlogres = rlogTransformation(resDESeq) + sampledists = dist( t( assay(rlogres) ) ) + sdmat = as.matrix(sampledists) + pdf("DESeq2_sample_distance_plot.pdf") + heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"), + col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255)) + dev.off() + sink() + result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) ) + if ("try-error" %in% class(result)) { + print.noquote('DESeq2 plotPCA failed.') + } else { + pdf("DESeq2_PCA_plot.pdf") + #### wtf - print? Seems needed to get this to work + print(ppca) + dev.off() + } + } + + if (doVoom == T) { + sink('VOOM.log') + pdf("VOOM_mean_variance_plot.pdf") + dat.voomed = voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor) + dev.off() + # Use limma to fit data + fit = lmFit(dat.voomed, mydesign) + fit = eBayes(fit) + rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none") + qqPlot(descr=paste(myTitle,'VOOM-limma QQ plot'),pvector=rvoom\$P.Value,outpdf='VOOM_qqplot.pdf') + rownames(rvoom) = rownames(workCM) + rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls) + srvoom = rvoom[order(rvoom\$P.Value),] + write.table(srvoom,paste(mt,'VOOM_topTable.xls',sep='_'), quote=FALSE, sep="\t",row.names=F) + # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma + topresults.voom = srvoom[which(rvoom\$adj.P.Val < fdrthresh), ] + voomcountsindex = which(allgenes %in% topresults.voom\$ID) + voomcounts = rep(0, length(allgenes)) + voomcounts[voomcountsindex] = 1 + cat("# VOOM top 50\n") + print(srvoom[1:50,]) + sink() + } + + if (doCamera) { + doGSEA(y=DGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle, + outfname=paste(mt,"GSEA.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype) + } + + if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) { + if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) { + vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, + VOOM_limma = voomcounts, row.names = allgenes) + } else if ((doDESeq2==T) && (doedgeR==T)) { + vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes) + } else if ((doVoom==T) && (doedgeR==T)) { + vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes) + } + + if (nrow(counts.dataframe > 1)) { + counts.venn = vennCounts(counts.dataframe) + vennf = "Venn_significant_genes_overlap.pdf" + pdf(vennf) + vennDiagram(counts.venn,main=vennmain,col="maroon") + dev.off() + } + } #### doDESeq2 or doVoom + #Return our main table + uoutput + +} +#### Done + +###sink(stdout(),append=T,type="message") +builtin_gmt="" +history_gmt="" +doDESeq2 = $DESeq2.doDESeq2 # make these T or F +doVoom = $doVoom +doCamera = F +doedgeR = $edgeR.doedgeR +edgeR_priordf = 0 + +#if $DESeq2.doDESeq2 == "T" + DESeq_fitType = "$DESeq2.DESeq_fitType" +#end if +#if $edgeR.doedgeR == "T" +edgeR_priordf = $edgeR.edgeR_priordf +#end if + + +Out_Dir = "$html_file.files_path" +Input = "$input1" +TreatmentName = "$treatment_name" +TreatmentCols = "$Treat_cols" +ControlName = "$control_name" +ControlCols= "$Control_cols" +outputfilename = "$outtab" +org = "$input1.dbkey" +if (org == "") { org = "hg19"} +fdrtype = "$fdrtype" +fdrthresh = $fdrthresh +useNDF = "$useNDF" +fQ = $fQ # non-differential centile cutoff +myTitle = "$title" +subjects = c($subjectids) +nsubj = length(subjects) +if (nsubj > 0) { +if (doDESeq2) { + print('WARNING - cannot yet use DESeq2 for 2 way anova - see the docs') + doDESeq2 = F + } +} +TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1 +CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1 +cat('Got TCols=') +cat(TCols) +cat('; CCols=') +cat(CCols) +cat('\n') +useCols = c(TCols,CCols) +if (file.exists(Out_Dir) == F) dir.create(Out_Dir) +Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header +snames = colnames(Count_Matrix) +nsamples = length(snames) +if (nsubj > 0 & nsubj != nsamples) { +options("show.error.messages"=T) +mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','), + 'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=',')) +write(mess, stderr()) +quit(save="no",status=4) +} + +Count_Matrix = Count_Matrix[,useCols] ### reorder columns +if (length(subjects) != 0) {subjects = subjects[useCols]} +rn = rownames(Count_Matrix) +islib = rn %in% c('librarySize','NotInBedRegions') +LibSizes = Count_Matrix[subset(rn,islib),][1] # take first +Count_Matrix = Count_Matrix[subset(rn,! islib),] +group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) ) #Build a group descriptor +group = factor(group, levels=c(ControlName,TreatmentName)) +colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_") #Relable columns +results = edgeIt(Count_Matrix=Count_Matrix,group=group,outputfilename=outputfilename, + fdrtype='BH',priordf=edgeR_priordf,fdrthresh=0.05,outputdir='.', + myTitle=myTitle,useNDF=F,libSize=c(),filterquantile=fQ,subjects=c(), + doDESeq2=doDESeq2,doVoom=doVoom,doCamera=doCamera,doedgeR=doedgeR,org=org, + histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fitType=DESeq_fitType) +sessionInfo() +]]> +</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. + +**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. + +***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> + +</tool> + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/test-data/edgeRtest1out.html Fri Jul 26 23:50:59 2013 -0400 @@ -0,0 +1,813 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> + <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> + <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> + <meta name="generator" content="Galaxy rgToolFactory.py tool output - see http://g2.trac.bx.psu.edu/" /> + <title></title> + <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> + </head> + <body> + <div class="toolFormBody"> + +<div class="infomessage">Galaxy Tool "edgeR" run at 12/06/2013 16:42:51</div><br/> +<div><table class="simple" cellpadding="2" cellspacing="2"> +<tr> +<td><a