# HG changeset patch # User fubar # Date 1375855775 14400 # Node ID c0fa3dde02d976498036b976e20b4ef8c9dcce9b # Parent 48d71bd383a1f12a4524b2876006762164cf7eb1 Uploaded diff -r 48d71bd383a1 -r c0fa3dde02d9 differential_count_models/rgToolFactory.py --- a/differential_count_models/rgToolFactory.py Wed Aug 07 01:58:28 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,631 +0,0 @@ -# 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 -# -# august 2013 -# found a problem with GS if $TMP or $TEMP missing - now inject /tmp and warn -# -# july 2013 -# added ability to combine images and individual log files into html output -# just make sure there's a log file foo.log and it will be output -# together with all images named like "foo_*.pdf -# otherwise old format for html -# -# January 2013 -# problem pointed out by Carlos Borroto -# added escaping for <>$ - thought I did that ages ago... -# -# August 11 2012 -# changed to use shell=False and cl as a sequence - -# This is a Galaxy tool factory for simple scripts in python, R or whatever ails ye. -# It also serves as the wrapper for the new tool. -# -# you paste and run your script -# Only works for simple scripts that read one input from the history. -# Optionally can write one new history dataset, -# and optionally collect any number of outputs into links on an autogenerated HTML page. - -# DO NOT install on a public or important site - please. - -# installed generated tools are fine if the script is safe. -# They just run normally and their user cannot do anything unusually insecure -# but please, practice safe toolshed. -# Read the fucking code before you install any tool -# especially this one - -# After you get the script working on some test data, you can -# optionally generate a toolshed compatible gzip file -# containing your script safely wrapped as an ordinary Galaxy script in your local toolshed for -# safe and largely automated installation in a production Galaxy. - -# If you opt for an HTML output, you get all the script outputs arranged -# as a single Html history item - all output files are linked, thumbnails for all the pdfs. -# Ugly but really inexpensive. -# -# Patches appreciated please. -# -# -# long route to June 2012 product -# Behold the awesome power of Galaxy and the toolshed with the tool factory to bind them -# derived from an integrated script model -# called rgBaseScriptWrapper.py -# Note to the unwary: -# This tool allows arbitrary scripting on your Galaxy as the Galaxy user -# There is nothing stopping a malicious user doing whatever they choose -# Extremely dangerous!! -# Totally insecure. So, trusted users only -# -# preferred model is a developer using their throw away workstation instance - ie a private site. -# no real risk. The universe_wsgi.ini admin_users string is checked - only admin users are permitted to run this tool. -# - -import sys -import shutil -import subprocess -import os -import time -import tempfile -import optparse -import tarfile -import re -import shutil -import math - -progname = os.path.split(sys.argv[0])[1] -myversion = '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.temp_warned = False # we want only one warning if $TMP not set - self.treatbashSpecial = treatbashSpecial - if opts.output_dir: # simplify for the tool tarball - os.chdir(opts.output_dir) - self.thumbformat = 'png' - self.opts = opts - self.toolname = re.sub('[^a-zA-Z0-9_]+', '', opts.tool_name) # a sanitizer now does this but.. - self.toolid = self.toolname - self.myname = sys.argv[0] # get our name because we write ourselves out as a tool later - self.pyfile = self.myname # crude but efficient - the cruft won't hurt much - self.xmlfile = '%s.xml' % self.toolname - s = open(self.opts.script_path,'r').readlines() - s = [x.rstrip() for x in s] # remove pesky dos line endings if needed - self.script = '\n'.join(s) - fhandle,self.sfile = tempfile.mkstemp(prefix=self.toolname,suffix=".%s" % (opts.interpreter)) - tscript = open(self.sfile,'w') # use self.sfile as script source for Popen - tscript.write(self.script) - tscript.close() - self.indentedScript = '\n'.join([' %s' % x for x in s]) # for restructured text in help - self.escapedScript = '\n'.join([html_escape(x) for x in s]) - self.elog = os.path.join(self.opts.output_dir,"%s_error.log" % self.toolname) - if opts.output_dir: # may not want these complexities - self.tlog = os.path.join(self.opts.output_dir,"%s_runner.log" % self.toolname) - art = '%s.%s' % (self.toolname,opts.interpreter) - artpath = os.path.join(self.opts.output_dir,art) # need full path - artifact = open(artpath,'w') # use self.sfile as script source for Popen - artifact.write(self.script) - artifact.close() - self.cl = [] - self.html = [] - a = self.cl.append - a(opts.interpreter) - if self.treatbashSpecial and opts.interpreter in ['bash','sh']: - a(self.sfile) - else: - a('-') # stdin - a(opts.input_tab) - a(opts.output_tab) - self.outFormats = 'tabular' # TODO make this an option at tool generation time - self.inputFormats = 'tabular' # TODO make this an option at tool generation time - self.test1Input = '%s_test1_input.xls' % self.toolname - self.test1Output = '%s_test1_output.xls' % self.toolname - self.test1HTML = '%s_test1_output.html' % self.toolname - - def makeXML(self): - """ - Create a Galaxy xml tool wrapper for the new script as a string to write out - fixme - use templating or something less fugly than this example of what we produce - - - a tabular file - - reverse.py --script_path "$runMe" --interpreter "python" - --tool_name "reverse" --input_tab "$input1" --output_tab "$tab_file" - - - - - - - - - - - -**What it Does** - -Reverse the columns in a tabular file - - - - - -# reverse order of columns in a tabular file -import sys -inp = sys.argv[1] -outp = sys.argv[2] -i = open(inp,'r') -o = open(outp,'w') -for row in i: - rs = row.rstrip().split('\t') - rs.reverse() - o.write('\t'.join(rs)) - o.write('\n') -i.close() -o.close() - - - - - - - """ - newXML=""" - %(tooldesc)s - %(command)s - - %(inputs)s - - - %(outputs)s - - - - %(script)s - - - %(tooltests)s - - %(help)s - - """ # needs a dict with toolname, toolid, interpreter, scriptname, command, inputs as a multi line string ready to write, outputs ditto, help ditto - - newCommand=""" - %(toolname)s.py --script_path "$runMe" --interpreter "%(interpreter)s" - --tool_name "%(toolname)s" %(command_inputs)s %(command_outputs)s - """ # may NOT be an input or htmlout - tooltestsTabOnly = """ - - - - - """ - tooltestsHTMLOnly = """ - - - - - """ - tooltestsBoth = """ - - - - - - """ - xdict = {} - xdict['tool_version'] = self.opts.tool_version - xdict['test1Input'] = self.test1Input - xdict['test1HTML'] = self.test1HTML - xdict['test1Output'] = self.test1Output - if self.opts.make_HTML and self.opts.output_tab <> 'None': - xdict['tooltests'] = tooltestsBoth % xdict - elif self.opts.make_HTML: - xdict['tooltests'] = tooltestsHTMLOnly % xdict - else: - xdict['tooltests'] = tooltestsTabOnly % xdict - xdict['script'] = self.escapedScript - # configfile is least painful way to embed script to avoid external dependencies - # but requires escaping of <, > and $ to avoid Mako parsing - if self.opts.help_text: - xdict['help'] = open(self.opts.help_text,'r').read() - else: - xdict['help'] = 'Please ask the tool author for help as none was supplied at tool generation' - coda = ['**Script**','Pressing execute will run the following code over your input file and generate some outputs in your history::'] - coda.append(self.indentedScript) - coda.append('**Attribution** This Galaxy tool was created by %s at %s\nusing the Galaxy Tool Factory.' % (self.opts.user_email,timenow())) - coda.append('See %s for details of that project' % (toolFactoryURL)) - coda.append('Please cite: Creating re-usable tools from scripts: The Galaxy Tool Factory. Ross Lazarus; Antony Kaspi; Mark Ziemann; The Galaxy Team. ') - coda.append('Bioinformatics 2012; doi: 10.1093/bioinformatics/bts573') - xdict['help'] = '%s\n%s' % (xdict['help'],'\n'.join(coda)) - if self.opts.tool_desc: - xdict['tooldesc'] = '%s' % self.opts.tool_desc - else: - xdict['tooldesc'] = '' - xdict['command_outputs'] = '' - xdict['outputs'] = '' - if self.opts.input_tab <> 'None': - xdict['command_inputs'] = '--input_tab "$input1" ' # the space may matter a lot if we append something - xdict['inputs'] = ' \n' % self.inputFormats - else: - xdict['command_inputs'] = '' # assume no input - eg a random data generator - xdict['inputs'] = '' - xdict['inputs'] += ' \n' % self.toolname - xdict['toolname'] = self.toolname - xdict['toolid'] = self.toolid - xdict['interpreter'] = self.opts.interpreter - xdict['scriptname'] = self.sfile - if self.opts.make_HTML: - xdict['command_outputs'] += ' --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" ' - xdict['outputs'] += ' \n' - if self.opts.output_tab <> 'None': - xdict['command_outputs'] += ' --output_tab "$tab_file"' - xdict['outputs'] += ' \n' % self.outFormats - xdict['command'] = newCommand % xdict - xmls = newXML % xdict - xf = open(self.xmlfile,'w') - xf.write(xmls) - xf.write('\n') - xf.close() - # ready for the tarball - - - def makeTooltar(self): - """ - a tool is a gz tarball with eg - /toolname/tool.xml /toolname/tool.py /toolname/test-data/test1_in.foo ... - """ - retval = self.run() - if retval: - print >> sys.stderr,'## Run failed. Cannot build yet. Please fix and retry' - sys.exit(1) - self.makeXML() - tdir = self.toolname - os.mkdir(tdir) - if self.opts.input_tab <> 'None': # no reproducible test otherwise? TODO: maybe.. - testdir = os.path.join(tdir,'test-data') - os.mkdir(testdir) # make tests directory - shutil.copyfile(self.opts.input_tab,os.path.join(testdir,self.test1Input)) - if self.opts.output_tab <> 'None': - shutil.copyfile(self.opts.output_tab,os.path.join(testdir,self.test1Output)) - if self.opts.make_HTML: - shutil.copyfile(self.opts.output_html,os.path.join(testdir,self.test1HTML)) - if self.opts.output_dir: - shutil.copyfile(self.tlog,os.path.join(testdir,'test1_out.log')) - op = '%s.py' % self.toolname # new name - outpiname = os.path.join(tdir,op) # path for the tool tarball - pyin = os.path.basename(self.pyfile) # our name - we rewrite ourselves (TM) - notes = ['# %s - a self annotated version of %s generated by running %s\n' % (op,pyin,pyin),] - notes.append('# to make a new Galaxy tool called %s\n' % self.toolname) - notes.append('# User %s at %s\n' % (self.opts.user_email,timenow())) - pi = open(self.pyfile,'r').readlines() # our code becomes new tool wrapper (!) - first Galaxy worm - notes += pi - outpi = open(outpiname,'w') - outpi.write(''.join(notes)) - outpi.write('\n') - outpi.close() - stname = os.path.join(tdir,self.sfile) - if not os.path.exists(stname): - shutil.copyfile(self.sfile, stname) - xtname = os.path.join(tdir,self.xmlfile) - if not os.path.exists(xtname): - shutil.copyfile(self.xmlfile,xtname) - tarpath = "%s.gz" % self.toolname - tar = tarfile.open(tarpath, "w:gz") - tar.add(tdir,arcname=self.toolname) - tar.close() - shutil.copyfile(tarpath,self.opts.new_tool) - shutil.rmtree(tdir) - ## TODO: replace with optional direct upload to local toolshed? - return retval - - - def compressPDF(self,inpdf=None,thumbformat='png'): - """need absolute path to pdf - note that GS gets confoozled if no $TMP or $TEMP - so we set it - """ - assert os.path.isfile(inpdf), "## Input %s supplied to %s compressPDF not found" % (inpdf,self.myName) - our_env = os.environ.copy() - if not (our_env.get('TMP',None) or our_env.get('TEMP',None)): - our_env['TMP'] = '/tmp' - if not self.temp_warned: - print >> sys.stdout,'## WARNING - no $TMP or $TEMP!!! Please fix - using /tmp temporarily' - self.temp_warned = True - hlog = os.path.join(self.opts.output_dir,"compress_%s.txt" % os.path.basename(inpdf)) - 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,env=our_env) - retval1 = x.wait() - sto.close() - if retval1 == 0: - os.unlink(inpdf) - shutil.move(outpdf,inpdf) - os.unlink(hlog) - else: - x = open(hlog,'r').readlines() - print >> sys.stdout,x - hlog = os.path.join(self.opts.output_dir,"thumbnail_%s.txt" % os.path.basename(inpdf)) - sto = open(hlog,'w') - outpng = '%s.%s' % (os.path.splitext(inpdf)[0],thumbformat) - if self.useGM: - cl2 = ['gm', 'convert', inpdf, outpng] - else: # assume imagemagick - cl2 = ['convert', inpdf, outpng] - x = subprocess.Popen(cl2,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env) - retval2 = x.wait() - sto.close() - if retval2 <> 0: - x = open(hlog,'r').readlines() - print >> sys.stdout,x - else: - os.unlink(hlog) - retval = retval1 or retval2 - return retval - - - def getfSize(self,fpath,outpath): - """ - format a nice file size string - """ - size = '' - fp = os.path.join(outpath,fpath) - if os.path.isfile(fp): - size = '0 B' - n = float(os.path.getsize(fp)) - if n > 2**20: - size = '%1.1f MB' % (n/2**20) - elif n > 2**10: - size = '%1.1f KB' % (n/2**10) - elif n > 0: - size = '%d B' % (int(n)) - return size - - def makeHtml(self): - """ Create an HTML file content to list all the artifacts found in the output_dir - """ - - galhtmlprefix = """ - - - - - - - -
- """ - galhtmlattr = """
This tool (%s) was generated by the Galaxy Tool Factory

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

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

') - html.append('
All output files available for downloading
\n') - html += fhtml # add all non-pdf files to the end of the display - else: - html.append('
### Error - %s returned no files - please confirm that parameters are sane
' % self.opts.interpreter) - html.append(galhtmlpostfix) - htmlf = file(self.opts.output_html,'w') - htmlf.write('\n'.join(html)) - htmlf.write('\n') - htmlf.close() - self.html = html - - - def run(self): - """ - scripts must be small enough not to fill the pipe! - """ - if self.treatbashSpecial and self.opts.interpreter in ['bash','sh']: - retval = self.runBash() - else: - if self.opts.output_dir: - ste = open(self.elog,'w') - sto = open(self.tlog,'w') - sto.write('## Toolfactory generated command line = %s\n' % ' '.join(self.cl)) - sto.flush() - p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=ste,stdin=subprocess.PIPE,cwd=self.opts.output_dir) - else: - p = subprocess.Popen(self.cl,shell=False,stdin=subprocess.PIPE) - p.stdin.write(self.script) - p.stdin.close() - retval = p.wait() - if self.opts.output_dir: - sto.close() - ste.close() - err = open(self.elog,'r').readlines() - if retval <> 0 and err: # problem - print >> sys.stderr,err - if self.opts.make_HTML: - self.makeHtml() - return retval - - def runBash(self): - """ - cannot use - for bash so use self.sfile - """ - if self.opts.output_dir: - s = '## Toolfactory generated command line = %s\n' % ' '.join(self.cl) - sto = open(self.tlog,'w') - sto.write(s) - sto.flush() - p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=sto,cwd=self.opts.output_dir) - else: - p = subprocess.Popen(self.cl,shell=False) - retval = p.wait() - if self.opts.output_dir: - sto.close() - if self.opts.make_HTML: - self.makeHtml() - return retval - - -def main(): - u = """ - This is a Galaxy wrapper. It expects to be called by a special purpose tool.xml as: - rgBaseScriptWrapper.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript" - - """ - op = optparse.OptionParser() - a = op.add_option - a('--script_path',default=None) - a('--tool_name',default=None) - a('--interpreter',default=None) - a('--output_dir',default=None) - a('--output_html',default=None) - a('--input_tab',default="None") - a('--output_tab',default="None") - a('--user_email',default='Unknown') - a('--bad_user',default=None) - a('--make_Tool',default=None) - a('--make_HTML',default=None) - a('--help_text',default=None) - a('--tool_desc',default=None) - a('--new_tool',default=None) - a('--tool_version',default=None) - opts, args = op.parse_args() - assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user) - assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq' - assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript' - assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R' - if opts.output_dir: - try: - os.makedirs(opts.output_dir) - except: - pass - r = ScriptRunner(opts) - if opts.make_Tool: - retcode = r.makeTooltar() - else: - retcode = r.run() - os.unlink(r.sfile) - if retcode: - sys.exit(retcode) # indicate failure to job runner - - -if __name__ == "__main__": - main() - - diff -r 48d71bd383a1 -r c0fa3dde02d9 differential_count_models/rgedgeRpaired.xml --- a/differential_count_models/rgedgeRpaired.xml Wed Aug 07 01:58:28 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1084 +0,0 @@ - - models using BioConductor packages - - biocbasics - r3 - graphicsmagick - ghostscript - - - - rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts" - --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - edgeR['doedgeR'] == "T" - - - DESeq2['doDESeq2'] == "T" - - - doVoom == "T" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - nsamp) { - dm =dm[1:nsamp,] - #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total') - } - newcolnames = substr(colnames(dm),1,20) - colnames(dm) = newcolnames - pdf(outpdfname) - heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none', - Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5) - dev.off() -} - -hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here") -{ - # for 2 groups only was - #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"} - #pcols = unlist(lapply(group,col.map)) - gu = unique(group) - colours = rainbow(length(gu),start=0.3,end=0.6) - pcols = colours[match(group,gu)] - nrows = nrow(cmat) - mtitle = paste(myTitle,'Heatmap: n contigs =',nrows) - if (nrows > nsamp) { - cmat = cmat[c(1:nsamp),] - mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='') - } - newcolnames = substr(colnames(cmat),1,20) - colnames(cmat) = newcolnames - pdf(outpdfname) - heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols) - dev.off() -} - -qqPlot = function(descr='qqplot',pvector, outpdf='qqplot.pdf',...) -# stolen from https://gist.github.com/703512 -{ - o = -log10(sort(pvector,decreasing=F)) - e = -log10( 1:length(o)/length(o) ) - o[o==-Inf] = reallysmall - o[o==Inf] = reallybig - maint = descr - pdf(outpdf) - plot(e,o,pch=19,cex=1, main=maint, ..., - xlab=expression(Expected~~-log[10](italic(p))), - ylab=expression(Observed~~-log[10](italic(p))), - xlim=c(0,max(e)), ylim=c(0,max(o))) - lines(e,e,col="red") - grid(col = "lightgray", lty = "dotted") - dev.off() -} - -smearPlot = function(DGEList,deTags, outSmear, outMain) - { - pdf(outSmear) - plotSmear(DGEList,de.tags=deTags,main=outMain) - grid(col="lightgray", lty="dotted") - dev.off() - } - -boxPlot = function(rawrs,cleanrs,maint,myTitle,pdfname) -{ # - nc = ncol(rawrs) - for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA} - fullnames = colnames(rawrs) - newcolnames = substr(colnames(rawrs),1,20) - colnames(rawrs) = newcolnames - newcolnames = substr(colnames(cleanrs),1,20) - colnames(cleanrs) = newcolnames - defpar = par(no.readonly=T) - print.noquote('raw contig counts by sample:') - print.noquote(summary(rawrs)) - print.noquote('normalised contig counts by sample:') - print.noquote(summary(cleanrs)) - pdf(pdfname) - par(mfrow=c(1,2)) - boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('Raw:',maint)) - grid(col="lightgray",lty="dotted") - boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('After ',maint)) - grid(col="lightgray",lty="dotted") - dev.off() - pdfname = "sample_counts_histogram.pdf" - nc = ncol(rawrs) - print.noquote(paste('Using ncol rawrs=',nc)) - ncroot = round(sqrt(nc)) - if (ncroot*ncroot < nc) { ncroot = ncroot + 1 } - m = c() - for (i in c(1:nc)) { - rhist = hist(rawrs[,i],breaks=100,plot=F) - m = append(m,max(rhist\$counts)) - } - ymax = max(m) - ncols = length(fullnames) - if (ncols > 20) - { - scale = 7*ncols/20 - pdf(pdfname,width=scale,height=scale) - } else { - pdf(pdfname) - } - par(mfrow=c(ncroot,ncroot)) - for (i in c(1:nc)) { - hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon", - breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax)) - } - dev.off() - par(defpar) - -} - -cumPlot = function(rawrs,cleanrs,maint,myTitle) -{ # updated to use ecdf - pdfname = "Filtering_rowsum_bar_charts.pdf" - defpar = par(no.readonly=T) - lrs = log(rawrs,10) - lim = max(lrs) - pdf(pdfname) - par(mfrow=c(2,1)) - hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)", - ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1) - grid(col="lightgray", lty="dotted") - lrs = log(cleanrs,10) - hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)", - ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1) - grid(col="lightgray", lty="dotted") - dev.off() - par(defpar) -} - -cumPlot1 = function(rawrs,cleanrs,maint,myTitle) -{ # updated to use ecdf - pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_') - pdf(pdfname) - par(mfrow=c(2,1)) - lastx = max(rawrs) - rawe = knots(ecdf(rawrs)) - cleane = knots(ecdf(cleanrs)) - cy = 1:length(cleane)/length(cleane) - ry = 1:length(rawe)/length(rawe) - plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads", - ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) - grid(col="blue") - plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads", - ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) - grid(col="blue") - dev.off() -} - - - -doGSEAold = function(y=NULL,design=NULL,histgmt="", - bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", - ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") -{ - sink('Camera.log') - genesets = c() - if (bigmt > "") - { - bigenesets = readLines(bigmt) - genesets = bigenesets - } - if (histgmt > "") - { - hgenesets = readLines(histgmt) - if (bigmt > "") { - genesets = rbind(genesets,hgenesets) - } else { - genesets = hgenesets - } # use only history if no bi - } - print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) - genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n - outf = outfname - head=paste(myTitle,'edgeR GSEA') - write(head,file=outfname,append=F) - ntest=length(genesets) - urownames = toupper(rownames(y)) - upcam = c() - downcam = c() - for (i in 1:ntest) { - gs = unlist(genesets[i]) - g = gs[1] # geneset_id - u = gs[2] - if (u > "") { u = paste("",u,"",sep="") } - glist = gs[3:length(gs)] # member gene symbols - glist = toupper(glist) - inglist = urownames %in% glist - nin = sum(inglist) - if ((nin > minnin) && (nin < maxnin)) { - ### print(paste('@@found',sum(inglist),'genes in glist')) - camres = camera(y=y,index=inglist,design=design) - if (! is.null(camres)) { - rownames(camres) = g # gene set name - camres = cbind(GeneSet=g,URL=u,camres) - if (camres\$Direction == "Up") - { - upcam = rbind(upcam,camres) } else { - downcam = rbind(downcam,camres) - } - } - } - } - uscam = upcam[order(upcam\$PValue),] - unadjp = uscam\$PValue - uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) - nup = max(10,sum((uscam\$adjPValue < fdrthresh))) - dscam = downcam[order(downcam\$PValue),] - unadjp = dscam\$PValue - dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) - ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) - write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) - write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) - print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) - write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) - print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) - write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) - sink() -} - - - - -doGSEA = function(y=NULL,design=NULL,histgmt="", - bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", - ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") -{ - sink('Camera.log') - genesets = c() - if (bigmt > "") - { - bigenesets = readLines(bigmt) - genesets = bigenesets - } - if (histgmt > "") - { - hgenesets = readLines(histgmt) - if (bigmt > "") { - genesets = rbind(genesets,hgenesets) - } else { - genesets = hgenesets - } # use only history if no bi - } - print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) - genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n - outf = outfname - head=paste(myTitle,'edgeR GSEA') - write(head,file=outfname,append=F) - ntest=length(genesets) - urownames = toupper(rownames(y)) - upcam = c() - downcam = c() - incam = c() - urls = c() - gsids = c() - for (i in 1:ntest) { - gs = unlist(genesets[i]) - gsid = gs[1] # geneset_id - url = gs[2] - if (url > "") { url = paste("",url,"",sep="") } - glist = gs[3:length(gs)] # member gene symbols - glist = toupper(glist) - inglist = urownames %in% glist - nin = sum(inglist) - if ((nin > minnin) && (nin < maxnin)) { - incam = c(incam,inglist) - gsids = c(gsids,gsid) - urls = c(urls,url) - } - } - incam = as.list(incam) - names(incam) = gsids - allcam = camera(y=y,index=incam,design=design) - allcamres = cbind(geneset=gsids,allcam,URL=urls) - for (i in 1:ntest) { - camres = allcamres[i] - res = try(test = (camres\$Direction == "Up")) - if ("try-error" %in% class(res)) { - cat("test failed, camres = :") - print.noquote(camres) - } else { if (camres\$Direction == "Up") - { upcam = rbind(upcam,camres) - } else { downcam = rbind(downcam,camres) - } - - } - } - uscam = upcam[order(upcam\$PValue),] - unadjp = uscam\$PValue - uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) - nup = max(10,sum((uscam\$adjPValue < fdrthresh))) - dscam = downcam[order(downcam\$PValue),] - unadjp = dscam\$PValue - dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) - ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) - write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) - write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) - print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) - write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) - print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) - write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) - sink() - } - - -edgeIt = function (Count_Matrix=c(),group=c(),out_edgeR=F,out_VOOM=F,out_DESeq2=F,fdrtype='fdr',priordf=5, - fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F, - filterquantile=0.2, subjects=c(),mydesign=NULL, - doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19', - histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", - doCook=F,DESeq_fitType="parameteric") -{ - # Error handling - if (length(unique(group))!=2){ - print("Number of conditions identified in experiment does not equal 2") - q() - } - require(edgeR) - options(width = 512) - mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ") - allN = nrow(Count_Matrix) - nscut = round(ncol(Count_Matrix)/2) - colTotmillionreads = colSums(Count_Matrix)/1e6 - counts.dataframe = as.data.frame(c()) - rawrs = rowSums(Count_Matrix) - nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes - nzN = nrow(nonzerod) - nzrs = rowSums(nonzerod) - zN = allN - nzN - print('# Quantiles for non-zero row counts:',quote=F) - print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F) - if (useNDF == T) - { - gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut - lo = colSums(Count_Matrix[!gt1rpin3,]) - workCM = Count_Matrix[gt1rpin3,] - cleanrs = rowSums(workCM) - cleanN = length(cleanrs) - meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="") - print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F) - maint = paste('Filter >=1/million reads in >=',nscut,'samples') - } else { - useme = (nzrs > quantile(nzrs,filterquantile)) - workCM = nonzerod[useme,] - lo = colSums(nonzerod[!useme,]) - cleanrs = rowSums(workCM) - cleanN = length(cleanrs) - meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="") - print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F) - maint = paste('Filter below',filterquantile,'quantile') - } - cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle) - allgenes = rownames(workCM) - reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" - genecards=" 0.8) # is ucsc style string - { - print("@@ using ucsc substitution for urls") - contigurls = paste0(ucsc,"&position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"") - } else { - print("@@ using genecards substitution for urls") - contigurls = paste0(genecards,allgenes,"\'>",allgenes,"") - } - print.noquote("# urls") - print.noquote(head(contigurls)) - print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F) - cmrowsums = rowSums(workCM) - TName=unique(group)[1] - CName=unique(group)[2] - if (is.null(mydesign)) { - if (length(subjects) == 0) - { - mydesign = model.matrix(~group) - } - else { - subjf = factor(subjects) - mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it - } - } - print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=','))) - print.noquote('Using design matrix:') - print.noquote(mydesign) - if (doedgeR) { - sink('edgeR.log') - #### Setup DGEList object - DGEList = DGEList(counts=workCM, group = group) - DGEList = calcNormFactors(DGEList) - - DGEList = estimateGLMCommonDisp(DGEList,mydesign) - comdisp = DGEList\$common.dispersion - DGEList = estimateGLMTrendedDisp(DGEList,mydesign) - if (edgeR_priordf > 0) { - print.noquote(paste("prior.df =",edgeR_priordf)) - DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = edgeR_priordf) - } else { - DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) - } - DGLM = glmFit(DGEList,design=mydesign) - DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed - efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors - normData = (1e+06*DGEList\$counts/efflib) - uoutput = cbind( - Name=as.character(rownames(DGEList\$counts)), - DE\$table, - adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), - Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums,normData, - DGEList\$counts - ) - soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable - goodness = gof(DGLM, pcutoff=fdrthresh) - if (sum(goodness\$outlier) > 0) { - print.noquote('GLM outliers:') - print(paste(rownames(DGLM)[(goodness\$outlier)],collapse=','),quote=F) - } else { - print('No GLM fit outlier genes found\n') - } - z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) - pdf("edgeR_GoodnessofFit.pdf") - qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") - abline(0,1,lwd=3) - points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon") - dev.off() - estpriorn = getPriorN(DGEList) - print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F) - efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors - normData = (1e+06*DGEList\$counts/efflib) - uniqueg = unique(group) - #### Plot MDS - sample_colors = match(group,levels(group)) - sampleTypes = levels(factor(group)) - print.noquote(sampleTypes) - pdf("edgeR_MDSplot.pdf") - plotMDS.DGEList(DGEList,main=paste("edgeR MDS for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors) - legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19) - grid(col="blue") - dev.off() - colnames(normData) = paste( colnames(normData),'N',sep="_") - print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=','))) - nzd = data.frame(log(nonzerod + 1e-2,10)) - try( boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle,pdfname="edgeR_raw_norm_counts_box.pdf") ) - write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F) - tt = cbind( - Name=as.character(rownames(DGEList\$counts)), - DE\$table, - adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), - Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums - ) - print.noquote("# edgeR Top tags\n") - tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely - tt = tt[order(DE\$table\$PValue),] - print.noquote(tt[1:50,]) - deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,]) - nsig = length(deTags) - print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F) - deColours = ifelse(deTags,'red','black') - pdf("edgeR_BCV_vs_abundance.pdf") - plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance") - dev.off() - dg = DGEList[order(DE\$table\$PValue),] - #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg))) - efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors - normData = (1e+06*dg\$counts/efflib) - outpdfname="edgeR_top_100_heatmap.pdf" - hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('edgeR Heatmap',myTitle)) - outSmear = "edgeR_smearplot.pdf" - outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') - smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain) - qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf='edgeR_qqplot.pdf') - norm.factor = DGEList\$samples\$norm.factors - topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ] - edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR)) - edgeRcounts = rep(0, length(allgenes)) - edgeRcounts[edgeRcountsindex] = 1 # Create venn diagram of hits - sink() - } ### doedgeR - if (doDESeq2 == T) - { - sink("DESeq2.log") - # DESeq2 - require('DESeq2') - library('RColorBrewer') - if (length(subjects) == 0) - { - pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM)) - deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ Rx)) - } else { - pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM)) - deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ subjects + Rx)) - } - #DESeq2 = DESeq(deSEQds,fitType='local',pAdjustMethod=fdrtype) - #rDESeq = results(DESeq2) - #newCountDataSet(workCM, group) - deSeqDatsizefac = estimateSizeFactors(deSEQds) - deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType) - resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype) - rDESeq = as.data.frame(results(resDESeq)) - rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls) - srDESeq = rDESeq[order(rDESeq\$pvalue),] - qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf') - cat("# DESeq top 50\n") - print.noquote(srDESeq[1:50,]) - write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F) - topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ] - DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq)) - DESeqcounts = rep(0, length(allgenes)) - DESeqcounts[DESeqcountsindex] = 1 - pdf("DESeq2_dispersion_estimates.pdf") - plotDispEsts(resDESeq) - dev.off() - ysmall = abs(min(rDESeq\$log2FoldChange)) - ybig = abs(max(rDESeq\$log2FoldChange)) - ylimit = min(4,ysmall,ybig) - pdf("DESeq2_MA_plot.pdf") - plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit)) - dev.off() - rlogres = rlogTransformation(resDESeq) - sampledists = dist( t( assay(rlogres) ) ) - sdmat = as.matrix(sampledists) - pdf("DESeq2_sample_distance_plot.pdf") - heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"), - col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255)) - dev.off() - ###outpdfname="DESeq2_top50_heatmap.pdf" - ###hmap2(sresDESeq,nsamp=50,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('DESeq2 vst rlog Heatmap',myTitle)) - sink() - result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) ) - if ("try-error" %in% class(result)) { - print.noquote('DESeq2 plotPCA failed.') - } else { - pdf("DESeq2_PCA_plot.pdf") - #### wtf - print? Seems needed to get this to work - print(ppca) - dev.off() - } - } - - if (doVoom == T) { - sink('VOOM.log') - if (doedgeR == F) { - #### Setup DGEList object - DGEList = DGEList(counts=workCM, group = group) - DGEList = calcNormFactors(DGEList) - DGEList = estimateGLMCommonDisp(DGEList,mydesign) - DGEList = estimateGLMTrendedDisp(DGEList,mydesign) - DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) - DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) - norm.factor = DGEList\$samples\$norm.factors - } - pdf("VOOM_mean_variance_plot.pdf") - dat.voomed = voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor) - dev.off() - # Use limma to fit data - fit = lmFit(dat.voomed, mydesign) - fit = eBayes(fit) - rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none") - qqPlot(descr=paste(myTitle,'VOOM-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf='VOOM_qqplot.pdf') - rownames(rvoom) = rownames(workCM) - rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls) - srvoom = rvoom[order(rvoom\$P.Value),] - cat("# VOOM top 50\n") - print(srvoom[1:50,]) - write.table(srvoom,file=out_VOOM, quote=FALSE, sep="\t",row.names=F) - # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma - topresults.voom = rvoom[which(rvoom\$adj.P.Val < fdrthresh), ] - voomcountsindex = which(allgenes %in% topresults.voom\$ID) - voomcounts = rep(0, length(allgenes)) - voomcounts[voomcountsindex] = 1 - sink() - } - - if (doCamera) { - doGSEA(y=DGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle, - outfname=paste(mt,"GSEA.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype) - } - - if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) { - if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) { - vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh) - counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, - VOOM_limma = voomcounts, row.names = allgenes) - } else if ((doDESeq2==T) && (doedgeR==T)) { - vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh) - counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes) - } else if ((doVoom==T) && (doedgeR==T)) { - vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh) - counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes) - } - - if (nrow(counts.dataframe > 1)) { - counts.venn = vennCounts(counts.dataframe) - vennf = "Venn_significant_genes_overlap.pdf" - pdf(vennf) - vennDiagram(counts.venn,main=vennmain,col="maroon") - dev.off() - } - } #### doDESeq2 or doVoom - -} -#### Done - -###sink(stdout(),append=T,type="message") -builtin_gmt = "" -history_gmt = "" -history_gmt_name = "" -out_edgeR = F -out_DESeq2 = F -out_VOOM = "$out_VOOM" -doDESeq2 = $DESeq2.doDESeq2 # make these T or F -doVoom = $doVoom -doCamera = F -doedgeR = $edgeR.doedgeR -edgeR_priordf = 0 - - -#if $doVoom == "T": - out_VOOM = "$out_VOOM" -#end if - -#if $DESeq2.doDESeq2 == "T": - out_DESeq2 = "$out_DESeq2" - DESeq_fitType = "$DESeq2.DESeq_fitType" -#end if - -#if $edgeR.doedgeR == "T": - out_edgeR = "$out_edgeR" - edgeR_priordf = $edgeR.edgeR_priordf -#end if - - - -if (sum(c(doedgeR,doVoom,doDESeq2)) == 0) -{ -write("No methods chosen - nothing to do! Please try again after choosing one or more methods", stderr()) -quit(save="no",status=2) -} - -Out_Dir = "$html_file.files_path" -Input = "$input1" -TreatmentName = "$treatment_name" -TreatmentCols = "$Treat_cols" -ControlName = "$control_name" -ControlCols= "$Control_cols" -org = "$input1.dbkey" -if (org == "") { org = "hg19"} -fdrtype = "$fdrtype" -fdrthresh = $fdrthresh -useNDF = $useNDF -fQ = $fQ # non-differential centile cutoff -myTitle = "$title" -sids = strsplit("$subjectids",',') -subjects = unlist(sids) -nsubj = length(subjects) -TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1 -CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1 -cat('Got TCols=') -cat(TCols) -cat('; CCols=') -cat(CCols) -cat('\n') -useCols = c(TCols,CCols) -if (file.exists(Out_Dir) == F) dir.create(Out_Dir) -Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header -snames = colnames(Count_Matrix) -nsamples = length(snames) -if (nsubj > 0 & nsubj != nsamples) { -options("show.error.messages"=T) -mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','), - 'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=',')) -write(mess, stderr()) -quit(save="no",status=4) -} -if (length(subjects) != 0) {subjects = subjects[useCols]} -Count_Matrix = Count_Matrix[,useCols] ### reorder columns -rn = rownames(Count_Matrix) -islib = rn %in% c('librarySize','NotInBedRegions') -LibSizes = Count_Matrix[subset(rn,islib),][1] # take first -Count_Matrix = Count_Matrix[subset(rn,! islib),] -group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) ) #Build a group descriptor -group = factor(group, levels=c(ControlName,TreatmentName)) -colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_") #Relable columns -results = edgeIt(Count_Matrix=Count_Matrix,group=group, out_edgeR=out_edgeR, out_VOOM=out_VOOM, out_DESeq2=out_DESeq2, - fdrtype='BH',mydesign=NULL,priordf=edgeR_priordf,fdrthresh=fdrthresh,outputdir='.', - myTitle=myTitle,useNDF=F,libSize=c(),filterquantile=fQ,subjects=subjects, - doDESeq2=doDESeq2,doVoom=doVoom,doCamera=doCamera,doedgeR=doedgeR,org=org, - histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fitType=DESeq_fitType) -sessionInfo() -]]> - - - - -**What it does** - -Allows short read sequence counts from controlled experiments to be analysed for differentially expressed genes. -Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design. - -**Input** - -Requires a count matrix as a tabular file. These are best made using the companion HTSeq_ based counter Galaxy wrapper -and your fave gene model to generate inputs. Each row is a genomic feature (gene or exon eg) and each column the -non-negative integer count of reads from one sample overlapping the feature. -The matrix must have a header row uniquely identifying the source samples, and unique row names in -the first column. Typically the row names are gene symbols or probe ids for downstream use in GSEA and other methods. - -**Specifying comparisons** - -This is basically dumbed down for two factors - case vs control. - -More complex interfaces are possible but painful at present. -Probably need to specify a phenotype file to do this better. -Work in progress. Send code. - -If you have (eg) paired samples and wish to include a term in the GLM to account for some other factor (subject in the case of paired samples), -put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or -A list of integers, one for each subject or an empty string if samples are all independent. -If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix. -Integers for samples that are not in the analysis *must* be present in the string as filler even if not used. - -So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones -eg if you had 6 samples with the first two independent but the second and third pairs each being from independent subjects. you might use -8,9,1,1,2,2 -as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6 - -**Methods available** - -You can run 3 popular Bioconductor packages available for count data. - -edgeR - see edgeR_ for details - -VOOM/limma - see limma_VOOM_ for details - -DESeq2 - see DESeq2_ for details - -and optionally camera in edgeR which works better if MSigDB is installed. - -**Outputs** - -Some helpful plots and analysis results. Note that most of these are produced using R code -suggested by the excellent documentation and vignettes for the Bioconductor -packages invoked. The Tool Factory is used to automatically lay these out for you to enjoy. - -**Note on Voom** - -The voom from limma version 3.16.6 help in R includes this from the authors - but you should read the paper to interpret this method. - -This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma. - -voom is an acronym for mean-variance modelling at the observational level. -The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation. -Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend. -This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance. -The weights are then used in the linear modelling process to adjust for heteroscedasticity. - -In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess. -The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag. -The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag. -Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays. -Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation. -This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data. - - -Author(s) - -Charity Law and Gordon Smyth - -References - -Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia. - -Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts. -Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia. -http://www.statsci.org/smyth/pubs/VoomPreprint.pdf - -See Also - -A voom case study is given in the edgeR User's Guide. - -vooma is a similar function but for microarrays instead of RNA-seq. - - -***old rant on changes to Bioconductor package variable names between versions*** - -The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue) -breaking this and all other code that assumed the old name for this variable, -between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). -This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing -to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly -when their old scripts break. This tool currently now works with 2.4.6. - -**Note on prior.N** - -http://seqanswers.com/forums/showthread.php?t=5591 says: - -*prior.n* - -The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. -You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood -in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your -tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the -common likelihood the weight of one observation. - -In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, -or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that -you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation -(squeezing) of the tagwise dispersions. How many samples do you have in your experiment? -What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10. -If you have more samples, then the tagwise dispersion estimates will be more reliable, -so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. - - -From Bioconductor Digest, Vol 118, Issue 5, Gordon writes: - -Dear Dorota, - -The important settings are prior.df and trend. - -prior.n and prior.df are related through prior.df = prior.n * residual.df, -and your experiment has residual.df = 36 - 12 = 24. So the old setting of -prior.n=10 is equivalent for your data to prior.df = 240, a very large -value. Going the other way, the new setting of prior.df=10 is equivalent -to prior.n=10/24. - -To recover old results with the current software you would use - - estimateTagwiseDisp(object, prior.df=240, trend="none") - -To get the new default from old software you would use - - estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE) - -Actually the old trend method is equivalent to trend="loess" in the new -software. You should use plotBCV(object) to see whether a trend is -required. - -Note you could also use - - prior.n = getPriorN(object, prior.df=10) - -to map between prior.df and prior.n. - ----- - -**Attributions** - -edgeR - edgeR_ - -VOOM/limma - limma_VOOM_ - -DESeq2 - DESeq2_ for details - -See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package. - -Galaxy_ (that's what you are using right now!) for gluing everything together - -Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is -licensed to you under the LGPL_ like other rgenetics artefacts - -.. _LGPL: http://www.gnu.org/copyleft/lesser.html -.. _HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/index.html -.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html -.. _DESeq2: http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html -.. _limma_VOOM: http://www.bioconductor.org/packages/release/bioc/html/limma.html -.. _Galaxy: http://getgalaxy.org - - - - - diff -r 48d71bd383a1 -r c0fa3dde02d9 differential_count_models/rgedgeRpaired_nocamera.xml --- a/differential_count_models/rgedgeRpaired_nocamera.xml Wed Aug 07 01:58:28 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1072 +0,0 @@ - - models using BioConductor packages - - biocbasics - r3 - graphicsmagick - ghostscript - - - - rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts" - --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - edgeR['doedgeR'] == "T" - - - DESeq2['doDESeq2'] == "T" - - - doVoom == "T" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - nsamp) { - dm =dm[1:nsamp,] - #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total') - } - newcolnames = substr(colnames(dm),1,20) - colnames(dm) = newcolnames - pdf(outpdfname) - heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none', - Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5) - dev.off() -} - -hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here") -{ - # for 2 groups only was - #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"} - #pcols = unlist(lapply(group,col.map)) - gu = unique(group) - colours = rainbow(length(gu),start=0.3,end=0.6) - pcols = colours[match(group,gu)] - nrows = nrow(cmat) - mtitle = paste(myTitle,'Heatmap: n contigs =',nrows) - if (nrows > nsamp) { - cmat = cmat[c(1:nsamp),] - mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='') - } - newcolnames = substr(colnames(cmat),1,20) - colnames(cmat) = newcolnames - pdf(outpdfname) - heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols) - dev.off() -} - -qqPlot = function(descr='qqplot',pvector, outpdf='qqplot.pdf',...) -# stolen from https://gist.github.com/703512 -{ - o = -log10(sort(pvector,decreasing=F)) - e = -log10( 1:length(o)/length(o) ) - o[o==-Inf] = reallysmall - o[o==Inf] = reallybig - maint = descr - pdf(outpdf) - plot(e,o,pch=19,cex=1, main=maint, ..., - xlab=expression(Expected~~-log[10](italic(p))), - ylab=expression(Observed~~-log[10](italic(p))), - xlim=c(0,max(e)), ylim=c(0,max(o))) - lines(e,e,col="red") - grid(col = "lightgray", lty = "dotted") - dev.off() -} - -smearPlot = function(DGEList,deTags, outSmear, outMain) - { - pdf(outSmear) - plotSmear(DGEList,de.tags=deTags,main=outMain) - grid(col="lightgray", lty="dotted") - dev.off() - } - -boxPlot = function(rawrs,cleanrs,maint,myTitle,pdfname) -{ # - nc = ncol(rawrs) - for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA} - fullnames = colnames(rawrs) - newcolnames = substr(colnames(rawrs),1,20) - colnames(rawrs) = newcolnames - newcolnames = substr(colnames(cleanrs),1,20) - colnames(cleanrs) = newcolnames - defpar = par(no.readonly=T) - print.noquote('raw contig counts by sample:') - print.noquote(summary(rawrs)) - print.noquote('normalised contig counts by sample:') - print.noquote(summary(cleanrs)) - pdf(pdfname) - par(mfrow=c(1,2)) - boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('Raw:',maint)) - grid(col="lightgray",lty="dotted") - boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('After ',maint)) - grid(col="lightgray",lty="dotted") - dev.off() - pdfname = "sample_counts_histogram.pdf" - nc = ncol(rawrs) - print.noquote(paste('Using ncol rawrs=',nc)) - ncroot = round(sqrt(nc)) - if (ncroot*ncroot < nc) { ncroot = ncroot + 1 } - m = c() - for (i in c(1:nc)) { - rhist = hist(rawrs[,i],breaks=100,plot=F) - m = append(m,max(rhist\$counts)) - } - ymax = max(m) - ncols = length(fullnames) - if (ncols > 20) - { - scale = 7*ncols/20 - pdf(pdfname,width=scale,height=scale) - } else { - pdf(pdfname) - } - par(mfrow=c(ncroot,ncroot)) - for (i in c(1:nc)) { - hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon", - breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax)) - } - dev.off() - par(defpar) - -} - -cumPlot = function(rawrs,cleanrs,maint,myTitle) -{ # updated to use ecdf - pdfname = "Filtering_rowsum_bar_charts.pdf" - defpar = par(no.readonly=T) - lrs = log(rawrs,10) - lim = max(lrs) - pdf(pdfname) - par(mfrow=c(2,1)) - hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)", - ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1) - grid(col="lightgray", lty="dotted") - lrs = log(cleanrs,10) - hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)", - ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1) - grid(col="lightgray", lty="dotted") - dev.off() - par(defpar) -} - -cumPlot1 = function(rawrs,cleanrs,maint,myTitle) -{ # updated to use ecdf - pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_') - pdf(pdfname) - par(mfrow=c(2,1)) - lastx = max(rawrs) - rawe = knots(ecdf(rawrs)) - cleane = knots(ecdf(cleanrs)) - cy = 1:length(cleane)/length(cleane) - ry = 1:length(rawe)/length(rawe) - plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads", - ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) - grid(col="blue") - plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads", - ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) - grid(col="blue") - dev.off() -} - - - -doGSEAold = function(y=NULL,design=NULL,histgmt="", - bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", - ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") -{ - sink('Camera.log') - genesets = c() - if (bigmt > "") - { - bigenesets = readLines(bigmt) - genesets = bigenesets - } - if (histgmt > "") - { - hgenesets = readLines(histgmt) - if (bigmt > "") { - genesets = rbind(genesets,hgenesets) - } else { - genesets = hgenesets - } # use only history if no bi - } - print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) - genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n - outf = outfname - head=paste(myTitle,'edgeR GSEA') - write(head,file=outfname,append=F) - ntest=length(genesets) - urownames = toupper(rownames(y)) - upcam = c() - downcam = c() - for (i in 1:ntest) { - gs = unlist(genesets[i]) - g = gs[1] # geneset_id - u = gs[2] - if (u > "") { u = paste("",u,"",sep="") } - glist = gs[3:length(gs)] # member gene symbols - glist = toupper(glist) - inglist = urownames %in% glist - nin = sum(inglist) - if ((nin > minnin) && (nin < maxnin)) { - ### print(paste('@@found',sum(inglist),'genes in glist')) - camres = camera(y=y,index=inglist,design=design) - if (! is.null(camres)) { - rownames(camres) = g # gene set name - camres = cbind(GeneSet=g,URL=u,camres) - if (camres\$Direction == "Up") - { - upcam = rbind(upcam,camres) } else { - downcam = rbind(downcam,camres) - } - } - } - } - uscam = upcam[order(upcam\$PValue),] - unadjp = uscam\$PValue - uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) - nup = max(10,sum((uscam\$adjPValue < fdrthresh))) - dscam = downcam[order(downcam\$PValue),] - unadjp = dscam\$PValue - dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) - ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) - write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) - write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) - print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) - write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) - print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) - write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) - sink() -} - - - - -doGSEA = function(y=NULL,design=NULL,histgmt="", - bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", - ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") -{ - sink('Camera.log') - genesets = c() - if (bigmt > "") - { - bigenesets = readLines(bigmt) - genesets = bigenesets - } - if (histgmt > "") - { - hgenesets = readLines(histgmt) - if (bigmt > "") { - genesets = rbind(genesets,hgenesets) - } else { - genesets = hgenesets - } # use only history if no bi - } - print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) - genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n - outf = outfname - head=paste(myTitle,'edgeR GSEA') - write(head,file=outfname,append=F) - ntest=length(genesets) - urownames = toupper(rownames(y)) - upcam = c() - downcam = c() - incam = c() - urls = c() - gsids = c() - for (i in 1:ntest) { - gs = unlist(genesets[i]) - gsid = gs[1] # geneset_id - url = gs[2] - if (url > "") { url = paste("",url,"",sep="") } - glist = gs[3:length(gs)] # member gene symbols - glist = toupper(glist) - inglist = urownames %in% glist - nin = sum(inglist) - if ((nin > minnin) && (nin < maxnin)) { - incam = c(incam,inglist) - gsids = c(gsids,gsid) - urls = c(urls,url) - } - } - incam = as.list(incam) - names(incam) = gsids - allcam = camera(y=y,index=incam,design=design) - allcamres = cbind(geneset=gsids,allcam,URL=urls) - for (i in 1:ntest) { - camres = allcamres[i] - res = try(test = (camres\$Direction == "Up")) - if ("try-error" %in% class(res)) { - cat("test failed, camres = :") - print.noquote(camres) - } else { if (camres\$Direction == "Up") - { upcam = rbind(upcam,camres) - } else { downcam = rbind(downcam,camres) - } - - } - } - uscam = upcam[order(upcam\$PValue),] - unadjp = uscam\$PValue - uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) - nup = max(10,sum((uscam\$adjPValue < fdrthresh))) - dscam = downcam[order(downcam\$PValue),] - unadjp = dscam\$PValue - dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) - ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) - write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) - write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) - print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) - write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) - print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) - write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) - sink() - } - - -edgeIt = function (Count_Matrix=c(),group=c(),out_edgeR=F,out_VOOM=F,out_DESeq2=F,fdrtype='fdr',priordf=5, - fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F, - filterquantile=0.2, subjects=c(),mydesign=NULL, - doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19', - histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", - doCook=F,DESeq_fitType="parameteric") -{ - # Error handling - if (length(unique(group))!=2){ - print("Number of conditions identified in experiment does not equal 2") - q() - } - require(edgeR) - options(width = 512) - mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ") - allN = nrow(Count_Matrix) - nscut = round(ncol(Count_Matrix)/2) - colTotmillionreads = colSums(Count_Matrix)/1e6 - counts.dataframe = as.data.frame(c()) - rawrs = rowSums(Count_Matrix) - nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes - nzN = nrow(nonzerod) - nzrs = rowSums(nonzerod) - zN = allN - nzN - print('# Quantiles for non-zero row counts:',quote=F) - print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F) - if (useNDF == T) - { - gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut - lo = colSums(Count_Matrix[!gt1rpin3,]) - workCM = Count_Matrix[gt1rpin3,] - cleanrs = rowSums(workCM) - cleanN = length(cleanrs) - meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="") - print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F) - maint = paste('Filter >=1/million reads in >=',nscut,'samples') - } else { - useme = (nzrs > quantile(nzrs,filterquantile)) - workCM = nonzerod[useme,] - lo = colSums(nonzerod[!useme,]) - cleanrs = rowSums(workCM) - cleanN = length(cleanrs) - meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="") - print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F) - maint = paste('Filter below',filterquantile,'quantile') - } - cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle) - allgenes = rownames(workCM) - reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" - genecards=" 0.8) # is ucsc style string - { - print("@@ using ucsc substitution for urls") - contigurls = paste0(ucsc,"&position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"") - } else { - print("@@ using genecards substitution for urls") - contigurls = paste0(genecards,allgenes,"\'>",allgenes,"") - } - print.noquote("# urls") - print.noquote(head(contigurls)) - print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F) - cmrowsums = rowSums(workCM) - TName=unique(group)[1] - CName=unique(group)[2] - if (is.null(mydesign)) { - if (length(subjects) == 0) - { - mydesign = model.matrix(~group) - } - else { - subjf = factor(subjects) - mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it - } - } - print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=','))) - print.noquote('Using design matrix:') - print.noquote(mydesign) - if (doedgeR) { - sink('edgeR.log') - #### Setup DGEList object - DGEList = DGEList(counts=workCM, group = group) - DGEList = calcNormFactors(DGEList) - - DGEList = estimateGLMCommonDisp(DGEList,mydesign) - comdisp = DGEList\$common.dispersion - DGEList = estimateGLMTrendedDisp(DGEList,mydesign) - if (edgeR_priordf > 0) { - print.noquote(paste("prior.df =",edgeR_priordf)) - DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = edgeR_priordf) - } else { - DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) - } - DGLM = glmFit(DGEList,design=mydesign) - DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed - efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors - normData = (1e+06*DGEList\$counts/efflib) - uoutput = cbind( - Name=as.character(rownames(DGEList\$counts)), - DE\$table, - adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), - Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums,normData, - DGEList\$counts - ) - soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable - goodness = gof(DGLM, pcutoff=fdrthresh) - if (sum(goodness\$outlier) > 0) { - print.noquote('GLM outliers:') - print(paste(rownames(DGLM)[(goodness\$outlier)],collapse=','),quote=F) - } else { - print('No GLM fit outlier genes found\n') - } - z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) - pdf("edgeR_GoodnessofFit.pdf") - qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") - abline(0,1,lwd=3) - points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon") - dev.off() - estpriorn = getPriorN(DGEList) - print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F) - efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors - normData = (1e+06*DGEList\$counts/efflib) - uniqueg = unique(group) - #### Plot MDS - sample_colors = match(group,levels(group)) - sampleTypes = levels(factor(group)) - print.noquote(sampleTypes) - pdf("edgeR_MDSplot.pdf") - plotMDS.DGEList(DGEList,main=paste("edgeR MDS for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors) - legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19) - grid(col="blue") - dev.off() - colnames(normData) = paste( colnames(normData),'N',sep="_") - print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=','))) - nzd = data.frame(log(nonzerod + 1e-2,10)) - try( boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle,pdfname="edgeR_raw_norm_counts_box.pdf") ) - write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F) - tt = cbind( - Name=as.character(rownames(DGEList\$counts)), - DE\$table, - adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), - Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums - ) - print.noquote("# edgeR Top tags\n") - tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely - tt = tt[order(DE\$table\$PValue),] - print.noquote(tt[1:50,]) - deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,]) - nsig = length(deTags) - print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F) - deColours = ifelse(deTags,'red','black') - pdf("edgeR_BCV_vs_abundance.pdf") - plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance") - dev.off() - dg = DGEList[order(DE\$table\$PValue),] - #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg))) - efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors - normData = (1e+06*dg\$counts/efflib) - outpdfname="edgeR_top_100_heatmap.pdf" - hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('edgeR Heatmap',myTitle)) - outSmear = "edgeR_smearplot.pdf" - outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') - smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain) - qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf='edgeR_qqplot.pdf') - norm.factor = DGEList\$samples\$norm.factors - topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ] - edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR)) - edgeRcounts = rep(0, length(allgenes)) - edgeRcounts[edgeRcountsindex] = 1 # Create venn diagram of hits - sink() - } ### doedgeR - if (doDESeq2 == T) - { - sink("DESeq2.log") - # DESeq2 - require('DESeq2') - library('RColorBrewer') - if (length(subjects) == 0) - { - pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM)) - deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ Rx)) - } else { - pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM)) - deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ subjects + Rx)) - } - #DESeq2 = DESeq(deSEQds,fitType='local',pAdjustMethod=fdrtype) - #rDESeq = results(DESeq2) - #newCountDataSet(workCM, group) - deSeqDatsizefac = estimateSizeFactors(deSEQds) - deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType) - resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype) - rDESeq = as.data.frame(results(resDESeq)) - rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls) - srDESeq = rDESeq[order(rDESeq\$pvalue),] - qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf') - cat("# DESeq top 50\n") - print.noquote(srDESeq[1:50,]) - write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F) - topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ] - DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq)) - DESeqcounts = rep(0, length(allgenes)) - DESeqcounts[DESeqcountsindex] = 1 - pdf("DESeq2_dispersion_estimates.pdf") - plotDispEsts(resDESeq) - dev.off() - ysmall = abs(min(rDESeq\$log2FoldChange)) - ybig = abs(max(rDESeq\$log2FoldChange)) - ylimit = min(4,ysmall,ybig) - pdf("DESeq2_MA_plot.pdf") - plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit)) - dev.off() - rlogres = rlogTransformation(resDESeq) - sampledists = dist( t( assay(rlogres) ) ) - sdmat = as.matrix(sampledists) - pdf("DESeq2_sample_distance_plot.pdf") - heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"), - col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255)) - dev.off() - ###outpdfname="DESeq2_top50_heatmap.pdf" - ###hmap2(sresDESeq,nsamp=50,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('DESeq2 vst rlog Heatmap',myTitle)) - sink() - result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) ) - if ("try-error" %in% class(result)) { - print.noquote('DESeq2 plotPCA failed.') - } else { - pdf("DESeq2_PCA_plot.pdf") - #### wtf - print? Seems needed to get this to work - print(ppca) - dev.off() - } - } - - if (doVoom == T) { - sink('VOOM.log') - if (doedgeR == F) { - #### Setup DGEList object - DGEList = DGEList(counts=workCM, group = group) - DGEList = calcNormFactors(DGEList) - DGEList = estimateGLMCommonDisp(DGEList,mydesign) - DGEList = estimateGLMTrendedDisp(DGEList,mydesign) - DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) - DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) - norm.factor = DGEList\$samples\$norm.factors - } - pdf("VOOM_mean_variance_plot.pdf") - dat.voomed = voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor) - dev.off() - # Use limma to fit data - fit = lmFit(dat.voomed, mydesign) - fit = eBayes(fit) - rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none") - qqPlot(descr=paste(myTitle,'VOOM-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf='VOOM_qqplot.pdf') - rownames(rvoom) = rownames(workCM) - rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls) - srvoom = rvoom[order(rvoom\$P.Value),] - cat("# VOOM top 50\n") - print(srvoom[1:50,]) - write.table(srvoom,file=out_VOOM, quote=FALSE, sep="\t",row.names=F) - # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma - topresults.voom = rvoom[which(rvoom\$adj.P.Val < fdrthresh), ] - voomcountsindex = which(allgenes %in% topresults.voom\$ID) - voomcounts = rep(0, length(allgenes)) - voomcounts[voomcountsindex] = 1 - sink() - } - - if (doCamera) { - doGSEA(y=DGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle, - outfname=paste(mt,"GSEA.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype) - } - - if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) { - if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) { - vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh) - counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, - VOOM_limma = voomcounts, row.names = allgenes) - } else if ((doDESeq2==T) && (doedgeR==T)) { - vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh) - counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes) - } else if ((doVoom==T) && (doedgeR==T)) { - vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh) - counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes) - } - - if (nrow(counts.dataframe > 1)) { - counts.venn = vennCounts(counts.dataframe) - vennf = "Venn_significant_genes_overlap.pdf" - pdf(vennf) - vennDiagram(counts.venn,main=vennmain,col="maroon") - dev.off() - } - } #### doDESeq2 or doVoom - -} -#### Done - -###sink(stdout(),append=T,type="message") -builtin_gmt = "" -history_gmt = "" -history_gmt_name = "" -out_edgeR = F -out_DESeq2 = F -out_VOOM = "$out_VOOM" -doDESeq2 = $DESeq2.doDESeq2 # make these T or F -doVoom = $doVoom -doCamera = F -doedgeR = $edgeR.doedgeR -edgeR_priordf = 0 - - -#if $doVoom == "T": - out_VOOM = "$out_VOOM" -#end if - -#if $DESeq2.doDESeq2 == "T": - out_DESeq2 = "$out_DESeq2" - DESeq_fitType = "$DESeq2.DESeq_fitType" -#end if - -#if $edgeR.doedgeR == "T": - out_edgeR = "$out_edgeR" - edgeR_priordf = $edgeR.edgeR_priordf -#end if - - -if (sum(c(doedgeR,doVoom,doDESeq2)) == 0) -{ -write("No methods chosen - nothing to do! Please try again after choosing one or more methods", stderr()) -quit(save="no",status=2) -} - -Out_Dir = "$html_file.files_path" -Input = "$input1" -TreatmentName = "$treatment_name" -TreatmentCols = "$Treat_cols" -ControlName = "$control_name" -ControlCols= "$Control_cols" -org = "$input1.dbkey" -if (org == "") { org = "hg19"} -fdrtype = "$fdrtype" -fdrthresh = $fdrthresh -useNDF = $useNDF -fQ = $fQ # non-differential centile cutoff -myTitle = "$title" -sids = strsplit("$subjectids",',') -subjects = unlist(sids) -nsubj = length(subjects) -TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1 -CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1 -cat('Got TCols=') -cat(TCols) -cat('; CCols=') -cat(CCols) -cat('\n') -useCols = c(TCols,CCols) -if (file.exists(Out_Dir) == F) dir.create(Out_Dir) -Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header -snames = colnames(Count_Matrix) -nsamples = length(snames) -if (nsubj > 0 & nsubj != nsamples) { -options("show.error.messages"=T) -mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','), - 'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=',')) -write(mess, stderr()) -quit(save="no",status=4) -} -if (length(subjects) != 0) {subjects = subjects[useCols]} -Count_Matrix = Count_Matrix[,useCols] ### reorder columns -rn = rownames(Count_Matrix) -islib = rn %in% c('librarySize','NotInBedRegions') -LibSizes = Count_Matrix[subset(rn,islib),][1] # take first -Count_Matrix = Count_Matrix[subset(rn,! islib),] -group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) ) #Build a group descriptor -group = factor(group, levels=c(ControlName,TreatmentName)) -colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_") #Relable columns -results = edgeIt(Count_Matrix=Count_Matrix,group=group, out_edgeR=out_edgeR, out_VOOM=out_VOOM, out_DESeq2=out_DESeq2, - fdrtype='BH',mydesign=NULL,priordf=edgeR_priordf,fdrthresh=fdrthresh,outputdir='.', - myTitle=myTitle,useNDF=F,libSize=c(),filterquantile=fQ,subjects=subjects, - doDESeq2=doDESeq2,doVoom=doVoom,doCamera=doCamera,doedgeR=doedgeR,org=org, - histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fitType=DESeq_fitType) -sessionInfo() -]]> - - - - -**What it does** - -Allows short read sequence counts from controlled experiments to be analysed for differentially expressed genes. -Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design. - -**Input** - -Requires a count matrix as a tabular file. These are best made using the companion HTSeq_ based counter Galaxy wrapper -and your fave gene model to generate inputs. Each row is a genomic feature (gene or exon eg) and each column the -non-negative integer count of reads from one sample overlapping the feature. -The matrix must have a header row uniquely identifying the source samples, and unique row names in -the first column. Typically the row names are gene symbols or probe ids for downstream use in GSEA and other methods. - -**Specifying comparisons** - -This is basically dumbed down for two factors - case vs control. - -More complex interfaces are possible but painful at present. -Probably need to specify a phenotype file to do this better. -Work in progress. Send code. - -If you have (eg) paired samples and wish to include a term in the GLM to account for some other factor (subject in the case of paired samples), -put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or -A list of integers, one for each subject or an empty string if samples are all independent. -If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix. -Integers for samples that are not in the analysis *must* be present in the string as filler even if not used. - -So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones -eg if you had 6 samples with the first two independent but the second and third pairs each being from independent subjects. you might use -8,9,1,1,2,2 -as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6 - -**Methods available** - -You can run 3 popular Bioconductor packages available for count data. - -edgeR - see edgeR_ for details - -VOOM/limma - see limma_VOOM_ for details - -DESeq2 - see DESeq2_ for details - -and optionally camera in edgeR which works better if MSigDB is installed. - -**Outputs** - -Some helpful plots and analysis results. Note that most of these are produced using R code -suggested by the excellent documentation and vignettes for the Bioconductor -packages invoked. The Tool Factory is used to automatically lay these out for you to enjoy. - -**Note on Voom** - -The voom from limma version 3.16.6 help in R includes this from the authors - but you should read the paper to interpret this method. - -This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma. - -voom is an acronym for mean-variance modelling at the observational level. -The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation. -Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend. -This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance. -The weights are then used in the linear modelling process to adjust for heteroscedasticity. - -In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess. -The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag. -The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag. -Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays. -Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation. -This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data. - - -Author(s) - -Charity Law and Gordon Smyth - -References - -Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia. - -Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts. -Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia. -http://www.statsci.org/smyth/pubs/VoomPreprint.pdf - -See Also - -A voom case study is given in the edgeR User's Guide. - -vooma is a similar function but for microarrays instead of RNA-seq. - - -***old rant on changes to Bioconductor package variable names between versions*** - -The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue) -breaking this and all other code that assumed the old name for this variable, -between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). -This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing -to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly -when their old scripts break. This tool currently now works with 2.4.6. - -**Note on prior.N** - -http://seqanswers.com/forums/showthread.php?t=5591 says: - -*prior.n* - -The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. -You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood -in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your -tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the -common likelihood the weight of one observation. - -In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, -or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that -you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation -(squeezing) of the tagwise dispersions. How many samples do you have in your experiment? -What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10. -If you have more samples, then the tagwise dispersion estimates will be more reliable, -so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. - - -From Bioconductor Digest, Vol 118, Issue 5, Gordon writes: - -Dear Dorota, - -The important settings are prior.df and trend. - -prior.n and prior.df are related through prior.df = prior.n * residual.df, -and your experiment has residual.df = 36 - 12 = 24. So the old setting of -prior.n=10 is equivalent for your data to prior.df = 240, a very large -value. Going the other way, the new setting of prior.df=10 is equivalent -to prior.n=10/24. - -To recover old results with the current software you would use - - estimateTagwiseDisp(object, prior.df=240, trend="none") - -To get the new default from old software you would use - - estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE) - -Actually the old trend method is equivalent to trend="loess" in the new -software. You should use plotBCV(object) to see whether a trend is -required. - -Note you could also use - - prior.n = getPriorN(object, prior.df=10) - -to map between prior.df and prior.n. - ----- - -**Attributions** - -edgeR - edgeR_ - -VOOM/limma - limma_VOOM_ - -DESeq2 - DESeq2_ for details - -See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package. - -Galaxy_ (that's what you are using right now!) for gluing everything together - -Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is -licensed to you under the LGPL_ like other rgenetics artefacts - -.. _LGPL: http://www.gnu.org/copyleft/lesser.html -.. _HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/index.html -.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html -.. _DESeq2: http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html -.. _limma_VOOM: http://www.bioconductor.org/packages/release/bioc/html/limma.html -.. _Galaxy: http://getgalaxy.org - - - - - diff -r 48d71bd383a1 -r c0fa3dde02d9 differential_count_models/test-data/edgeRtest1out.html --- a/differential_count_models/test-data/edgeRtest1out.html Wed Aug 07 01:58:28 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,733 +0,0 @@ - - - - - - - - -
- -
Galaxy Tool "DifferentialCounts" run at 07/08/2013 15:46:55

-
DESeq2 images and outputs
-(Click on a thumbnail image to download the corresponding original PDF image)
-
- - - - - - - - - - - - - -
Image called DESeq2_MA_plot.pdfImage called DESeq2_PCA_plot.pdfImage called DESeq2_dispersion_estimates.pdf
Image called DESeq2_qqplot.pdfImage called DESeq2_sample_distance_plot.pdf 
- -
DESeq2 log output
- -
-
-# DESeq top 50
-
-                     Contig     baseMean log2FoldChange     lfcSE        pvalue          padj  NReads                                                                                               URL
-
-Mir192               Mir192 271352.97636       6.965264 0.2150593 4.096936e-230 4.576278e-227 2325567               Mir192
-
-Mir122a             Mir122a  10112.31117      10.312083 0.3292695 2.649323e-215 1.479647e-212   90428             Mir122a
-
-Mir149               Mir149    810.35429      -6.911118 0.2341392 1.735537e-191 6.461982e-189    6164               Mir149
-
-Mir23a               Mir23a   1289.18043      -3.104086 0.1191688 1.424246e-149 3.977206e-147   10118               Mir23a
-
-Mir181d             Mir181d    275.22797      -3.581172 0.1778187  3.329371e-90  7.437816e-88    2139             Mir181d
-
-Mir204               Mir204    347.57397      -7.284200 0.3771119  3.959336e-83  7.370965e-81    2601               Mir204
-
-Mir23b               Mir23b   2028.55377      -2.065110 0.1085802  1.182361e-80  1.886711e-78   16387               Mir23b
-
-Mir27a               Mir27a   2788.72629      -3.016676 0.1688167  2.036708e-71  2.843754e-69   21886               Mir27a
-
-Mir195               Mir195    519.86200      -3.152795 0.1784796  7.838123e-70  9.727982e-68    3962               Mir195
-
-Mir194-2           Mir194-2    391.65678       5.222911 0.3099275  1.013490e-63  1.132068e-61    3570           Mir194-2
-
-Mir208b             Mir208b   1649.77924     -11.396172 0.6771238  1.464479e-63  1.487112e-61   14756             Mir208b
-
-Mir10b               Mir10b  27820.40551      -5.071453 0.3044889  2.754493e-62  2.563974e-60  197340               Mir10b
-
-Mir181c             Mir181c   2765.96510      -3.660964 0.2275711  3.141153e-58  2.698975e-56   23605             Mir181c
-
-Mir208a             Mir208a    616.76981     -10.356524 0.6559217  3.688385e-56  2.942804e-54    4638             Mir208a
-
-Mir490               Mir490    220.99790      -8.059660 0.5142876  2.369067e-55  1.764165e-53    1741               Mir490
-
-Mir203               Mir203    772.92882       1.990849 0.1274099  4.877239e-55  3.404923e-53    6739               Mir203
-
-Mir215               Mir215    152.78082      -3.004380 0.1939090  3.822339e-54  2.511502e-52    1182               Mir215
-
-Dnm3os               Dnm3os    179.61643      -3.278392 0.2166491  9.922020e-52  6.157165e-50    1401               Dnm3os
-
-Mir214               Mir214    134.69038      -3.216444 0.2154916  2.230148e-50  1.311093e-48    1048               Mir214
-
-Mir21                 Mir21  26121.31011       2.963903 0.2008617  2.817434e-49  1.573537e-47  229120                 Mir21
-
-Mir1948             Mir1948    263.89527       7.074045 0.4867225  7.374030e-48  3.922282e-46    2404             Mir1948
-
-Mir27b               Mir27b  76478.05753      -1.904653 0.1312889  1.088626e-47  5.527251e-46  625308               Mir27b
-
-Rabggtb             Rabggtb   2257.19195       1.988368 0.1401741  1.134862e-45  5.511484e-44   19535             Rabggtb
-
-Mir499               Mir499    712.45950     -10.577061 0.7528467  7.766408e-45  3.614616e-43    6527               Mir499
-
-Mir101b             Mir101b   6846.19683       3.791681 0.2809666  1.670548e-41  7.464007e-40   59019             Mir101b
-
-Mir132               Mir132    106.46062      -2.797928 0.2083376  4.046163e-41  1.738294e-39     857               Mir132
-
-Mir143hg           Mir143hg 180217.77425      -2.169143 0.1685614  6.764675e-38  2.798571e-36 1407364           Mir143hg
-
-Mir143               Mir143 179219.35960      -2.170303 0.1696199  1.746403e-37  6.966899e-36 1399819               Mir143
-
-Mir155               Mir155     57.66182      -3.788079 0.3056585  2.845488e-35  1.096004e-33     463               Mir155
-
-Mir322               Mir322    899.53469      -3.126011 0.2622595  9.363374e-33  3.486296e-31    7074               Mir322
-
-Mir378               Mir378    483.21548      -2.994300 0.2577321  3.343457e-31  1.204723e-29    4075               Mir378
-
-Mir24-2             Mir24-2    424.48288      -2.712674 0.2361028  1.491830e-30  5.049617e-29    3470             Mir24-2
-
-Mir3074-2         Mir3074-2    424.48288      -2.712674 0.2361028  1.491830e-30  5.049617e-29    3470         Mir3074-2
-
-Mir199b             Mir199b     47.84725      -5.294373 0.4644474  4.215162e-30  1.384805e-28     370             Mir199b
-
-Mir802               Mir802    166.83414       8.816580 0.7782636  9.478527e-30  3.025004e-28    1514               Mir802
-
-Mir125b-2         Mir125b-2    493.08516      -2.919341 0.2631193  1.324797e-28  4.110551e-27    3837         Mir125b-2
-
-Mir301               Mir301    260.53406      -1.676984 0.1526772  4.570133e-28  1.379686e-26    2119               Mir301
-
-Snord104           Snord104   3851.90119       2.386573 0.2173857  4.847914e-28  1.425032e-26   33458           Snord104
-
-Mir150               Mir150    553.20599      -2.836881 0.2595088  8.127991e-28  2.327940e-26    4229               Mir150
-
-Mir148a             Mir148a 118994.46955       2.678852 0.2481801  3.675045e-27  1.026256e-25 1002397             Mir148a
-
-5430416N02Rik 5430416N02Rik     62.15966       3.089960 0.2941123  8.101331e-26  2.207119e-24     564 5430416N02Rik
-
-Mir193               Mir193     45.70861       4.991530 0.4814098  3.446492e-25  9.166027e-24     421               Mir193
-
-Mir3073             Mir3073     98.93199       8.208709 0.7944742  5.036320e-25  1.308272e-23     904             Mir3073
-
-Mir125b-1         Mir125b-1     79.01988      -3.020660 0.2937360  8.355633e-25  2.121191e-23     609         Mir125b-1
-
-2610203C20Rik 2610203C20Rik     79.17666      -3.023491 0.2948614  1.136165e-24  2.820214e-23     610 2610203C20Rik
-
-Mir181a-1         Mir181a-1     59.53826      -3.151487 0.3211628  9.923707e-23  2.409735e-21     506         Mir181a-1
-
-Mir184               Mir184     32.23796      -4.865023 0.4962776  1.092606e-22  2.596683e-21     247               Mir184
-
-Mir199a-2         Mir199a-2     44.84878      -3.422216 0.3545647  4.826269e-22  1.123113e-20     352         Mir199a-2
-
-Snord91a           Snord91a    168.95251       2.700421 0.2835464  1.670595e-21  3.808275e-20    1437           Snord91a
-
-Mir200b             Mir200b     87.13638       5.940702 0.6338554  7.094881e-21  1.584996e-19     888             Mir200b
-
-
-
- -
DifferentialCounts log output
- -
-
-Loading required package: gtools
-
-Loading required package: gdata
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-gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
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-gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
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-Attaching package: ‘gdata’
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-The following object is masked from ‘package:stats’:
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-    nobs
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-The following object is masked from ‘package:utils’:
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-    object.size
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-Loading required package: caTools
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-Loading required package: grid
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-Loading required package: KernSmooth
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-KernSmooth 2.23 loaded
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-Copyright M. P. Wand 1997-2009
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-Loading required package: MASS
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-Attaching package: ‘gplots’
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-The following object is masked from ‘package:stats’:
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-    lowess
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-Loading required package: methods
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-Loading required package: limma
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-Loading required package: splines
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-Loading required package: DESeq2
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-Loading required package: GenomicRanges
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-Loading required package: BiocGenerics
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-Loading required package: parallel
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-Attaching package: ‘BiocGenerics’
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-The following object is masked from ‘package:parallel’:
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-    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB
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-Loading required package: Biobase
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-Welcome to Bioconductor
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-    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'.