href="edgeRtest_BCV_vs_abundance.pdf"><img src="edgeRtest_BCV_vs_abundance.jpg" title="Click to download a PDF of edgeRtest_BCV_vs_abundance.pdf" hspace="5" width="400" + alt="Image called edgeRtest_BCV_vs_abundance.pdf"/></a></td> + +<td><a href="edgeRtest_MDSplot.pdf"><img src="edgeRtest_MDSplot.jpg" title="Click to download a PDF of edgeRtest_MDSplot.pdf" hspace="5" width="400" + alt="Image called edgeRtest_MDSplot.pdf"/></a></td> + +<td><a href="edgeRtest_RowsumCum.pdf"><img src="edgeRtest_RowsumCum.jpg" title="Click to download a PDF of edgeRtest_RowsumCum.pdf" hspace="5" width="400" + alt="Image called edgeRtest_RowsumCum.pdf"/></a></td> +</tr> +<tr> +<td><a href="edgeRtest_Smearplot.pdf"><img src="edgeRtest_Smearplot.jpg" title="Click to download a PDF of edgeRtest_Smearplot.pdf" hspace="5" width="400" + alt="Image called edgeRtest_Smearplot.pdf"/></a></td> + +<td><a href="edgeRtest_heatmap.pdf"><img src="edgeRtest_heatmap.jpg" title="Click to download a PDF of edgeRtest_heatmap.pdf" hspace="5" width="400" + alt="Image called edgeRtest_heatmap.pdf"/></a></td> + +<td><a href="edgeRtest_pval_qq.pdf"><img src="edgeRtest_pval_qq.jpg" title="Click to download a PDF of edgeRtest_pval_qq.pdf" hspace="5" width="400" + alt="Image called edgeRtest_pval_qq.pdf"/></a></td> +</tr> +<tr> +<td><a href="edgeRtest_sampleBoxplot.pdf"><img src="edgeRtest_sampleBoxplot.jpg" title="Click to download a PDF of edgeRtest_sampleBoxplot.pdf" hspace="5" width="400" + alt="Image called edgeRtest_sampleBoxplot.pdf"/></a></td> + +<td><a href="edgeRtest_samplehistplot.pdf"><img src="edgeRtest_samplehistplot.jpg" title="Click to download a PDF of edgeRtest_samplehistplot.pdf" hspace="5" width="400" + alt="Image called edgeRtest_samplehistplot.pdf"/></a></td> + +<td><a href="edgeRtest_venn.pdf"><img src="edgeRtest_venn.jpg" title="Click to download a PDF of edgeRtest_venn.pdf" hspace="5" width="400" + alt="Image called edgeRtest_venn.pdf"/></a></td> +</tr> +<tr> +<td><a href="edgeRtest_voomplot.pdf"><img src="edgeRtest_voomplot.jpg" title="Click to download a PDF of edgeRtest_voomplot.pdf" hspace="5" width="400" + alt="Image called edgeRtest_voomplot.pdf"/></a></td> + +<td> </td><td> </td> +</tr></table></div> + +<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr> + +<tr><td><a href="edgeR.Rscript">edgeR.Rscript</a></td><td>19.7 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeR_runner.log">edgeR_runner.log</a></td><td>79.2 KB)</td></tr> +<tr><td><a href="edgeRtest_BCV_vs_abundance.pdf">edgeRtest_BCV_vs_abundance.pdf</a></td><td>16.4 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeRtest_DESeq_TopTable.xls">edgeRtest_DESeq_TopTable.xls</a></td><td>135.1 KB)</td></tr> +<tr><td><a href="edgeRtest_MDSplot.pdf">edgeRtest_MDSplot.pdf</a></td><td>4.9 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeRtest_RowsumCum.pdf">edgeRtest_RowsumCum.pdf</a></td><td>6.3 KB)</td></tr> +<tr><td><a href="edgeRtest_Smearplot.pdf">edgeRtest_Smearplot.pdf</a></td><td>16.7 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeRtest_VOOM_topTable.xls">edgeRtest_VOOM_topTable.xls</a></td><td>131.4 KB)</td></tr> +<tr><td><a href="edgeRtest_heatmap.pdf">edgeRtest_heatmap.pdf</a></td><td>11.3 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeRtest_pval_qq.pdf">edgeRtest_pval_qq.pdf</a></td><td>15.9 KB)</td></tr> +<tr><td><a href="edgeRtest_sampleBoxplot.pdf">edgeRtest_sampleBoxplot.pdf</a></td><td>7.8 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeRtest_samplehistplot.pdf">edgeRtest_samplehistplot.pdf</a></td><td>10.9 KB)</td></tr> +<tr><td><a href="edgeRtest_venn.pdf">edgeRtest_venn.pdf</a></td><td>9.7 KB)</td></tr> +<tr class="odd_row"><td><a href="edgeRtest_voomplot.pdf">edgeRtest_voomplot.pdf</a></td><td>16.7 KB)</td></tr> +</table></div><br/> +<div class="toolFormTitle">Rscript log</div><pre> + +## Toolfactory generated command line = Rscript - None /data/home/rlazarus/galaxy-central/database/files/000/dataset_2.dat + +Loading required package: gtools + +Loading required package: gdata + +gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED. + +gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED. + +Attaching package: ‘gdata’ + +The following object(s) are masked from ‘package:stats’: + + nobs + +The following object(s) are masked from ‘package:utils’: + + object.size + +Loading required package: caTools + +Loading required package: grid + +Loading required package: KernSmooth + +KernSmooth 2.23 loaded + +Copyright M. P. Wand 1997-2009 + +Loading required package: MASS + +Attaching package: ‘gplots’ + +The following object(s) are masked from ‘package:stats’: + + lowess + +Loading required package: Biobase + +Loading required package: BiocGenerics + +Loading required package: methods + +Attaching package: ‘BiocGenerics’ + +The following object(s) are masked from ‘package:gdata’: + + combine + +The following object(s) are masked from ‘package:stats’: + + xtabs + +The following object(s) are masked from ‘package:base’: + + anyDuplicated, cbind, colnames, duplicated, eval, Filter, Find, + + get, intersect, lapply, Map, mapply, mget, order, paste, pmax, + + pmax.int, pmin, pmin.int, Position, rbind, Reduce, rep.int, + + rownames, sapply, setdiff, table, tapply, union, unique + +Welcome to Bioconductor + + Vignettes contain introductory material; view with + + 'browseVignettes()'. To cite Bioconductor, see + + 'citation("Biobase")', and for packages 'citation("pkgname")'. + +Loading required package: locfit + +locfit 1.5-9.1 2013-03-22 + +Loading required package: lattice + +Warning message: + +found methods to import for function ‘eapply’ but not the generic itself + +Loading required package: limma + +Attaching package: ‘limma’ + +The following object(s) are masked from ‘package:DESeq’: + + plotMA + +# got TCols=2 3 4 8; CCols=1 5 6 7 + +[1] # Quantiles for non-zero row counts: + + 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% + + 1.0 1.0 2.0 3.0 4.0 8.0 13.0 24.0 86.6 753.0 2325567.0 + +[1] Read 3242 contigs. Removed 1494 with no reads. After filtering at count quantile =0.3, there are 1141 contigs + +[1] # Total low count contigs per sample = 145,111,155,120,170,67,203,86 + +Calculating library sizes from column totals. + +[1] Using samples: case_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam,case_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam,case_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam,case_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam,control_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam,control_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam,control_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam,control_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam + +[1] Using design matrix: + + (Intercept) groupcase + +1 1 1 + +2 1 1 + +3 1 1 + +4 1 1 + +5 1 0 + +6 1 0 + +7 1 0 + +8 1 0 + +attr(,"assign") + +[1] 0 1 + +attr(,"contrasts") + +attr(,"contrasts")$group + +[1] contr.treatment + +[1] "No GLM fit outlier genes found\n" + +[1] Common Dispersion = 0.24088423096811 CV = 0.490799583300669 getPriorN = 3.33333333333333 + +[1] "Raw sample read totals 1440960,1341813,2888924,1428365,2443751,1644652,1682104,1806045" + +[1] raw contig counts by sample: + + case_X11706He_AGTTCC case_X11699He_GGCTAC case_X11698He_TAGCTT case_X11700He_CTTGTA control_X11706Liv_CA control_X11700Liv_AT control_X11698Liv_AC control_X11699Liv_AT + + Min. :0.0043 Min. :0.0043 Min. :0.0043 Min. :0.0043 Min. :0.0043 Min. :0.0043 Min. :0.0043 Min. :0.0043 + + 1st Qu.:0.0043 1st Qu.:0.0043 1st Qu.:0.0043 1st Qu.:0.0043 1st Qu.:0.3032 1st Qu.:0.0043 1st Qu.:0.3032 1st Qu.:0.0043 + + Median :0.4786 Median :0.4786 Median :0.4786 Median :0.4786 Median :0.7789 Median :0.6031 Median :0.6998 Median :0.6031 + + Mean :0.9210 Mean :0.9428 Mean :1.0205 Mean :0.9753 Mean :1.0519 Mean :1.0343 Mean :0.9855 Mean :0.9966 + + 3rd Qu.:1.5770 3rd Qu.:1.5855 3rd Qu.:1.7161 3rd Qu.:1.6022 3rd Qu.:1.5410 3rd Qu.:1.6335 3rd Qu.:1.4473 3rd Qu.:1.5534 + + Max. :5.3609 Max. :5.3589 Max. :5.6967 Max. :5.3702 Max. :5.8209 Max. :5.6905 Max. :5.7999 Max. :5.7215 + + NA's :902 NA's :957 NA's :821 NA's :950 NA's :650 NA's :969 NA's :664 NA's :886 + +[1] normalised contig counts by sample: + + case_X11706He_AGTTCC case_X11699He_GGCTAC case_X11698He_TAGCTT case_X11700He_CTTGTA control_X11706Liv_CA control_X11700Liv_AT control_X11698Liv_AC control_X11699Liv_AT + + Min. :-Inf Min. :-Inf Min. :-Inf Min. :-Inf Min. :-Inf Min. :-Inf Min. :-Inf Min. :-Inf + + 1st Qu.:-Inf 1st Qu.:-Inf 1st Qu.:-Inf 1st Qu.:-Inf 1st Qu.: 0 1st Qu.:-Inf 1st Qu.: 0 1st Qu.:-Inf + + Median : 0 Median : 0 Median : 0 Median : 0 Median : 0 Median : 0 Median : 0 Median : 0 + + Mean :-Inf Mean :-Inf Mean :-Inf Mean :-Inf Mean :-Inf Mean :-Inf Mean :-Inf Mean :-Inf + + 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1 + + Max. : 5 Max. : 5 Max. : 6 Max. : 5 Max. : 6 Max. : 6 Max. : 6 Max. : 5 + +[1] Using ncol rawrs= 8 + +[1] # writing output + +[1] # urls + +<a href='http://www.genecards.org/index.php?path=/Search/keyword/0610005C13Rik'></a>0610005C13Rik</a> <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610007N19Rik'></a>0610007N19Rik</a> <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610008F07Rik'></a>0610008F07Rik</a> <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610009L18Rik'></a>0610009L18Rik</a> <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610012G03Rik'></a>0610012G03Rik</a> <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610031O16Rik'></a>0610031O16Rik</a># Top tags + + Name logFC logCPM LR PValue adj.p.value Dispersion totreads case_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam control_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam + +Mir122a Mir122a -10.661348 12.537949 445.91203 5.594743e-99 6.383602e-96 0.10205510 90428 6.644669 2.864648e+00 2.012369e+01 4.756523e+00 2.084166e+04 1.681895e+04 + +Mir192 Mir192 -7.109019 17.247415 366.13166 1.301558e-81 7.425390e-79 0.07765487 2325567 1779.663879 1.294412e+03 4.493546e+03 1.933526e+03 4.635990e+05 3.403123e+05 + +Mir208b Mir208b 13.423720 9.822338 355.41782 2.801296e-79 1.065426e-76 0.12444059 14756 1245.907022 5.701390e+02 4.930344e+03 8.819024e+02 7.453220e-01 0.000000e+00 + +Mir208a Mir208a 11.977586 8.388290 348.81540 7.675451e-78 2.189422e-75 0.09936798 4638 433.789590 7.125278e+02 1.006599e+03 6.497485e+02 0.000000e+00 0.000000e+00 + +Mir499 Mir499 14.488216 8.612770 291.12794 2.823507e-65 6.443244e-63 0.13481620 6527 608.439028 5.066577e+02 2.521609e+03 2.703574e+02 0.000000e+00 0.000000e+00 + +Mir149 Mir149 6.869731 8.785665 253.01986 5.702930e-57 1.084507e-54 0.09834684 6164 774.657675 4.595714e+02 1.710514e+03 7.663947e+02 1.750400e+01 6.246465e+00 + +Mir490 Mir490 8.453930 6.909596 251.05610 1.528281e-56 2.491098e-54 0.09745005 1741 209.881557 2.177498e+02 5.205387e+02 1.957941e+02 1.038505e+00 5.537224e-01 + +Mir802 Mir802 -12.499678 6.629391 237.56161 1.337812e-53 1.908055e-51 0.10939981 1514 0.000000 0.000000e+00 0.000000e+00 0.000000e+00 3.056548e+02 1.641034e+02 + +Mir204 Mir204 7.371179 7.558996 237.12302 1.667365e-53 2.113848e-51 0.10292005 2601 425.578854 3.264164e+02 6.462477e+02 3.754819e+02 3.040281e+00 6.923364e-01 + +Mir1948 Mir1948 -7.583326 7.293456 204.79641 1.875897e-46 2.140399e-44 0.11833999 2404 1.216112 6.923364e-01 1.661167e+00 1.636942e+00 6.476848e+02 3.852783e+02 + +Mir3073 Mir3073 -11.748556 5.881462 200.03350 2.053625e-45 2.130170e-43 0.11068562 904 0.000000 0.000000e+00 0.000000e+00 0.000000e+00 2.073471e+02 7.546467e+01 + +Mir194-2 Mir194-2 -5.439827 7.860048 165.57239 6.859199e-38 6.521955e-36 0.10671020 3570 10.520726 