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-gene-wise dispersion estimates
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-final dispersion estimates
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-final dispersion estimates
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-Warning messages:
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-1: In bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group ==  :
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-  Outlier (-Inf) in boxplot 1 is not drawn
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-2: In bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group ==  :
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-3: In bxp(list(stats = c(-0.430723026286372, -0.127900608036896, 0.474159383291067,  :
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-  some notches went outside hinges ('box'): maybe set notch=FALSE
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-4: In par(defpar) : calling par(new=TRUE) with no plot
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VOOM images and outputs
-(Click on a thumbnail image to download the corresponding original PDF image)
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Image called VOOM_mean_variance_plot.pdfImage called VOOM_qqplot.pdf
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VOOM log output
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-# VOOM top 50
-
-                         ID      logFC    AveExpr          t      P.Value    adj.P.Val         B  NReads                                                                                               URL
-
-Mir192               Mir192   6.948883 14.6763803  42.722954 2.301199e-16 2.625668e-13 27.266471 2325567               Mir192
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-Mir208a             Mir208a -11.015018  3.9395538 -23.252407 1.118938e-12 6.383542e-10 17.208662    4638             Mir208a
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-Mir122a             Mir122a  10.426125  8.1698641  21.722912 2.859682e-12 1.087633e-09 17.760171   90428             Mir122a
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-Mir149               Mir149  -7.030463  6.3160807 -20.883835 4.915491e-12 1.402144e-09 17.277609    6164               Mir149
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-Mir208b             Mir208b -12.433228  4.6076218 -19.592458 1.179199e-11 2.690931e-09 15.683666   14756             Mir208b
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-Mir10b               Mir10b  -5.130915 12.2628672 -18.242023 3.124991e-11 4.963978e-09 16.221503  197340               Mir10b
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-Mir143hg           Mir143hg  -2.249221 16.2444825 -18.082481 3.521739e-11 4.963978e-09 16.026695 1407364           Mir143hg
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-Mir143               Mir143  -2.251067 16.2358599 -18.081481 3.524391e-11 4.963978e-09 16.026084 1399819               Mir143
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-Mir499               Mir499 -11.567529  3.7874598 -17.942086 3.915496e-11 4.963978e-09 14.821741    6527               Mir499
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-Mir802               Mir802   9.158434  2.9157675  17.316522 6.338616e-11 7.232360e-09 14.381577    1514               Mir802
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-Mir3073             Mir3073   8.420542  2.5457189  16.702657 1.033066e-10 1.036045e-08 13.985845     904             Mir3073
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-Mir148a             Mir148a   2.638213 15.4435820  16.548188 1.171186e-10 1.036045e-08 14.814792 1002397             Mir148a
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-Mir101b             Mir101b   3.765722 10.8508440  16.538566 1.180419e-10 1.036045e-08 14.900027   59019             Mir101b
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-Mir490               Mir490  -8.474378  3.7506957 -16.259650 1.484816e-10 1.210125e-08 13.424617    1741               Mir490
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-Mir21                 Mir21   2.938537 13.1642917  15.375404 3.148335e-10 2.394833e-08 13.867698  229120                 Mir21
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-Mir181c             Mir181c  -3.742560  9.6295577 -15.242361 3.537063e-10 2.522368e-08 13.810405   23605             Mir181c
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-Mir204               Mir204  -7.684425  4.7751735 -15.033484 4.254268e-10 2.855364e-08 12.822427    2601               Mir204
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-Mir23a               Mir23a  -3.165768  8.7896592 -14.631179 6.110682e-10 3.873493e-08 13.269174   10118               Mir23a
-
-Mir181d             Mir181d  -3.636211  6.3713218 -14.317073 8.157508e-10 4.898798e-08 12.956333    2139             Mir181d
-
-Mir133b             Mir133b  -6.493549  1.2544862 -13.969968 1.129934e-09 6.446275e-08 11.982684     159             Mir133b
-
-Mir27a               Mir27a  -3.106935  9.9255796 -13.838251 1.281011e-09 6.960160e-08 12.513086   21886               Mir27a
-
-Mir194-2           Mir194-2   5.264136  6.0897615  13.044012 2.792884e-09 1.448491e-07 11.715753    3570           Mir194-2
-
-Mir195               Mir195  -3.216595  7.4509350 -12.869478 3.332788e-09 1.653353e-07 11.587523    3962               Mir195
-
-Mir27b               Mir27b  -1.976376 15.0957731 -11.756036 1.082197e-08 5.144946e-07 10.127719  625308               Mir27b
-
-Mir378               Mir378  -3.097393  7.3832049 -11.684163 1.171371e-08 5.346138e-07 10.329692    4075               Mir378
-
-Snord104           Snord104   2.337374 10.6109024  11.495675 1.444482e-08 6.339052e-07 10.023395   33458           Snord104
-
-Mir1983             Mir1983  -5.895500  0.9931851 -11.445812 1.527548e-08 6.441605e-07  9.749260     101             Mir1983
-
-Mir322               Mir322  -3.296618  8.2153415 -11.415362 1.580762e-08 6.441605e-07 10.008472    7074               Mir322
-
-Mir200a             Mir200a   6.191561  1.7981309  11.322172 1.756229e-08 6.909853e-07  9.662295     264             Mir200a
-
-Mir215               Mir215  -3.045873  5.7544234 -11.148134 2.141822e-08 8.082459e-07  9.753268    1182               Mir215
-
-Dnm3os               Dnm3os  -3.363344  5.8607432 -11.092261 2.283960e-08 8.082459e-07  9.689496    1401               Dnm3os
-
-Mir182               Mir182   4.903995  7.1511683  11.074468 2.331304e-08 8.082459e-07  9.658842    7189               Mir182
-
-Mir181a-2         Mir181a-2  -3.048298  6.9414651 -11.072128 2.337609e-08 8.082459e-07  9.644017    2817         Mir181a-2
-
-Mir1948             Mir1948   7.195525  4.5513493  11.005492 2.524936e-08 8.473388e-07  9.341794    2404             Mir1948
-
-Mir214               Mir214  -3.280874  5.4784451 -10.768257 3.332555e-08 1.086413e-06  9.318504    1048               Mir214
-
-Mir153               Mir153  -5.963803  1.4386315 -10.727082 3.498742e-08 1.093990e-06  9.035569     140               Mir153
-
-Cyp3a25             Cyp3a25   6.318200  1.4888933  10.698226 3.620443e-08 1.093990e-06  9.024973     226             Cyp3a25
-
-Gm5441               Gm5441  -5.982176  1.4484953 -10.692891 3.643436e-08 1.093990e-06  9.000362     142               Gm5441
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-Mir125b-2         Mir125b-2  -3.077678  7.4316058 -10.446668 4.893073e-08 1.431538e-06  8.884250    3837         Mir125b-2
-
-Mir133a-1         Mir133a-1  -5.144671  0.5903264 -10.358205 5.447229e-08 1.553822e-06  8.575535      60         Mir133a-1
-
-1110038B12Rik 1110038B12Rik   2.226702 10.8487089  10.194609 6.655312e-08 1.852125e-06  8.439308   37066 1110038B12Rik
-
-Mir132               Mir132  -2.847559  5.3211839 -10.110952 7.380297e-08 2.004981e-06  8.531491     857               Mir132
-
-Rabggtb             Rabggtb   1.935779  9.9874171   9.928995 9.262821e-08 2.457879e-06  8.133384   19535             Rabggtb
-
-Mir504               Mir504  -5.256127  0.6221088  -9.892894 9.693595e-08 2.513725e-06  8.068853      69               Mir504
-
-Mir150               Mir150  -2.938531  7.6297870  -9.842102 1.033602e-07 2.620755e-06  8.116464    4229               Mir150
-
-Mir199b             Mir199b  -5.752816  2.8805143  -9.823920 1.057683e-07 2.623514e-06  7.979387     370             Mir199b
-
-Mir23b               Mir23b  -2.124129  9.8141190  -9.806316 1.081569e-07 2.625681e-06  7.979464   16387               Mir23b
-
-Mir24-2             Mir24-2  -2.833979  7.3083691  -9.767192 1.136724e-07 2.646944e-06  8.030550    3470             Mir24-2
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-Mir3074-2         Mir3074-2  -2.833979  7.3083691  -9.767192 1.136724e-07 2.646944e-06  8.030550    3470         Mir3074-2
-
-Mir155               Mir155  -3.906600  3.9899000  -9.732173 1.188627e-07 2.712448e-06  8.046518     463               Mir155
-
-
-
- -
edgeR images and outputs
-(Click on a thumbnail image to download the corresponding original PDF image)
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- - - - - - - - - - - - - - - - - - -
Image called edgeR_BCV_vs_abundance.pdfImage called edgeR_GoodnessofFit.pdfImage called edgeR_MDSplot.pdf
Image called edgeR_qqplot.pdfImage called edgeR_raw_norm_counts_box.pdfImage called edgeR_smearplot.pdf
Image called edgeR_top_100_heatmap.pdf  
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edgeR log output
- -
-
-[1] prior.df = 8
-
-[1] "No GLM fit outlier genes found\n"
-
-[1] Common Dispersion = 0.228651460998105 CV =  0.478175136323613 getPriorN =  3.33333333333333
-
-[1] heart liver
-
-[1] "Raw sample read totals 2443751,1644652,1682104,1806045,1440960,1341813,2888924,1428365"
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-[1] raw contig counts by sample:
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- liver_X11706Liv_CAAA liver_X11700Liv_ATTC liver_X11698Liv_ACTG liver_X11699Liv_ATGA heart_X11706He_AGTTC heart_X11699He_GGCTA heart_X11698He_TAGCT heart_X11700He_CTTGT
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- Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043      
-
- 1st Qu.:0.3032       1st Qu.:0.0043       1st Qu.:0.3032       1st Qu.:0.0043       1st Qu.:0.0043       1st Qu.:0.0043       1st Qu.:0.0043       1st Qu.:0.0043      
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- Median :0.7789       Median :0.6031       Median :0.6998       Median :0.6031       Median :0.4786       Median :0.4786       Median :0.4786       Median :0.4786      
-
- Mean   :1.0519       Mean   :1.0343       Mean   :0.9855       Mean   :0.9966       Mean   :0.9210       Mean   :0.9428       Mean   :1.0205       Mean   :0.9753      
-
- 3rd Qu.:1.5410       3rd Qu.:1.6335       3rd Qu.:1.4473       3rd Qu.:1.5534       3rd Qu.:1.5770       3rd Qu.:1.5855       3rd Qu.:1.7161       3rd Qu.:1.6022      
-
- Max.   :5.8209       Max.   :5.6905       Max.   :5.7999       Max.   :5.7215       Max.   :5.3609       Max.   :5.3589       Max.   :5.6967       Max.   :5.3702      
-
- NA's   :650          NA's   :969          NA's   :664          NA's   :886          NA's   :902          NA's   :957          NA's   :821          NA's   :950         
-
-[1] normalised contig counts by sample:
-
- liver_X11706Liv_CAAA liver_X11700Liv_ATTC liver_X11698Liv_ACTG liver_X11699Liv_ATGA heart_X11706He_AGTTC heart_X11699He_GGCTA heart_X11698He_TAGCT heart_X11700He_CTTGT
-
- Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf        
-
- 1st Qu.:   0         1st Qu.:-Inf         1st Qu.:   0         1st Qu.:-Inf         1st Qu.:-Inf         1st Qu.:-Inf         1st Qu.:-Inf         1st Qu.:-Inf        
-
- Median :   0         Median :   0         Median :   0         Median :   0         Median :   0         Median :   0         Median :   0         Median :   0        
-
- Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf        
-
- 3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1        
-
- Max.   :   6         Max.   :   6         Max.   :   6         Max.   :   5         Max.   :   5         Max.   :   5         Max.   :   6         Max.   :   5        
-
-[1] Using ncol rawrs= 8
-
-[1] # edgeR Top tags\n
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-                       Name      logFC    logCPM        LR        PValue   adj.p.value Dispersion totreads                                                                                               URL
-
-Mir208a             Mir208a -11.840751  8.465017 594.16946 3.104543e-131 3.542284e-128 0.05171220     4638             Mir208a
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-Mir149               Mir149  -7.008984  8.861767 484.30321 2.473909e-107 1.411365e-104 0.04959937     6164               Mir149
-
-Mir208b             Mir208b -13.291635  9.905945 417.69758  7.737463e-93  2.942815e-90 0.10508096    14756             Mir208b
-
-Mir122a             Mir122a  10.514683 12.478088 415.17429  2.740525e-92  7.817349e-90 0.10803882    90428             Mir122a
-
-Mir204               Mir204  -7.498162  7.634507 341.30678  3.313430e-76  7.561247e-74 0.06907958     2601               Mir204
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-Mir499               Mir499 -13.577454  8.700078 325.79199  7.930755e-73  1.508165e-70 0.12042284     6527               Mir499
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-Mir490               Mir490  -8.534394  6.991023 303.17184  6.710366e-68  1.093790e-65 0.07949711     1741               Mir490
-
-Mir192               Mir192   6.953853 17.169364 217.22867  3.638307e-49  5.189135e-47 0.12700995  2325567               Mir192
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-Mir802               Mir802  11.440805  6.593380 212.88059  3.231644e-48  4.097007e-46 0.12273671     1514               Mir802
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-Mir1948             Mir1948   7.418142  7.252734 195.66958  1.840248e-44  2.099723e-42 0.12060221     2404             Mir1948
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-Mir194-2           Mir194-2   5.298950  7.811522 191.85588  1.250960e-43  1.297587e-41 0.08670751     3570           Mir194-2
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-Mir23a               Mir23a  -3.153807  9.529402 177.53185  1.676248e-40  1.593833e-38 0.04442763    10118               Mir23a
-
-Mir181c             Mir181c  -3.767686 10.639598 169.87390  7.883295e-39  6.919107e-37 0.06368883    23605             Mir181c
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-Mir3073             Mir3073  10.686337  5.859950 164.86740  9.778593e-38  7.969554e-36 0.14069249      904             Mir3073
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-Mir181d             Mir181d  -3.643963  7.300371 162.18591  3.767663e-37  2.865936e-35 0.05729574     2139             Mir181d
-
-Mir195               Mir195  -3.203683  8.215089 150.20548  1.563314e-34  1.114838e-32 0.05235020     3962               Mir195
-
-Mir10b               Mir10b  -5.182616 13.946466 147.24793  6.926819e-34  4.649118e-32 0.12268790   197340               Mir10b
-
-Mir101b             Mir101b   3.759962 11.863187 136.31359  1.703812e-31  1.080028e-29 0.07961343    59019             Mir101b
-
-Mir378               Mir378  -3.115599  8.119617 126.76408  2.092233e-29  1.256441e-27 0.05942391     4075               Mir378
-
-Mir27a               Mir27a  -3.064687 10.642480 124.98911  5.117477e-29  2.919520e-27 0.06113852    21886               Mir27a
-
-Mir182               Mir182   5.057509  8.846381 123.17765  1.275060e-28  6.927826e-27 0.13653707     7189               Mir182
-
-Mir322               Mir322  -3.194159  9.012888 107.34926  3.732413e-25  1.935765e-23 0.07536483     7074               Mir322
-
-Mir199b             Mir199b  -5.520119  4.792610 102.10724  5.259607e-24  2.609223e-22 0.13417024      370             Mir199b
-
-Mir181a-2         Mir181a-2  -3.000177  7.637692 101.38361  7.578821e-24  3.603098e-22 0.06896654     2817         Mir181a-2
-
-Mir125b-2         Mir125b-2  -2.987759  8.144514  91.72544  9.957640e-22  4.488356e-20 0.07737381     3837         Mir125b-2
-
-Dnm3os               Dnm3os  -3.331215  6.686950  91.67250  1.022763e-21  4.488356e-20 0.08810497     1401               Dnm3os
-
-Mir184               Mir184  -5.111350  4.234160  84.35542  4.133639e-20  1.686711e-18 0.13502324      247               Mir184
-
-Mir215               Mir215  -3.058208  6.447966  84.35278  4.139167e-20  1.686711e-18 0.08138517     1182               Mir215
-
-Mir133b             Mir133b  -8.383611  3.584760  83.96681  5.031517e-20  1.960318e-18 0.17482280      159             Mir133b
-
-Mir150               Mir150  -2.883446  8.307765  83.91918  5.154210e-20  1.960318e-18 0.08008123     4229               Mir150
-
-Mir3074-2         Mir3074-2  -2.778308  7.935651  83.74839  5.619282e-20  2.040616e-18 0.07424646     3470         Mir3074-2
-
-Mir24-2             Mir24-2  -2.778307  7.935651  83.71222  5.723024e-20  2.040616e-18 0.07427992     3470             Mir24-2
-
-Mir193               Mir193   5.176579  4.801090  83.19222  7.445011e-20  2.574169e-18 0.14794861      421               Mir193
-
-Scarna17           Scarna17   2.182159  9.244479  81.91330  1.421894e-19  4.771710e-18 0.04982909     9224           Scarna17
-
-Mir214               Mir214  -3.271172  6.271755  80.43948  2.997458e-19  9.771712e-18 0.09566584     1048               Mir214
-
-Snord104           Snord104   2.330488 11.053611  79.50529  4.809369e-19  1.524303e-17 0.05915990    33458           Snord104
-
-Mir200a             Mir200a   7.201555  4.139422  77.35503  1.428304e-18  4.365755e-17 0.19287764      264             Mir200a
-
-Mir200b             Mir200b   6.525423  5.752604  77.31985  1.453976e-18  4.365755e-17 0.26237966      888             Mir200b
-
-Mir21                 Mir21   2.923147 13.825255  75.51798  3.620938e-18  1.059357e-16 0.09395834   229120                 Mir21
-
-Mir203               Mir203   1.956427  8.767610  75.17870  4.299815e-18  1.226522e-16 0.04381710     6739               Mir203
-
-Mir155               Mir155  -3.886731  5.068563  73.81316  8.587210e-18  2.389758e-16 0.12522673      463               Mir155
-
-Cyp3a25             Cyp3a25   8.681501  3.972085  72.29680  1.851471e-17  5.029829e-16 0.23125383      226             Cyp3a25
-
-Rabggtb             Rabggtb   1.934093 10.298211  72.02043  2.129809e-17  5.651422e-16 0.04596646    19535             Rabggtb
-
-Mir23b               Mir23b  -2.100584 10.184110  71.44225  2.854935e-17  7.403367e-16 0.05416378    16387               Mir23b
-
-Snord52             Snord52   2.207491 10.217554  71.27974  3.100027e-17  7.860292e-16 0.05941483    18059             Snord52
-
-Gm5441               Gm5441  -6.881248  3.538457  70.05615  5.764004e-17  1.429724e-15 0.20097284      142               Gm5441
-
-Mir153               Mir153  -6.857671  3.517446  69.37600  8.137282e-17  1.975455e-15 0.20158808      140               Mir153
-
-Mir132               Mir132  -2.858294  5.938312  64.52507  9.531204e-16  2.265647e-14 0.09274248      857               Mir132
-
-1110038B12Rik 1110038B12Rik   2.195962 11.253090  62.92015  2.152583e-15  5.012443e-14 0.06712174    37066 1110038B12Rik
-
-Snord91a           Snord91a   2.654072  6.557504  62.40549  2.795431e-15  6.379174e-14 0.08637410     1437           Snord91a
-
-[1] # 416 tags significant at adj p= 0.05
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-
- -
Other images and outputs
-(Click on a thumbnail image to download the corresponding original PDF image)
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- - - - - - - - - -
Image called Filtering_rowsum_bar_charts.pdfImage called Venn_significant_genes_overlap.pdf
Image called sample_counts_histogram.pdf 
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Other log output
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-
-## Toolfactory generated command line = Rscript - None None
-
-Got TCols=1 5 6 7; CCols=2 3 4 8
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-[1] # Quantiles for non-zero row counts:
-
-       0%       10%       20%       30%       40%       50%       60%       70%       80%       90%      100% 
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-      1.0       1.0       2.0       3.0       4.0       8.0      13.0      24.0      86.6     753.0 2325567.0 
-
-[1] Read 3242 contigs. Removed 1494 with no reads. After filtering at count quantile =0.3, there are 1141 contigs
-
-[1] "@@ using genecards substitution for urls"
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-[1] # urls
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-[1] 0610005C13Rik 0610007N19Rik 0610008F07Rik 0610009L18Rik 0610012G03Rik
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-[6] 0610031O16Rik
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-[1] # Total low count contigs per sample =  170,67,203,86,145,111,155,120
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-[1] Using samples: liver_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam
-
-[1] Using design matrix:
-
-  (Intercept) groupliver
-
-1           1          1
-
-2           1          1
-
-3           1          1
-
-4           1          1
-
-5           1          0
-
-6           1          0
-
-7           1          0
-
-8           1          0
-
-attr(,"assign")
-
-[1] 0 1
-
-attr(,"contrasts")
-
-attr(,"contrasts")$group
-
-[1] contr.treatment
-
-R version 3.0.1 (2013-05-16)
-
-Platform: x86_64-unknown-linux-gnu (64-bit)
-
-locale:
-
- [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C               LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8     LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8    LC_PAPER=C                 LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       
-
-attached base packages:
-
- [1] parallel  splines   methods   grid      stats     graphics  grDevices utils     datasets  base     
-
-other attached packages:
-
- [1] RColorBrewer_1.0-5      DESeq2_1.0.18           RcppArmadillo_0.3.900.7 Rcpp_0.10.4             lattice_0.20-15         Biobase_2.20.1          GenomicRanges_1.12.4    IRanges_1.18.2          BiocGenerics_0.6.0      edgeR_3.2.4             limma_3.16.7            gplots_2.11.3           MASS_7.3-28             KernSmooth_2.23-10      caTools_1.14            gdata_2.13.2            gtools_3.0.0            stringr_0.6.2          
-
-loaded via a namespace (and not attached):
-
- [1] annotate_1.38.0      AnnotationDbi_1.22.6 bitops_1.0-5         DBI_0.2-7            genefilter_1.42.0    locfit_1.5-9.1       RSQLite_0.11.4       stats4_3.0.1         survival_2.37-4      XML_3.98-1.1         xtable_1.7-1        
-
-
-
- -
All output files available for downloading
- -
- - - - - - - - - - - - - - - - - - - - - - - - -
Output File Name (click to view)Size
DESeq2.log10.0 KB
DESeq2_MA_plot.pdf14.7 KB
DESeq2_PCA_plot.pdf4.9 KB
DESeq2_dispersion_estimates.pdf188.4 KB
DESeq2_qqplot.pdf14.4 KB
DESeq2_sample_distance_plot.pdf9.5 KB
DifferentialCounts.Rscript27.1 KB
DifferentialCounts_error.log3.1 KB
DifferentialCounts_runner.log3.2 KB
Filtering_rowsum_bar_charts.pdf6.3 KB
VOOM.log10.1 KB
VOOM_mean_variance_plot.pdf16.7 KB
VOOM_qqplot.pdf17.6 KB
Venn_significant_genes_overlap.pdf9.7 KB
edgeR.log13.1 KB
edgeR_BCV_vs_abundance.pdf17.4 KB
edgeR_GoodnessofFit.pdf13.1 KB
edgeR_MDSplot.pdf4.9 KB
edgeR_qqplot.pdf15.2 KB
edgeR_raw_norm_counts_box.pdf7.8 KB
edgeR_smearplot.pdf16.6 KB
edgeR_top_100_heatmap.pdf11.3 KB
sample_counts_histogram.pdf11.0 KB

-
- diff -r 48d71bd383a1 -r c0fa3dde02d9 differential_count_models/test-data/edgeRtest1out.xls --- a/differential_count_models/test-data/edgeRtest1out.xls Wed Aug 07 01:58:28 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1142 +0,0 @@ -ID logFC AveExpr t P.Value adj.P.Val B NReads URL -Mir192 6.94888256843679 14.6763802609023 42.7229535356942 2.30119906424271e-16 2.62566813230094e-13 27.2664713266936 2325567 Mir192 -Mir208a -11.0150177152075 3.93955375669227 -23.2524066836307 1.11893807599952e-12 6.38354172357727e-10 17.2086622097974 4638 Mir208a -Mir122a 10.4261254701779 8.16986409392255 21.7229119192922 2.85968233611017e-12 1.08763251516723e-09 17.760171141852 90428 Mir122a -Mir149 -7.03046258655617 6.31608073609863 -20.8838348040628 4.91549082404237e-12 1.40214375755809e-09 17.2776088871455 6164 Mir149 -Mir208b -12.4332279840446 4.60762179736006 -19.5924575126382 1.17919871718875e-11 2.69093147262473e-09 15.6836663826186 14756 Mir208b -Mir10b -5.1309149063532 12.2628671946242 -18.2420234752943 3.12499057505143e-11 4.96397841614262e-09 16.2215027882858 197340 Mir10b -Mir143hg -2.24922058313374 16.2444825488726 -18.0824813146443 3.52173903971276e-11 4.96397841614262e-09 16.0266951625541 1407364 Mir143hg -Mir143 -2.25106712131643 16.235859869169 -18.0814805993441 3.524391092512e-11 4.96397841614262e-09 16.0260836456534 1399819 Mir143 -Mir499 -11.5675289490546 3.78745976580796 -17.9420857279689 3.91549568319751e-11 4.96397841614262e-09 14.8217405828874 6527 Mir499 -Mir802 9.15843445824816 2.91576747878654 17.3165224121399 6.33861560587965e-11 7.23236040630868e-09 14.381577240531 1514 Mir802 -Mir3073 8.42054159318439 2.54571889776166 16.7026571721381 1.03306635740721e-10 1.03604453339228e-08 13.9858447292853 904 Mir3073 -Mir148a 2.63821345578617 15.4435819751152 16.5481882215215 1.17118649515038e-10 1.03604453339228e-08 14.8147917664862 1002397 Mir148a -Mir101b 3.76572195114225 10.8508440499081 16.5385659719288 1.1804188373444e-10 1.03604453339228e-08 14.9000274171241 59019 Mir101b -Mir490 -8.47437764634465 3.75069567634692 -16.2596504905533 1.48481644820999e-10 1.21012540529114e-08 13.4246171016517 1741 Mir490 -Mir21 2.93853744034991 13.1642916950886 15.3754036511693 3.14833456057776e-10 2.39483315574615e-08 13.8676979022068 229120 Mir21 -Mir181c -3.74256009957124 9.62955774646065 -15.2423608550805 3.53706264458683e-10 2.52236779842098e-08 13.8104046176901 23605 Mir181c -Mir204 -7.68442507149438 4.77517348536933 -15.0334839919296 4.2542677795722e-10 2.85536443323052e-08 12.8224274879526 2601 Mir204 -Mir23a -3.16576837850821 8.78965917558611 -14.6311785109623 6.11068192724496e-10 3.87349337721472e-08 13.2691736804205 10118 Mir23a -Mir181d -3.63621106402109 6.37132182424908 -14.3170733565449 8.15750840848868e-10 4.89879847057136e-08 12.9563328312209 2139 Mir181d -Mir133b -6.49354876170712 1.25448620431148 -13.969968060601 1.12993427319653e-09 6.44627502858619e-08 11.9826837063041 159 Mir133b -Mir27a -3.10693537246128 9.92557960348829 -13.8382510839158 1.28101104196848e-09 6.96015999469543e-08 12.5130856443239 21886 Mir27a -Mir194-2 5.26413595786074 6.08976151689046 13.0440120203829 2.79288399641768e-09 1.44849119996026e-07 11.7157527118771 3570 Mir194-2 -Mir195 -3.21659545049586 7.4509349905835 -12.869478368273 3.33278798407795e-09 1.65335264775345e-07 11.5875227405737 3962 Mir195 -Mir27b -1.97637614533106 15.0957731023791 -11.75603589654 1.08219717999805e-08 5.14494575990741e-07 10.1277185662145 625308 Mir27b -Mir378 -3.09739319841142 7.38320489393809 -11.6841625470748 1.17137125863851e-08 5.34613842442616e-07 10.3296922348831 4075 Mir378 -Snord104 2.33737428989677 10.6109023861403 11.4956750870273 1.44448164322638e-08 6.33905213431269e-07 10.0233949189609 33458 Snord104 -Mir1983 -5.89550024150745 0.993185099223749 -11.4458119994178 1.52754786535047e-08 6.44160462853232e-07 9.74926029381244 101 Mir1983 -Mir322 -3.29661750880005 8.21534154356388 -11.4153616003567 1.58076187203247e-08 6.44160462853232e-07 10.0084716002011 7074 Mir322 -Mir200a 6.19156065085543 1.79813092499896 11.3221723123067 1.75622912046568e-08 6.9098531946598e-07 9.66229453831667 264 Mir200a -Mir215 -3.04587333807051 5.75442336214621 -11.1481336257529 2.14182153707674e-08 8.08245865886245e-07 9.75326755116029 1182 Mir215 -Dnm3os -3.36334357719079 5.86074322417943 -11.0922610835813 2.28395969947309e-08 8.08245865886245e-07 9.68949616901383 1401 Dnm3os -Mir182 4.90399541739044 7.1511683493624 11.0744681203078 2.33130367310143e-08 8.08245865886245e-07 9.65884218207857 7189 Mir182 -Mir181a-2 -3.04829832099813 6.94146510070354 -11.0721276255975 2.33760855164295e-08 8.08245865886245e-07 9.64401697815694 2817 Mir181a-2 -Mir1948 7.19552540631629 4.5513492833967 11.0054920626234 2.52493600829575e-08 8.47338819254543e-07 9.34179361673467 2404 Mir1948 -Mir214 -3.28087400431203 5.47844506177362 -10.7682572190636 3.3325545851092e-08 1.0864127947456e-06 9.3185039394887 1048 Mir214 -Mir153 -5.9638030672045 1.43863148956561 -10.7270821099311 3.49874201497232e-08 1.09398957489501e-06 9.03556928822473 140 Mir153 -Cyp3a25 6.3181999278218 1.48889330889732 10.698226339624 3.62044305804168e-08 1.09398957489501e-06 9.02497280953977 226 Cyp3a25 -Gm5441 -5.98217559924296 1.44849534030207 -10.6928905219357 3.64343591989574e-08 1.09398957489501e-06 9.00036161342169 142 Gm5441 -Mir125b-2 -3.07767777869034 7.43160584496732 -10.4466676018996 4.89307304174606e-08 1.43153752323904e-06 8.88425047860251 3837 Mir125b-2 -Mir133a-1 -5.1446707114636 0.590326422018506 -10.3582048674166 5.44722862183524e-08 1.5538219643785e-06 8.57553468686339 60 Mir133a-1 -1110038B12Rik 2.2267024913641 10.8487089345135 10.1946092089644 6.6553123680318e-08 1.85212473461568e-06 8.43930767784171 37066 1110038B12Rik -Mir132 -2.84755882904953 5.32118389129247 -10.1109518209472 7.38029688527407e-08 2.00498065383279e-06 8.53149092529729 857 Mir132 -Rabggtb 1.93577875106629 9.98741711657765 9.92899544482161 9.26282059254969e-08 2.45787867351144e-06 8.13338404178509 19535 Rabggtb -Mir504 -5.25612736011945 0.622108812393712 -9.89289391617729 9.69359452558378e-08 2.51372530765707e-06 8.06885345567596 69 Mir504 -Mir150 -2.93853062743745 7.62978695478889 -9.84210172881295 1.0336018288322e-07 2.62075485932786e-06 8.11646444295523 4229 Mir150 -Mir199b -5.75281586680568 2.88051428778536 -9.82392040215114 1.05768314120536e-07 2.62351405242461e-06 7.97938664656761 370 Mir199b -Mir23b -2.12412853458823 9.81411903779331 -9.80631552439508 1.08156895246613e-07 2.62568122290182e-06 7.97946421769546 16387 Mir23b -Mir24-2 -2.83397931671342 7.30836912335631 -9.76719188905173 1.13672435494913e-07 2.64694385509584e-06 8.03055035937719 3470 Mir24-2 -Mir3074-2 -2.83397931671342 7.30836912335631 -9.76719188905173 1.13672435494913e-07 2.64694385509584e-06 8.03055035937719 3470 Mir3074-2 -Mir155 -3.90660012470502 3.98990000150695 -9.73217278591154 1.18862734970705e-07 2.71244761203149e-06 8.04651834419983 463 Mir155 -Snord52 2.24297808473649 9.80695706696893 9.65508926768283 1.31194241930095e-07 2.93514960867135e-06 7.78317356265822 18059 Snord52 -Scarna17 2.22135685073443 8.84690751573681 9.60031550237377 1.4077767610154e-07 3.08898708522803e-06 7.75116302071276 9224 Scarna17 -Mir201 -5.13478218238105 0.574435171385817 -9.36375298852343 1.91556946938131e-07 4.12389578219637e-06 7.45101629313275 63 Mir201 -Mir184 -5.19740389169544 2.57741455998865 -9.28503096123923 2.12504578894189e-07 4.42570763295992e-06 7.34294491810118 247 Mir184 -Snord91a 2.64601724328653 5.9737770599439 9.28208610647144 2.1333384733812e-07 4.42570763295992e-06 7.45176303063652 1437 Snord91a -Mir193 4.93970055277848 3.14663535122407 9.21323530535893 2.33732739541295e-07 4.76230456815389e-06 7.28752724029761 421 Mir193 -Mir470 5.07947201153691 1.71052510073336 8.84181308472679 3.85901653714362e-07 7.72480327873838e-06 6.83971839651152 157 Mir470 -0610031O16Rik 4.84298796174609 0.739739561937192 8.8004727262221 4.08431072700958e-07 8.03482506813436e-06 6.77016207746292 78 0610031O16Rik -Mir203 1.9396775595845 8.44497102033975 8.70156015908293 4.6818189744087e-07 9.05416177932259e-06 6.53224466632053 6739 Mir203 -1810019D21Rik 5.37088028736873 1.02158093294187 8.63951664435106 5.10331246821015e-07 9.70479921037964e-06 6.58253399607689 117 1810019D21Rik -D7Ertd143e 4.74135480360802 0.690096187370941 8.59182875086527 5.45451616539556e-07 1.02026277782235e-05 6.49996146777963 73 D7Ertd143e -Mir181a-1 -3.20886334319296 4.33388500974742 -8.528721562826 5.95908204570219e-07 1.09666332486229e-05 6.48742379588458 506 Mir181a-1 -Mir547 -4.64199116509563 0.384583900671288 -8.48569977551612 6.33121735643368e-07 1.14665381010966e-05 6.34288346197563 42 Mir547 -Mir3102 -4.82802034015575 1.26816340424779 -8.40617041640129 7.08539703084848e-07 1.26319343940595e-05 6.2533959548051 87 Mir3102 -Mir194-1 3.86792676846218 4.28030595336869 8.2361953191898 9.03449017918911e-07 1.5859005068392e-05 6.08687440143381 635 Mir194-1 -Mir199a-2 -3.62193724804755 3.76746624824118 -8.22528846947687 9.17754251311817e-07 1.58660242537391e-05 6.07294487982063 352 Mir199a-2 -Mir375 4.36561952992499 1.70304479419652 8.20239976081094 9.48559026077637e-07 1.61538186381281e-05 5.9804850351652 123 Mir375 -Mir128-1 -2.79500838795041 4.68404652058909 -8.16636754121193 9.99287948408945e-07 1.67674639578619e-05 5.95537435854342 527 Mir128-1 -Mir1a-1 -4.50930254765404 0.235845136893643 -8.15352480043541 1.01805558742015e-06 1.68348032644405e-05 5.89233114702952 42 Mir1a-1 -Mir17hg 1.26324530315481 13.08692945461 8.08792426896809 1.11990826888155e-06 1.80004069127122e-05 5.39248242771111 145451 Mir17hg -Mir92-1 1.26323093975761 13.0869221993444 8.08780961117952 1.12009543453336e-06 1.80004069127122e-05 5.39230857078851 145450 Mir92-1 -Mir881 4.55329779507755 0.622217688176746 7.98090604480001 1.30985575977083e-06 2.06385233899717e-05 5.68307629847666 64 Mir881 -Mir125b-1 -3.19780406116102 4.7293501044755 -7.96524973476641 1.340381181277e-06 2.06385233899717e-05 5.66721192306587 609 Mir125b-1 -2610203C20Rik -3.20042083590071 4.73071629949528 -7.96367997663051 1.3434829565975e-06 2.06385233899717e-05 5.66487879544241 610 2610203C20Rik -Scnn1b -4.63822537309801 0.37290502628334 -7.9570799219013 1.35660758479218e-06 2.06385233899717e-05 5.63911983957 45 Scnn1b -Mir193b -3.19095319216208 3.51292960275358 -7.90681185253976 1.46112366345347e-06 2.19360802631633e-05 5.62073482323402 260 Mir193b -Mir183 4.30421971951602 3.92737361468754 7.78136892736858 1.76081314682182e-06 2.60215226213659e-05 5.44000583749589 563 Mir183 -5430416N02Rik 3.07746071410873 4.3937689770433 7.77455061595853 1.77885956570249e-06 2.60215226213659e-05 5.40856341816749 564 5430416N02Rik -Mir125a -2.6112312467571 10.9299667279975 -7.67497504879118 2.06586606398761e-06 2.98373820127831e-05 4.87458713303236 37867 Mir125a -Mir200b 6.64163006978911 3.12821174098826 7.58298811849222 2.37461716273863e-06 3.38679772835598e-05 5.13195387121669 888 Mir200b -C430049B03Rik -1.92074554462898 10.5496297843175 -7.55549117760725 2.4760890707944e-06 3.48792299972396e-05 4.70418935748661 25716 C430049B03Rik -Terc 4.24682289892723 0.87844372666952 7.3826580782883 3.2281439398588e-06 4.49184418948646e-05 4.83681905205792 66 Terc -Mir871 5.21513327520529 2.43957936484213 7.24439581531717 4.00226877508459e-06 5.50191406309821e-05 4.63418699567578 290 Mir871 -Mir30e -1.90475431278526 13.2693114305891 -7.14894534136424 4.6492913699815e-06 6.28199839913465e-05 3.89856793405104 186796 Mir30e -Mir24-1 -3.10183666628555 3.10602318965769 -7.13671325687185 4.73984945200915e-06 6.28199839913465e-05 4.48008371426601 189 Mir24-1 -Mir3074-1 -3.10183666628555 3.10602318965769 -7.13671325687185 4.73984945200915e-06 6.28199839913465e-05 4.48008371426601 189 Mir3074-1 -Mir592 4.27837071128313 0.882966518707851 7.13005059451541 4.78995495814825e-06 6.28199839913465e-05 4.46386515426502 70 Mir592 -Serpina4-ps1 6.28609584979245 1.44994282762173 7.10887070838901 4.95297318411398e-06 6.42198000349325e-05 4.43976042902202 295 Serpina4-ps1 -Mirlet7e -1.98704484524714 6.38418687649397 -6.91936550313624 6.69984149789542e-06 8.58934735853783e-05 3.9044291222869 1576 Mirlet7e -Mir3470b 4.76222273641772 1.545054677666 6.90067154705821 6.90430035193184e-06 8.75311855728247e-05 4.11927719036494 132 Mir3470b -Mir497 -2.37449282835512 5.72555109896591 -6.88407718002354 7.09129622786183e-06 8.89139450108829e-05 3.89837376299718 963 Mir497 -Mir30d -1.6250750764544 13.1824457426439 -6.86162843142705 7.35278174335401e-06 9.11904779257275e-05 3.42562585807056 161730 Mir30d -Mir107 1.66807649183652 7.69910580818381 6.8393447582273 7.62239107335082e-06 9.35177227386374e-05 3.6976878474277 3843 Mir107 -Mir301 -1.74763355751926 6.9632575863254 -6.82958967456103 7.7436706688371e-06 9.39949812036503e-05 3.72033936484245 2119 Mir301 -Mir455 2.46320289727146 5.30404932290074 6.81532109954182 7.92473126605604e-06 9.51801934165257e-05 3.83331724079003 895 Mir455 -Mir429 4.86439995826456 3.23263249974835 6.78791698606005 8.28507245768243e-06 9.84715382730797e-05 3.94480735955982 499 Mir429 -1700003L19Rik -3.76057540139707 -0.126261497218445 -6.63064169305684 1.07147990447279e-05 0.000126033542791258 3.68122560260964 23 1700003L19Rik -Mir99a -2.28512022818228 9.92572736694444 -6.62442810768864 1.08249668655068e-05 0.000126033542791258 3.2109507357583 17575 Mir99a -Mir145 -1.97697703068737 8.78484403570673 -6.5439327901519 1.23647594606541e-05 0.000141146069525717 3.13555880659816 7539 Mir145 -Mir34b -4.12275476924448 0.708468030075098 -6.54365850253687 1.23703829558034e-05 0.000141146069525717 3.55740492991767 54 Mir34b -Mir31 2.28911995870681 6.51541747970347 6.52931766820141 1.26681737242473e-05 0.000143112734845209 3.25621845009615 2078 Mir31 -Mir181b-1 -3.60801009779468 -0.132126628141882 -6.46494720684188 1.41005553764556e-05 0.000157732683181724 3.42410398459059 19 Mir181b-1 -Gm6307 -4.1578316997704 0.656018151435715 -6.45273564502753 1.43909587108892e-05 0.000159418290185675 3.41462429299529 52 Gm6307 -Scarna6 1.69929242985022 7.02301841351445 6.42269285853436 1.51324405034754e-05 0.000166020332831399 3.03238527080513 2398 Scarna6 -Snord45b 1.94022355970573 6.80826479898751 6.39728013438808 1.57909283820436e-05 0.000171594755084873 3.00447879542696 2199 Snord45b -Mir212 -2.84645310030162 3.44913875490104 -6.35557690856707 1.6937466935156e-05 0.000182317450688802 3.21240862806012 232 Mir212 -Mir488 -4.10203604485773 1.24180834532503 -6.29103817686794 1.88870111723183e-05 0.000201402614463693 3.15543310741006 78 Mir488 -Mir1943 -2.71589064856915 3.44876720857909 -6.23580114025192 2.07423167962376e-05 0.000219138735782473 3.00834314558259 215 Mir1943 -Gas5 1.24087717560169 10.5129034284973 6.13133629490868 2.4792457454819e-05 0.000259524715192188 2.31915097100889 24887 Gas5 -Mir1960 -3.55886465849503 0.603332307653829 -6.02805967736536 2.96173517543744e-05 0.000307212712288556 2.72737825973636 36 Mir1960 -Snord72 1.83422077226714 4.99202771858388 6.01300538064759 3.03988327484681e-05 0.000312478091585605 2.48059356064701 603 Snord72 -Mir126 -1.4123938474776 14.7113257499272 -5.9690966101592 3.28038341776394e-05 0.000334189060684701 1.77670178050375 420720 Mir126 -Mir181b-2 -3.5537298874584 2.37784481058975 -5.74465057345259 4.86143755138839e-05 0.000490876127976474 2.24688113349229 154 Mir181b-2 -4930581F22Rik 3.25147651228939 0.329341147921603 5.72231806379132 5.05741508633493e-05 0.000506185141535803 2.2176701707354 33 4930581F22Rik -Taf1d 1.56967838004803 6.27598295057893 5.62971411199061 5.96235762303463e-05 0.000591569569381088 1.67884064404776 1421 Taf1d -Mir324 -2.38077905852261 3.65370681185719 -5.61738196066422 6.09504435595697e-05 0.000599521173288526 1.9104544791018 248 Mir324 -Mtmr2 -2.30493988089979 4.77764446891153 -5.57410629776523 6.58554908247018e-05 0.000642231752401579 1.7123130370103 580 Mtmr2 -Mir137 3.41092416262533 0.0324356592441258 5.50287186084007 7.48455682327983e-05 0.000723718587742567 1.84456682895684 29 Mir137 -Snhg1 0.975366701589908 11.1969347059892 5.49575538415102 7.5811401213261e-05 0.000726897552809502 1.12394645028672 37837 Snhg1 -Mir34a -2.05826883405221 3.36051649740781 -5.47484354411999 7.87253643157781e-05 0.000748547005702523 1.67723038143596 180 Mir34a -Mir298 -3.44904214184284 0.481437816526014 -5.39740371005322 9.05725273791661e-05 0.000854076477187012 1.66441956376117 37 Mir298 -Mir30b -1.37954359870678 9.2115221848988 -5.37206514503938 9.4840390621554e-05 0.000886990866386828 1.00959570224732 10011 Mir30b -2310001H17Rik 3.60260656982356 0.877139295086231 5.35615735698035 9.76262576906534e-05 0.000902593356272145 1.57720822721783 54 2310001H17Rik -Pex16 3.10814004056272 0.242483417469106 5.35355087544425 9.80907766676126e-05 0.000902593356272145 1.58678494520955 30 Pex16 -Mir741 3.33919133389387 0.432231427661093 5.2950378042311 0.000109146860855228 0.000996292545886519 1.47796989545021 36 Mir741 -Gm6313 -3.15860344908017 -0.430119305523511 -5.24231069726794 0.000120220067795944 0.00108865950281883 1.40353714409938 15 Gm6313 -Mir328 -1.36488724959 7.2282210636054 -5.23221518519415 0.000122470141866871 0.00110030261315039 0.870852174596449 2362 Mir328 -Snord33 1.25647224562943 7.71222650703403 5.19862342341357 0.000130277220055511 0.00116129928190108 0.778490918357933 3442 Snord33 -Rpph1 1.87063945690961 4.43992987975591 5.19124070458341 0.000132061294296316 0.0011680770293961 1.05386997375291 430 Rpph1 -Mir291a 3.17904342101315 -0.0791999402663102 5.18540568400292 0.000133489301957871 0.00117026083393732 1.2956738181937 24 Mir291a -Mir186 -1.38629871411117 10.176089899221 -5.181881716386 0.000134359482248719 0.00117026083393732 0.594672202880173 18235 Mir186 -Gm16596 -3.02811906442667 -0.421976649282355 -5.15901843295933 0.000140150118907224 0.00121144913388744 1.25840752971795 12 Gm16596 -Mirlet7i -1.21381140575271 10.6031742297066 -5.15326347701587 0.000141648110216644 0.00121519168238489 0.514620675402163 26242 Mirlet7i -Sra1 2.97648314190951 -0.188256184051246 5.14486604509538 0.000143863812461334 0.00122498962700285 1.22868620554498 19 Sra1 -Mir700 -3.05275751205424 3.03686027981893 -5.10658051111472 0.00015443097639702 0.00130522773384445 1.06937466394294 177 Mir700 -Mir30c-1 -1.44640830149114 5.51774362365196 -5.09058727395353 0.000159080836478697 0.0013266290585589 0.730639691134108 732 Mir30c-1 -Mir674 -1.94702895981213 4.45877434664642 -5.08988436247691 0.000159288502210841 0.0013266290585589 0.846234772297753 392 Mir674 -Snord45c 1.44139263505882 6.31780757188042 5.0752407566119 0.000163679307853258 0.00135146340163194 0.643628779392595 1427 Snord45c -Ank1 -1.70157147221109 10.9929711198162 -5.07209350467361 0.000164639275045433 0.00135146340163194 0.337810380148678 33776 Ank1 -Mirlet7b -1.09478453248563 9.30463614623057 -5.06223764308063 0.000167683437276746 0.00136662001380548 0.418305977430397 10012 Mirlet7b -0610008F07Rik 3.20935973501609 0.342617899865038 5.04843460516125 0.000172045196108449 0.00139222389191305 1.0441746904328 34 0610008F07Rik -Snord4a 1.57395983217788 5.94089477513251 5.00029990452424 0.000188198499627571 0.00151221470475393 0.535941755206456 1151 Snord4a -Mir3107 -1.70018651164628 9.99212523723331 -4.97672369154541 0.000196676176463643 0.00155838553711817 0.215189363582583 16873 Mir3107 -Mir486 -1.70018651164628 9.99212523723331 -4.97672369154541 0.000196676176463643 0.00155838553711817 0.215189363582583 16873 Mir486 -Mir100 -1.97076444192024 8.99815411447499 -4.96949043197095 0.000199355724522589 0.00156872332193292 0.262493038912075 8837 Mir100 -6430411K18Rik -3.18910263916585 -0.373872894349982 -4.91488268129579 0.000220845557812913 0.00170486995980212 0.826251830470595 17 6430411K18Rik -Mir433 -3.18910263916585 -0.373872894349982 -4.91488268129579 0.000220845557812913 0.00170486995980212 0.826251830470595 17 Mir433 -Mir190 -2.07544392773473 3.1544454577673 -4.91417324518583 0.000221140012314386 0.00170486995980212 0.671557118650327 163 Mir190 -Mir668 -2.84374273013661 -0.157703843092941 -4.86996462339487 0.000240314465841743 0.00184026043976798 0.746753746471687 15 Mir668 -0610012G03Rik 2.84115361016785 -0.244188556172649 4.84933497977366 0.000249843031682205 0.00190047266099598 0.705829411755822 17 0610012G03Rik -Mirlet7d -1.19906882560952 10.0080528514984 -4.83828196963314 0.000255108219097298 0.00192767203966899 -0.0535917366982233 16847 Mirlet7d -Mir320 -1.40563797109219 6.563348272824 -4.77568906174122 0.000287168062262582 0.00215564973053688 0.0467165593444996 1651 Mir320 -Snord66 1.15430111322029 8.03351541888005 4.75658981303321 0.00029775850741536 0.00222053893438514 -0.0877963597335594 4212 Snord66 -Hgd 4.54425664237216 0.975547692617975 4.73952572490202 0.000307561641064749 0.00225560361403978 0.450492806487889 137 Hgd -Aqp9 4.93135714830861 0.753510541775538 4.73814880056307 0.000308367063909922 0.00225560361403978 0.445426512401092 161 Aqp9 -Mir221 -1.08377350661343 8.47402248983799 -4.73470478811828 0.000310391167513083 0.00225560361403978 -0.161171563827692 5659 Mir221 -Atf5 3.74327970750113 0.168725495917353 4.73192727585554 0.00031203354916423 0.00225560361403978 0.467422722695712 50 Atf5 -Ahsg 7.43012370228481 2.43819500653074 4.73140267365542 0.000312344759875797 0.00225560361403978 0.392509485594371 1524 Ahsg -Cyp2d13 3.62058562658928 0.099076105394318 4.71784217799444 0.000320501474638856 0.00229995083372915 0.445950346991055 44 Cyp2d13 -Mir292 3.55331832828793 0.0808135496327816 4.67052458846538 0.000350727059524427 0.00250112234323357 0.360392052911509 41 Mir292 -Proc 4.16579682989808 0.376893953921531 4.63475223165321 0.000375520073648099 0.00266129443498435 0.275862327084029 83 Proc -2610019E17Rik 1.58277820027238 5.58959301890268 4.63093795449771 0.00037826821496869 0.00266422242764985 -0.140287526130874 892 2610019E17Rik -Mir379 -2.08294744907303 3.05372097245691 -4.61798165733268 0.000387758909131681 0.00271431236392177 0.128998572399559 153 Mir379 -Mir96 3.092187161072 -0.127577830654966 4.61264491367143 0.000391739223724717 0.00272545398945063 0.269031262574771 24 Mir96 -5033403H07Rik 3.03100334960097 -0.167104636116587 4.55138913141517 0.00044055961680218 0.00304390944849929 0.159657330767462 23 5033403H07Rik -Mir582 -4.60557103590561 3.35812677089552 -4.5486926708253 0.00044284747454065 0.00304390944849929 0.0591283272554897 566 Mir582 -Mir335 -1.27174458468766 6.43063411797007 -4.54414079104836 0.000446737391037914 0.00305225965972611 -0.393123932477534 1318 Mir335 -Gm5424 4.79310957596335 1.09020146508355 4.53505892344961 0.000454603987554657 0.00308751874880871 0.0640172708933422 181 Gm5424 -Snord96a 1.64486304749729 4.67116544357615 4.51910388563813 0.0004687711663005 0.00316489882099923 -0.255892987917286 487 Snord96a -Fam120b 3.09294836244015 0.400892342309139 4.51279274228026 0.000474499826623265 0.00318473118927733 0.0711882890790463 39 Fam120b -1810032O08Rik 1.41990709594509 5.47749401704587 4.44622801412284 0.000539494231805467 0.00359978314906455 -0.491596569959665 801 1810032O08Rik -D830005E20Rik -2.66421484690988 -0.635454055345691 -4.43818702356919 0.00054794279494766 0.00363489958741442 -0.0273233727843643 9 D830005E20Rik -Mir3057 -1.76308589999293 3.90842103747244 -4.39033832828025 0.000601107772422842 0.00396453160886973 -0.421971049333088 244 Mir3057 -Dcaf11 3.64616824902598 1.34504089418874 4.36305371925593 0.000633768357283662 0.00415591779115321 -0.228339632338981 105 Dcaf11 -Mir141 2.71262314324362 4.77357067250837 4.34309768158673 0.000658805704226011 0.00429541319155359 -0.576407055554532 835 Mir141 -Clip4 -2.70686636040313 -0.590132795422603 -4.22339289420565 0.000831845216151027 0.00538076227850526 -0.424317219340228 10 Clip4 -Atp2a2 -3.03468731889576 3.9897529348455 -4.22163834350324 0.000834701948549895 0.00538076227850526 -0.712425757555907 394 Atp2a2 -Snord11 1.70196648813426 4.30076703857411 4.20393833047899 0.000864088763598198 0.00553890606328957 -0.81986210274439 399 Snord11 -Mir1a-2 -2.99188005456984 -0.157703843092941 -4.1964183364679 0.00087689254654494 0.0055585244200432 -0.484125725482271 23 Mir1a-2 -Mir1b -2.99188005456984 -0.157703843092941 -4.1964183364679 0.00087689254654494 0.0055585244200432 -0.484125725482271 23 Mir1b -Snord95 0.986915429703335 7.67764085008967 4.16122119437911 0.000939451338082392 0.00592217666713818 -1.23233286437982 3258 Snord95 -Tardbp 2.97062100907675 0.883541928525332 4.15354257136734 0.000953695266999763 0.00597893571234467 -0.601444869463529 52 Tardbp -Mir34c -4.34256582352703 3.19074386781529 -4.14603488500754 0.000967835650645262 0.00601077590779499 -0.705590893704782 715 Mir34c -Nagpa 2.44212396887249 0.350172381597785 4.14525853315587 0.000969310049986221 0.00601077590779499 -0.597611598821245 24 Nagpa -Bmp1 2.83284622628297 0.517967853857585 4.09387916064743 0.00107217035218767 0.00661268309106014 -0.708641495529353 39 Bmp1 -Crem 2.33712898304139 -0.121515515943567 4.0829392931282 0.00109547464985413 0.00672008911550302 -0.694023102630085 16 Crem -AI506816 2.42270659752045 -0.452690836228738 4.06592993902701 0.00113274010846169 0.00690883244123217 -0.71823549330855 12 AI506816 -Snhg12 1.2392736392255 8.22309194102909 4.06341770887408 0.0011383527598174 0.00690883244123217 -1.46153826275546 5403 Snhg12 -Snord99 1.60658132000619 7.61494864267921 4.05085172286681 0.00116685579559478 0.00702317443883207 -1.44229619547632 3970 Snord99 -Mir450b -1.85261764183761 3.54483087111489 -4.0485135276699 0.00117223928473074 0.00702317443883207 -1.03659851317493 205 Mir450b -Mir218-1 -2.37943795492075 -0.788253108012747 -4.04703416313971 0.00117565847310861 0.00702317443883207 -0.747038393026922 7 Mir218-1 -Mir140 -0.901201558594089 9.41159056411459 -4.03552071597731 0.00120261909580773 0.00711569212542235 -1.59661829698077 10795 Mir140 -Mir483 -2.42347249247516 -0.756810762138252 -4.03017784461274 0.0012153437165189 0.00711569212542235 -0.779102255351839 7 Mir483 -Sqrdl 3.73159563362893 0.945260485192632 4.02973478085716 0.00121640506624817 0.00711569212542235 -0.861776345505721 102 Sqrdl -Mir144 -2.29129119700542 9.41554506116587 -4.02892270273821 0.00121835283652175 0.00711569212542235 -1.60506726133582 13709 Mir144 -Mir210 -1.50896749399836 4.99944311707418 -4.02726966499444 0.00122232748166764 0.00711569212542235 -1.27568646998696 592 Mir210 -Mir136 -1.91584100290298 5.18425364003902 -4.01919516322722 0.00124193308478254 0.0071567962107923 -1.30273093425232 753 Mir136 -Mir3071 -1.91584100290298 5.18425364003902 -4.01919516322722 0.00124193308478254 0.0071567962107923 -1.30273093425232 753 Mir3071 -Snord43 1.12653255459757 7.42275234629614 4.00820447995329 0.00126913582767258 0.00727680391645436 -1.51733899954002 2975 Snord43 -Nlrp5-ps 3.06966395518949 -0.135132312387713 3.98319245863032 0.00133332885542151 0.00760664112017971 -0.902428549559341 34 Nlrp5-ps -4833418N02Rik 1.82386047796749 3.16360132579728 3.94914311648112 0.00142607040510993 0.00809525538423099 -1.16130756295351 170 4833418N02Rik -Pknox1 2.58217103686749 -0.386376496641391 3.94009175499237 0.00145181139000837 0.00820057819801758 -0.95836656931183 16 Pknox1 -Mir1843 1.0607275776375 7.37281183848222 3.92128643575477 0.00150681377235572 0.008458947103908 -1.6871582740604 2782 Mir1843 -Scarna3b 1.05890367361984 7.37450114126396 3.91942250502631 0.00151237967501948 0.008458947103908 -1.69099763322655 2784 Scarna3b -Phyhd1 2.69871613662387 -0.324381809808589 3.91199503566496 0.00153476742516205 0.00850676625406398 -1.01680363882686 20 Phyhd1 -Gm14207 -2.37304409257741 -0.416502509079365 -3.91164184680055 0.00153584035787658 0.00850676625406398 -1.00193629125355 11 Gm14207 -2700038G22Rik -1.20941383309973 5.69714561045198 -3.89423392377302 0.00158967928624185 0.00876243509952633 -1.61494260853442 872 2700038G22Rik -Mir1249 -1.57997335358757 3.92571996538164 -3.86749708921129 0.00167613304205796 0.00914790116608117 -1.45071018307216 240 Mir1249 -Mir680-2 2.69993600843854 2.33940488575521 3.86630244603638 0.00168010559076576 0.00914790116608117 -1.19260507987069 134 Mir680-2 -4732471J01Rik 2.47123788102083 1.83426864599497 3.86203852417355 0.00169436257731633 0.00914790116608117 -1.16621943210526 78 4732471J01Rik -0610005C13Rik 3.75552246080227 1.36486301235638 3.86197876879019 0.00169456324819645 0.00914790116608117 -1.18181214765895 126 0610005C13Rik -Mir297a-3 -2.83038541581836 0.620898493219045 -3.86045228910567 0.00169969767502998 0.00914790116608117 -1.12583690632738 32 Mir297a-3 -D830015G02Rik -2.85288965105576 -0.0430116131169372 -3.8540007067199 0.00172157316996324 0.00918761905215916 -1.135758525707 19 D830015G02Rik -Mir190b -2.58480150088673 0.118920372323834 -3.85352956525721 0.00172318183800356 0.00918761905215916 -1.12342997930169 19 Mir190b -Cacnb2 -2.36615720731133 -0.788253108012747 -3.84657108578939 0.00174711947070295 0.00927192240033519 -1.12169301350773 7 Cacnb2 -Mtfr1 2.77427419358368 0.492589466798896 3.83997888411189 0.001770108543009 0.00935043447950588 -1.18729669638225 40 Mtfr1 -Snord1a 1.56162899910724 3.64400301591315 3.83681604346118 0.0017812471515648 0.00936313497745819 -1.45598973323054 230 Snord1a -Snord68 0.957663081634941 8.79334935483305 3.83464756418373 0.00178892500007527 0.00936313497745819 -1.95644313626433 7303 Snord68 -Mir878 2.41936085459412 -0.447944854953861 3.82652263259005 0.00181799260036803 0.00947182446127819 -1.16702401112308 13 Mir878 -Mir511 1.70981318188926 3.27929603694437 3.81643804724114 0.0018547387918105 0.00959956329857923 -1.44128807747474 183 Mir511 -Gm15787 2.48153800186339 -0.386376496641391 3.81519042194731 0.00185933697544786 0.00959956329857923 -1.19313192146596 14 Gm15787 -Surf1 2.36126838851735 0.0872159088448913 3.80305260835126 0.00190467958178741 0.00978936667936682 -1.23097217655323 20 Surf1 -Mir1968 2.37800084613411 0.673754504632647 3.77926083620018 0.00199683807660974 0.0102052996591412 -1.28734965758831 33 Mir1968 -0610007N19Rik -1.90120438734532 2.49555567542219 -3.77758577340756 0.00200349441161053 0.0102052996591412 -1.41487533930946 104 0610007N19Rik -Snhg8 1.59039445480745 3.20715500905903 3.75615085858512 0.00209068421652196 0.0106020919602291 -1.55245580151111 164 Snhg8 -Mir99b -1.91682795135645 10.9172256229833 -3.75211079306552 0.0021075435523792 0.0106402973153304 -2.25375326353787 35086 Mir99b -Mir365-2 2.40700496454038 0.263026808334256 3.74756102414217 0.00212669484901157 0.0106896864437102 -1.33894003797924 25 Mir365-2 -A630019I02Rik -2.46429396746277 -0.331334129436178 -3.72894212518103 0.00220692150206125 0.0110442869905785 -1.34913426897275 12 A630019I02Rik -Snora24 1.55151202645698 3.18622291582758 3.72443395692917 0.00222680324291416 0.011095120088057 -1.61270421930364 160 Snora24 -Tmem205 3.35434252485109 1.34402776298411 3.71826658220279 0.00225429637910924 0.0111803962803989 -1.43852442151233 106 Tmem205 -Chka 2.66265038819564 -0.367431606769972 3.71621547263374 0.00226351581136911 0.0111803962803989 -1.38147433178837 20 Chka -Il15ra 3.0322333836799 1.34537734368953 3.69802409752219 0.00234697158113459 0.0115426490261835 -1.46743653610507 78 Il15ra -Mir340 1.08524047523296 8.40150339094243 3.68614126686871 0.00240316001921397 0.0117592747718197 -2.22640527546981 5906 Mir340 -Mir222 -0.989531454809674 6.83748363675873 -3.68437434098812 0.00241163040894463 0.0117592747718197 -2.12318215364121 1840 Mir222 -Snord16a 2.29129601947442 3.76378890459482 3.66271871458069 0.0025179297139219 0.0122253523556804 -1.78739319047154 368 Snord16a -Snora16a 1.52372874357926 4.55536943029929 3.65510898252504 0.0025563971250754 0.0123595301682671 -1.93830245300744 464 Snora16a -Meg3 -2.68467060880573 -0.63693223477029 -3.65193847372806 0.00257259886012651 0.0123853810101449 -1.49159048882037 12 Meg3 -Mir503 -1.62803161355467 2.50957996141068 -3.64484640281096 0.00260921685489431 0.0125088925690521 -1.68081269699905 88 Mir503 -Snord34 1.9527050627559 2.43578559807808 3.64115899694926 0.00262846334208935 0.0125484379636985 -1.64754683241552 110 Snord34 -Mir330 -1.20046349365495 3.89404790732931 -3.62347773804946 0.00272276191759244 0.0129444639498874 -1.93549975651123 231 Mir330 -Snord123 -1.85009039423829 2.46689582455698 -3.62068933774574 0.00273794187669866 0.012962621084287 -1.71578244093404 102 Snord123 -1700123M08Rik -2.53907013725051 -0.265019789848831 -3.61128151512854 0.0027897908425338 0.0131535179807069 -1.57539508080877 14 1700123M08Rik -U05342 2.83058680402627 0.79675702914225 3.60519476819122 0.00282386325723748 0.0132525688403876 -1.63602002228639 54 U05342 -Mir331 -1.23366463697674 4.50384654608554 -3.60339313514846 0.00283402874413198 0.0132525688403876 -2.05839651689923 359 Mir331 -Snord47 1.75254142711948 3.676071658915 3.56574123782182 0.00305512804060726 0.014228167731971 -1.9866272780716 277 Snord47 -1810026B05Rik 2.40748340219702 -0.486051677644913 3.55958270725964 0.00309291230781102 0.014345581069969 -1.66670102684768 15 1810026B05Rik -Mir1933 -2.60309478178739 0.220871127749556 -3.55529124527092 0.00311951960536729 0.0144104124280327 -1.68059425943861 26 Mir1933 -Gm9899 -2.07308991765323 -0.941052160679803 -3.54532493149369 0.00318220422627889 0.0146407057346138 -1.68493975204853 5 Gm9899 -Hspa13 2.27524541038784 -0.523818455835255 3.54060461550118 0.00321233452906321 0.0147199746894021 -1.69974419035356 12 Hspa13 -Mir3096b -1.20474634794453 4.70053095823646 -3.5153757202842 0.00337831126227612 0.0153571838655659 -2.25812559332021 435 Mir3096b -Snord93 -1.20474634794453 4.70053095823646 -3.5153757202842 0.00337831126227612 0.0153571838655659 -2.25812559332021 435 Snord93 -Adhfe1 2.52527478072387 0.179068747865736 3.50530102423767 0.00344697804598443 0.0156071505970962 -1.80634560509756 25 Adhfe1 -Gm10768 3.5358301921593 0.021906952859762 3.48225041539546 0.00360941220951063 0.0162780210713503 -1.87207612241023 59 Gm10768 -Snord85 1.10352244207212 9.7813263715169 3.47953980186612 0.00362901230761682 0.0163019804842157 -2.72788678002978 15092 Snord85 -AF357399 1.79554087095325 2.33898958600026 3.47297259387192 0.00367694357668826 0.0164098488394083 -1.96818697726206 89 AF357399 -Mir551b 2.15397612089142 -0.590132795422603 3.47231349486416 0.00368178904722922 0.0164098488394083 -1.82422890137775 10 Mir551b -Def8 2.41874321422316 -0.465993593620758 3.46859651584658 0.00370923535603452 0.0164678503550015 -1.83970905317218 16 Def8 -Mir664 -1.01764634401405 5.37432688059702 -3.45211961435443 0.00383339754528852 0.0168755824717367 -2.46240607108039 632 Mir664 -Mir467c 2.31849317866793 0.658275206086465 3.45045370722881 0.00384618107849534 0.0168755824717367 -1.9100187677291 35 Mir467c -Mir345 -0.912671478268366 6.07047283018279 -3.44931934620133 0.00385491021641726 0.0168755824717367 -2.53238717399997 1042 Mir345 -Tysnd1 2.30243120612363 -0.498012096151827 3.44862877647117 0.0038602340272772 0.0168755824717367 -1.87482817968793 13 Tysnd1 -Szrd1 3.00773623992391 1.76282092941222 3.43919964294803 0.00393366880940514 0.0171175138601232 -1.97326991386 108 Szrd1 -Snord35a 1.64122011441228 2.9111550248254 3.43698444459166 0.00395112336041969 0.0171175138601232 -2.13032503281622 131 Snord35a -Snora36b -1.01237028448432 5.37669976851797 -3.43578817017266 0.00396058164686462 0.0171175138601232 -2.49502841436006 633 Snora36b -Mir3471-1 -1.1190522551011 4.8388269488082 -3.42504935436875 0.00404651146785495 0.017401703908276 -2.45454758690256 469 Mir3471-1 -Snord42b 1.04806162755878 6.10341205402338 3.42377413826923 0.00405683894794165 0.017401703908276 -2.57887542149696 1095 Snord42b -Snord23 2.9296105505117 1.31458313757935 3.40028372003562 0.00425187802628573 0.0181700105917304 -2.02991542506104 83 Snord23 -Wac 2.7504919030552 0.283618002868323 3.39632013594792 0.00428570220488658 0.0182462172230433 -2.02643747960115 36 Wac -Mir98 -0.933678340542421 7.94963852128455 -3.39430346367777 0.00430301532596754 0.0182518233714831 -2.78058118585178 4140 Mir98 -4930483K19Rik 2.52533407356931 -0.00211602021024845 3.38443072134124 0.00438878944604477 0.0185466991034707 -2.0252331979185 24 4930483K19Rik -9530036O11Rik -2.18021083734 -0.880373807283523 -3.36944259471363 0.00452229090218708 0.0190403465660349 -2.01615459630653 6 9530036O11Rik -Airn -3.32728005388875 1.05242623359369 -3.36289553627823 0.00458187540512432 0.0192202935192899 -2.09508526477381 66 Airn -Mir542 -1.22316159239699 3.81531438686763 -3.32742457590211 0.00491862125207608 0.0205573144638052 -2.50522353523098 225 Mir542 -Mir350 -1.56832295120394 3.2654986223895 -3.31591379494715 0.00503313311490562 0.0209591419128004 -2.43484812644587 172 Mir350 -Mir467d -2.52245592103549 0.453062661337275 -3.31293337335217 0.00506321480472689 0.0210077385170668 -2.14396740548906 27 Mir467d -Gm10069 5.68607477678649 3.08099524867498 3.3046846231052 0.00514741011835455 0.0212796918298643 -2.27892670543982 1309 Gm10069 -Wwp1 3.00070653341197 1.74135246228108 3.28733760371041 0.00532906211683886 0.0219415999009116 -2.26148023738523 118 Wwp1 -Mir431 -2.52814898361935 0.729842447535775 -3.28575204794379 0.0053459813956647 0.0219415999009116 -2.20212311657876 36 Mir431 -1810058I24Rik 2.62271409770273 0.785774819657178 3.27879786751028 0.00542082417887067 0.0221690336490732 -2.25054020283081 50 1810058I24Rik -Mir1950 2.90261468429164 1.12507136246027 3.27585207867393 0.00545284200159248 0.0222203311564894 -2.2663579061757 78 Mir1950 -1700045H11Rik 2.17220411664475 -0.157703843092941 3.2269553992009 0.00601270540485873 0.0244145795976648 -2.30225973272088 15 1700045H11Rik -Mir3098 1.50327989079112 