6.138532e+00 1.490644e+01 9.513045e+00 9.214105e+02 6.003547e+02 + +Mir10b Mir10b 5.032898 13.880770 160.06580 1.094640e-36 9.607570e-35 0.10795434 197340 25838.822630 3.216361e+04 3.631730e+04 3.446522e+04 1.825385e+03 3.738617e+02 + +Mir101b Mir101b -3.925287 11.936090 120.62427 4.618143e-28 3.763787e-26 0.09663075 59019 219.816815 3.012250e+02 8.119232e+02 4.270695e+02 1.014507e+04 7.747970e+03 + +Mir182 Mir182 -5.244384 8.942708 118.92815 1.085926e-27 8.260276e-26 0.14993443 7189 16.962242 1.439678e+01 2.209871e+01 3.130352e+01 9.840056e+02 6.154406e+02 + +Mir200b Mir200b -6.725348 5.765187 115.73189 5.440989e-27 3.880105e-25 0.16712490 888 2.432225 0.000000e+00 1.661167e+00 0.000000e+00 3.696797e+02 9.750871e+01 + +Mir181c Mir181c 3.611867 10.569815 107.82011 2.943153e-25 1.975375e-23 0.09385270 23605 2420.852087 1.892879e+03 4.093093e+03 1.902590e+03 2.320365e+02 2.727878e+02 + +Mir181d Mir181d 3.489976 7.232064 102.18661 5.053019e-24 3.203053e-22 0.08738498 2139 310.053946 2.021522e+02 5.398233e+02 2.956660e+02 3.648337e+01 1.453906e+01 + +Mir21 Mir21 -3.077771 13.881500 91.37324 1.189745e-21 7.144733e-20 0.08494272 229120 3207.212585 2.190826e+03 4.348911e+03 2.010566e+03 2.715400e+04 3.322794e+04 + +Mir199b Mir199b 5.411476 4.695869 86.70372 1.260596e-20 7.191701e-19 0.15656183 370 43.403268 4.481024e+01 9.924938e+01 4.925255e+01 1.384673e+00 5.537224e-01 + +Mir193 Mir193 -5.360157 4.775477 84.66127 3.541205e-20 1.924055e-18 0.15546497 421 1.490644 1.189131e+00 2.800640e+00 6.940516e-01 1.015454e+02 3.496299e+01 + +Mir23a Mir23a 3.003071 9.467420 82.28684 1.177029e-19 6.104501e-18 0.08920345 10118 1149.889322 1.127958e+03 2.471518e+03 1.096325e+03 1.505832e+02 1.268024e+02 + +Mir195 Mir195 3.060546 8.152797 81.39727 1.846126e-19 9.158392e-18 0.09133016 3962 429.134881 2.831909e+02 9.227086e+02 4.708957e+02 1.106253e+02 7.773378e+01 + +Ahsg Ahsg -10.101343 6.638589 79.82133 4.098447e-19 1.923759e-17 0.42092199 1524 0.000000 5.537224e-01 0.000000e+00 0.000000e+00 3.484153e+02 1.260288e+01 + +Mir148a Mir148a -2.789375 16.047169 79.76589 4.215073e-19 1.923759e-17 0.08196054 1002397 15925.598251 8.528200e+03 2.207082e+04 1.059020e+04 1.927239e+05 1.054456e+05 + +Dnm3os Dnm3os 3.189443 6.618834 79.19614 5.623979e-19 2.468062e-17 0.09496531 1401 134.000827 8.716715e+01 3.652078e+02 1.825316e+02 3.710848e+01 2.498586e+01 + +Mir27a Mir27a 2.921772 10.581277 77.26988 1.491231e-18 6.301832e-17 0.09125059 21886 2383.612353 2.512174e+03 5.321988e+03 2.361690e+03 3.285136e+02 3.477377e+02 + +Mir429 Mir429 -5.341934 4.951103 76.38758 2.331128e-18 9.499346e-17 0.18324117 499 1.783696 7.001600e-01 2.082155e+00 1.824168e+00 9.173458e+01 4.540524e+01 + +Mir214 Mir214 3.125906 6.202347 74.28213 6.771387e-18 2.664191e-16 0.09532762 1048 110.058161 5.642542e+01 1.982326e+02 9.371492e+01 3.353949e+01 1.545870e+01 + +Mir200a Mir200a -7.592263 4.059943 73.71426 9.028450e-18 3.433821e-16 0.22417820 264 0.000000 0.000000e+00 5.945653e-01 0.000000e+00 9.022671e+01 3.222698e+01 + +Mir194-1 Mir194-1 -3.970219 5.393899 73.22380 1.157517e-17 4.260408e-16 0.13095886 635 3.567392 9.102080e+00 5.552413e+00 3.648337e+00 7.615701e+01 8.859559e+01 + +Mir378 Mir378 2.954320 8.053368 73.09647 1.234655e-17 4.402315e-16 0.09566655 4075 425.697272 3.921392e+02 1.155915e+03 1.883155e+02 7.641369e+01 3.683119e+01 + +Mir322 Mir322 3.058569 8.948242 72.83721 1.407969e-17 4.868160e-16 0.10365695 7074 1012.892580 6.718588e+02 1.784708e+03 8.550716e+02 1.587027e+02 7.892635e+01 + +Mir215 Mir215 2.908934 6.381924 70.31105 5.065317e-17 1.699861e-15 0.09002036 1182 123.075020 1.610368e+02 2.727623e+02 1.222193e+02 1.938542e+01 1.716540e+01 + +Cyp3a25 Cyp3a25 -9.778602 3.913257 70.02642 5.851533e-17 1.907600e-15 0.26445237 226 0.000000 0.000000e+00 0.000000e+00 0.000000e+00 5.664447e+01 1.783696e+01 + +Mir183 Mir183 -4.214462 5.247929 66.31101 3.851010e-16 1.220556e-14 0.15955164 563 8.401920 1.388103e+00 2.432225e+00 2.423177e+00 8.638070e+01 2.905572e+01 + +Mir181a-2 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7.871042 57.68174 3.081509e-14 7.175515e-13 0.09908656 3470 479.987359 2.746892e+02 9.620198e+02 3.442496e+02 9.850509e+01 4.396336e+01 + +5430416N02Rik 5430416N02Rik -3.267228 5.202367 56.75137 4.945455e-14 1.128553e-12 0.12456687 564 6.080561 1.730841e+00 1.273562e+01 4.910826e+00 1.453378e+02 5.886197e+01 + + control_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam case_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam control_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam + +Mir122a 7.242557e+03 8.468313e+03 12 7 27 8 29767 + +Mir192 3.835916e+05 1.822922e+05 3214 3163 6029 3252 662133 + +Mir208b 0.000000e+00 0.000000e+00 2049 1647 8904 2155 1 + +Mir208a 0.000000e+00 5.537224e-01 1060 956 1693 928 0 + +Mir499 0.000000e+00 0.000000e+00 869 730 4147 781 0 + +Mir149 4.864449e+00 5.538691e+00 1399 1123 2295 1289 25 + +Mir490 0.000000e+00 7.453220e-01 353 311 750 322 3 + +Mir802 1.557723e+02 2.092870e+02 0 0 0 0 552 + +Mir204 5.537224e-01 3.683119e+00 571 549 923 541 5 + +Mir1948 1.813414e+02 4.282299e+02 2 2 3 4 869 + +Mir3073 7.253764e+01 8.757639e+01 0 0 0 0 341 + +Mir194-2 2.669366e+02 3.046280e+02 19 15 20 16 1316 + +Mir10b 6.583760e+02 7.272114e+02 34668 54096 51870 49658 3002 + +Mir101b 1.128042e+04 6.628420e+03 635 544 1984 573 17063 + +Mir182 2.366022e+03 6.536603e+02 49 26 54 42 1655 + +Mir200b 4.621056e+01 1.075780e+02 4 0 3 0 496 + +Mir181c 2.283131e+02 2.989683e+02 3488 3113 11824 3436 567 + +Mir181d 2.547123e+01 1.555095e+01 416 340 771 426 60 + +Mir21 2.570722e+04 3.537908e+04 4621 3603 12563 3631 66353 + +Mir199b 0.000000e+00 2.981288e+00 73 64 143 81 4 + +Mir193 3.377707e+01 3.396654e+01 2 2 4 1 167 + +Mir23a 8.921333e+01 2.437203e+02 1934 1611 3561 1803 435 + +Mir195 4.560421e+01 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9.716723e+00 0 0 0 0 145 + +Mir133b 0.000000e+00 0.000000e+00 32 26 77 24 0 + +Gm10069 2.868741e+02 2.757691e+01 2 2 1 1 553 + +Mir871 1.003888e+01 5.149619e+01 3 1 1 0 82 + +Mir184 1.400320e+00 1.388103e+00 55 41 90 53 4 + +Mir132 2.100480e+01 1.943345e+01 136 125 329 149 38 + +Snord104 4.430701e+03 4.212863e+03 886 720 2493 929 9351 + +Mir3074-2 4.844593e+01 8.323914e+01 644 462 1374 496 162 + +Mir24-2 3.599196e+01 5.729296e+01 644 462 1374 496 162 + +5430416N02Rik 5.531264e+01 9.786128e+01 10 5 23 12 195 + + control_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam ntotreads URL + +Mir122a 24233 11911 24463 90428 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir122a'></a>Mir122a</a> + +Mir192 490327 630849 526600 2325567 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir192'></a>Mir192</a> + +Mir208b 0 0 0 14756 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208b'></a>Mir208b</a> + +Mir208a 0 0 1 4638 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208a'></a>Mir208a</a> + +Mir499 0 0 0 6527 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir499'></a>Mir499</a> + +Mir149 9 8 16 6164 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir149'></a>Mir149</a> + +Mir490 1 0 1 1741 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir490'></a>Mir490</a> + +Mir802 401 209 352 1514 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir802'></a>Mir802</a> + +Mir204 2 1 9 2601 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir204'></a>Mir204</a> + +Mir1948 648 259 617 2404 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1948'></a>Mir1948</a> + +Mir3073 218 131 214 904 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3073'></a>Mir3073</a> + +Mir194-2 865 439 880 3570 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-2'></a>Mir194-2</a> + +Mir10b 1080 1189 1777 197340 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir10b'></a>Mir10b</a> + +Mir101b 11066 16253 10901 59019 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir101b'></a>Mir101b</a> + +Mir182 879 3409 1075 7189 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir182'></a>Mir182</a> + +Mir200b 164 66 155 888 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200b'></a>Mir200b</a> + +Mir181c 366 384 427 23605 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181c'></a>Mir181c</a> + +Mir181d 42 46 38 2139 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181d'></a>Mir181d</a> + +Mir21 44582 43237 50530 229120 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir21'></a>Mir21</a> + +Mir199b 1 0 4 370 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199b'></a>Mir199b</a> + +Mir193 101 61 83 421 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir193'></a>Mir193</a> + +Mir23a 229 218 327 10118 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23a'></a>Mir23a</a> + +Mir195 112 75 120 3962 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir195'></a>Mir195</a> + +Ahsg 18 879 40 1524 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Ahsg'></a>Ahsg</a> + +Mir148a 177349 256441 193465 1002397 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir148a'></a>Mir148a</a> + +Dnm3os 36 20 40 1401 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Dnm3os'></a>Dnm3os</a> + +Mir27a 628 400 760 21886 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27a'></a>Mir27a</a> + +Mir429 82 41 101 499 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir429'></a>Mir429</a> + +Mir214 26 17 29 1048 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir214'></a>Mir214</a> + +Mir200a 53 33 47 264 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200a'></a>Mir200a</a> + +Mir194-1 160 81 141 635 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-1'></a>Mir194-1</a> + +Mir378 90 123 106 4075 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir378'></a>Mir378</a> + +Mir322 228 91 224 7074 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir322'></a>Mir322</a> + +Mir215 31 28 36 1182 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir215'></a>Mir215</a> + +Cyp3a25 30 93 27 226 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Cyp3a25'></a>Cyp3a25</a> + +Mir183 71 181 130 563 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir183'></a>Mir183</a> + +Mir181a-2 69 115 82 2817 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181a-2'></a>Mir181a-2</a> + +Mir155 3 8 9 463 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir155'></a>Mir155</a> + +Snord91a 256 358 285 1437 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord91a'></a>Snord91a</a> + +Mir125b-2 149 60 133 3837 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-2'></a>Mir125b-2</a> + +Serpina4-ps1 19 117 14 295 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Serpina4-ps1'></a>Serpina4-ps1</a> + +Mir133b 0 0 0 159 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir133b'></a>Mir133b</a> + +Gm10069 12 701 37 1309 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Gm10069'></a>Gm10069</a> + +Mir871 81 29 93 290 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir871'></a>Mir871</a> + +Mir184 0 2 2 247 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir184'></a>Mir184</a> + +Mir132 22 30 28 857 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir132'></a>Mir132</a> + +Snord104 5610 7452 6017 33458 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord104'></a>Snord104</a> + +Mir3074-2 127 65 140 3470 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3074-2'></a>Mir3074-2</a> + +Mir24-2 127 65 140 3470 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir24-2'></a>Mir24-2</a> + +5430416N02Rik 99 79 141 564 <a href='http://www.genecards.org/index.php?path=/Search/keyword/5430416N02Rik'></a>5430416N02Rik</a> + +[1] # 445 tags significant at adj p= 0.05 + +[1] # deTags + +[1] "0610005C13Rik" "0610007N19Rik" "0610008F07Rik" "0610012G03Rik" "0610031O16Rik" "1110038B12Rik" + +# DESeq top 50 + + id baseMean baseMeanA baseMeanB foldChange log2FoldChange pval padj + +536 Mir122a 10112.31117 2.021162e+04 1.300527e+01 6.434550e-04 -10.601873 5.488369e-57 6.262229e-54 + +645 Mir208a 616.76981 2.430915e-01 1.233297e+03 5.073385e+03 12.308733 6.046036e-53 3.449263e-50 + +600 Mir192 271352.97636 5.385927e+05 4.113233e+03 7.637001e-03 -7.032778 1.311798e-38 4.989204e-36 + +568 Mir149 810.35429 1.224267e+01 1.608466e+03 1.313819e+02 7.037623 7.073079e-37 2.017596e-34 + +791 Mir490 220.99790 9.450198e-01 4.410508e+02 4.667107e+02 8.866385 1.354865e-36 3.091803e-34 + +642 Mir204 347.57397 3.737736e+00 6.914102e+02 1.849810e+02 7.531234 8.058436e-36 1.532446e-33 + +844 Mir802 166.83414 3.336683e+02 0.000000e+00 0.000000e+00 -Inf 3.255469e-35 5.306414e-33 + +698 Mir3073 98.93199 1.978640e+02 0.000000e+00 0.000000e+00 -Inf 1.111351e-28 1.585065e-26 + +646 Mir208b 1649.77924 1.445675e-01 3.299414e+03 2.282265e+04 14.478178 7.505934e-28 9.515857e-26 + +614 Mir1948 263.89527 5.246983e+02 3.092210e+00 5.893310e-03 -7.406706 1.534188e-26 1.750509e-24 + +608 Mir194-2 391.65678 7.637703e+02 1.954329e+01 2.558791e-02 -5.288394 1.622682e-24 1.683163e-22 + +796 Mir499 712.45950 0.000000e+00 1.424919e+03 Inf Inf 2.577318e-19 2.450600e-17 + +585 Mir181c 2765.96510 3.955399e+02 5.136390e+03 1.298577e+01 3.698860 1.942959e-17 1.705320e-15 + +631 Mir199b 47.84725 1.818862e+00 9.387565e+01 5.161231e+01 5.689643 4.012622e-15 3.270287e-13 + +530 Mir10b 27820.40551 1.501200e+03 5.413961e+04 3.606421e+01 5.172496 3.069912e-14 2.335180e-12 + +586 Mir181d 275.22797 4.254569e+01 5.079103e+02 1.193800e+01 3.577489 5.204519e-14 3.711473e-12 + +602 Mir193 45.70861 8.913819e+01 2.279021e+00 2.556728e-02 -5.289558 1.657854e-13 1.112713e-11 + +637 Mir200a 27.09009 5.402561e+01 1.545578e-01 2.860824e-03 -8.449354 2.466964e-13 1.563781e-11 + +722 Mir322 899.53469 1.797868e+02 1.619283e+03 9.006682e+00 3.170996 1.359804e-12 8.165978e-11 + +756 Mir378 483.21548 1.056050e+02 8.608260e+02 8.151374e+00 3.027043 2.046396e-12 1.160965e-10 + +523 Mir101b 6846.19683 1.280838e+04 8.840090e+02 6.901800e-02 -3.856884 2.136745e-12 1.160965e-10 + +552 Mir133b 19.46050 0.000000e+00 3.892101e+01 Inf Inf 2.388227e-12 1.238621e-10 + +663 Mir23a 1289.18043 2.671574e+02 2.311203e+03 8.651093e+00 3.112882 3.093049e-12 1.534421e-10 + +671 Mir27a 2788.72629 6.066392e+02 4.970813e+03 8.194019e+00 3.034571 5.970859e-12 2.838646e-10 + +616 Mir195 519.86200 1.038507e+02 9.358733e+02 9.011721e+00 3.171803 6.245320e-12 2.850364e-10 + +607 Mir194-1 71.10636 1.325374e+02 9.675354e+00 7.300095e-02 -3.775941 1.405195e-11 6.166645e-10 + +344 Dnm3os 179.61643 3.285433e+01 3.263785e+02 9.934110e+00 3.312391 1.503324e-11 6.352936e-10 + +590 Mir184 32.23796 1.645700e+00 6.283021e+01 3.817841e+01 5.254685 1.644760e-11 6.702396e-10 + +582 Mir181a-2 347.82506 8.313904e+01 6.125111e+02 7.367310e+00 2.881138 2.735424e-11 1.076248e-09 + +541 Mir125b-2 493.08516 1.122867e+02 8.738836e+02 7.782608e+00 2.960254 5.662328e-11 2.153572e-09 + +573 Mir155 57.66182 7.341434e+00 1.079822e+02 1.470860e+01 3.878588 6.173765e-11 2.272344e-09 + +464 Gm5441 20.24317 2.430915e-01 4.024325e+01 1.655478e+02 7.371104 6.503611e-11 2.318944e-09 + +571 Mir153 19.90411 2.430915e-01 3.956513e+01 1.627582e+02 7.346586 8.675688e-11 2.999685e-09 + +649 Mir21 26121.31011 4.639963e+04 5.842995e+03 1.259276e-01 -2.989333 1.048681e-10 3.519252e-09 + +654 Mir214 134.69038 2.546966e+01 2.439111e+02 9.576536e+00 3.259504 1.338023e-10 4.361954e-09 + +638 Mir200b 87.13638 1.725548e+02 1.717931e+00 9.955853e-03 -6.650239 2.397950e-10 7.600169e-09 + +666 Mir24-2 424.48288 1.104079e+02 7.385578e+02 6.689355e+00 2.741867 4.542021e-10 1.363802e-08 + +700 Mir3074-2 424.48288 1.104079e+02 7.385578e+02 6.689355e+00 2.741867 4.542021e-10 1.363802e-08 + +845 Mir871 33.03360 6.461640e+01 1.450790e+00 2.245235e-02 -5.476990 5.839129e-10 1.708320e-08 + +767 Mir429 49.75018 9.677270e+01 2.727653e+00 2.818618e-02 -5.148868 6.903556e-10 1.969239e-08 + +655 Mir215 152.78082 3.329953e+01 2.722621e+02 8.176156e+00 3.031423 8.994273e-10 2.503040e-08 + +566 Mir148a 118994.46955 2.065090e+05 3.147993e+04 1.524386e-01 -2.713700 1.022762e-09 2.772155e-08 + +569 Mir150 553.20599 1.318948e+02 9.745171e+02 7.388592e+00 2.885299 1.044721e-09 2.772155e-08 + +628 Mir1983 14.04997 0.000000e+00 2.809994e+01 Inf Inf 2.859722e-09 7.415778e-08 + +581 Mir181a-1 59.53826 1.179305e+01 1.072835e+02 9.097179e+00 3.185419 3.537731e-09 8.970115e-08 + +784 Mir470 18.37716 3.608521e+01 6.691037e-01 1.854232e-02 -5.753034 7.467127e-09 1.852172e-07 + +630 Mir199a-2 44.84878 7.384673e+00 8.231288e+01 1.114645e+01 3.478512 8.433815e-09 2.047443e-07 + +550 Mir132 106.46062 2.691980e+01 1.860014e+02 6.909467e+00 2.788574 1.329950e-08 3.161402e-07 + +76 2610203C20Rik 79.17666 1.649993e+01 1.418534e+02 8.597210e+00 3.103869 1.594438e-08 3.712764e-07 + +540 Mir125b-1 79.01988 1.649993e+01 1.415398e+02 8.578206e+00 3.100676 1.650666e-08 3.766821e-07 + +# VOOM top 50 + + ID logFC AveExpr t P.Value adj.P.Val B + +600 Mir192 -6.948883 14.6763803 -42.722954 2.301199e-16 2.625668e-13 27.266471 + +536 Mir122a -10.426125 8.1698641 -21.722912 2.859682e-12 1.087633e-09 17.760171 + +568 Mir149 7.030463 6.3160807 20.883835 4.915491e-12 1.402144e-09 17.277609 + +645 Mir208a 11.015018 3.9395538 23.252407 1.118938e-12 6.383542e-10 17.208662 + +530 Mir10b 5.130915 12.2628672 18.242023 3.124991e-11 4.963978e-09 16.221503 + +561 Mir143hg 2.249221 16.2444825 18.082481 3.521739e-11 4.963978e-09 16.026695 + +560 Mir143 2.251067 16.2358599 18.081481 3.524391e-11 4.963978e-09 16.026084 + +646 Mir208b 12.433228 4.6076218 19.592458 1.179199e-11 2.690931e-09 15.683666 + +523 Mir101b -3.765722 10.8508440 -16.538566 1.180419e-10 1.036045e-08 