3.77759040172564 3.22488371497116 0.00603765136079478 0.0244289368888895 -2.67812879077923 259 Mir3098 -Mir92b -1.33550563727296 3.82392790661174 -3.21643129376509 0.00614050279704137 0.0247572921958452 -2.71829841094789 237 Mir92b -Gm16157 2.66162389621631 -0.365224948706732 3.20356821793981 0.00630038071552899 0.0253124450578119 -2.34859094591427 22 Gm16157 -Snord19 0.841113010089355 7.11055248976838 3.19415039125178 0.00642005717801694 0.0257027552284819 -3.11414489267883 2203 Snord19 -Schip1 -2.57567954952992 -0.153384190338395 -3.18549465102036 0.00653204236936578 0.0260045455633494 -2.38778174828683 17 Schip1 -Snord69 0.84304704580235 7.14897332827513 3.18307678347552 0.00656366958087888 0.0260045455633494 -3.13872359988574 2301 Snord69 -Mir3061 -1.52813229940924 2.87430787227175 -3.18306594661466 0.00656381167593746 0.0260045455633494 -2.63327753238114 119 Mir3061 -Mir29a -0.877942320051101 11.0487374837586 -3.17676979444805 0.00664688724206556 0.0262425548207502 -3.41112827127731 33081 Mir29a -Gm10319 2.14644292502742 -0.590132795422603 3.17164218489659 0.00671531580422408 0.0264212942504127 -2.38832170685348 12 Gm10319 -Mir127 -1.25650791022161 9.78648771687729 -3.16887488380797 0.00675253616433696 0.0264764390498573 -3.34593460449095 14429 Mir127 -B330016D10Rik -1.92161264416092 -0.63693223477029 -3.1657281651115 0.0067951088367248 0.0264822665438203 -2.39563418410943 8 B330016D10Rik -Eci2 3.49526347553937 0.0311983516108461 3.16533536523053 0.00680044180310197 0.0264822665438203 -2.47536158350563 69 Eci2 -Mir128-2 -2.89379537629014 0.203236815749758 -3.14346309940017 0.00710405107482255 0.0275704839332399 -2.47229052342811 35 Mir128-2 -Snora65 1.80564358716833 2.25630817287283 3.13994836128033 0.00715407873897124 0.0276705214954786 -2.59842313949207 83 Snora65 -Snord38a 1.14948303605283 3.92210478649851 3.12106491021558 0.00742890451562957 0.0286364190957207 -2.90862714087175 255 Snord38a -Snord49b 1.06413907223879 6.40649794888962 3.10982427775322 0.0075974432295226 0.0291874839221727 -3.22360916919111 1485 Snord49b -G630025P09Rik 2.04699188377197 -0.244188556172649 3.10780445430196 0.00762812710513572 0.0292070235804022 -2.51605087870259 15 G630025P09Rik -Mir329 -2.25038749201629 0.143148280966294 -3.10219089596341 0.00771405119681339 0.0294372321590772 -2.52468319230879 18 Mir329 -Gm5105 -2.16408947287434 -0.894252721332116 -3.09357723434137 0.00784776462691287 0.029847664797692 -2.53204083131101 7 Gm5105 -1700001L05Rik 2.36321176153954 -0.504303555595595 3.08655241783997 0.00795851133538617 0.0301683104108825 -2.55344737484833 16 1700001L05Rik -Snord2 0.691526499495304 8.00175923757187 3.08142494474571 0.008040321980135 0.030265949526277 -3.40026204758547 3954 Snord2 -Mir22 -0.847615802202714 16.7326675324719 -3.08005897908952 0.00806225653232276 0.030265949526277 -3.96615097802407 1751447 Mir22 -Mir22hg -0.847524583315551 16.7327250619969 -3.07996011844832 0.00806384632426661 0.030265949526277 -3.96634849509949 1751491 Mir22hg -1700012B15Rik 2.2557460290944 -0.13987829366259 3.07437268195085 0.00815420485488848 0.0305047466866484 -2.59032088872728 19 1700012B15Rik -Mir185 -1.40702024721662 4.08694622913669 -3.05877067430607 0.008411855287644 0.0313657741281105 -3.06664608794292 331 Mir185 -Slc25a44 2.21491041672761 1.66497498687079 3.04421973608031 0.00865939699909311 0.032183622071548 -2.71149512021232 77 Slc25a44 -Mir495 -1.8776791373757 -0.650811148818883 -3.04203753845606 0.00869713645483206 0.0322189373213097 -2.62552265945633 7 Mir495 -Sec16b 3.05043595272399 0.157027195014887 3.02828873074633 0.00893867714364734 0.0330065715886784 -2.71905679017603 57 Sec16b -Mir152 -0.718432101776587 7.28616446583531 -3.01300789436766 0.00921491113521454 0.0339168180815477 -3.48441058966127 2398 Mir152 -Snora3 2.07140605143005 1.76186432056615 3.0107219189642 0.00925695325975535 0.0339620053677841 -2.78114890188917 69 Snora3 -2010003O02Rik 1.94956844186534 -0.0201000379109987 2.97528778647613 0.00993335310087066 0.0362443709129751 -2.76683258555442 17 2010003O02Rik -Mir704 1.80973957230357 -0.756810762138252 2.9728569191809 0.00998150161417436 0.0362443709129751 -2.75225460174575 7 Mir704 -D130020L05Rik 2.51176492481985 1.19972413900365 2.97271570851407 0.00998430564726682 0.0362443709129751 -2.82735067383733 52 D130020L05Rik -Mir3076 -2.06400546664394 -0.027699510367659 -2.97161874120075 0.0100061146692262 0.0362443709129751 -2.75226107651724 17 Mir3076 -Mirlet7c-1 -0.837889959835657 5.12475158025001 -2.96511158812865 0.0101364526370322 0.0366002925913093 -3.39152169935695 536 Mirlet7c-1 -Mir540 -2.19546334412823 0.490670971257216 -2.96107695486126 0.0102181042879641 0.0367787286831769 -2.78918578498352 25 Mir540 -Mir1947 -1.28776936588478 3.27191581050928 -2.95683139992983 0.0103047236916254 0.0369738670822157 -3.13442994008256 146 Mir1947 -9130019P16Rik 2.19950155178161 0.0491090861538386 2.93627487051494 0.0107344511680762 0.0383950118582288 -2.85952778968786 20 9130019P16Rik -Mir5121 1.92484055344077 0.963995516493567 2.91681375354519 0.0111574945019998 0.0397834413336931 -2.90528656811773 37 Mir5121 -B430319G15Rik -1.98479980231837 -0.986373420602892 -2.90027155266244 0.0115299314828684 0.0408786538511516 -2.88966321680022 6 B430319G15Rik -Mir351 -0.833417329407842 8.58289887759314 -2.89999295816495 0.0115363072217974 0.0408786538511516 -3.79447142137588 5692 Mir351 -D330050I16Rik 1.78527438588565 -0.392012482832458 2.89505348884379 0.0116499250396066 0.0411534503721088 -2.89805092182303 11 D330050I16Rik -BC024386 2.76807972012068 -0.350592172752806 2.88853915590151 0.0118014481853742 0.0415600382083702 -2.93769383343029 35 BC024386 -Mir666 -2.07579086945099 -0.498012096151827 -2.87668024285998 0.0120822641583993 0.0424180412453341 -2.935960499869 9 Mir666 -Mir19a 1.09579604803789 3.73141492889975 2.86437745877971 0.0123805124227085 0.0433317934794797 -3.37709843179263 223 Mir19a -Numb 1.68732685590953 2.04652538144382 2.86121813573409 0.0124582585716259 0.0434705597254591 -3.09454193475081 80 Numb -Mir743b 1.75236219547595 -0.788253108012747 2.85924586889851 0.0125070353152586 0.043507705166799 -2.96109586080316 7 Mir743b -Mir466f-3 2.69172899990669 1.87893214035262 2.85764483454932 0.012546768496086 0.0435132609545109 -3.07809643101419 134 Mir466f-3 -Snora64 2.4307644292688 0.761833001151837 2.85484959589604 0.0126164349935154 0.0435532556947262 -3.03394915069077 47 Snora64 -Trmt61b 2.31260437934785 -0.537538901613448 2.85283128922496 0.0126669731509781 0.0435532556947262 -2.98304494636545 18 Trmt61b -C130080G10Rik -1.86190650840199 -1.04705177399917 -2.85259853175366 0.0126728141022341 0.0435532556947262 -2.97869388313859 5 C130080G10Rik -Mir146 -0.747535639173072 9.8698371568429 -2.82836188364647 0.0132956711142425 0.045554137729091 -4.01615829841473 13917 Mir146 -Mir449a -1.79604146328396 -1.07849411987367 -2.8268741085019 0.0133348659084281 0.045554137729091 -3.02729704133418 5 Mir449a -Ncaph2 2.43172128495717 0.531053368370468 2.81514840260429 0.0136477529969634 0.0464838393120456 -3.10990472720523 39 Ncaph2 -Tmem41a 2.13366400352273 -0.61462282166951 2.81019216927593 0.0137821511685655 0.0468018883432536 -3.05559526679725 14 Tmem41a -Mir337 -2.00512882554678 1.46245475734951 -2.790176090596 0.0143381997019222 0.0484912300051582 -3.15685281038144 50 Mir337 -E430016F16Rik -1.97871005920294 -0.986373420602892 -2.78924386549867 0.0143646237876805 0.0484912300051582 -3.09307014868296 6 E430016F16Rik -1700003F17Rik -2.01422348895548 0.00986412853889808 -2.78534021594328 0.0144757903878217 0.0487223505383617 -3.09728477834654 16 1700003F17Rik -5730522E02Rik 1.77943144260577 -0.402623595030772 2.78153548351825 0.0145849474469889 0.0489453677559245 -3.10514832323792 12 5730522E02Rik -Snord32a 0.731701165103898 7.36041411624865 2.77936808794083 0.0146474880967064 0.0490110965347272 -3.93874361373315 2646 Snord32a -Ccl25 1.82691173511948 -0.742931848089659 2.76634551467819 0.0150287990280197 0.0501399406168728 -3.13119236947949 8 Ccl25 -Bc1 1.4447966027473 5.60396756501899 2.76417137258444 0.015093394998458 0.0502086405050747 -3.80973281446833 1034 Bc1 -Mir92-2 0.841738777296982 7.67150490176917 2.75570725400377 0.015347458592434 0.0509053786452535 -4.00630959541651 3238 Mir92-2 -Gm16023 2.83755782890821 0.231660734523689 2.74997768567304 0.0155217992534112 0.051334414342441 -3.23123254571433 61 Gm16023 -Mir463 1.7496368661484 -0.788253108012747 2.74746508869923 0.0155988599062727 0.051440170962593 -3.16503205634054 7 Mir463 -1810008I18Rik 2.42034344843247 -0.486051677644913 2.74392263242908 0.0157081389909388 0.0516512581805798 -3.18685172400794 25 1810008I18Rik -Gm16119 2.15928464558089 0.141491903029062 2.7402336372813 0.0158227303686605 0.0517925809899256 -3.22270297555294 24 Gm16119 -2010308F09Rik -1.83277349898908 -0.650811148818883 -2.73961897562678 0.0158419025113795 0.0517925809899256 -3.18111223859539 7 2010308F09Rik -Gm8883 2.16409799450603 -0.590132795422603 2.72505563686563 0.0163028203034417 0.0529872269292577 -3.21224705829732 16 Gm8883 -Zfp862 2.16409799450603 -0.590132795422603 2.72505563686563 0.0163028203034417 0.0529872269292577 -3.21224705829732 16 Zfp862 -Snhg7 1.72599658448324 2.92713305382255 2.72369233450925 0.0163466291666071 0.0529872269292577 -3.48946421579912 173 Snhg7 -B830017H08Rik -2.23343112594958 -0.833574367935836 -2.72209538930301 0.016398091528245 0.0530034629850638 -3.21732724703038 8 B830017H08Rik -1810064F22Rik 2.10429563053184 -0.628239618113655 2.71233407077621 0.0167160942777747 0.0537469782312529 -3.23263196069571 14 1810064F22Rik -Mir297c -2.59766718982369 -0.523818455835255 -2.71214450340864 0.0167223288975414 0.0537469782312529 -3.25125579861584 16 Mir297c -Mir3065 -1.15841548117293 4.89782194523564 -2.69932982501886 0.0171490634567944 0.0546270939470388 -3.86797461891443 469 Mir3065 -Mir338 -1.15841548117293 4.89782194523564 -2.69932982501886 0.0171490634567944 0.0546270939470388 -3.86797461891443 469 Mir338 -Snord12 0.862355865323009 7.21183801132978 2.69866680699734 0.017171427527834 0.0546270939470388 -4.08039835847175 2566 Snord12 -Gm16551 1.95751791415313 -0.691386427467094 2.69401162790859 0.0173292489773132 0.0546270939470388 -3.26351851959596 11 Gm16551 -Mir1938 -1.6694250746775 -1.13917247326995 -2.69395153729379 0.0173312953626889 0.0546270939470388 -3.27380411394135 4 Mir1938 -Mir384 -1.6694250746775 -1.13917247326995 -2.69395153729379 0.0173312953626889 0.0546270939470388 -3.27380411394135 4 Mir384 -Sec14l1 -1.6694250746775 -1.13917247326995 -2.69395153729379 0.0173312953626889 0.0546270939470388 -3.27380411394135 4 Sec14l1 -A530013C23Rik 2.10076433042777 -0.628239618113655 2.67776214138051 0.0178912486859939 0.056236679754047 -3.29508901202161 14 A530013C23Rik -Gm9920 1.6285204207954 -0.848931461409027 2.67099225409632 0.0181305686626977 0.0566766543675016 -3.30261681920118 6 Gm9920 -Mir3096 1.6285204207954 -0.848931461409027 2.67099225409632 0.0181305686626977 0.0566766543675016 -3.30261681920118 6 Mir3096 -Runx2 -2.05940172171601 0.19321552216426 -2.66798439607752 0.0182378909516821 0.0568298554460666 -3.31709929184916 18 Runx2 -Mir1199 -1.80452318263558 -0.286012869513089 -2.66623221676357 0.0183006932250553 0.0568298554460666 -3.30697738722689 11 Mir1199 -2810013P06Rik 1.73280899888147 -0.254570523638594 2.66544441649986 0.0183289980755061 0.0568298554460666 -3.31814833648003 13 2810013P06Rik -Mir139 -0.71676951546528 5.60987527682552 -2.6551603087927 0.0187024067912338 0.0578304773680157 -4.03578216454232 751 Mir139 -Syce2 2.36455525863438 -0.504303555595595 2.65072370278831 0.0188657609520281 0.0581779276926056 -3.35376683182341 26 Syce2 -Hsf4 1.75853048276747 -0.027699510367659 2.63272367541842 0.0195427890611213 0.060103294659675 -3.38335065718976 17 Hsf4 -9530026P05Rik -2.07695780402641 0.308348068257344 -2.63023447836339 0.0196382434958373 0.0602345049159956 -3.38900744489907 20 9530026P05Rik -F730035M05Rik 1.78278284441709 0.0460524556882713 2.60073804597223 0.0208042043193282 0.0636396705853981 -3.45006336955945 17 F730035M05Rik -4931440P22Rik 1.81357503948527 -0.34669122290937 2.59692706678062 0.0209596355730796 0.0639437010397962 -3.44244562957201 12 4931440P22Rik -Mir300 -1.68375330243594 3.41000156536173 -2.5819475209075 0.0215814969001033 0.0656653012347144 -3.85223959755949 222 Mir300 -Gm9961 1.6141478502722 -0.484133182103234 2.57010065554055 0.0220858578259184 0.0668716722449952 -3.48214581462349 10 Gm9961 -2900060B14Rik -1.19518787351076 3.46457469939205 -2.56923876538494 0.0221229909127292 0.0668716722449952 -3.89704508052604 185 2900060B14Rik -Mir411 -1.00605950591069 6.99892994877789 -2.56852458872886 0.0221538055290168 0.0668716722449952 -4.310335894974 2196 Mir411 -Mir673 -1.3090170207406 1.82295295242957 -2.56480697753964 0.0223148777778054 0.0671801465553455 -3.62107180274171 51 Mir673 -Gm5088 -1.68701668543175 -1.13917247326995 -2.55941235619776 0.0225506148115993 0.0677111881579862 -3.51525248418991 4 Gm5088 -Tor2a 2.07995668744553 -0.61904848868591 2.55252291882178 0.0228551574897157 0.0684454978891487 -3.51995132920037 16 Tor2a -Mir1929 -1.69214227634269 -1.13917247326995 -2.5510179798629 0.0229222065165283 0.0684665906684784 -3.53018502209241 4 Mir1929 -Gm12295 -1.69666721026032 -1.13917247326995 -2.54771186173241 0.0230701674123488 0.0687286188446215 -3.53606163444577 4 Gm12295 -Wdr13 -1.72526707110706 -0.147092730894626 -2.53924168587016 0.0234534382441355 0.0696884714493714 -3.52799276824936 12 Wdr13 -St7l 1.62230300798708 0.897522708635819 2.52666370286734 0.0240338880626592 0.0712277046220627 -3.60118587779941 32 St7l -Snord88c 0.793290174312999 4.47693446317466 2.50505317099491 0.0250634970146661 0.0740866582739222 -4.17079027962479 341 Snord88c -9330175M20Rik -1.65022439160042 -0.360570136957963 -2.50294066783667 0.0251663831454311 0.0741985611600436 -3.59490203212403 9 9330175M20Rik -Snora52 1.11047151370588 2.69319347264049 2.50050576985066 0.0252854729408082 0.0742992649502222 -3.88083546486857 105 Snora52 -4933405D12Rik 2.28373557503359 -0.125999379613997 2.49952793100309 0.0253334503629621 0.0742992649502222 -3.64207314883416 26 4933405D12Rik -Mir188 -1.84047092737597 1.30222680574037 -2.49825793655251 0.0253958924895589 0.0742992649502222 -3.67062190136747 41 Mir188 -Mir205 1.92476770177287 -0.714181147408868 2.49436665703146 0.0255881349007889 0.0746702350941181 -3.61910043615221 13 Mir205 -Mir29b-2 -1.76343390631061 1.19462262736195 -2.491486533954 0.0257313191461785 0.0747071028176607 -3.66952491758492 41 Mir29b-2 -Snord100 0.630343774928686 6.02538660524635 2.49147851197563 0.0257317190248384 0.0747071028176607 -4.3727044437044 1006 Snord100 -Mir103-1 1.66401353804538 -0.178535076769122 2.48869237583317 0.0258709623692558 0.0749123841663655 -3.63209662533681 14 Mir103-1 -Snord67 1.44797500570103 2.44125660341038 2.48744103910754 0.0259337350970328 0.0749123841663655 -3.84438090974652 108 Snord67 -Mir378b -1.87217458287095 -1.04230579272429 -2.48543799390718 0.0260345203956202 0.07501360548334 -3.63953394774132 8 Mir378b -Stap2 1.82543646576299 0.360783493796099 2.47215410233116 0.0267124555123547 0.0766751025232241 -3.68285167340431 25 Stap2 -Abhd1 1.9251911184693 -0.706743520940286 2.47151360035917 0.0267455659984603 0.0766751025232241 -3.65951891456996 13 Abhd1 -Crebzf 2.18345173240976 1.01056858325673 2.46941691788336 0.0268542277090025 0.0766788467965674 -3.72491986207361 48 Crebzf -AW495222 3.12693001673928 0.217900170575655 2.46889622052332 0.0268812784562901 0.0766788467965674 -3.7522647303971 101 AW495222 -Lrrc28 2.42525008262091 0.708273211214943 2.46119616740811 0.0272843555867779 0.077634537966368 -3.75212597460025 60 Lrrc28 -Mir3086 -1.56502610075238 0.163979514642475 -2.45070863513763 0.0278426426188866 0.079026008030223 -3.67658416235536 15 Mir3086 -Gm16548 1.56554530187766 -0.880373807283523 2.43719245534725 0.028578244472197 0.0809125978728953 -3.7171315266521 6 Gm16548 -Vsig8 0.720909981822672 5.46827442991139 2.4297652846464 0.0289903098093856 0.081876097753735 -4.42992368125479 711 Vsig8 -Mllt10 1.64313940342435 2.00106077397987 2.42643449288836 0.0291769377903763 0.0821624443172407 -3.88522168534726 76 Mllt10 -Snord37 0.718235154642642 5.26607014192141 2.42421441307507 0.0293019662978149 0.0821624443172407 -4.41840621005442 605 Snord37 -2310044G17Rik 1.83521282323275 0.25478388047673 2.42411236612262 0.0293077255364741 0.0821624443172407 -3.77290246486377 21 2310044G17Rik -Mir5109 1.91674990639075 5.26141207092908 2.4205606649453 0.0295088463344336 0.0825235138911489 -4.39344655492679 1282 Mir5109 -1700028E10Rik 1.54839823984953 -0.345213043484771 2.41736388881311 0.0296909918731214 0.0828298819736711 -3.7511992998099 11 1700028E10Rik -Mea1 1.54661886587012 -0.101771470971538 2.4138331460667 0.0298934095831866 0.0831911715473558 -3.76260805956638 13 Mea1 -Snora74a -1.73489546483347 1.01966577101052 -2.40803895916142 0.0302284388032876 0.0839188532227522 -3.80206412811815 36 Snora74a -Mir191 -0.655767170712639 13.5617906330993 -2.39884764518688 0.0307672305375651 0.0851401619055862 -5.06132700567092 174890 Mir191 -Mir1949 -1.81819327722704 -1.07849411987367 -2.39799614454486 0.0308176046161324 0.0851401619055862 -3.79424293296871 5 Mir1949 -Mir1198 1.58891563084634 1.17557415725746 2.39059542110177 0.0312587378593833 0.086150289607624 -3.8609699445768 43 Mir1198 -Cldn25 1.85104613563129 -0.734239231433024 2.38535835022853 0.0315745192814028 0.0868109072291099 -3.80901008552036 12 Cldn25 -3110045C21Rik 1.65443039841412 0.203826634362574 2.38155453756473 0.0318057752099395 0.0872365132561082 -3.82495974541955 21 3110045C21Rik -A330009N23Rik -1.65876461460145 -1.13917247326995 -2.36252069958505 0.0329872919163081 0.0902246398210002 -3.86075992930999 6 A330009N23Rik -4930412C18Rik -1.71539919467515 -1.13917247326995 -2.36022700442918 0.0331324488036802 0.0902246398210002 -3.86472183787187 4 4930412C18Rik -Gm17769 -1.71539919467515 -1.13917247326995 -2.36022700442918 0.0331324488036802 0.0902246398210002 -3.86472183787187 4 Gm17769 -Fth1 2.86103494155137 3.45595627169968 2.35821583980086 0.033260223411433 0.0903569402677262 -4.19586341531867 625 Fth1 -4833417C18Rik 1.47974694653215 -0.544811535499514 2.35068477635695 0.0337428496416542 0.0914503359646734 -3.86474671238307 9 4833417C18Rik -Snora61 0.958904821800182 3.29767563160608 2.349125503029 0.0338436003101147 0.0915060378053102 -4.26190631619714 164 Snora61 -Mirlet7f-2 0.650708366569944 9.71888537299185 2.34186730999564 0.0343163384594052 0.0925648751351804 -4.91362837250212 14033 Mirlet7f-2 -Mir484 -0.729527631490827 7.08906245513894 -2.3224356166388 0.0356128817086712 0.095835608560363 -4.76719481541216 2052 Mir484 -A630066F11Rik 1.44515529589515 -0.941052160679803 2.31716732167662 0.0359722924599672 0.0963708221549233 -3.92438209949561 5 A630066F11Rik -A730017L22Rik 2.05481659238372 -0.239213430165402 2.31696857375682 0.0359859181283499 0.0963708221549233 -3.9446476842384 21 A730017L22Rik -Ankrd10 1.8458518819756 -0.032400500918623 2.31581421766513 0.0360651543033762 0.0963708221549233 -3.94922969635093 18 Ankrd10 -9330179D12Rik -1.85045093530144 1.28320177813767 -2.31096269706071 0.0363999711565358 0.0970382408635685 -3.9951686511153 40 9330179D12Rik -Snora62 1.7847261774294 -0.244188556172649 2.29775908931167 0.0373260968205211 0.0992752365319687 -3.96525604386916 17 Snora62 -BC024582 1.59980554588654 -0.452690836228738 2.2959268866433 0.0374563520512198 0.0993899946289343 -3.96118314063755 10 BC024582 -Gm10804 1.52979741848186 -0.355824155683085 2.29375082460336 0.0376116093012649 0.0994728784445405 -3.96360070348265 12 Gm10804 -Mir1195 1.10164801174588 3.09590694872149 2.29304709119496 0.0376619487187042 0.0994728784445405 -4.32472042528613 144 Mir1195 -Gorasp2 1.73210701934952 0.154556788162208 2.28600861467189 0.03816892734941 0.100579090313341 -3.99587343487878 24 Gorasp2 -5730420D15Rik 1.62853621883357 -0.848931461409027 2.27860458389794 0.0387091654979492 0.101767644776867 -3.99077412899541 8 5730420D15Rik -Mirlet7f-1 -0.614882576643158 5.68980618928649 -2.27248837673306 0.0391608536660756 0.102718469041361 -4.74250560785076 779 Mirlet7f-1 -0610009L18Rik -1.61889807816313 0.265553460945444 -2.26526136567524 0.0397009617671157 0.103777261528111 -3.99536063610116 21 0610009L18Rik -Snhg3 1.25378785890833 2.38956644178022 2.26464378812804 0.0397474393896485 0.103777261528111 -4.23895965644335 94 Snhg3 -Mir297-2 1.46684984163613 1.40872493183085 2.26345073111919 0.0398373712088629 0.103777261528111 -4.09858373085422 47 Mir297-2 -Etfb 2.40847795385732 0.265851908477543 2.26169079632117 0.0399703828812269 0.103886576007927 -4.07904638296427 53 Etfb -Mir708 -1.85451172422601 -0.280376883322022 -2.25778324055568 0.0402671991288784 0.104420168650114 -4.0216739515904 16 Mir708 -Rchy1 1.62884124887726 -0.438811922180145 2.25361813300649 0.0405858566094605 0.105007851227652 -4.03354975134699 11 Rchy1 -A730056A06Rik -1.68694694851363 -1.13917247326995 -2.24683042206407 0.0411102355769964 0.105912931059682 -4.05854864097368 6 A730056A06Rik -4933406I18Rik 1.77699338800155 -0.768738207773087 2.24668779416224 0.0411213220503411 0.105912931059682 -4.04580334564085 11 4933406I18Rik -Mir134 -1.97599424729025 0.963837048223167 -2.23531953342299 0.0420140490275288 0.107968535901825 -4.10273890592774 40 Mir134 -0610040F04Rik 1.48024531718237 -0.126261497218445 2.21863530367332 0.0433571530862421 0.111169689149219 -4.09406770500723 13 0610040F04Rik -Hyi 1.7208723887239 -0.798864220211061 2.21727225982332 0.0434686369972673 0.111205638596148 -4.09496822692958 10 Hyi -Mir19b-2 1.46199574456486 -0.157703843092941 2.21491518136265 0.0436620562951831 0.111450573227749 -4.09725345928943 13 Mir19b-2 -Dnajc5 1.72175747703832 0.127791187493102 2.20641034213855 0.0443666638363991 0.112996346958329 -4.1264696449174 24 Dnajc5 -A330023F24Rik -0.702900153988052 8.59775002327938 -2.19591230206849 0.0452510530552774 0.114819027522333 -5.09936581845105 6103 A330023F24Rik -Mir5117 -0.936491371721633 3.3130102793186 -2.19552988269139 0.0452835779010078 0.114819027522333 -4.54124266085371 153 Mir5117 -Mir376a -1.47455098239027 -0.835052547360434 -2.1925218271152 0.0455401753877667 0.11521361445109 -4.13790833966528 5 Mir376a -C530005A16Rik 1.59621643209164 -0.0517042297735718 2.19129548146478 0.0456451756588884 0.115223773068123 -4.14802186280035 15 C530005A16Rik -Mir29c -0.711325240372844 8.57976498765981 -2.18303555556971 0.0463582979168776 0.116551708876032 -5.12055952980097 6038 Mir29c -Snora17 1.61038572109377 2.63473165643875 2.18283746936672 0.046375526581699 0.116551708876032 -4.41075432236771 155 Snora17 -Gm12992 1.67719387198237 -0.452690836228738 2.17669534214676 0.04691271219153 0.117642647495683 -4.16199311590905 14 Gm12992 -4933425B07Rik 1.566045114104 -0.880373807283523 2.1731277221669 0.0472273890461801 0.118135825145526 -4.16761720498767 8 4933425B07Rik -4932412D23Rik -1.44767473084559 -1.24517208658932 -2.17095503836684 0.0474199894098607 0.118135825145526 -4.19574836919915 4 4932412D23Rik -Smpx -1.44767473084559 -1.24517208658932 -2.17095503836684 0.0474199894098607 0.118135825145526 -4.19574836919915 4 Smpx -Gm16833 1.43772363562533 0.00422814234783093 2.16359562114833 0.0480778206167906 0.119513710945007 -4.18236836922897 15 Gm16833 -Mir3089 -1.46210970943343 -1.24517208658932 -2.15543507336627 0.0488171825310707 0.120734101973115 -4.22155997764311 4 Mir3089 -Snora7a 1.7230694099937 1.73375263955762 2.15504822558148 0.048852492789084 0.120734101973115 -4.31592015811305 80 Snora7a -2310015A10Rik 1.51712536676038 -0.90486383353043 2.15439473012054 0.0489121957425213 0.120734101973115 -4.19854386809234 8 2310015A10Rik -AW112010 1.99789847267928 0.0701015602482627 2.15352220222733 0.0489920150863736 0.120734101973115 -4.22796047652863 34 AW112010 -Pglyrp2 2.13000407982332 -0.625897547790957 2.1382953912996 0.050404600122275 0.123947518835163 -4.23118827095858 27 Pglyrp2 -R74862 1.46156334047426 0.026537555448611 2.13628776933242 0.0505936418662866 0.124144828751469 -4.22999882095606 15 R74862 -Snora28 1.12037820753744 2.12526039705926 2.12930512434667 0.0512562751008916 0.125500879592526 -4.42817337501701 71 Snora28 -Cd40 -1.37088395935442 -1.27661443246381 -2.12752810799839 0.0514261885899383 0.125647283043083 -4.27058401928142 4 Cd40 -Mug-ps1 2.13180518001054 -0.635454055345691 2.12264061264248 0.0518962135703567 0.126524742914053 -4.25630236233275 31 Mug-ps1 -A630072M18Rik 1.38270587147115 0.396971820945472 2.11893446805287 0.0522552789377958 0.127117232913158 -4.26481829202589 20 A630072M18Rik -9430083A17Rik 1.47463750425962 -0.925695067206612 2.11783696007545 0.0523620503673834 0.127117232913158 -4.25875097666518 7 9430083A17Rik -Mir1934 -1.57424505068045 -0.635454055345691 -2.11625143263769 0.0525166556449856 0.127221877050804 -4.25830313630768 9 Mir1934 -Snhg4 -1.12748951163344 1.68874582221391 -2.10271220257256 0.0538541790241838 0.130185631920749 -4.40346908432753 47 Snhg4 -Mir669e 1.4372851263053 -0.148570910319225 2.09754754034625 0.0543726361448265 0.131161052518493 -4.29239386401532 13 Mir669e -9430008C03Rik 1.75693578729127 0.363840124261666 2.09398906309311 0.054732534541466 0.131750679138845 -4.32757583432609 26 9430008C03Rik -C030037D09Rik 1.44249836924774 -0.941052160679803 2.08964460798292 0.0551749074500188 0.132204324416187 -4.30482301369224 7 C030037D09Rik -Wdr73 1.44249836924774 -0.941052160679803 2.08964460798292 0.0551749074500188 0.132204324416187 -4.30482301369224 7 Wdr73 -A230108P19Rik 1.49352479961928 0.250614055858426 2.08783829894219 0.0553598038831607 0.132204324416187 -4.31611624705347 21 A230108P19Rik -B230206H07Rik 1.44241701736279 -0.941052160679803 2.08759786205388 0.0553844584320223 0.132204324416187 -4.3081590960531 7 B230206H07Rik -Snord55 0.601021636432394 6.97866331241363 2.08546096371218 0.0556040229210925 0.132208091094402 -5.17041448286456 2070 Snord55 -Ralgps2 1.88483656314977 0.227262055965901 2.08532736079326 0.0556177771475135 0.132208091094402 -4.34269584383277 30 Ralgps2 -Mir33 0.994045785258037 2.79490734914157 2.08206634259786 0.055954469010935 0.132731910897041 -4.63235497043256 118 Mir33 -Mir26a-1 -0.807612985022876 4.30478785875254 -2.07928199044436 0.0562434329588863 0.13314057470143 -4.90515725875701 302 Mir26a-1 -Gm16845 1.58909272532801 0.693110936601907 2.07661019420121 0.0565220086427535 0.133523005924186 -4.35651524620671 30 Gm16845 -Gm15417 1.5342975077552 0.98095079124996 2.07236996254055 0.0569667294167582 0.134295533604382 -4.38415298913751 35 Gm15417 -Tmem161b 1.62214843774913 -0.848931461409027 2.06567840236373 0.0576751098770028 0.135602143587659 -4.3444683065412 8 Tmem161b -0610039K10Rik 1.59784774087558 0.19965680974427 2.0648940719261 0.0577586693984243 0.135602143587659 -4.35652602439924 21 0610039K10Rik -E230016K23Rik -1.72852657300674 -1.13917247326995 -2.05875394572702 0.0584166748798269 0.136730583937275 -4.37016708474507 6 E230016K23Rik -2900056M20Rik -1.65506739394547 -0.558690449548107 -2.05452050389929 0.0588743595154887 0.136730583937275 -4.36084200194152 10 2900056M20Rik -Mir671 0.562605162336286 5.98852427457086 2.05429817680062 0.0588984865622872 0.136730583937275 -5.14108074312137 973 Mir671 -4931408D14Rik 1.96514847475323 -0.696132408741971 2.05392305419837 0.0589392156613288 0.136730583937275 -4.36610698383729 22 4931408D14Rik -Gm16998 1.69004828050554 0.625421285257375 2.05268667282661 0.0590736396779562 0.136730583937275 -4.40968404758219 28 Gm16998 -Gm16880 1.44362454501385 -0.941052160679803 2.05264516314336 0.0590781576521265 0.136730583937275 -4.36487132437809 7 Gm16880 -Gm20605 1.44362454501385 -0.941052160679803 2.05264516314336 0.0590781576521265 0.136730583937275 -4.36487132437809 7 Gm20605 -Angel2 2.00212254672889 -0.0888847843510248 2.04370859875966 0.0600582524306836 0.13871754255751 -4.40649208108809 28 Angel2 -Mir3082 -0.840503124788145 2.98078155227528 -2.04091866162716 0.0603672767363008 0.139149621729534 -4.74512204880153 112 Mir3082 -4930556M19Rik 1.91670992783053 -0.130687164234845 2.03879415031149 0.0606035740806922 0.139412657310624 -4.40877400780406 23 4930556M19Rik -Rian -1.1485219400328 2.35394460295566 -2.03597388605946 0.0609185674267399 0.139771726308962 -4.63934157315238 88 Rian -Mir410 -0.901221750135118 3.70006848443484 -2.03462058748079 0.0610702490918199 0.139771726308962 -4.88099717429878 208 Mir410 -3110039I08Rik -1.63610179345525 -1.18449373319304 -2.03411361106907 0.0611271616373111 0.139771726308962 -4.41449053166305 5 3110039I08Rik -Jpx 1.4439778868564 -0.386376496641391 2.0256963387538 0.0620792090771524 0.141664755114062 -4.40640628270017 12 Jpx -Ranbp3 1.65127100064997 0.13253716876798 2.01854426538914 0.0628988113300898 0.143248590274716 -4.43559297253071 21 Ranbp3 -Ano4 -1.60469225892927 -0.386376496641391 -2.01415852435434 0.0634062900341383 0.144116687109466 -4.41831749461231 12 Ano4 -Raver1-fdx1l 1.84484701704264 0.152704788521028 2.00550228504778 0.0644189220532073 0.146127216824472 -4.45962561575273 32 Raver1-fdx1l -Mir1191 1.82343613876431 0.355147507605031 2.00159544245338 0.064880780262737 0.14678508924003 -4.47205950977787 34 Mir1191 -Snord42a -1.13379631100394 4.75607203025783 -2.00076940150347 0.0649788196577846 0.14678508924003 -5.09261777286222 438 Snord42a -Elmo1 -1.44814767972568 -0.848931461409027 -1.99979301527205 0.0650948774368582 0.14678508924003 -4.45311869203765 6 Elmo1 -B430010I23Rik 1.34949047728334 -0.986373420602892 1.99444973304055 0.0657333644405924 0.147932482892931 -4.45809589212282 6 B430010I23Rik -Zfp809 1.61960355427207 -0.452690836228738 1.9814089454698 0.0673157207393185 0.151195349140871 -4.47945908882588 14 Zfp809 -2310050B05Rik -1.59274071576353 -0.590132795422603 -1.97545789269397 0.0680492897692784 0.152490055886233 -4.48612649748884 10 2310050B05Rik -Gm19705 -1.53197912631465 -1.24517208658932 -1.97349298532553 0.0682930925134662 0.152490055886233 -4.5170833713595 4 Gm19705 -Mir701 -1.53197912631465 -1.24517208658932 -1.97349298532553 0.0682930925134662 0.152490055886233 -4.5170833713595 4 Mir701 -F630028O10Rik -0.622888913014848 5.94290954470824 -1.96287969360653 0.0696237734116029 0.154761005415237 -5.29318401111469 928 F630028O10Rik -Mir223 -0.622888913014848 5.94290954470824 -1.96287969360653 0.0696237734116029 0.154761005415237 -5.29318401111469 928 Mir223 -Snord71 0.746584913495821 4.40398042673262 1.96214265492746 0.0697170523956459 0.154761005415237 -5.10586352740556 341 Snord71 -Mir3087 -1.55280633805545 -0.590132795422603 -1.94082835991025 0.0724641808981051 0.160546855154831 -4.54065638515475 8 Mir3087 -Gm12718 