14.900027 + +796 Mir499 11.567529 3.7874598 17.942086 3.915496e-11 4.963978e-09 14.821741 + +566 Mir148a -2.638213 15.4435820 -16.548188 1.171186e-10 1.036045e-08 14.814792 + +844 Mir802 -9.158434 2.9157675 -17.316522 6.338616e-11 7.232360e-09 14.381577 + +698 Mir3073 -8.420542 2.5457189 -16.702657 1.033066e-10 1.036045e-08 13.985845 + +649 Mir21 -2.938537 13.1642917 -15.375404 3.148335e-10 2.394833e-08 13.867698 + +585 Mir181c 3.742560 9.6295577 15.242361 3.537063e-10 2.522368e-08 13.810405 + +791 Mir490 8.474378 3.7506957 16.259650 1.484816e-10 1.210125e-08 13.424617 + +663 Mir23a 3.165768 8.7896592 14.631179 6.110682e-10 3.873493e-08 13.269174 + +586 Mir181d 3.636211 6.3713218 14.317073 8.157508e-10 4.898798e-08 12.956333 + +642 Mir204 7.684425 4.7751735 15.033484 4.254268e-10 2.855364e-08 12.822427 + +671 Mir27a 3.106935 9.9255796 13.838251 1.281011e-09 6.960160e-08 12.513086 + +552 Mir133b 6.493549 1.2544862 13.969968 1.129934e-09 6.446275e-08 11.982684 + +608 Mir194-2 -5.264136 6.0897615 -13.044012 2.792884e-09 1.448491e-07 11.715753 + +616 Mir195 3.216595 7.4509350 12.869478 3.332788e-09 1.653353e-07 11.587523 + +756 Mir378 3.097393 7.3832049 11.684163 1.171371e-08 5.346138e-07 10.329692 + +672 Mir27b 1.976376 15.0957731 11.756036 1.082197e-08 5.144946e-07 10.127719 + +1017 Snord104 -2.337374 10.6109024 -11.495675 1.444482e-08 6.339052e-07 10.023395 + +722 Mir322 3.296618 8.2153415 11.415362 1.580762e-08 6.441605e-07 10.008472 + +655 Mir215 3.045873 5.7544234 11.148134 2.141822e-08 8.082459e-07 9.753268 + +628 Mir1983 5.895500 0.9931851 11.445812 1.527548e-08 6.441605e-07 9.749260 + +344 Dnm3os 3.363344 5.8607432 11.092261 2.283960e-08 8.082459e-07 9.689496 + +637 Mir200a -6.191561 1.7981309 -11.322172 1.756229e-08 6.909853e-07 9.662295 + +587 Mir182 -4.903995 7.1511683 -11.074468 2.331304e-08 8.082459e-07 9.658842 + +582 Mir181a-2 3.048298 6.9414651 11.072128 2.337609e-08 8.082459e-07 9.644017 + +614 Mir1948 -7.195525 4.5513493 -11.005492 2.524936e-08 8.473388e-07 9.341794 + +654 Mir214 3.280874 5.4784451 10.768257 3.332555e-08 1.086413e-06 9.318504 + +571 Mir153 5.963803 1.4386315 10.727082 3.498742e-08 1.093990e-06 9.035569 + +318 Cyp3a25 -6.318200 1.4888933 -10.698226 3.620443e-08 1.093990e-06 9.024973 + +464 Gm5441 5.982176 1.4484953 10.692891 3.643436e-08 1.093990e-06 9.000362 + +541 Mir125b-2 3.077678 7.4316058 10.446668 4.893073e-08 1.431538e-06 8.884250 + +551 Mir133a-1 5.144671 0.5903264 10.358205 5.447229e-08 1.553822e-06 8.575535 + +550 Mir132 2.847559 5.3211839 10.110952 7.380297e-08 2.004981e-06 8.531491 + +13 1110038B12Rik -2.226702 10.8487089 -10.194609 6.655312e-08 1.852125e-06 8.439308 + +922 Rabggtb -1.935779 9.9874171 -9.928995 9.262821e-08 2.457879e-06 8.133384 + +569 Mir150 2.938531 7.6297870 9.842102 1.033602e-07 2.620755e-06 8.116464 + +800 Mir504 5.256127 0.6221088 9.892894 9.693595e-08 2.513725e-06 8.068853 + +573 Mir155 3.906600 3.9899000 9.732173 1.188627e-07 2.712448e-06 8.046518 + +666 Mir24-2 2.833979 7.3083691 9.767192 1.136724e-07 2.646944e-06 8.030550 + +700 Mir3074-2 2.833979 7.3083691 9.767192 1.136724e-07 2.646944e-06 8.030550 + +664 Mir23b 2.124129 9.8141190 9.806316 1.081569e-07 2.625681e-06 7.979464 + +631 Mir199b 5.752816 2.8805143 9.823920 1.057683e-07 2.623514e-06 7.979387 + +Warning messages: + +1: In bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group == : + + Outlier (-Inf) in boxplot 5 is not drawn + +2: In bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group == : + + Outlier (-Inf) in boxplot 7 is not drawn + +3: In bxp(list(stats = c(-Inf, -Inf, -0.158608221176912, 0.826586442285183, : + + some notches went outside hinges ('box'): maybe set notch=FALSE + +4: In par(defpar) : calling par(new=TRUE) with no plot + +R version 2.15.1 (2012-06-22) + +Platform: x86_64-unknown-linux-gnu (64-bit) + +locale: + + [1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8 LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8 LC_PAPER=C LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C + +attached base packages: + +[1] methods grid stats graphics grDevices utils datasets base + +other attached packages: + + [1] edgeR_3.0.8 limma_3.14.4 DESeq_1.10.1 lattice_0.20-15 locfit_1.5-9.1 Biobase_2.18.0 BiocGenerics_0.4.0 gplots_2.11.0 MASS_7.3-23 KernSmooth_2.23-10 caTools_1.14 gdata_2.12.0.2 gtools_2.7.1 stringr_0.6.2 + +loaded via a namespace (and not attached): + + [1] annotate_1.36.0 AnnotationDbi_1.20.7 bitops_1.0-5 DBI_0.2-7 genefilter_1.40.0 geneplotter_1.36.0 IRanges_1.16.6 parallel_2.15.1 RColorBrewer_1.0-5 RSQLite_0.11.3 splines_2.15.1 stats4_2.15.1 survival_2.37-4 XML_3.96-1.1 xtable_1.7-1 + +</pre> + +<hr/><div class="infomessage">This tool (edgeR) was generated by the <a href="https://bitbucket.org/fubar/galaxytoolfactory/overview">Galaxy Tool Factory</a></div><br/> +</div></body></html> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/test-data/edgeRtest1out.xls Fri Jul 26 23:50:59 2013 -0400 @@ -0,0 +1,1142 @@ +Name logFC logCPM LR PValue adj.p.value Dispersion totreads case_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam control_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam case_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam case_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam control_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam control_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam +Mir122a -10.6613482198735 12.5379487184339 445.912028851371 5.59474310450852e-99 6.38360188224422e-96 0.102055096028295 90428 6.64466912039531 2.86464823551992 20.1236936368881 4.75652253016081 20841.6623198401 16818.9531619257 7242.55663282834 8468.31297344994 12 7 27 8 29767 24233 11911 24463 +Mir192 -7.10901851688288 17.2474145508888 366.131659138235 1.3015583263717e-81 7.42539025195057e-79 0.0776548700185199 2325567 1779.66387941254 1294.41176699279 4493.54625691845 1933.52640851037 463599.032378899 340312.253828562 383591.60517699 182292.180510107 3214 3163 6029 3252 662133 490327 630849 526600 +Mir208b 13.4237202575152 9.82233816515324 355.417815392181 2.80129611458365e-79 1.06542628891331e-76 0.124440587141633 14756 1245.90702213628 570.139045385768 4930.34448733332 881.902421077918 0.74532198655141 0 0 0 2049 1647 8904 2155 1 0 0 0 +Mir208a 11.9775863683342 8.38828977985358 348.815401742963 7.675450754893e-78 2.18942232783323e-75 0.0993679816515115 4638 433.789589950159 712.527819143148 1006.59908044528 649.748467524829 0 0 0 0.553722426699609 1060 956 1693 928 0 0 0 1 +Mir499 14.4882160574998 8.61277040381698 291.127944921827 2.82350748745294e-65 6.4432440863676e-63 0.134816198797062 6527 608.439028317971 506.657690265579 2521.60879492395 270.357373677161 0 0 0 0 869 730 4147 781 0 0 0 0 +Mir149 6.86973108218652 8.78566474384905 253.019861795716 5.70293016120625e-57 1.08450721898939e-54 0.0983468375157501 6164 774.657674952754 459.571424069838 1710.51395913549 766.394692672161 17.5039996639232 6.24646467450714 4.86444908593961 5.5386913941544 1399 1123 2295 1289 25 9 8 16 +Mir490 8.4539298899245 6.90959613094456 251.056095815646 1.52828085290668e-56 2.4910977902379e-54 0.0974500488770791 1741 209.881556643346 217.749755819205 520.538722875595 195.794075709069 1.03850463640395 0.553722426699609 0 0.74532198655141 353 311 750 322 3 1 0 1 +Mir802 -12.4996776940066 6.62939088571757 237.561606597444 1.33781241656995e-53 1.90805495913289e-51 0.10939980863872 1514 0 0 0 0 305.654779538184 164.10342034907 155.772295189245 209.286991327076 0 0 0 0 552 401 209 352 +Mir204 7.37117920479641 7.55899555710097 237.12302218121 1.66736458270285e-53 2.11384776540439e-51 0.10292005004102 2601 425.578854320855 326.416358632286 646.247667592045 375.481932100929 3.04028067871226 0.6923364242693 0.553722426699609 3.68311915995418 571 549 923 541 5 2 1 9 +Mir1948 -7.58332571104735 7.29345576009458 204.796406017043 1.87589722530299e-46 2.14039873407072e-44 0.118339985035871 2404 1.2161122714849 0.6923364242693 1.66116728009883 1.63694184886853 647.684806313175 385.278324943026 181.341436518244 428.229856018989 2 2 3 4 869 648 259 617 +Mir3073 -11.7485555893732 5.88146234349958 200.033500954155 2.05362547312175e-45 2.13016969530174e-43 0.110685621278186 904 0 0 0 0 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/test-data/gentestdata.sh Fri Jul 26 23:50:59 2013 -0400 @@ -0,0 +1,9 @@ +#!/bin/bash +# generate test data for rgGSEA +# ross lazarus June 2013 +# adjust gseajar_path ! +GSEAJAR_PATH=/home/rlazarus/galaxy-central/tool_dependency_dir/gsea_jar/2.0.12/fubar/rg_gsea_test/8e291f464aa0/jars/gsea2-2.0.12.jar +python ../rgGSEA.py --input_tab "gsea_test_DGE.xls" --adjpvalcol "5" --signcol "2" --idcol "1" --outhtml "gseatestout.html" --input_name "gsea_test" --setMax "500" --setMin "15" --nPerm "10" --plotTop "20" --gsea_jar "$GSEAJAR_PATH" --output_dir "gseatestout" --mode "Max_probe" +--title "GSEA test" --builtin_gmt "gseatestdata.gmt" + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/test-data/test_bams2mx.xls Fri Jul 26 23:50:59 2013 -0400 @@ -0,0 +1,3243 @@ +Contigname 11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam 11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam 11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam 11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam 11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam 11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam 11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam 11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam +0610005C13Rik 40 0 2 0 6 70 6 2 +0610007N19Rik 10 17 11 42 2 6 6 10 +0610008F07Rik 16 0 0 0 8 5 4 1 +0610009B14Rik 0 0 0 1 0 0 0 0 +0610009L18Rik 3 2 2 11 0 1 1 1 +0610012G03Rik 6 0 0 0 4 5 2 0 +0610031O16Rik 33 0 0 0 10 25 10 0 +0610038B21Rik 0 0 0 2 0 0 0 0 +0610038L08Rik 0 0 0 0 0 3 0 0 +0610039K10Rik 9 0 0 1 3 1 4 3 +0610040B10Rik 0 0 0 0 0 0 2 0 +0610040F04Rik 2 1 0 1 2 5 2 0 +0610043K17Rik 9 2 0 4 4 12 0 1 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeR/tool_dependencies.xml Fri Jul 26 23:50:59 2013 -0400 @@ -0,0 +1,28 @@ +<?xml version="1.0"?> +<tool_dependency> + <package name="r3" version="3.0.1"> + <repository changeset_revision="6e89ec508745" name="package_r3" owner="fubar" prior_installation_required="True" toolshed="http://testtoolshed.g2.bx.psu.edu/" /> + </package> + <package name="biocbasics" version="2.12"> + <install version="1.0"> + <actions> + <action type="set_environment_for_install"> + <repository changeset_revision="6e89ec508745" name="package_r3" owner="fubar" toolshed="http://testtoolshed.g2.bx.psu.edu/"> + <package name="r3" version="3.0.1" /> + </repository> + </action> + <action type="make_directory">$INSTALL_DIR</action> + <action type="shell_command">echo "source('http://bioconductor.org/biocLite.R')" > $INSTALL_DIR/runme.R</action> + <action type="shell_command">echo "installme=c('edgeR','limma','DESeq','DESeq2')" >> $INSTALL_DIR/runme.R</action> + <action type="shell_command">echo "biocLite()" >> $INSTALL_DIR/runme.R</action> + <action type="shell_command">echo "biocLite(installme)" >> $INSTALL_DIR/runme.R</action> + <action type="shell_command">echo "install.packages(c('stringr','gplots'),dependencies=T)" >> $INSTALL_DIR/runme.R</action> + <action type="shell_command">echo "quit(save='no')" >> $INSTALL_DIR/runme.R</action> + <action type="shell_command"> export PATH=$PATH && export R_HOME=$R_HOME && export R_LIBS=$R_LIBS && R CMD BATCH $INSTALL_DIR/runme.R </action> + </actions> + </install> + <readme>Installs some basic bioc packages for the edgeR wrapper + It's clunky but this is the only way I could get anything installed into the package_r3 R + </readme> + </package> +</tool_dependency>