1.35418552228114 -0.986373420602892 1.93287179314716 0.073514592902694 0.162558431205376 -4.55523156925391 6 Gm12718 -AF357359 -1.68799855890395 1.29585702757533 -1.92491777610303 0.0745784347261457 0.163934632876851 -4.62972173041495 55 AF357359 -Gm10125 -1.45199246964296 -0.848931461409028 -1.92479193749794 0.0745953769075101 0.163934632876851 -4.57142675996109 6 Gm10125 -Tmem179b 1.72738705406917 -0.803788399998572 1.92472040076264 0.0746050097519669 0.163934632876851 -4.56937203477109 16 Tmem179b -5430405H02Rik 1.78645846006788 -0.165684653346019 1.92319520065518 0.0748106549472968 0.163934632876851 -4.58774074751784 20 5430405H02Rik -Snora20 1.38262094231094 0.0963488416186067 1.92286428347317 0.0748553406913579 0.163934632876851 -4.56861037921303 18 Snora20 -4930519F09Rik -1.5565120089996 -1.23129317254072 -1.9216903287951 0.0750140617025739 0.163967518012714 -4.59735520927714 5 4930519F09Rik -Snord15a 1.10276132756029 1.79014108710226 1.91104926615851 0.0764666941361036 0.166823131948937 -4.72655247154268 57 Snord15a -Dmr 1.68073345913718 -0.8263599307038 1.89430363868892 0.0788041640078884 0.171285514017207 -4.61631543051433 15 Dmr -A930005H10Rik 1.49847756821754 -0.371019403168199 1.89424576390839 0.0788123530754021 0.171285514017207 -4.61401782584198 14 A930005H10Rik -C920021L13Rik 1.45659708523928 0.785774819657178 1.89280562125827 0.0790163750358231 0.171402440904704 -4.64971051817534 32 C920021L13Rik -4931403E22Rik 1.70378545689415 -0.814059467696176 1.88880372983809 0.079585814935806 0.172310085088719 -4.62498282958674 16 4931403E22Rik -Mir1964 -0.871113074799274 2.86663339631991 -1.88648279477582 0.0799177575555235 0.172701063202372 -4.9744960348716 105 Mir1964 -2410004N09Rik 2.12780696213311 -0.132941404588628 1.87600039224749 0.0814325355366182 0.175611886412351 -4.66535335652261 50 2410004N09Rik -4930545L23Rik 1.35692949813202 -0.986373420602892 1.87504023129734 0.0815725677463155 0.175611886412351 -4.6448850582387 6 4930545L23Rik -C330022C24Rik 1.66684006383984 -0.832108206363072 1.87245175628895 0.0819511559395892 0.176094668412564 -4.64990032998496 15 C330022C24Rik -Gm7854 1.41521331892746 0.409161384184755 1.87132675965834 0.0821161885893659 0.176117615000877 -4.66097795086114 23 Gm7854 -Zfp410 1.71265563446791 -0.233577443974335 1.86771686993171 0.0826477630092813 0.176925136198105 -4.66576692182148 20 Zfp410 -Srsf3 1.22390265356443 1.85404652627685 1.85691167526583 0.0842573902310767 0.180033112834567 -4.81976844441289 57 Srsf3 -Mir466n 1.94888590100719 1.13742198871721 1.85224203398279 0.084961673426306 0.181198634354047 -4.7515811957887 64 Mir466n -Ldha 1.23623349077625 1.31382141784026 1.85067176230469 0.0851996867355077 0.181367243591818 -4.75855841349533 43 Ldha -Jmjd4 1.44086756566094 -0.101771470971538 1.84911253075739 0.0854366173626727 0.181532924414915 -4.69022441726304 15 Jmjd4 -Snord90 1.29002743641748 2.8519744150179 1.84711128368238 0.0857415782389845 0.181842269090486 -5.00704775229682 171 Snord90 -Apol7d 1.22452963991045 -1.04705177399917 1.84402849240442 0.0862132574777033 0.182165419966777 -4.69431475678792 5 Apol7d -Gm13944 1.22452963991045 -1.04705177399917 1.84402849240442 0.0862132574777033 0.182165419966777 -4.69431475678792 5 Gm13944 -Snora68 0.821345299486193 3.6276738274756 1.84196855216361 0.0865297291862233 0.182496157119188 -5.16790668162727 213 Snora68 -Mir3470a 1.39115943125415 1.51311326753652 1.83385231031511 0.0877867742768492 0.184805736992408 -4.80714219046788 61 Mir3470a -1700042O10Rik 1.25969390520503 -1.03317285995058 1.82371980602793 0.0893789810710051 0.186983832297381 -4.72463210359103 6 1700042O10Rik -Cox18 1.25969390520503 -1.03317285995058 1.82371980602793 0.0893789810710051 0.186983832297381 -4.72463210359103 6 Cox18 -2010310C07Rik 1.30127564765566 -0.600743907620917 1.82236178655336 0.0895943251318701 0.186983832297381 -4.72220804437159 9 2010310C07Rik -9530080O11Rik 1.2333343214217 -1.04705177399917 1.82206906601016 0.0896408030382712 0.186983832297381 -4.72761567183742 5 9530080O11Rik -Gm10658 1.2333343214217 -1.04705177399917 1.82206906601016 0.0896408030382712 0.186983832297381 -4.72761567183742 5 Gm10658 -AV039307 1.68466781700848 -0.822165860739921 1.8179655824626 0.0902946176139629 0.188003939229072 -4.7329903325485 18 AV039307 -Mir653 1.31080939375484 -0.0354571313841903 1.81352557566678 0.0910068355861608 0.18914171111805 -4.73339615516883 15 Mir653 -Snord15b 0.765111056122759 3.75912830759914 1.811253812561 0.0913731790974714 0.189557813364027 -5.24290697168161 219 Snord15b -A330032B11Rik 1.15351830091647 -0.544811535499514 1.805581783419 0.092293585640648 0.191024884842792 -4.74543310731249 9 A330032B11Rik -Gpr137b-ps -1.34164266279755 -0.941052160679803 -1.80483613693327 0.0924151940694311 0.191024884842792 -4.76257477080841 5 Gpr137b-ps -Gm3230 1.32839510964763 -0.996984532801206 1.80296443093399 0.0927210811440096 0.191159424553245 -4.75462931995263 7 Gm3230 -BC090627 -1.21125462055415 1.58389205620882 -1.80195893201754 0.0928857782098881 0.191159424553245 -4.85973163462932 47 BC090627 -Mir1981 0.697255970236885 4.21228670526814 1.80136677676051 0.0929828927493876 0.191159424553245 -5.33355766780014 295 Mir1981 -2700069I18Rik 1.2258276117016 -1.04705177399917 1.79737646930347 0.0936396672658505 0.192163417896287 -4.7647729602822 5 2700069I18Rik -1700018L02Rik 1.60638305633868 -0.05968504002665 1.79496471819782 0.0940386190778224 0.192635663137873 -4.77706326738907 20 1700018L02Rik -Rnu12 1.34545573987153 3.12465170168855 1.79074555187797 0.0947401853209974 0.193675928813183 -5.15435248901701 212 Rnu12 -Aes 1.97580787988901 1.50102830623892 1.7898725456979 0.0948859283142588 0.193675928813183 -4.87690265891261 113 Aes -AW011738 1.35994322320792 -0.986373420602892 1.78331122652731 0.0959876815075987 0.195574901071732 -4.78377188602086 6 AW011738 -Mir1247 0.717962491463309 4.29587920045115 1.77899645447695 0.09671837092938 0.196712408610379 -5.37990996340849 300 Mir1247 -Snora26 1.32536884557828 -0.188256184051246 1.77664788038335 0.0971181605178584 0.197174058987325 -4.79082437782362 13 Snora26 -C130046K22Rik -1.66206124112149 -0.401145415606174 -1.77343531333303 0.0976673947466025 0.197936940330148 -4.79605188690242 13 C130046K22Rik -4930546K05Rik 1.23872623697392 -1.04705177399917 1.76682617714789 0.0988059745301037 0.199889391735547 -4.81031354370822 5 4930546K05Rik -Mir199a-1 1.28133340693594 0.249147894285663 1.75988001617421 0.100015250730246 0.201977701032231 -4.81578597695043 18 Mir199a-1 -Mir434 -0.892989649406754 5.12926720938992 -1.7580843415124 0.100329984081606 0.202255321266984 -5.5313213323858 623 Mir434 -Snord53 0.741893342123238 4.09583299151738 1.75305135186545 0.101216797040749 0.203683184168421 -5.3852498459864 290 Snord53 -Mir466g 1.83795214574926 0.185777895695677 1.74292337320769 0.103022330922592 0.206606620810476 -4.86892478985408 38 Mir466g -D930015M05Rik 1.30968823250333 0.15849335658765 1.7428712395644 0.103031697845014 0.206606620810476 -4.83950381088453 20 D930015M05Rik -Mir532 0.395164755537383 7.64371024412024 1.7408125217348 0.103402190719686 0.206985788791511 -5.7782897714965 3010 Mir532 -Mir103-2 0.773766228848679 2.45632927480254 1.73913662142218 0.103704656419595 0.207227693475933 -5.11879080206363 81 Mir103-2 -1810014B01Rik 1.26181707046536 -1.03317285995058 1.73762861899304 0.103977485505656 0.207409634548871 -4.85288674502469 6 1810014B01Rik -Snora69 1.22315416187213 0.909823171643444 1.73588809106924 0.104293168525467 0.207676274498356 -4.89225702628115 33 Snora69 -Zc3h14 1.41408821776149 0.00675479495901958 1.72847661834252 0.105646860853607 0.21000534535534 -4.86420908405344 19 Zc3h14 -Nkx6-2 -1.27800503999609 -0.941052160679803 -1.7146791107628 0.108208119548433 0.214722546790891 -4.8960301602504 5 Nkx6-2 -Mir26b 0.351712467695003 11.5635032907349 1.7045779906641 0.110117579874109 0.21763806583767 -6.09597193580631 46666 Mir26b -Mir299 -0.955766682393671 1.55229988219809 -1.70455586992184 0.110121793629752 0.21763806583767 -5.00394219885035 43 Mir299 -Mir3068 -0.644710617273588 4.67886513002661 -1.70302081836154 0.110414548820487 0.21763806583767 -5.55943820497418 431 Mir3068 -4930512B01Rik 1.20898643105347 -0.682253494693378 1.70288569810342 0.110440350674856 0.21763806583767 -4.89782103905022 9 4930512B01Rik -Fam187b 1.23742378935899 -1.04705177399917 1.69414248393857 0.112121161065231 0.220569387543842 -4.9166784968792 5 Fam187b -Scarna13 1.31946224597966 -0.590132795422603 1.68885863838395 0.113147737124684 0.221824000101829 -4.91936341824324 10 Scarna13 -Snhg10 1.31946224597966 -0.590132795422603 1.68885863838395 0.113147737124684 0.221824000101829 -4.91936341824324 10 Snhg10 -Gm10791 1.38935319993587 -0.966858520363231 1.68575319044044 0.113754902225617 0.222389287455916 -4.92671603809347 10 Gm10791 -H2-K2 1.16076121091899 -1.07849411987367 1.68539104137817 0.113825892966043 0.222389287455916 -4.93053846380363 5 H2-K2 -Mir676 -0.437089730569779 6.48681209189413 -1.6806647861103 0.114755912374217 0.223823070117918 -5.77991725097014 1363 Mir676 -Rcbtb2 1.34162849838235 0.642696874272247 1.67798906730309 0.115285364113241 0.22447201442527 -4.95274844960655 29 Rcbtb2 -5730422E09Rik 1.18020228563473 -0.696132408741971 1.67428783670958 0.116021244081938 0.224957555323422 -4.93882350178396 8 5730422E09Rik -Ythdf3 1.18280803863615 1.44825173729247 1.6741256859047 0.116053576278149 0.224957555323422 -5.03651124662328 52 Ythdf3 -C730027H18Rik 1.17258184372084 -1.07849411987367 1.67277419424211 0.116323363401244 0.224957555323422 -4.94864972108029 5 C730027H18Rik -Gm8615 1.17258184372084 -1.07849411987367 1.67277419424211 0.116323363401244 0.224957555323422 -4.94864972108029 5 Gm8615 -2900009J06Rik 1.34828416289482 -0.986373420602892 1.66877503516158 0.117124882491955 0.226124350124061 -4.9510598544989 10 2900009J06Rik -Rnf44 2.04490507739992 1.01150037846895 1.66529647991714 0.117825963606828 0.227093622424646 -5.02208296723603 88 Rnf44 -A630010A05Rik -1.25675269332335 0.631280460684989 -1.66260430434796 0.118371056366922 0.227453417799216 -4.9530858854327 24 A630010A05Rik -Rad51d 1.39060859991726 2.02739737762503 1.66240581191988 0.118411332316156 0.227453417799216 -5.13161638361473 92 Rad51d -Gm12359 1.48198839903639 0.110062763084683 1.65776269040705 0.119356866775459 0.228884344522352 -4.96622809648743 26 Gm12359 -4933417D19Rik 1.18269745295452 -1.07849411987367 1.65430499284386 0.120065250928896 0.229856461929313 -4.97500052644218 5 4933417D19Rik -Gm9895 1.21903665624816 -0.63693223477029 1.65194097135226 0.120551668835456 0.230401095713995 -4.97151843176298 8 Gm9895 -1110020A21Rik 1.25194593270892 -0.49237610996076 1.63876864711163 0.123293382787791 0.235247073178712 -4.9892549623361 12 1110020A21Rik -Alg11 1.32531249978621 -0.590132795422603 1.6361767652414 0.123839170710122 0.235510431606847 -4.99444823996257 12 Alg11 -Vaultrc5 -0.448072317249509 7.10631262637587 -1.63615281653852 0.123844223456712 0.235510431606847 -5.89478683307987 2022 Vaultrc5 -Mir3060 -1.21202833172584 0.27171942499089 -1.6351645637142 0.124052882574934 0.235514707184691 -4.96812309442586 19 Mir3060 -Mir1955 1.13186286307835 -0.682253494693378 1.63345540817796 0.124414470052149 0.235669705476309 -4.9969826223679 7 Mir1955 -Gm14005 -1.44880310133897 0.48022170504698 -1.63282712337373 0.124547618231564 0.235669705476309 -4.99064879469161 26 Gm14005 -C730036E19Rik 1.2943846006491 -1.01217978028632 1.63063554865266 0.125013028232036 0.236158717239657 -5.00596783361666 9 C730036E19Rik -4930405P13Rik 1.34966504178449 -0.986373420602892 1.62725241064407 0.125734431753086 0.236979623633452 -5.00991537352707 10 4930405P13Rik -Dancr 1.3062030182448 1.18459288047694 1.62665135682264 0.125862972762377 0.236979623633452 -5.07677615617175 48 Dancr -Ankrd11 1.37844840409545 1.82869495288249 1.62469304300553 0.126282563660683 0.237377932680131 -5.15746897731049 86 Ankrd11 -2610020C07Rik 1.50920105232855 -0.317940522228579 1.6135231103667 0.128698986652833 0.241522275938952 -5.03116671787619 19 2610020C07Rik -1600002D24Rik -1.25321204977434 -0.756810762138252 -1.61125552509827 0.12919437339977 0.242053826024856 -5.03110633443407 7 1600002D24Rik -BC031361 1.03166169729911 -1.13917247326995 1.608779945121 0.129737072495046 0.242672130683356 -5.043580985531 4 BC031361 -5730408K05Rik 1.05171391832391 -1.13917247326995 1.60382914837461 0.130828274941307 0.244312703286467 -5.05048396234822 4 5730408K05Rik -4833422C13Rik 1.56072686239259 0.473647969476496 1.59330634063312 0.133173818859074 0.248286482546083 -5.0792948158275 34 4833422C13Rik -Mir15b -0.423479234767332 6.16430836474156 -1.59205186812547 0.133455834392276 0.248406373640436 -5.88344797905857 1064 Mir15b -Snord17 0.588180610794611 6.40434740068647 1.5909226382288 0.133710132153462 0.248415310064625 -5.90270306066359 1360 Snord17 -2700089E24Rik 1.9346637528719 0.708144926037837 1.59009813142159 0.13389606984202 0.248415310064625 -5.10719912908875 70 2700089E24Rik -1300002E11Rik 1.66663408255978 -0.437333742755547 1.58582291431296 0.134863747546901 0.249804441478919 -5.0690582710292 28 1300002E11Rik -4930474G06Rik -1.090892671974 -1.03317285995058 -1.58193904835456 0.135748030192849 0.250441466610603 -5.09374604062072 4 4930474G06Rik -A430093F15Rik -1.090892671974 -1.03317285995058 -1.58193904835456 0.135748030192849 0.250441466610603 -5.09374604062072 4 A430093F15Rik -Rreb1 1.35338804346774 2.10846654001091 1.58142192959551 0.135866141833447 0.250441466610603 -5.26419259030567 113 Rreb1 -Mir485 -1.2897278302183 -0.360570136957963 -1.57792310950414 0.136667596032013 0.250873719680857 -5.05792690495308 13 Mir485 -Adam17 1.1761458064096 0.445349711334128 1.57783328945761 0.136688223694767 0.250873719680857 -5.07292679688905 22 Adam17 -Mir154 -1.41177845571614 0.000731195765182737 -1.57751972062112 0.136760257354508 0.250873719680857 -5.05355498603271 18 Mir154 -1700034P13Rik 1.03573302470771 -1.13917247326995 1.5706655582452 0.138342934232158 0.251258367802273 -5.09634899411597 4 1700034P13Rik -Fam120aos 1.03573302470771 -1.13917247326995 1.5706655582452 0.138342934232158 0.251258367802273 -5.09634899411597 4 Fam120aos -1700102H20Rik -1.01261850877746 -1.42941348513087 -1.56975595848976 0.138554138577694 0.251258367802273 -5.13526440107113 4 1700102H20Rik -9230112J17Rik -1.01261850877746 -1.42941348513087 -1.56975595848976 0.138554138577694 0.251258367802273 -5.13526440107113 4 9230112J17Rik -Mir3100 -1.01261850877746 -1.42941348513087 -1.56975595848976 0.138554138577694 0.251258367802273 -5.13526440107113 4 Mir3100 -Mir294 1.03626141233244 -1.13917247326995 1.56969188750367 0.138569025899642 0.251258367802273 -5.09768561064634 4 Mir294 -Snora70 1.03626141233244 -1.13917247326995 1.56969188750367 0.138569025899642 0.251258367802273 -5.09768561064634 4 Snora70 -Mir129-1 1.05464886354904 -1.13917247326995 1.56899253420323 0.138731614123954 0.251258367802273 -5.09864530153619 4 Mir129-1 -Gm10516 1.05631363335724 -1.13917247326995 1.56670193467872 0.139265281483992 0.251825176185792 -5.1017865162441 4 Gm10516 -4930451G09Rik 1.06510899754063 -1.13917247326995 1.56184533087519 0.140402574895956 0.252848126899102 -5.10843610513404 4 4930451G09Rik -4933421O10Rik 1.06510899754063 -1.13917247326995 1.56184533087519 0.140402574895956 0.252848126899102 -5.10843610513404 4 4933421O10Rik -St5 1.16930236967868 0.570831252021079 1.5614487016406 0.140495804078905 0.252848126899102 -5.1040121162741 24 St5 -Snhg6 0.414987169877994 6.88812749706196 1.55780210312341 0.141355425161323 0.25372730599006 -5.98952812049796 1762 Snhg6 -Senp2 1.23316924476294 -1.04230579272429 1.55749058186328 0.141429068018999 0.25372730599006 -5.10845162064594 8 Senp2 -Mir3069 1.1967490330554 -0.650811148818883 1.55602233545589 0.141776597800618 0.25395148836814 -5.10475733476948 9 Mir3069 -Snord87 0.414265796353463 6.8857147404585 1.55208332473347 0.142712543117168 0.255227291060641 -5.99741848701943 1759 Snord87 -4932702P03Rik 1.50538536018542 -0.190947829244951 1.54735549642484 0.143842852742846 0.256846158027523 -5.12545513466049 20 4932702P03Rik -Mir341 -1.11910417431075 -0.268449437687187 -1.54504024071338 0.144399143660684 0.257436598307563 -5.09444510471904 10 Mir341 -Fam19a2 -1.08108870526458 -1.03317285995058 -1.54274205574173 0.144953138847169 0.258021109866802 -5.14743110946704 4 Fam19a2 -Mir2137 -1.25397648150412 -0.986373420602892 -1.53902949676667 0.145851890715621 0.259216522284305 -5.1478345461744 6 Mir2137 -D130009I18Rik 1.27231702999427 -0.381630515366513 1.53514141676594 0.146798196085364 0.260339335086546 -5.13021329550442 15 D130009I18Rik -Elp3 -1.44496494213962 1.49755589579473 -1.53456064882656 0.146939992809584 0.260339335086546 -5.2298474338907 55 Elp3 -Six3os1 -1.3837215389721 -1.31472125515486 -1.53340154891498 0.147223338498394 0.260436944537469 -5.17353312572401 8 Six3os1 -Rmst -1.34494122456533 -0.508623208350141 -1.52875491880875 0.148363873123417 0.262048265067831 -5.13210633012727 10 Rmst -4930564K09Rik 1.15715309457904 -1.07849411987367 1.51860435848876 0.150881394880579 0.266082954495736 -5.16248906856691 7 4930564K09Rik -Gm16793 1.16848451363027 -1.0728581336826 1.51349594367936 0.152161963356188 0.267725701552132 -5.16901059939246 8 Gm16793 -Mir200c 1.09203191538954 1.0686845390219 1.51301819802264 0.152282191329828 0.267725701552132 -5.21562223006706 43 Mir200c -Mir500 -0.959184822315059 1.55556768404836 -1.50996567016574 0.153052273761653 0.268308865049533 -5.27805724838699 47 Mir500 -Brd2 1.15193277255657 1.46278337085446 1.5098394012633 0.153084199077341 0.268308865049533 -5.26772609337448 52 Brd2 -1700061I17Rik -0.993032112528657 -1.07849411987367 -1.5078450002038 0.153589200235581 0.268618908035033 -5.19885089576796 5 1700061I17Rik -2010009K17Rik 1.39848757206625 0.0129207590044654 1.50658142705804 0.153909874663779 0.268618908035033 -5.1753515727655 21 2010009K17Rik -4930549G23Rik 1.15784945023494 -1.07849411987367 1.50450440196419 0.154438215311991 0.268618908035033 -5.18132752967772 7 4930549G23Rik -Arfrp1 1.15784945023494 -1.07849411987367 1.50450440196419 0.154438215311991 0.268618908035033 -5.18132752967772 7 Arfrp1 -Gm16675 1.15784945023494 -1.07849411987367 1.50450440196419 0.154438215311991 0.268618908035033 -5.18132752967772 7 Gm16675 -9230116N13Rik 1.06454128715679 -1.13917247326995 1.50290619358259 0.154845796366435 0.268917889884478 -5.18797830371707 4 9230116N13Rik -4930515G01Rik 0.992431794232819 -0.788253108012747 1.49297552868103 0.157398682710309 0.272936013635962 -5.18861224143696 7 4930515G01Rik -Mir381 -0.585788303821621 4.02795786800028 -1.48635287193065 0.159120749771416 0.275503452942618 -5.77368217940814 263 Mir381 -Mir377 -1.26023741335144 -0.742931848089659 -1.46946309676428 0.163584151533594 0.282783957164269 -5.22241603606054 8 Mir377 -Slc25a43 -1.17913476508088 -1.42941348513087 -1.46857624117987 0.163821380968959 0.282783957164269 -5.27142485692471 4 Slc25a43 -Gm15850 -1.19142307125329 -1.42941348513087 -1.46628124024459 0.164436616186004 0.283417188924819 -5.27443785148495 4 Gm15850 -4930577N17Rik 0.996261471651621 -0.788253108012747 1.46486383853075 0.164817550391935 0.283645286571943 -5.22556084614497 7 4930577N17Rik -Gm1976 1.17098543636347 -1.0728581336826 1.46338871742997 0.165214778610828 0.283900696377943 -5.23522027677804 8 Gm1976 -Gnas 1.20481163504504 3.01946961304055 1.44714466040454 0.169642076912222 0.291070089859918 -5.63385665345255 198 Gnas -Trpc4 -1.12214295081842 -0.63693223477029 -1.44324497998824 0.170719487323175 0.292478881435048 -5.24590464097049 8 Trpc4 -Mir1839 0.440343224508104 6.65996421341352 1.43932422020417 0.171808441988587 0.293903196865034 -6.13404680514468 1655 Mir1839 -Gm16861 1.02101890973297 0.0237430425874912 1.4372048018983 0.172399485659686 0.294472774158237 -5.24662386648199 15 Gm16861 -Rnu73b 0.921794509533217 1.40212948737186 1.43527079633787 0.17294029293129 0.294954968960541 -5.35714724474774 44 Rnu73b -9330162012Rik 1.1439446656217 -0.676617508502311 1.43366062517497 0.173391617780733 0.295283337145994 -5.26641582277432 10 9330162012Rik -1700008J07Rik 1.13211025049156 -0.682253494693378 1.43155460784931 0.173983398326015 0.295849564068529 -5.26903519525208 9 1700008J07Rik -Snora23 1.20628823206499 -0.684171990235058 1.42281586307131 0.176456826615396 0.299171979403534 -5.28019781733805 14 Snora23 -4930506C21Rik 0.970252362879898 -0.392012482832458 1.42250461225319 0.176545456709643 0.299171979403534 -5.27331937270512 9 4930506C21Rik -Mir142 0.410046157034538 10.2906694825108 1.4218785186046 0.17672385111129 0.299171979403534 -6.41435699163406 19662 Mir142 -Snora21 0.959353356143857 0.507946560272087 1.41623429110422 0.178338802850048 0.301115991489993 -5.28552716691184 20 Snora21 -Snord65 -1.23356235450304 4.06885013060286 -1.41596621878151 0.178415806740733 0.301115991489993 -5.85809835998808 320 Snord65 -Mir501 0.955024236284725 4.54147542300529 1.41510313454645 0.178663914319654 0.301115991489993 -5.93664817702189 395 Mir501 -2410006H16Rik 0.361629145305105 7.57050758085267 1.41328401324661 0.179187781889619 0.301553479551704 -6.23986339628376 2927 2410006H16Rik -4930455H04Rik 1.13172981083616 -0.682253494693378 1.40835127249367 0.180614676369773 0.303507136580134 -5.29864005434158 9 4930455H04Rik -D330041H03Rik 1.39156614960393 -0.558690449548107 1.40593276317899 0.181317688516695 0.304240415584632 -5.30345383247936 20 D330041H03Rik -Icmt 1.20612960113274 -0.684171990235058 1.40322513481035 0.18210741065202 0.305116821665132 -5.30510284062866 14 Icmt -2900076A07Rik 0.423795449217499 6.67156273621472 1.3964570180697 0.184093807847588 0.307992719580788 -6.19163088513208 1663 2900076A07Rik -Luc7l 1.4356244163844 0.36209982723262 1.39261137548867 0.185230378351142 0.309440500290854 -5.32601427080344 36 Luc7l -Pnpla2 1.02936952347131 0.947589949833786 1.38809066529025 0.186573812314756 0.31122912258938 -5.36176823700877 32 Pnpla2 -Rnu11 1.07261723735483 3.54085705017394 1.37958006862461 0.1891245750451 0.315023562228408 -5.8124177161749 297 Rnu11 -Scarna3a 0.358634347087909 6.84417523287242 1.37423633297104 0.190740692166723 0.317252375746693 -6.23449885762334 1742 Scarna3a -Mir879 -0.865443524827827 0.513582546463155 -1.37321027685045 0.191052290488549 0.31730809817676 -5.32105187421976 19 Mir879 -4930417O13Rik 1.18361492567874 -0.461823769002454 1.37210884759797 0.191387240468858 0.317402385719429 -5.34431284007925 12 4930417O13Rik -Mir1843b 0.356514470405414 6.84310339090516 1.36899584712098 0.192336508638543 0.318513724755555 -6.24116798547559 1740 Mir1843b -4930592A05Rik 1.03052531511564 -1.13917247326995 1.36610709134638 0.193220824547368 0.319052041691096 -5.36399273876038 6 4930592A05Rik -Gm20300 1.03052531511564 -1.13917247326995 1.36610709134638 0.193220824547368 0.319052041691096 -5.36399273876038 6 Gm20300 -Psmd2 1.47834506664869 0.852672302453096 1.36471212861521 0.193649039068748 0.319087273512383 -5.39396285673084 58 Psmd2 -Mir339 0.40343749694094 5.26814352346546 1.36421616220313 0.193801472869484 0.319087273512383 -6.10182981490915 599 Mir339 -2810055G20Rik -0.97189420184314 0.494067646223494 -1.36078572848097 0.194858481133459 0.320365312641608 -5.33994058231233 19 2810055G20Rik -Mir196b -1.13510810552843 -0.405891396881051 -1.35478140249157 0.196719850556781 0.322960215086743 -5.34712927474264 10 Mir196b -Gm6981 1.0403319439215 -0.727574754616467 1.3501513241177 0.198165030794737 0.324865373759763 -5.3710798023265 8 Gm6981 -Mir296 -1.05016591608505 -0.376655389359266 -1.3390650655849 0.201660348607318 0.330121173258177 -5.36030020128242 11 Mir296 -Snord64 -0.5066981906967 3.71324562206005 -1.33655464337627 0.202458725817311 0.330953303950647 -5.91647756097912 197 Snord64 -Snord35b 1.09275289003475 -0.00490479042588459 1.32292989560528 0.206836338140382 0.337625553387949 -5.39467067778802 15 Snord35b -2810454H06Rik -1.03206410146289 0.244172768278416 -1.32151347019798 0.207295773208692 0.337892110330168 -5.37505541531076 18 2810454H06Rik -Gm10532 0.930350784049422 -1.18449373319304 1.30182675888512 0.213766681711239 0.347446985516415 -5.4461738806082 5 Gm10532 -Gm16907 0.930350784049422 -1.18449373319304 1.30182675888512 0.213766681711239 0.347446985516415 -5.4461738806082 5 Gm16907 -Snord82 0.521973959385618 3.65015969513716 1.2980839555761 0.215015018125258 0.34897885587613 -5.94664789550243 191 Snord82 -Snhg5 -0.967225523765167 0.0824699275700137 -1.29541157390313 0.2159098951753 0.349918908831395 -5.40370928399367 13 Snhg5 -Mir363 1.15445492135561 -0.478497195912167 1.29452460128447 0.216207564177154 0.349918908831395 -5.43848438880553 13 Mir363 -Gm19710 0.93508992538424 -1.18449373319304 1.28764646778302 0.218526999893145 0.352672286956263 -5.46306405992466 5 Gm19710 -Slc35c2 0.93508992538424 -1.18449373319304 1.28764646778302 0.218526999893145 0.352672286956263 -5.46306405992466 5 Slc35c2 -Snord57 0.262437228600292 9.4077044747777 1.2842500554542 0.219679620562366 0.35403170488935 -6.53234377843161 10268 Snord57 -Scarna9 0.753642588298107 2.12767296655412 1.27094440177951 0.224241711620433 0.36087417906758 -5.69229097174786 67 Scarna9 -9330178D15Rik -1.05713219200343 -1.07849411987367 -1.27002691260158 0.224559037706864 0.360875862004974 -5.49789518736927 5 9330178D15Rik -Mir130b -0.530010375624871 3.21349184345499 -1.26785063937428 0.225313153729231 0.36157849283411 -5.9082089021571 135 Mir130b -Sh2d3c -0.946567512897814 0.0824699275700137 -1.25923701193745 0.228317603257779 0.365885372636413 -5.44614755916012 13 Sh2d3c -Snord49a 0.381301583110176 6.11567405056436 1.25241371523562 0.230719961977962 0.369216657246641 -6.32557176857517 1114 Snord49a -Dtnb 1.34663394444782 -0.393930978374138 1.24318551402563 0.234000665323399 0.373942239683471 -5.50079398296816 26 Dtnb -4933404O12Rik 0.960987626070486 -1.18449373319304 1.2390904903603 0.235468171833537 0.375761096590301 -5.51979500865598 5 4933404O12Rik -9330133O14Rik 0.902410788307409 -1.19510484539135 1.23568661810827 0.236693481520096 0.377188913986634 -5.52481098009737 6 9330133O14Rik -Mir1970 0.981623687672708 -1.16497883295338 1.23358760674581 0.237451560697612 0.377869220022281 -5.52422742180909 7 Mir1970 -4933433G15Rik -0.845091810799975 -1.13917247326995 -1.21970156103377 0.242514607267494 0.384823868748979 -5.56216873762972 4 4933433G15Rik -Mir487b -0.845091810799975 -1.13917247326995 -1.21970156103377 0.242514607267494 0.384823868748979 -5.56216873762972 4 Mir487b -4930413G21Rik 0.989353987464206 -0.386376496641391 1.21883417397784 0.242833641980074 0.384823868748979 -5.51908435781945 12 4930413G21Rik -Snord83b 0.418249712013688 3.86840948135021 1.21625843057086 0.24378295710375 0.385792446678751 -6.08239291387437 221 Snord83b -Mir744 0.375928995244175 7.06487761533261 1.21046077244345 0.245930308976254 0.388148264070044 -6.45312420350352 2175 Mir744 -Gm11602 1.0569663896266 1.47452012926735 1.21040249722871 0.245951967504506 0.388148264070044 -5.64187054631556 61 Gm11602 -Clk1 1.34983134183554 0.892119610003834 1.20797593928329 0.246855136854761 0.388972207417494 -5.58134407870492 54 Clk1 -Rn45s 0.683447675537728 14.2197149824842 1.20716947910787 0.247155872373079 0.388972207417494 -6.94183060696878 360404 Rn45s -Orc6 0.828069658072878 -0.835052547360434 1.19938377098925 0.250073865655696 0.393012592445325 -5.54966196863703 5 Orc6 -Gm10336 -0.862983197557129 -1.13917247326995 -1.19601713252826 0.251343875564565 0.393012592445325 -5.58891156888004 4 Gm10336 -Gm16897 -0.862983197557129 -1.13917247326995 -1.19601713252826 0.251343875564565 0.393012592445325 -5.58891156888004 4 Gm16897 -Abhd11 0.914456828775136 -0.664690062867476 1.19552627276665 0.251529460444447 0.393012592445325 -5.550699097181 8 Abhd11 -D030028A08Rik 0.921530541409756 -1.19510484539135 1.19301988845778 0.25247872941492 0.393012592445325 -5.57311877933865 6 D030028A08Rik -Dennd2d 0.921530541409756 -1.19510484539135 1.19301988845778 0.25247872941492 0.393012592445325 -5.57311877933865 6 Dennd2d -Gm14403 0.921530541409756 -1.19510484539135 1.19301988845778 0.25247872941492 0.393012592445325 -5.57311877933865 6 Gm14403 -Gm3219 0.921530541409756 -1.19510484539135 1.19301988845778 0.25247872941492 0.393012592445325 -5.57311877933865 6 Gm3219 -Wbscr25 -0.93987296072539 -1.13917247326995 -1.18824159745951 0.254296125357236 0.395302287510363 -5.59759867475912 4 Wbscr25 -Gabpb1 0.916773949402714 -0.788253108012747 1.18369591465584 0.256034399575008 0.397462925054535 -5.56549504794546 7 Gabpb1 -Mir211 0.825074748111626 -0.880373807283523 1.17378618405685 0.259855584968479 0.40284676963184 -5.57990075138058 6 Mir211 -Nt5c2 1.05516837907923 0.326231814341724 1.17272736320358 0.260266441095664 0.402936240556516 -5.56509032003189 26 Nt5c2 -1500015L24Rik 0.808616217238156 -1.24517208658932 1.16726413306531 0.262394269069057 0.404583595956478 -5.60687790222948 4 1500015L24Rik -Cyp2d37-ps 0.808616217238156 -1.24517208658932 1.16726413306531 0.262394269069057 0.404583595956478 -5.60687790222948 4 Cyp2d37-ps -Trpt1 0.808616217238156 -1.24517208658932 1.16726413306531 0.262394269069057 0.404583595956478 -5.60687790222948 4 Trpt1 -BC029722 0.823443920149512 -1.23129317254072 1.16607728334613 0.262858281841981 0.404752091203375 -5.60671637501778 5 BC029722 -1810062O18Rik 0.922758966567288 -0.788253108012747 1.16111625399114 0.264804653612883 0.407199608857547 -5.5903045818008 7 1810062O18Rik -Snord89 0.738345291933225 1.20312081396993 1.15974138941317 0.265346001348298 0.407482890361249 -5.65593005904902 36 Snord89 -Polg2 0.884660693310263 -1.24517208658932 1.1577939018345 0.266114262880999 0.407565602613717 -5.61723207863543 4 Polg2 -Sgsm1 0.884660693310263 -1.24517208658932 1.1577939018345 0.266114262880999 0.407565602613717 -5.61723207863543 4 Sgsm1 -Mir3058 -0.978203405704609 -0.498012096151827 -1.14953116886071 0.269392696348615 0.411904246746464 -5.58396014066336 9 Mir3058 -Mirlet7c-2 -0.313141269327901 5.8169620327074 -1.14883798280248 0.269669125608772 0.411904246746464 -6.42193298909956 861 Mirlet7c-2 -Mir376b -0.99103045209925 1.03829066633399 -1.14279150067575 0.272089498256322 0.414737087115015 -5.63858333409662 42 Mir376b -Mir30a -0.151237258601805 15.2301476884988 -1.14239018110005 0.272250725897586 0.414737087115015 -7.0852176231084 565907 Mir30a -Zfp414 0.85018887029495 -1.23129317254072 1.14035831952976 0.273068126226575 0.415093483425908 -5.63464742242361 5 Zfp414 -2610001J05Rik -0.86697193885018 -0.544811535499514 -1.14000047811695 0.273212275243521 0.415093483425908 -5.59553124660726 7 2610001J05Rik -Gm5089 1.19134922509429 0.268662794525323 1.1381213178655 0.273970202991538 0.415691491507108 -5.60872857801353 34 Gm5089 -Ankzf1 1.12914177457743 -0.218382196489221 1.13664393067542 0.274567199850722 0.416044057144321 -5.60992873862588 18 Ankzf1 -Rpl22 1.09218207381924 0.761804412553155 1.13340898087306 0.275877847845008 0.417475629165987 -5.63984950101587 40 Rpl22 -Gm9999 0.881407773684291 -1.23129317254072 1.13199470748295 0.276452328431551 0.417576407133251 -5.64361970581462 5 Gm9999 -Mir1965 -0.872346376244262 -0.941052160679803 -1.1314437021438 0.276676392456387 0.417576407133251 -5.64344814416069 5 Mir1965 -1700113A16Rik 0.751208638851327 -0.101771470971538 1.12825054116647 0.277977580239225 0.418986022527021 -5.60264200827995 11 1700113A16Rik -Parp6 1.01660160898912 0.172934248903557 1.10488791379887 0.287638347248928 0.432975401333808 -5.63192280524991 24 Parp6 -Gm10649 -0.892846942526544 -0.742931848089659 -1.09635581501043 0.291228443757408 0.437316695864095 -5.65951959369185 6 Gm10649 -Mir5128 0.980265466022246 -0.0477575943918147 1.0962126985773 0.291288947288968 0.437316695864095 -5.64043620544502 20 Mir5128 -AA465934 -0.998174655804847 -0.455382481422444 -1.09422848561995 0.292128753507906 0.438001192841683 -5.6384531760545 15 AA465934 -Mirg -0.474362072277141 6.82767679984071 -1.08788511862449 0.294825638847012 0.44112747945308 -6.57223781945416 1940 Mirg -Krit1 -0.738246664477569 -0.835052547360435 -1.08750674463041 0.294987087487029 0.44112747945308 -5.67818166859644 5 Krit1 -Zfp326 0.916204728436975 0.141670101541695 1.08461400360127 0.296223563256996 0.441769310816773 -5.64946191365176 19 Zfp326 -9430060I03Rik 0.893039869828009 -0.484133182103234 1.08424508768217 0.296381529077199 0.441769310816773 -5.66824954289941 10 9430060I03Rik -Mphosph9 0.825946492812557 -0.835052547360434 1.08364147818961 0.296640122891534 0.441769310816773 -5.67407248575821 7 Mphosph9 -Mir465 0.753847528822331 -1.27661443246381 1.08288371043005 0.296964996841775 0.441769310816773 -5.70008050633046 4 Mir465 -Mir7-1 -0.462285314925654 2.79727444411883 -1.07768367150356 0.299201499765338 0.444023980943917 -6.04570260584071 96 Mir7-1 -4930502E09Rik 0.820061649339301 -1.27661443246381 1.07755051682844 0.299258931942044 0.444023980943917 -5.70556790028921 4 4930502E09Rik -Tug1 0.943551372770822 1.74501709615624 1.07570169377573 0.300057207710029 0.444071701003612 -5.82821799993449 78 Tug1 -4930414N06Rik 0.732304356782422 -1.27661443246381 1.07567328957759 0.300069484201389 0.444071701003612 -5.70749397255429 4 4930414N06Rik -Pvt1 0.777499165180762 -0.452690836228738 1.07397292650801 0.300805069325891 0.444583658161712 -5.67426367019689 8 Pvt1 -Mir880 0.798518477299392 -1.27661443246381 1.06858448026948 0.303144932062895 0.446380139558595 -5.7147416680859 4 Mir880 -9330151L19Rik 0.797000406484024 -1.27661443246381 1.06847129963835 0.303194222750142 0.446380139558595 -5.71485705720905 4 9330151L19Rik -Lins 0.797000406484024 -1.27661443246381 1.06847129963835 0.303194222750142 0.446380139558595 -5.71485705720905 4 Lins -A730020M07Rik 0.759617567138328 -1.27661443246381 1.06479234302223 0.304799642362463 0.447013357243663 -5.7186021728347 4 A730020M07Rik -F630206G17Rik 0.759617567138328 -1.27661443246381 1.06479234302223 0.304799642362463 0.447013357243663 -5.7186021728347 4 F630206G17Rik -Gm21944 0.759617567138328 -1.27661443246381 1.06479234302223 0.304799642362463 0.447013357243663 -5.7186021728347 4 Gm21944 -Snord8 0.659763836479369 1.10794348423359 1.06250387902545 0.305801430794694 0.447906845361676 -5.74488769750243 32 Snord8 -Afg3l1 0.944955196582147 0.447268206875807 1.06058601378491 0.306642849914595 0.448563450964811 -5.68339660157891 27 Afg3l1 -Vps39 0.961960880735596 -0.76568157730752 1.05915803437452 0.307270446487918 0.448905991603988 -5.69739803344418 12 Vps39 -Mrpl48 0.838840541796115 -0.239213430165402 1.05463054356545 0.309266509401883 0.451244357068476 -5.68718625388737 11 Mrpl48 -Wls -0.798306174379161 -0.590132795422603 -1.04838398068904 0.312036040279375 0.454149609721631 -5.69285981724256 8 Wls -Mir101c 0.678674341657184 -0.941052160679803 1.04834423627082 0.312053719563329 0.454149609721631 -5.71644979537614 5 Mir101c -4930565N06Rik -0.760881657711942 -0.452690836228738 -1.04536813721085 0.313379640481894 0.455414522982691 -5.68467326756101 8 4930565N06Rik -Gm19782 -0.824138055093141 -1.04705177399917 -1.04419216716558 0.313904694022045 0.455414522982691 -5.74437221530442 5 Gm19782 -E130317F20Rik 0.769245257418443 -0.498012096151827 1.04291674364268 0.3144748770515 0.455414522982691 -5.7049158335577 9 E130317F20Rik -Mir672 -0.747248291924879 -1.04705177399917 -1.04198352749126 0.314892552981749 0.455414522982691 -5.74657924729597 5 Mir672 -Mir20a -0.370723568916546 5.31004708857036 -1.04192548554098 0.314918543938074 0.455414522982691 -6.48442003496676 643 Mir20a -0610043K17Rik 0.984989107300095 0.566661427402775 1.03734276139365 0.316975604930417 0.457287012691463 -5.71838241477771 32 0610043K17Rik -2610307P16Rik -0.91429583632135 -0.954931074728396 -1.03725537976782 0.317014922908806 0.457287012691463 -5.74201320442599 6 2610307P16Rik -B630019K06Rik 0.719990612273558 -0.756810762138252 1.03218755693734 0.319301294119003 0.46000350579518 -5.72479023365126 7 B630019K06Rik -Gm14872 0.835812662260224 -0.835052547360434 1.03077941192774 0.319938704270154 0.460340556837637 -5.72736949007166 7 Gm14872 -Cops5 0.955225694844395 -0.407547774818283 1.02575302603018 0.322221466881596 0.463041175959573 -5.72322668671274 18 Cops5 -Gm19461 0.915494510219759 -0.788253108012747 1.02058918961822 0.324578899608511 0.465842169123662 -5.73572336698256 11 Gm19461 -Mir702 -0.826707259520221 -0.299891783561682 -1.01839078954777 0.325586298913891 0.465882836241176 -5.70404861077289 12 Mir702 -E130307A14Rik -0.788814584438982 -0.590132795422603 -1.01803072764692 0.325751509050542 0.465882836241176 -5.72282760292051 8 E130307A14Rik -Mir677 -0.46535805282509 2.7048223776038 -1.01785498662811 0.325832167677878 0.465882836241176 -6.08983176917819 94 Mir677 -2900008C10Rik -0.828374176793865 -0.558690449548107 -1.01087466362321 0.329047523081663 0.469891394037769 -5.72790706263801 8 2900008C10Rik -Scarna10 0.743056669215782 0.528777793948269 1.00234095871278 0.333009304922059 0.474954521145087 -5.74101823672795 23 Scarna10 -Snord7 -0.835470371413723 -0.676617508502311 -1.00141282716469 0.333442241670381 0.474978274339456 -5.74740702860384 10 Snord7 -Oaz1-ps 0.674104425949451 1.49609964982675 0.999155033797244 0.334497093796054 0.475886763118825 -5.86801256135581 41 Oaz1-ps -Kcnq1ot1 0.910278085334024 -0.218382196489221 0.997758138243239 0.335150923261523 0.476223167423908 -5.74375068180754 16 Kcnq1ot1 -4921513I03Rik -0.898652409766853 -0.954931074728396 -0.996357559472656 0.335807392489445 0.476352069705573 -5.7819683962692 6 4921513I03Rik -Snord111 0.487167361404432 2.40322325564047 0.995476712980872 0.336220726321058 0.476352069705573 -6.04721452535901 82 Snord111 -Mir326 -0.382358415670825 3.27011368251419 -0.994342337417545 0.336753562180123 0.476352069705573 -6.21830968460093 144 Mir326 -A330035P11Rik 0.965364268939122 -0.768738207773087 0.993167927399008 0.337305836954342 0.476352069705573 -5.76224148604484 11 A330035P11Rik -Wiz 0.750657520168244 0.574260899859435 0.993118546042969 0.337329073025507 0.476352069705573 -5.7530631806465 26 Wiz -4930573O16Rik 0.724242940462577 -0.880373807283523 0.988574355151803 0.33947219472701 0.478785876617451 -5.77014512800664 6 4930573O16Rik -C030016D13Rik 0.736627595056793 -0.880373807283523 0.98365906844454 0.341801211615667 0.48147553389318 -5.7748146431183 6 C030016D13Rik -Mir106b -0.272040824179646 6.30534021503053 -0.978912859532382 0.344060849311784 0.483787015120022 -6.6415010931846 1230 Mir106b -Mir93 0.231991919214371 8.92958242253252 0.978432473677121 0.344290145729586 0.483787015120022 -6.83817344932306 7480 Mir93 -A130049A11Rik 0.749480333675935 -0.90486383353043 0.973825967230468 0.346494399579791 0.485688095725481 -5.78593949448688 8 A130049A11Rik -Ppp4r1l-ps 0.749480333675935 -0.90486383353043 0.973825967230468 0.346494399579791 0.485688095725481 -5.78593949448688 8 Ppp4r1l-ps -Snora44 0.286033309288083 5.62130667581717 0.971157099851877 0.3477760255746 0.48688643580444 -6.58548506620032 724 Snora44 -Kat5 0.866499552085197 -0.402623595030772 0.964975242015777 0.35075746085532 0.490458655436177 -5.78144072211864 12 Kat5 -Osbpl1a 1.09737003164025 0.516465901027703 0.963410623524111 0.351514898897857 0.490916156233114 -5.78884397041858 52 Osbpl1a -Mir1982 0.739352688973082 0.294469154208751 0.962125981745475 0.35213765737009 0.491184678556568 -5.76656609837178 17 Mir1982 -Asb7 0.703526575105393 -0.925695067206612 0.958123449514082 0.354082936025685 0.492563942310599 -5.80224617247519 7 Asb7 -Hdac10 0.974865932277475 -1.17447243960754 0.957964659851365 0.354160264679881 0.492563942310599 -5.81150986834852 18 Hdac10 -Brat1 0.726590679354257 -0.880373807283523 0.957428291454004 0.354421557087644 0.492563942310599 -5.79939146334398 6 Brat1 -Mir491 -0.684898615634921 -0.835052547360435 -0.953861551192324 0.35616253103266 0.494381323489374 -5.80900377777519 5 Mir491 -Pum2 0.976211707535353 0.893848485511468 0.941843079399731 0.362072854082899 0.501974637312986 -5.83976051882873 52 Pum2 -Flcn 0.819178463384112 0.21104107159461 0.936286718836365 0.364828219319466 0.505180823111056 -5.78834781376735 22 Flcn -2310010J17Rik -0.781496807473563 -0.752064780863374 -0.934867786030351 0.365534182055843 0.505544850576626 -5.81765744708763 10 2310010J17Rik -Yipf2 0.803138506076097 -0.880373807283523 0.933170155190723 0.366380047226091 0.506101251676719 -5.82160275803741 12 Yipf2 -Pex11b 0.745690782417397 -0.742931848089659 0.932131479018354 0.366898247195751 0.506204232225335 -5.81894124952379 10 Pex11b -Mir301b -0.741931823310326 0.0963488416186067 -0.928572452406972 0.368677700638315 0.507450144605635 -5.78347200652652 14 Mir301b -Mir3066 -0.837579077861343 -0.558690449548107 -0.927767726846745 0.369080873917973 0.507450144605635 -5.80554388805339 8 Mir3066 -1110054M08Rik 0.762586926696282 -0.108062930415306 0.927658715560211 0.369135512728026 0.507450144605635 -5.79933690871854 16 1110054M08Rik -2810008D09Rik 0.62387744108405 2.75194116634391 0.919384284757436 0.373299112497706 0.512041876787549 -6.18722980685037 137 2810008D09Rik -Gm5512 0.771365549912191 -0.452690836228738 0.919237589387622 0.373373217780228 0.512041876787549 -5.82241315825623 12 Gm5512 -Rbbp8 0.886305125756175 -0.0477575943918147 0.917555243290929 0.374223801048332 0.512592265301497 -5.81282466752688 18 Rbbp8 -Gm11974 0.778082873591224 -0.447944854953861 0.909892000761802 0.37811508552564 0.517301334034479 -5.83079148689953 11 Gm11974 -Mir369 -0.74269677210509 0.589456147344549 -0.905344093956725 0.380437470711399 0.519596997836352 -5.82156063054654 26 Mir369 -Mir26a-2 0.485967334173185 2.96081541822386 0.904823661302764 0.380703847669755 0.519596997836352 -6.23863459210439 131 Mir26a-2 -Minos1 0.704827095217255 0.326231814341724 0.900400917247283 0.382972698552506 0.522069114753178 -5.81919593446542 22 Minos1 -Deaf1 0.779036313891313 -0.299891783561682 0.896459320434846 0.385002448889921 0.524209778261814 -5.83491078724319 14 Deaf1 -Gm19466 -0.749840672361975 -1.04705177399917 -0.887587437647754 0.389597708777317 0.529834309552942 -5.89076869788414 5 Gm19466 -Gm2518 0.63646451042641 -0.558690449548107 0.883457449799982 0.391749438497377 0.532126320625604 -5.85297878336349 8 Gm2518 -Mir505 0.574628179268003 0.386589853479527 0.877080062325064 0.395087742631044 0.535616337424576 -5.8408806452811 16 Mir505 -Gm13483 -0.650881842270608 -1.13917247326995 -0.876756483145742 0.395257630246707 0.535616337424576 -5.90961874985058 4 Gm13483 -A930016O22Rik -0.909159760956733 -0.165684653346019 -0.874712631220314 0.39633183750076 0.535965610314034 -5.83382312143566 20 A930016O22Rik -4930526L06Rik -0.646483610508766 -1.13917247326995 -0.874478831554822 0.39645484233571 0.535965610314034 -5.91157337325639 4 4930526L06Rik -1500011K16Rik 0.687958770706193 -0.941052160679803 0.866684426419982 0.400570191138535 0.540789721466861 -5.88463986982684 7 1500011K16Rik -Adamts10 0.766269359657236 -0.742931848089659 0.865927841035999 0.400971169466226 0.540789721466861 -5.87691249385262 10 Adamts10 -Mir206 -0.641735198874169 -1.04705177399917 -0.859684053228752 0.404290476936943 0.544080673710294 -5.91464885848361 5 Mir206 -Mirlet7a-1 0.288440707007994 4.74252865689731 0.859544351286645 0.404364952941568 0.544080673710294 -6.58293245452761 419 Mirlet7a-1 -Mir382 -0.527248187537959 0.0495494022709194 -0.854532688517629 0.407042720034116 0.547038567207216 -5.84404452207965 12 Mir382 -Snord73a 0.572583034995795 1.81609219396321 0.851752202751694 0.408533400862583 0.548396012216715 -6.06031448455445 64 Snord73a -AI450353 -0.739501717206301 -0.628239618113655 -0.84525729275282 0.412029489929552 0.551965918621367 -5.88226286676174 12 AI450353 -Rnf126 0.61640700457571 -0.941052160679803 0.844020033078334 0.412697708943189 0.551965918621367 -5.90363669815116 5 Rnf126 -4930452A19Rik -0.567623714118899 -1.13917247326995 -0.842981854548559 0.41325895737657 0.551965918621367 -5.93813552591444 4 4930452A19Rik -Mir20b -0.567623714118899 -1.13917247326995 -0.842981854548559 0.41325895737657 0.551965918621367 -5.93813552591444 4 Mir20b -Gm20757 -0.731130631705067 -1.04705177399917 -0.842329981431555 0.41361162175396 0.551965918621367 -5.92915547644012 5 Gm20757 -4931431C16Rik -0.591970897908513 -0.484133182103234 -0.841074383427949 0.414291458744729 0.552203761888197 -5.87182267817147 8 4931431C16Rik -Mir541 -0.389131733074012 6.47782397759286 -0.840213916014968 0.414757777334079 0.552203761888197 -6.78224992188811 1553 Mir541 -Klc2 0.629802083792426 -0.941052160679803 0.838734263576251 0.415560458996938 0.552479168421516 -5.9080019839148 5 Klc2 -Snapc4 0.677537959473713 -0.941052160679803 0.838048077598245 0.41593304616484 0.552479168421516 -5.90856686178304 9 Snapc4 -2210408F21Rik 0.673125158878238 3.63276510319773 0.835452055835978 0.417344619062095 0.553709546918431 -6.41859057863293 234 2210408F21Rik -Gm10865 0.652530700740246 -0.558690449548107 0.834529610770428 0.417846946728652 0.553732132656668 -5.8940864123663 8 Gm10865 -Lmf1 -0.698964865722747 -0.133213816846033 -0.831214508127132 0.419655480523413 0.555440326826189 -5.86465518441044 13 Lmf1 -B230208H11Rik -0.324105163507426 4.07425728624104 -0.830383638654244 0.42010955482121 0.555440326826189 -6.51194043474033 250 B230208H11Rik -Zfp207 0.813007063755825 -0.0836318824890616 0.828134659773233 0.421340237870797 0.556422698391875 -5.88719575711569 19 Zfp207 -Mir1945 0.597491165734377 -0.941052160679803 0.826047545337222 0.422484441705835 0.557288725995789 -5.91837835814761 5 Mir1945 -Snora41 0.348564473053555 4.54185649419262 0.819592789265972 0.426035844548422 0.560279811338197 -6.58753509611365 394 Snora41 -Oxsr1 0.724767749665156 0.244978069667359 0.819331396413772 0.426180068646681 0.560279811338197 -5.88476749078111 24 Oxsr1 -D630024D03Rik 0.728476694326676 -1.31472125515486 0.818971143090679 0.426378890981613 0.560279811338197 -5.94729714256845 8 D630024D03Rik -4933400L20Rik 0.515324865407562 -1.03317285995058 0.818083247017312 0.426869173233144 0.560279811338197 -5.9337772796173 4 4933400L20Rik -Xist 0.723357937638788 -1.31472125515486 0.816666748076131 0.427652095638525 0.560279811338197 -5.94915534796961 8 Xist -Dlg1 0.603909137429248 1.27329048309733 0.815752808761627 0.428157736836546 0.560279811338197 -5.99440605481118 45 Dlg1 -March2 0.877263658846623 0.62903919856468 0.815695775265458 0.428189303669507 0.560279811338197 -5.91500249911908 36 March2 -C230037L18Rik 0.602090880767705 -0.941052160679803 0.813307514125875 0.429512503460807 0.561367430067332 -5.92865426872929 5 C230037L18Rik -Gm13375 0.630370755592059 -0.941052160679803 0.811990507037629 0.430243308062424 0.561679192790876 -5.9297082862367 5 Gm13375 -Gt(ROSA)26Sor -0.733479640637234 0.703375227668527 -0.811102967738619 0.430736253048963 0.561680073975848 -5.91552151445348 26 Gt(ROSA)26Sor -Mir16-1 0.23749541505541 5.06012981661098 0.809632974888138 0.431553495333911 0.562103354082183 -6.66710805508667 499 Mir16-1 -4930523C07Rik 0.594215392165458 -0.544811535499514 0.807761652304698 0.432595297587676 0.562150931706581 -5.915729457308 7 4930523C07Rik -Mir374 0.289766495627774 4.44733832008286 0.806913394226144 0.433068070964141 0.562150931706581 -6.58616786299793 323 Mir374 -Mir374c 0.289766495627774 4.44733832008286 0.806913394226144 0.433068070964141 0.562150931706581 -6.58616786299793 323 Mir374c -Mir32 0.284410784956249 4.15923774757354 0.802255193307119 0.435670204852205 0.564332305440744 -6.54373122401893 274 Mir32 -4930481A15Rik 0.749607671194882 -0.676617508502311 0.802134533319532 0.435737739783782 0.564332305440744 -5.92746695739246 12 4930481A15Rik -Mir409 -0.359607138432163 2.88484526509054 -0.797779273016708 0.438179914112214 0.566851793653103 -6.32177877712439 114 Mir409 -Cdh23 0.599378997186575 -0.00965077170076204 0.794288413364285 0.440143672025998 0.568747372346165 -5.90268758787859 14 Cdh23 -Mir187 -0.410978066923546 2.47994750376 -0.791106453483068 0.441938529712543 0.570420658825805 -6.25179118381728 82 Mir187 -1700012D01Rik 0.583673007317414 -0.544811535499514 0.784412244834506 0.445729690119356 0.574663928165181 -5.93404385599654 7 1700012D01Rik -Tubgcp4 0.664451553261744 -0.360570136957963 0.781428132053519 0.447426298647817 0.576200233360225 -5.92858125197625 13 Tubgcp4 -D930016D06Rik 0.626414726591185 -0.925695067206612 0.770815021447429 0.453493261191949 0.58335491659528 -5.9606398937825 7 D930016D06Rik -Cep57 0.681384751801818 -0.90486383353043 0.766753154462571 0.455828781677903 0.585698918800099 -5.96205602441123 8 Cep57 -A930006I01Rik -0.665296203586878 -0.0564502110484493 -0.764249316031887 0.457272188017012 0.586892650761991 -5.91664059502587 14 A930006I01Rik -Spns1 0.563832082258649 -0.600743907620917 0.76139003304349 0.458923976722084 0.588350851056065 -5.9518091247888 9 Spns1 -Fut8 -0.594335457577261 -0.0202618838990762 -0.760096444549607 0.459672490960781 0.588649059692762 -5.91849005226079 13 Fut8 -Gm19522 -0.603683840589023 -0.590132795422603 -0.755842901826103 0.462139073141416 0.590653973169546 -5.94897829684297 8 Gm19522 -Mir874 0.295514313563059 4.89511091554138 0.755611590283585 0.462273442629627 0.590653973169546 -6.68933197812847 439 Mir874 -5031425E22Rik 0.426232500568612 1.59819729877775 0.753049543565295 0.463763358388416 0.591894845549421 -6.09850103143821 41 5031425E22Rik -2610203C22Rik 0.717338709985131 -0.478497195912167 0.74698345640789 0.46730279346032 0.595745795908632 -5.96274275496501 15 2610203C22Rik -BC039771 0.591670069535226 -0.941052160679803 0.731476967640175 0.476425656973266 0.606698297551894 -5.99117242861284 7 BC039771 -B230217O12Rik 0.561095991633646 -0.600743907620917 0.728108334120667 0.478421736774268 0.608560982897926 -5.97636714997683 9 B230217O12Rik -Pradc1 0.638558125216705 -0.941052160679803 0.720376726518506 0.48302220728867 0.613728662044959 -5.99918286949762 7 Pradc1 -4930471I20Rik 0.628368284837395 -0.567561264717375 0.716644411151915 0.48525251902964 0.615389020070981 -5.98432330499153 13 4930471I20Rik -Ubl4 0.588354263449582 -0.00490479042588451 0.716246202469665 0.485490840342292 0.615389020070981 -5.96107461669334 15 Ubl4 -Snora31 -0.36143449798853 4.31585910590137 -0.715484366086799 0.485946982545095 0.615389020070981 -6.63976294261444 320 Snora31 -Pisd-ps3 0.56333714379083 -0.0202618838990763 0.710204908076067 0.48911506227464 0.618714286092421 -5.96478675414579 15 Pisd-ps3 -Dleu2 0.219184098946397 8.12273369604978 0.708956946599182 0.489865734256984 0.618977633208437 -7.01267950795979 4399 Dleu2 -Ube2w 0.500646796546902 -0.0410931175752576 0.69543523444875 0.498043237514861 0.628614307527053 -5.97437115016694 12 Ube2w -2310040G24Rik 0.521668371480116 -0.848931461409027 0.693309917822119 0.499335854000457 0.629232647004551 -6.01645462006701 8 2310040G24Rik -Gm16973 0.520003601671916 -0.848931461409027 0.692816735376406 0.49963608955839 0.629232647004551 -6.01679548706659 8 Gm16973 -9430091E24Rik 0.632405272014803 1.08345345798668 0.690351426110223 0.501138492660048 0.630428908627469 -6.05646241852674 42 9430091E24Rik -4930599N23Rik 0.731549498463023 -0.297616209139484 0.687698634645228 0.502758111464513 0.631137207128483 -5.99772425069657 22 4930599N23Rik -Uqcc 0.60456771084976 0.00230964680615132 0.687617297753382 0.502807818825408 0.631137207128483 -5.98129860883947 18 Uqcc -AF357425 -0.469274766892277 0.0356704882223264 -0.683451535972865 0.505357486136066 0.633640540309067 -5.97309261164346 11 AF357425 -Lrrc57 0.524349090633768 -1.03317285995058 0.680981371472743 0.506872917877587 0.634389061854647 -6.03572221948629 6 Lrrc57 -Mir16-2 -0.292148003746823 3.19006469968901 -0.680666215921933 0.507066454348325 0.634389061854647 -6.46705009548147 133 Mir16-2 -1700052K11Rik -0.500246978727084 -0.360570136957963 -0.677562993700312 0.508974434943859 0.636078674995556 -5.98437458158914 9 1700052K11Rik -Snord98 0.255484149978109 5.08114085097502 0.673871566865267 0.511249489447753 0.638222830918913 -6.77177075901992 553 Snord98 -Pfkfb2 -0.545354000608178 0.424518477657946 -0.670346587478651 0.513427448101524 0.63926739732123 -5.99345507595512 21 Pfkfb2 -9130221H12Rik 0.493130187244428 -1.03317285995058 0.669887014710966 0.51371179596365 0.63926739732123 -6.04321414453495 6 9130221H12Rik -Btbd19 -0.547332034034489 -1.13917247326995 -0.669797729280709 0.513767049380866 0.63926739732123 -6.0682117317532 4 Btbd19 -4930509E16Rik 0.570473767057425 -0.600743907620917 0.65989169715578 0.519918547067217 0.646216843359145 -6.02351322613687 11 4930509E16Rik -Srsf9 -0.620398138974306 -0.299891783561682 -0.656391645137278 0.522102062684203 0.647794698934503 -5.99659806286004 14 Srsf9 -4930547E14Rik -0.479717332437278 -1.13917247326995 -0.656037147154269 0.522323508343333 0.647794698934503 -6.07735963771559 4 4930547E14Rik -Snord92 0.328191137503612 2.71880718071066 0.648008809237884 0.527352904688381 0.653322111020024 -6.3973107671481 106 Snord92 -4930526I15Rik -0.491752882645034 -0.742931848089659 -0.643842626515511 0.529973595048547 0.655856694089363 -6.04251967943561 8 4930526I15Rik -Rmi1 0.481912707053239 -0.63693223477029 0.642382362081087 0.53089389225232 0.656283782296747 -6.03629833075496 8 Rmi1 -E2f6 0.599323909462423 -0.393930978374138 0.639357845639497 0.532802879018816 0.656869353859217 -6.02831197673234 16 E2f6 -1190002F15Rik 0.49888080484846 -1.42941348513087 0.637507437962788 0.533972699009268 0.656869353859217 -6.09252279377585 4 1190002F15Rik -Tsix 0.49888080484846 -1.42941348513087 0.637507437962788 0.533972699009268 0.656869353859217 -6.09252279377585 4 Tsix -1700034H15Rik 0.543082796194865 -0.788253108012747 0.636899871034629 0.534357113785704 0.656869353859217 -6.04917319027871 7 1700034H15Rik -Adarb1 -0.552066785536743 -0.202135098099839 -0.636790015626986 0.534426637134148 0.656869353859217 -6.00439550621409 14 Adarb1 -Gm2061 0.463825041047814 -1.39322515798149 0.636165822738289 0.534821761380555 0.656869353859217 -6.08891943947055 5 Gm2061 -G730013B05Rik -0.555071724179367 -0.202135098099839 -0.634045128592704 0.536165415013138 0.657811546806441 -6.00613726849612 14 G730013B05Rik -Etohd2 0.50091939381951 -0.986373420602892 0.626287461136556 0.54109661778882 0.66314848646299 -6.06649536389095 6 Etohd2 -Fendrr 0.467194943660743 -1.03317285995058 0.625054288937282 0.541882799556935 0.663399435938265 -6.07231128308936 4 Fendrr -Mir15a 0.206050012741693 7.92756591993174 0.624069296235348 0.542511213731924 0.663456907682878 -7.05700019883519 3883 Mir15a -Mir29b-1 -0.568163238783593 -0.484133182103234 -0.613223964954576 0.549456958502096 0.670669357445426 -6.03590249794921 10 Mir29b-1 -Magix 0.459516554314059 -1.07849411987367 0.613025611681336 0.549584442779556 0.670669357445426 -6.08477332483564 5 Magix -Ngrn 0.440449993515304 -1.03317285995058 0.608380804083763 0.552574338249185 0.672951153459094 -6.08264752161091 4 Ngrn -Snord22 -0.33412784264152 2.12142786320235 -0.608047224549733 0.552789405985685 0.672951153459094 -6.31148705986951 69 Snord22 -1700012D14Rik 0.504465784559469 -0.182193869339848 0.606840054346948 0.553568081556802 0.672951153459094 -6.03643358657414 15 1700012D14Rik -Malat1 0.457988929028296 4.6947660854097 0.606459872307611 0.553813438298062 0.672951153459094 -6.7660970542376 560 Malat1 -Atp10d 0.608234437022461 -0.360570136957963 0.603954383897974 0.555431872187505 0.674199751240365 -6.05088643641914 17 Atp10d -Tk2 0.435766247183332 -0.299891783561682 0.584981614285355 0.567770032800142 0.688443791099854 -6.05317939429249 10 Tk2 -Sec23b 0.487360009845026 -0.996984532801206 0.581431884228508 0.570094526650815 0.69052850839552 -6.09488450593516 7 Sec23b -Yaf2 0.531934801396187 -0.78261712182168 0.57958011589666 0.571309127139827 0.691265868575337 -6.08386174660212 10 Yaf2 -4930562F07Rik 0.531934801396188 -0.78261712182168 0.573556610573136 0.575269449205912 0.694633374984294 -6.08736043819879 10 4930562F07Rik -Mir25 0.119476823021838 10.3640735387842 0.57349527419075 0.575309850447115 0.694633374984294 -7.25394152331292 19593 Mir25 -H2-T10 0.402558884087807 -1.07849411987367 0.57231748519401 0.576085928522387 0.694835142118439 -6.10908404449287 5 H2-T10 -Ndufs6 0.465406117300006 -0.646065167544005 0.565228910470871 0.580768330016121 0.69791113489181 -6.08343544608261 10 Ndufs6 -4931403G20Rik 0.396304995264058 -1.42941348513087 0.564863424364777 0.581010289039597 0.69791113489181 -6.13656822202315 4 4931403G20Rik -Gm4265 0.396304995264058 -1.42941348513087 0.564863424364777 0.581010289039597 0.69791113489181 -6.13656822202315 4 Gm4265 -Dnm1l 0.596675525388084 -0.10266422962704 0.564648152710883 0.581152827607449 0.69791113489181 -6.06110568505703 26 Dnm1l -1600020E01Rik -0.428368836775209 -1.24517208658932 -0.562576463668063 0.582525489062561 0.69791113489181 -6.14310756515597 4 1600020E01Rik -B230319C09Rik -0.413970740406424 -1.03317285995058 -0.56081648487133 0.583692934466204 0.69791113489181 -6.12332847161404 4 B230319C09Rik -C230091D08Rik 0.570836755523839 -0.373872894349982 0.560787405184822 0.583712234019791 0.69791113489181 -6.07595261398833 17 C230091D08Rik -Gm12216 0.527434247362 -0.218382196489221 0.560377655595617 0.583984210925728 0.69791113489181 -6.06856161202066 14 Gm12216 -Myeov2 0.3752456737319 -1.07849411987367 0.560141154452764 0.584141221579034 0.69791113489181 -6.11604668250071 5 Myeov2 -2610035D17Rik -0.409572508644584 -1.03317285995058 -0.557270792901514 0.586048560018765 0.699024554798309 -6.12533252351616 4 2610035D17Rik -Gm10433 -0.411910596355978 -1.03317285995058 -0.556895131904771 0.586298421509187 0.699024554798309 -6.12554413845499 4 Gm10433 -Sfpq 0.534329997707399 0.889926201599616 0.555891096710229 0.586966499094674 0.69909057981944 -6.11137785471773 42 Sfpq -1700011J10Rik 0.529021416706896 0.170420802222486 0.543067267793 0.595533596473573 0.708554570986806 -6.06705821666725 22 1700011J10Rik -1700086O06Rik 0.427757523649846 0.607281696774899 0.539835103869891 0.597702825758803 0.709892527729479 -6.0911847020693 26 1700086O06Rik -Rbm7 0.453647315238698 -0.171582757141534 0.539018521642786 0.598251494919921 0.709892527729479 -6.07445491272463 14 Rbm7 -2410137F16Rik 0.346588847488861 -0.941052160679803 0.538612144779345 0.598524637752637 0.709892527729479 -6.12117886037737 5 2410137F16Rik -Snord1b 0.327856599452274 1.28882447100627 0.522934546604764 0.609109798041057 0.721697071199217 -6.18918715059754 37 Snord1b -Scarna8 0.41857211258756 -0.498012096151827 0.514323740288517 0.614962686208841 0.727875959506523 -6.10050180568233 9 Scarna8 -8430429K09Rik 0.414916540751085 -0.788253108012747 0.512906371671284 0.615928717589278 0.728263903387944 -6.12271326702371 11 8430429K09Rik -Fbxo34 0.40198566998122 0.1102277556672 0.509352622996849 0.618354078512004 0.730374744909107 -6.08031249836236 15 Fbxo34 -Gm12238 0.450619994317534 0.827986746000388 0.506098398216102 0.620579073491806 0.73224480129695 -6.13049162033361 32 Gm12238 -Abhd10 -0.374596139874294 -0.63693223477029 -0.499427866618794 0.625151920071888 0.736262362403135 -6.11479367410621 6 Abhd10 -Gm6793 0.389273388584781 -1.03317285995058 0.498559841769692 0.625748160602701 0.736262362403135 -6.14405724031514 6 Gm6793 -Gm6225 0.46855445481597 -0.833574367935836 0.498310037608462 0.625919799764278 0.736262362403135 -6.13203165562082 8 Gm6225 -Gm15545 -0.330712612254716 -1.03317285995058 -0.495988985275263 0.627515656816932 0.736611523333955 -6.15804139536778 4 Gm15545 -Gm11747 0.335098793325209 -0.0096507717007621 0.495972744971373 0.627526829783269 0.736611523333955 -6.08743592777084 12 Gm11747 -Mir423 -0.173689502560178 8.7519993946383 -0.492479800459781 0.629932094687387 0.736611523333955 -7.19207867477127 6997 Mir423 -Snora43 -0.378883433040629 -0.788253108012747 -0.492233452418481 0.63010189641288 0.736611523333955 -6.13378146663089 7 Snora43 -2610027K06Rik 0.418297137532964 -0.478497195912167 0.491895430681986 0.630334921865253 0.736611523333955 -6.11191956714245 11 2610027K06Rik -Mir147 -0.328652468204269 -1.03317285995058 -0.491676373837183 0.630485957074276 0.736611523333955 -6.16020521199643 4 Mir147 -Gm5095 -0.324568459168512 -1.03317285995058 -0.490626195985013 0.631210270949685 0.736611523333955 -6.16072936854254 4 Gm5095 -Gm9776 0.388741227193178 -0.941052160679803 0.490378296979785 0.631381305714819 0.736611523333955 -6.1463267385683 7 Gm9776 -Snord110 0.129319486094622 7.41597550306118 0.48578283224498 0.634555843906532 0.739197500965227 -7.09884262072819 2471 Snord110 -Mir30c-2 0.137775378234264 6.42581414345931 0.485294592286323 0.634893559111238 0.739197500965227 -7.01980953500918 1345 Mir30c-2 -4930555B11Rik 0.2949397151298 1.99987508225673 0.483042377674677 0.636452504372091 0.740257194177937 -6.35317786250865 61 4930555B11Rik -Mir667 -0.391305201966948 -0.848931461409027 -0.475887236012698 0.641417003923651 0.745271691931656 -6.14780781181069 6 Mir667 -A930015D03Rik 0.376646313021944 0.514898879899676 0.473277554456916 0.643232148337921 0.745696184804009 -6.11541875955504 22 A930015D03Rik -Mir148b -0.096502203766917 9.5098882524752 -0.472634911746632 0.643679496834764 0.745696184804009 -7.2530384831483 10703 Mir148b -Mir5114 -0.355477405873343 -0.848931461409028 -0.472543728748277 0.643742981623093 0.745696184804009 -6.14941854830243 6 Mir5114 -Gm11696 0.385891117774572 -1.07849411987367 0.464554766915719 0.649316322766449 0.751389375533994 -6.1657021159416 5 Gm11696 -Mir3473 -0.35240146171116 -0.63693223477029 -0.461057879770776 0.651762750418594 0.752701819182497 -6.13347427347714 6 Mir3473 -6330549D23Rik -0.331449829799571 -0.254570523638594 -0.461047742280118 0.651769848687385 0.752701819182497 -6.10197272310564 9 6330549D23Rik -2810001G20Rik -0.295155201235964 -1.24517208658932 -0.455859206316244 0.655407440132046 0.756137400597235 -6.19832965924427 4 2810001G20Rik -Plscr3 -0.307745114670155 -1.03317285995058 -0.449268914134516 0.660040873119404 0.760713773968929 -6.18050852643307 4 Plscr3 -Mir130a 0.0948947867813811 9.53816266066212 0.446663133142086 0.661876928287154 0.762060116221638 -7.26729031205233 11048 Mir130a -Scarna2 0.402232211609843 0.691735624844723 0.442282244736368 0.664968822143442 0.764848211759745 -6.14647900407425 32 Scarna2 -A430071A18Rik -0.354071454044044 -1.24517208658932 -0.433473215739963 0.671205080288204 0.770467803429417 -6.20849188803248 4 A430071A18Rik -Cd22 -0.354071454044044 -1.24517208658932 -0.433473215739963 0.671205080288204 0.770467803429417 -6.20849188803248 4 Cd22 -Neat1 0.312359287225155 1.92594411218741 0.430272777772821 0.673477061033794 0.772298820743276 -6.36297140623657 65 Neat1 -Rhno1 -0.354071454044043 -1.24517208658932 -0.428356577421971 0.674838950443228 0.773083576762774 -6.21074475219862 4 Rhno1 -4930426L09Rik 0.280363052731611 -1.13917247326995 0.425550041422787 0.676835754994839 0.774593376578848 -6.19000059303377 4 4930426L09Rik -Mdp1 -0.32178107969573 -0.848931461409027 -0.424206744137332 0.677792382558126 0.774910930359541 -6.17147301088386 6 Mdp1 -Mir5100 0.286079733326491 0.406104753719187 0.421137469236957 0.679980324136049 0.776133988892759 -6.12996948354496 18 Mir5100 -5430417L22Rik 0.279090327370704 -1.13917247326995 0.420797856848568 0.680222602009429 0.776133988892759 -6.19205319731848 4 5430417L22Rik -4930568G15Rik 0.325447035573598 -1.07849411987367 0.415950309958232 0.683684799051212 0.779205515291919 -6.18750841807313 5 4930568G15Rik -Adck5 0.296209487894226 -0.742931848089659 0.415117444464695 0.684280391167838 0.779205515291919 -6.1642627433001 6 Adck5 -A230020J21Rik -0.305089019444194 -0.452690836228738 -0.412631263938286 0.686059583143688 0.780372490447987 -6.13714270799574 8 A230020J21Rik -3110056K07Rik 0.321778367105555 -0.544811535499514 0.411774615698607 0.686673076608045 0.780372490447987 -6.15031511044177 9 3110056K07Rik -Mtf2 -0.351812353895571 -0.349382868103075 -0.407554645656379 0.689698567236507 0.783030910663537 -6.13041218971925 15 Mtf2 -BB123696 0.260421273935177 -1.13917247326995 0.404926598716839 0.691585522829203 0.784392725196939 -6.19874553892436 4 BB123696 -Irf3 0.298133825217692 -1.07849411987367 0.396206543932785 0.697861753754696 0.790725184740922 -6.19569620036444 5 Irf3 -Mir18 -0.210024487848092 2.16031654478057 -0.393337083693313 0.699932088731125 0.792284239327593 -6.42958083480241 62 Mir18 -Khdrbs1 0.267969897244776 -0.742931848089659 0.386850701364717 0.704621159953094 0.796801529738831 -6.17582087837821 6 Khdrbs1 -E030003E18Rik 0.292822130692984 -0.727574754616467 0.377280776298752 0.711562098861472 0.803190169478922 -6.17832119950389 8 E030003E18Rik -9530052E02Rik 0.240241697353475 0.0356704882223265 0.376911575171499 0.71183041230099 0.803190169478922 -6.13775517192755 11 9530052E02Rik -F420014N23Rik 0.382817630756644 -0.117306762957363 0.375641100841847 0.712754019798555 0.803190169478922 -6.1456273280473 22 F420014N23Rik -Mir5129 -0.298231732945833 -0.452690836228738 -0.375183978987274 0.713086451956309 0.803190169478922 -6.15215700511858 8 Mir5129 -Mir219-1 0.176264413953521 2.53670005639744 0.373534816743211 0.714286272130486 0.803748162229669 -6.50734777259605 90 Mir219-1 -Mir872 0.111217220149994 5.81048191699923 0.367908518024425 0.718385472265704 0.807166686595139 -7.01869601020665 859 Mir872 -Snhg9 -0.314719071095367 0.412167068430586 -0.365994199209784 0.719782263529738 0.807166686595139 -6.14978861932683 22 Snhg9 -Snora78 -0.314719071095367 0.412167068430586 -0.365994199209784 0.719782263529738 0.807166686595139 -6.14978861932683 22 Snora78 -Orc4 0.297794064812266 -0.544811535499514 0.36548500236509 0.720153976296101 0.807166686595139 -6.16865012392074 9 Orc4 -Dnajc24 0.266599549793499 -0.558690449548107 0.354534559090483 0.728165394656301 0.815345157313876 -6.17320373339602 8 Dnajc24 -6720483E21Rik 0.289583824911831 -0.147092730894626 0.350649982201204 0.731015369891102 0.817733859848772 -6.15445263472245 12 6720483E21Rik -2310001K24Rik -0.24591482502991 -1.03317285995058 -0.345230837127148 0.734998083052978 0.821383753930899 -6.22275270624135 4 2310001K24Rik -Alg9 0.309421795900789 -0.676617508502311 0.343236944974775 0.736465460344019 0.822218287918323 -6.18698179945559 10 Alg9 -Snora81 -0.326932802874368 0.0115007762338971 -0.341850898914962 0.737486130537647 0.822552956933974 -6.14759719106399 22 Snora81 -BB283400 -0.279424707462514 -1.03317285995058 -0.340367255810976 0.738579239356336 0.822967687603104 -6.22446215814992 6 BB283400 -Mir466d 0.248413625738754 -1.13917247326995 0.320824573653718 0.753031659736241 0.838252803667367 -6.2299984743374 4 Mir466d -Mir10a 0.0951038511256634 14.8586469353474 0.311728572155646 0.759791705201772 0.844953543504115 -7.67496459069947 435679 Mir10a -Gm4890 -0.220857818504799 -1.24517208658932 -0.310290622719027 0.760862255053954 0.845320187942124 -6.25544681917468 4 Gm4890 -6720401G13Rik 0.289187406074058 -1.11660094256472 0.307349482960207 0.763053506555847 0.84693001068115 -6.23186816538886 9 6720401G13Rik -5430402O13Rik -0.217791204402388 -1.24517208658932 -0.304855090219651 0.764913568480844 0.847346001588973 -6.25716634200865 4 5430402O13Rik -Gm6297 -0.217791204402388 -1.24517208658932 -0.304855090219651 0.764913568480844 0.847346001588973 -6.25716634200865 4 Gm6297 -Mir877 -0.206666454163989 1.57934953392826 -0.297694706970739 0.77026137051895 0.852442506073833 -6.34009404640788 45 Mir877 -Nol8 0.264698853020979 -0.386376496641391 0.293585635346632 0.7733357709552 0.855015614980507 -6.18259191534761 14 Nol8 -Snord58b 0.282727046691085 4.4176355838932 0.288358932677936 0.777252081328622 0.858513673568207 -6.87726871780058 394 Snord58b -Gm10785 -0.210547737676215 -1.07849411987367 -0.283356828799375 0.781005991911092 0.861118429107723 -6.24760352460985 7 Gm10785 -1110019D14Rik -0.222492569746086 -0.941052160679803 -0.283205434775104 0.781119696868092 0.861118429107723 -6.23175173996211 5 1110019D14Rik -2010320M18Rik 0.206496077148502 -0.299891783561682 0.277852680238545 0.785143217405954 0.864718543494396 -6.18149015495467 10 2010320M18Rik -Ccdc11 0.204900539151867 -1.13917247326995 0.274068094032085 0.787991843292579 0.866356392494407 -6.24428661528185 4 Ccdc11 -Zfp821 -0.202147777847892 -1.24517208658932 -0.273859508838947 0.788148935503238 0.866356392494407 -6.26639816102087 4 Zfp821 -Rtel1 0.201978478856495 -1.13917247326995 0.269324663439279 0.791566624557495 0.869275763830705 -6.24561211960019 4 Rtel1 -Cyp4f41-ps 0.196688936671879 -1.04705177399917 0.266646009723831 0.793587488192855 0.870657042334661 -6.24445495366917 5 Cyp4f41-ps -Gm10033 0.211295954535854 -0.798864220211061 0.265102653487238 0.794752545801563 0.870977274346489 -6.2215520620046 10 Gm10033 -Gm15713 -0.213339795720713 -0.788253108012747 -0.264237229142785 0.795406064740615 0.870977274346489 -6.22144581162811 7 Gm15713 -9930014A18Rik 0.202831120802033 -0.590132795422603 0.261795875496793 0.797250491951153 0.872159934147906 -6.20377372854167 8 9930014A18Rik -4930429F24Rik -0.235017737171845 -0.590132795422603 -0.260029712720504 0.798585600641395 0.872783688057311 -6.20264241644327 8 4930429F24Rik -Slc12a4 0.197276741970114 -0.590132795422603 0.255487613570678 0.802022135394515 0.875700723909226 -6.2054471610575 8 Slc12a4 -Dos 0.183309425420969 -1.13917247326995 0.253065969585285 0.803856084462768 0.876864046244759 -6.24998160826913 4 Dos -Gm3086 0.181314808962686 -1.13917247326995 0.250796184607887 0.805576116449452 0.877901001784933 -6.25057017845262 4 Gm3086 -Svip -0.191457734911026 -1.13917247326995 -0.248964250053628 0.806965109263563 0.878457915019561 -6.26035672094373 4 Svip -4930467D21Rik 0.181314808962687 -1.13917247326995 0.248091662395482 0.807626952546468 0.878457915019561 -6.25126461835207 4 4930467D21Rik -Peg13 -0.205451957492413 -0.392012482832458 -0.240462116640139 0.813420288672865 0.88391671369118 -6.19004292428062 11 Peg13 -Pafah1b1 0.213436895760378 1.10038900250084 0.237624832608292 0.815577632686837 0.885417772498269 -6.26459265250452 40 Pafah1b1 -Snord88a -0.0731689826617632 5.35276818255428 -0.231022702481669 0.820603573853006 0.890027260234107 -7.01600278328706 589 Snord88a -Mir451 -0.0611495321193691 10.5875987299641 -0.22852689183222 0.822505679316192 0.891243096011182 -7.41440171692514 23834 Mir451 -Gm7367 -0.156676018697354 -0.941052160679803 -0.22725891434218 0.823472473631732 0.891444110449532 -6.24643601325685 5 Gm7367 -4921511C10Rik 0.182682113759512 -0.788253108012747 0.224188388947209 0.825814884315388 0.893132495738253 -6.23070631729261 7 4921511C10Rik -Rmrp 0.0655095097323271 7.670330005123 0.217766244863006 0.830719680954736 0.897584295976066 -7.21766694613943 3178 Rmrp -Ufd1l 0.195771351763775 -1.16497883295338 0.216430843996505 0.831740495954836 0.897584295976066 -6.26197687991093 7 Ufd1l -Snord70 0.10221174934082 2.60861620211668 0.21571068481477 0.832291135094371 0.897584295976066 -6.57150704500313 92 Snord70 -Mrpl15 0.14880859252168 0.33979041413184 0.210945385298102 0.835937006061044 0.900604863671905 -6.19219624410467 16 Mrpl15 -Prmt1 0.168995528929063 -0.61462282166951 0.209987168816182 0.836670600781962 0.900604863671905 -6.21815488399073 10 Prmt1 -Dio3os -0.170981896180084 -0.360570136957963 -0.203892664909895 0.841340111913074 0.904777632132722 -6.19710106994991 9 Dio3os -Mir5122 0.137338174750326 0.909823171643444 0.202469317623695 0.842431557085991 0.905098311332501 -6.24373086611934 25 Mir5122 -AF357426 0.146994795882059 -0.925695067206612 0.198977007139408 0.845110934201941 0.907122837181952 -6.25084617139261 7 AF357426 -Mir1940 -0.125092164153929 -0.148570910319225 -0.197867539305057 0.845962560042597 0.907183534782522 -6.18834145281798 9 Mir1940 -Zmynd8 -0.133555302246356 1.10038900250084 -0.196079492196291 0.847335481986915 0.907736573107103 -6.27212529839997 30 Zmynd8 -Rit1 0.164276960637274 -0.742931848089659 0.19512393531283 0.848069401342832 0.907736573107103 -6.2329898157428 8 Rit1 -A330093E20Rik -0.166436728635281 -0.529454442026323 -0.193988877004694 0.848941377328815 0.907818286346933 -6.21302114126874 9 A330093E20Rik -Zc3h7a -0.166703851628381 4.97783950558816 -0.189789214512805 0.852169421422477 0.910416956781879 -6.97955880875527 754 Zc3h7a -Ftx 0.0504324766144679 6.86722045285186 0.186700290456373 0.854545461173525 0.911489026597255 -7.16164366317441 1746 Ftx -C730002L08Rik -0.131614117041705 -0.941052160679803 -0.186271197576372 0.854875641216881 0.911489026597255 -6.25516561907766 5 C730002L08Rik -4930583K01Rik -0.148007765096514 -0.742931848089659 -0.185369656315026 0.855569454413374 0.911489026597255 -6.2348331965382 6 4930583K01Rik -Snord61 0.0907359318616479 3.7128223821274 0.180846592738436 0.859052196853419 0.914286401155283 -6.78522682866309 233 Snord61 -Fam214b 0.16820554523756 -0.193892170242314 0.179683525243946 0.859948249575887 0.914286401155283 -6.2001202438586 12 Fam214b -Nnt -0.179572835568388 2.32886502522719 -0.178838866016564 0.860599119054141 0.914286401155283 -6.52700074446877 121 Nnt -Rbmx 0.0865169681824078 3.76765216278315 0.174526413007477 0.863923795040316 0.916964697805581 -6.796283925268 240 Rbmx -4930473A02Rik 0.109648341099376 -1.04705177399917 0.164042278471314 0.872017542480434 0.924695182128416 -6.267217819505 5 4930473A02Rik -1110002L01Rik -0.117583232855061 -1.27661443246381 -0.15904757936708 0.875878740745282 0.926998609682585 -6.28744474569536 4 1110002L01Rik -Snora34 0.113255539654068 0.926496598553157 0.158111032892598 0.87660311478273 0.926998609682585 -6.25496403965504 26 Snora34 -Mir1306 -0.0980764865609146 0.590346152260739 -0.157138454831744 0.877355478875916 0.926998609682585 -6.21667496967709 20 Mir1306 -Fance -0.140169830118825 -1.13917247326995 -0.156010049757285 0.878228541143718 0.926998609682585 -6.27977120680639 6 Fance -Snora47 0.0984116028006845 0.141670101541695 0.15597977243771 0.878251969383764 0.926998609682585 -6.19661177976075 13 Snora47 -9330020H09Rik -0.118716898832216 -0.880373807283523 -0.15161129600864 0.881633474249955 0.928922046091183 -6.25469072285892 6 9330020H09Rik -Ppargc1a 0.131863677613015 0.0735991124007457 0.151522129473541 0.881702520523007 0.928922046091183 -6.1971928092207 18 Ppargc1a -Ccdc77 0.125473874965672 -0.788253108012747 0.147530169260755 0.884794715485482 0.931319898864331 -6.24537213273817 7 Ccdc77 -Gm16291 -0.137648313668149 -0.0655831438221648 -0.145738917274025 0.886182865432267 0.93192133590619 -6.19632184114116 14 Gm16291 -4930527F14Rik -0.115142188483818 -1.13917247326995 -0.142774358438664 0.888481131158281 0.932061058543482 -6.28181384714649 4 4930527F14Rik -Cnot2 0.138596259908812 -0.0491775771623832 0.142715474719377 0.888526791198434 0.932061058543482 -6.19956411508142 21 Cnot2 -Gm16039 0.0860006893041729 0.989643407906595 0.141187247205111 0.889711963452007 0.932061058543482 -6.26618211598301 27 Gm16039 -1700007J10Rik -0.113205756535242 -0.788253108012747 -0.140564172695348 0.890195249337243 0.932061058543482 -6.24719705198026 7 1700007J10Rik -Nup88 -0.124975737193874 -0.239213430165402 -0.139113735537381 0.891320451556099 0.932061058543482 -6.20078805666017 15 Nup88 -Mir1946b -0.11008548363065 0.157027195014887 -0.13736714896008 0.892675718878866 0.932061058543482 -6.19984394272254 15 Mir1946b -Mirlet7g 0.0395844887395015 11.1837033910963 0.136145707336603 0.893623705801407 0.932061058543482 -7.47148737731621 38152 Mirlet7g -Gm10548 -0.0899680772947313 -0.34669122290937 -0.136099070118153 0.89365990529298 0.932061058543482 -6.20842396396051 8 Gm10548 -1810034E14Rik -0.075352723109679 2.06893973454929 -0.136089081740893 0.893667658235381 0.932061058543482 -6.48017984570887 64 1810034E14Rik -4931440J10Rik 0.0849667984841879 0.325911500083247 0.126806897573872 0.900877218801809 0.938600049942131 -6.20615379472686 15 4931440J10Rik -D330022K07Rik -0.101032157727657 1.65698316208093 -0.125899476060991 0.901582519488672 0.938600049942131 -6.39470229837695 56 D330022K07Rik -C330013E15Rik -0.103267266578104 -0.742931848089659 -0.124450397809002 0.90270900568535 0.938916112567898 -6.24455208288828 8 C330013E15Rik -5930430L01Rik 0.136225847581219 -0.214062543734675 0.119769549531324 0.906349281099135 0.941260828400483 -6.20916672317144 18 5930430L01Rik -Mir496 0.0950067476259783 -0.0703291250970423 0.119430311967314 0.90661319054525 0.941260828400483 -6.20237905877004 13 Mir496 -Mir669a-1 -0.0874831731028778 0.143148280966294 -0.118249227240961 0.907532102973484 0.941358299538859 -6.20192887859536 14 Mir669a-1 -Thap6 -0.0878168955885893 -0.558690449548107 -0.115414375847903 0.909738243408161 0.942789587401192 -6.2280381796423 8 Thap6 -Chpt1 -0.127661446694368 0.920094239341047 -0.11067734520755 0.913426413354199 0.945752756476534 -6.25776602096988 61 Chpt1 -Mir101a 0.0244432527205966 10.3082648606422 0.106757481268119 0.916479919716901 0.948054023931989 -7.4177003681481 19692 Mir101a -E130102H24Rik 0.0240283211557467 10.3090843068977 0.10496799166705 0.917874358796149 0.94863645234276 -7.4179565369203 19702 E130102H24Rik -Mir425 0.033427373456698 6.66465089052376 0.101272505699857 0.920754905156141 0.950345151621533 -7.15835044873542 1583 Mir425 -Mir362 -0.0544522631314578 3.35231486548691 -0.100710009948844 0.921193459854001 0.950345151621533 -6.73276860148804 169 Mir362 -Mir17 0.0294271489414047 5.12380283689553 0.0952058023213346 0.925486243109504 0.953911294840058 -7.01165582330098 534 Mir17 -E230016M11Rik -0.0696354231248524 -0.941052160679803 -0.0918911504840257 0.928072541539687 0.955713691242584 -6.26870805660384 5 E230016M11Rik -Chkb 0.0776691859878007 0.62903919856468 0.0899505655256887 0.929587100348321 0.956253428651282 -6.22872128912026 28 Chkb -Arhgap1 0.0966234721391633 -0.198705450261483 0.0890720608925429 0.930272835935954 0.956253428651282 -6.21090676133964 18 Arhgap1 -2610002J02Rik -0.0573974454417385 -0.742931848089659 -0.0830721604333289 0.93495769271309 0.959921241425124 -6.24897822876549 6 2610002J02Rik -4921531C22Rik -0.0635851168193281 -0.941052160679803 -0.0822890634566592 0.93556934061789 0.959921241425124 -6.26957168238787 5 4921531C22Rik -Srsf7 0.0705092857951172 -0.798864220211061 0.0812705943953252 0.936364891942299 0.959921241425124 -6.25432076732456 8 Srsf7 -1700020I14Rik 0.0749473297339094 0.656660491848086 0.0792648453094152 0.937931837290533 0.960664476075851 -6.23371457269478 32 1700020I14Rik -2210015D19Rik 0.0647102732837292 -1.23129317254072 0.0766096103443329 0.940006587546541 0.961787864403415 -6.29126291736405 5 2210015D19Rik -4732416N19Rik 0.0594207089292347 -0.239213430165402 0.0757037539631176 0.940714510669773 0.961787864403415 -6.21193843190686 11 4732416N19Rik -Mir4660 0.0657262988065859 0.507946560272087 0.0713313665087823 0.944132233479521 0.964417975291077 -6.22141853049579 26 Mir4660 -Mir5113 0.0484940420860439 -0.452690836228738 0.0682297423584027 0.946557346616918 0.966030351064315 -6.22526905240972 8 Mir5113 -E530011L22Rik -0.0404713843408522 -1.27661443246381 -0.0613724386888617 0.951920883212834 0.970284520587158 -6.29857782441741 4 E530011L22Rik -Gm15441 0.0426104096369733 1.51577639605449 0.0604630248332405 0.952632381396158 0.970284520587158 -6.37051029075163 43 Gm15441 -Fam172a 0.0503754462959195 0.409161384184755 0.0596392630819191 0.953276904100091 0.970284520587158 -6.21618388102567 23 Fam172a -Rbm18 -0.0538668143348915 -0.299891783561682 -0.0557642901053547 0.956309184092742 0.972503368137093 -6.21475268708383 12 Rbm18 -Gm12191 -0.0354587706805078 -0.880373807283523 -0.0497068478436261 0.961050697355324 0.975480839012736 -6.26527518040501 6 Gm12191 -Rprl3 0.0408471478447264 -0.798864220211061 0.0488367850296659 0.961731876147215 0.975480839012736 -6.25649782741468 8 Rprl3 -H19 -0.0415019911233814 -0.880373807283523 -0.0480992443304892 0.962309326654761 0.975480839012736 -6.26535636348749 6 H19 -Ppifos -0.045813233148843 -0.325698143245111 -0.047655434486338 0.962656813959983 0.975480839012736 -6.21600758126971 15 Ppifos -Mirlet7a-2 -0.0146726577509692 3.37472927372453 -0.0386745178686727 0.969690141624003 0.981735981892624 -6.74139737131634 158 Mirlet7a-2 -9530082P21Rik -0.027891459987058 -1.27661443246381 -0.0346374807689275 0.972852600944678 0.983192929741255 -6.29990631175915 4 9530082P21Rik -Mir1196 -0.027891459987058 -1.27661443246381 -0.0346374807689275 0.972852600944678 0.983192929741255 -6.29990631175915 4 Mir1196 -Kcnk2 -0.0253403588431765 -0.835052547360434 -0.031369255395874 0.975413152136616 0.984908324414052 -6.26109829686273 7 Kcnk2 -A930011G23Rik -0.0241809579465951 -0.00965077170076215 -0.0291709235913898 0.977135635463562 0.985775207837245 -6.2074206456237 12 A930011G23Rik -Mir3103 0.0180714470554271 -1.13917247326995 0.0259355368025229 0.979670906552206 0.987459809519494 -6.2920042186438 4 Mir3103 -Snora75 -0.0172327656019052 -0.590132795422603 -0.021476287065286 0.983165572405378 0.989793060148976 -6.23767380603265 10 Snora75 -4930414L22Rik -0.0152743869054217 -0.484133182103234 -0.0207679670021376 0.983720710086713 0.989793060148976 -6.22874423282067 8 4930414L22Rik -Mipep 0.0164012673799433 -0.280376883322022 0.0179445082376201 0.985933647271923 0.991145631310365 -6.21618933242714 14 Mipep -Ippk -0.0101933209515463 0.0857377294202925 -0.0128542290662295 0.989923536727642 0.99288598158411 -6.20831119150974 15 Ippk -Mir598 -0.00787301950188535 1.52736379718861 -0.0127956250729132 0.989969473858512 0.99288598158411 -6.3747989571524 44 Mir598 -1500017E21Rik 0.0125149432592357 0.202348454937975 0.0124053255048405 0.990275413709656 0.99288598158411 -6.21004071500431 20 1500017E21Rik -Map1lc3a 0.00968300434227123 0.00717248334519302 0.0097742078067436 0.992337878036726 0.994080350166729 -6.20806203375241 21 Map1lc3a -Trappc13 -0.00465914834988075 -0.880373807283523 -0.00622703745231414 0.995118499154886 0.995991410119056 -6.26653097741332 6 Trappc13 -Mir1966 -0.00246570556544304 -0.590132795422603 -0.00337044327478241 0.997357828291427 0.997357828291427 -6.23790561933129 8 Mir1966 diff -r 48d71bd383a1 -r c0fa3dde02d9 differential_count_models/test-data/gentestdata.sh --- a/differential_count_models/test-data/gentestdata.sh Wed Aug 07 01:58:28 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,9 +0,0 @@ -#!/bin/bash -# generate test data for rgGSEA -# ross lazarus June 2013 -# adjust gseajar_path ! -GSEAJAR_PATH=/home/rlazarus/galaxy-central/tool_dependency_dir/gsea_jar/2.0.12/fubar/rg_gsea_test/8e291f464aa0/jars/gsea2-2.0.12.jar -python ../rgGSEA.py --input_tab "gsea_test_DGE.xls" --adjpvalcol "5" --signcol "2" --idcol "1" --outhtml "gseatestout.html" --input_name "gsea_test" --setMax "500" --setMin "15" --nPerm "10" --plotTop "20" --gsea_jar "$GSEAJAR_PATH" --output_dir "gseatestout" --mode "Max_probe" ---title "GSEA test" --builtin_gmt "gseatestdata.gmt" - - diff -r 48d71bd383a1 -r c0fa3dde02d9 differential_count_models/test-data/test_bams2mx.xls --- a/differential_count_models/test-data/test_bams2mx.xls Wed Aug 07 01:58:28 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,3243 +0,0 @@ -Contigname 11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam 11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam 11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam 11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam 11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam 11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam 11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam 11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam -0610005C13Rik 40 0 2 0 6 70 6 2 -0610007N19Rik 10 17 11 42 2 6 6 10 -0610008F07Rik 16 0 0 0 8 5 4 1 -0610009B14Rik 0 0 0 1 0 0 0 0 -0610009L18Rik 3 2 2 11 0 1 1 1 -0610012G03Rik 6 0 0 0 4 5 2 0 -0610031O16Rik 33 0 0 0 10 25 10 0 -0610038B21Rik 0 0 0 2 0 0 0 0 -0610038L08Rik 0 0 0 0 0 3 0 0 -0610039K10Rik 9 0 0 1 3 1 4 3 -0610040B10Rik 0 0 0 0 0 0 2 0 -0610040F04Rik 2 1 0 1 2 5 2 0 -0610043K17Rik 9 2 0 4 4 12 0 1 -1110002L01Rik 3 1 0 0 0 0 0 0 -1110006O24Rik 0 0 0 1 0 0 0 0 -1110015O18Rik 0 0 0 0 0 0 0 0 -1110017D15Rik 0 0 0 0 0 2 0 0 -1110019D14Rik 1 1 1 0 0 0 2 0 -1110020A21Rik 2 0 0 2 0 2 6 0 -1110028F11Rik 0 0 0 0 0 0 0 0 -1110028F18Rik 0 0 0 0 0 0 0 0 -1110035M17Rik 0 0 0 0 0 0 0 1 -1110036E04Rik 0 0 0 0 0 0 0 0 -1110038B12Rik 9406 1346 1212 1600 7604 6486 8084 1328 -1110046J04Rik 0 0 0 0 0 0 0 1 -1110054M08Rik 6 1 0 1 0 6 1 1 -1190002F15Rik 0 0 0 0 0 4 0 0 -1300002E11Rik 10 1 0 0 0 17 0 0 -1300015D01Rik 0 0 0 0 1 2 0 0 -1500002O10Rik 0 0 0 0 0 0 0 0 -1500004A13Rik 0 0 0 0 0 0 0 0 -1500009C09Rik 0 0 0 0 0 0 0 0 -1500011B03Rik 0 0 0 0 1 0 0 0 -1500011K16Rik 0 0 0 1 0 4 2 0 -1500012K07Rik 0 0 0 0 0 0 0 0 -1500015A07Rik 0 0 0 0 0 0 0 0 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1 1 -Tyms-ps 0 0 0 0 0 0 0 0 -Tysnd1 2 0 0 0 1 7 3 0 -Tyw5 1 1 0 0 0 0 0 1 -U05342 20 0 1 3 3 24 3 0 -U90926 0 0 0 0 0 0 0 0 -Ube2w 3 1 2 1 1 3 1 0 -Ubl4 3 1 0 1 0 5 3 2 -Ubxn11 0 0 0 0 0 0 0 0 -Uchl1as 0 0 0 0 0 0 0 1 -Ufd1l 0 0 1 0 0 6 0 0 -Uqcc 5 0 2 1 0 8 1 1 -Vash2 0 0 0 0 0 0 0 0 -Vaultrc5 243 258 161 383 159 262 286 270 -Vax2os 0 0 0 0 0 0 0 0 -Vmn1r-ps79 0 0 0 0 0 0 0 0 -Vmn2r-ps11 0 0 0 1 0 0 0 0 -Vmn2r-ps129 0 0 0 0 0 0 0 0 -Vmn2r-ps159 0 0 0 0 0 0 0 0 -Vmn2r-ps54 0 0 1 0 0 0 0 0 -Vmn2r-ps60 0 0 0 0 0 0 0 0 -Vmn2r29 2 0 0 0 0 0 0 0 -Vps39 3 1 0 0 0 8 0 0 -Vsig8 194 50 35 100 99 74 109 50 -Wac 15 0 0 0 2 15 2 2 -Wbscr25 0 2 1 0 0 1 0 0 -Wdr13 2 2 1 3 0 1 0 3 -Wdr73 4 0 0 0 0 2 1 0 -Wiz 10 0 1 6 1 3 3 2 -Wls 1 1 0 3 2 0 0 1 -Wwp1 40 0 2 2 4 56 10 4 -Xist 0 0 0 0 0 8 0 0 -Yaf2 2 0 0 0 0 6 0 2 -Yars2 1 0 0 0 1 1 0 0 -Yipf2 1 0 0 1 0 10 0 0 -Ythdf3 16 4 1 4 3 19 3 2 -Zbtb24 1 0 0 1 0 0 0 0 -Zc3h14 11 1 0 0 1 2 2 2 -Zc3h7a 336 148 60 20 17 99 14 60 -Zf12 0 0 0 0 0 0 0 0 -Zfa-ps 0 0 0 0 0 0 0 0 -Zfhx2as 0 0 0 0 0 1 0 0 -Zfp133-ps 0 0 0 0 0 0 0 0 -Zfp207 5 0 2 0 0 9 1 2 -Zfp326 7 2 1 1 0 4 4 0 -Zfp389 0 0 0 0 0 0 0 0 -Zfp410 10 0 2 0 0 6 2 0 -Zfp414 1 0 0 0 0 4 0 0 -Zfp57 0 0 0 0 0 0 0 0 -Zfp572 0 0 0 0 0 0 0 0 -Zfp672 0 0 0 0 0 2 0 0 -Zfp783 0 0 0 0 0 0 0 0 -Zfp809 7 1 0 0 0 4 2 0 -Zfp821 0 0 0 2 2 0 0 0 -Zfp862 4 0 0 0 0 10 2 0 -Zim3 0 0 0 0 0 0 0 0 -Zmynd8 3 5 4 4 3 8 2 1 -Znf41-ps 0 0 0 0 0 0 0 0 -Zp4-ps 0 0 0 0 0 0 0 0 -Zscan4a 0 0 0 0 0 0 0 0 -Zxda 0 0 0 0 0 0 0 0 diff -r 48d71bd383a1 -r c0fa3dde02d9 differential_count_models/tool_dependencies.xml --- a/differential_count_models/tool_dependencies.xml Wed Aug 07 01:58:28 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,35 +0,0 @@ - - - - - package_ghostscript_9_07 - - - - - - - - - - - - - - - $INSTALL_DIR - echo "source('http://bioconductor.org/biocLite.R')" > $INSTALL_DIR/runme.R - echo "installme=c('edgeR','limma','DESeq','DESeq2')" >> $INSTALL_DIR/runme.R - echo "biocLite()" >> $INSTALL_DIR/runme.R - echo "biocLite(installme)" >> $INSTALL_DIR/runme.R - echo "install.packages(c('stringr','gplots'),dependencies=T,repos='http://cran.us.r-project.org')" >> $INSTALL_DIR/runme.R - echo "quit(save='no')" >> $INSTALL_DIR/runme.R - export PATH=$PATH && export R_HOME=$R_HOME && export R_LIBS=$R_LIBS && R CMD BATCH $INSTALL_DIR/runme.R - - - Installs some basic bioc packages for the edgeR wrapper and dependencies graphicsmagick (replaces imagemagick) and ghostscript for compressing R's bloated pdfs - It's clunky but this is the most convenient way I could get anything installed into the package_r3 - Note we use cran at fred hutch since no fastest mirror thingy - - - diff -r 48d71bd383a1 -r c0fa3dde02d9 rgToolFactory.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgToolFactory.py Wed Aug 07 02:09:35 2013 -0400 @@ -0,0 +1,631 @@ +# 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 +# +# august 2013 +# found a problem with GS if $TMP or $TEMP missing - now inject /tmp and warn +# +# july 2013 +# added ability to combine images and individual log files into html output +# just make sure there's a log file foo.log and it will be output +# together with all images named like "foo_*.pdf +# otherwise old format for html +# +# January 2013 +# problem pointed out by Carlos Borroto +# added escaping for <>$ - thought I did that ages ago... +# +# August 11 2012 +# changed to use shell=False and cl as a sequence + +# This is a Galaxy tool factory for simple scripts in python, R or whatever ails ye. +# It also serves as the wrapper for the new tool. +# +# you paste and run your script +# Only works for simple scripts that read one input from the history. +# Optionally can write one new history dataset, +# and optionally collect any number of outputs into links on an autogenerated HTML page. + +# DO NOT install on a public or important site - please. + +# installed generated tools are fine if the script is safe. +# They just run normally and their user cannot do anything unusually insecure +# but please, practice safe toolshed. +# Read the fucking code before you install any tool +# especially this one + +# After you get the script working on some test data, you can +# optionally generate a toolshed compatible gzip file +# containing your script safely wrapped as an ordinary Galaxy script in your local toolshed for +# safe and largely automated installation in a production Galaxy. + +# If you opt for an HTML output, you get all the script outputs arranged +# as a single Html history item - all output files are linked, thumbnails for all the pdfs. +# Ugly but really inexpensive. +# +# Patches appreciated please. +# +# +# long route to June 2012 product +# Behold the awesome power of Galaxy and the toolshed with the tool factory to bind them +# derived from an integrated script model +# called rgBaseScriptWrapper.py +# Note to the unwary: +# This tool allows arbitrary scripting on your Galaxy as the Galaxy user +# There is nothing stopping a malicious user doing whatever they choose +# Extremely dangerous!! +# Totally insecure. So, trusted users only +# +# preferred model is a developer using their throw away workstation instance - ie a private site. +# no real risk. The universe_wsgi.ini admin_users string is checked - only admin users are permitted to run this tool. +# + +import sys +import shutil +import subprocess +import os +import time +import tempfile +import optparse +import tarfile +import re +import shutil +import math + +progname = os.path.split(sys.argv[0])[1] +myversion = '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.temp_warned = False # we want only one warning if $TMP not set + self.treatbashSpecial = treatbashSpecial + if opts.output_dir: # simplify for the tool tarball + os.chdir(opts.output_dir) + self.thumbformat = 'png' + self.opts = opts + self.toolname = re.sub('[^a-zA-Z0-9_]+', '', opts.tool_name) # a sanitizer now does this but.. + self.toolid = self.toolname + self.myname = sys.argv[0] # get our name because we write ourselves out as a tool later + self.pyfile = self.myname # crude but efficient - the cruft won't hurt much + self.xmlfile = '%s.xml' % self.toolname + s = open(self.opts.script_path,'r').readlines() + s = [x.rstrip() for x in s] # remove pesky dos line endings if needed + self.script = '\n'.join(s) + fhandle,self.sfile = tempfile.mkstemp(prefix=self.toolname,suffix=".%s" % (opts.interpreter)) + tscript = open(self.sfile,'w') # use self.sfile as script source for Popen + tscript.write(self.script) + tscript.close() + self.indentedScript = '\n'.join([' %s' % x for x in s]) # for restructured text in help + self.escapedScript = '\n'.join([html_escape(x) for x in s]) + self.elog = os.path.join(self.opts.output_dir,"%s_error.log" % self.toolname) + if opts.output_dir: # may not want these complexities + self.tlog = os.path.join(self.opts.output_dir,"%s_runner.log" % self.toolname) + art = '%s.%s' % (self.toolname,opts.interpreter) + artpath = os.path.join(self.opts.output_dir,art) # need full path + artifact = open(artpath,'w') # use self.sfile as script source for Popen + artifact.write(self.script) + artifact.close() + self.cl = [] + self.html = [] + a = self.cl.append + a(opts.interpreter) + if self.treatbashSpecial and opts.interpreter in ['bash','sh']: + a(self.sfile) + else: + a('-') # stdin + a(opts.input_tab) + a(opts.output_tab) + self.outFormats = 'tabular' # TODO make this an option at tool generation time + self.inputFormats = 'tabular' # TODO make this an option at tool generation time + self.test1Input = '%s_test1_input.xls' % self.toolname + self.test1Output = '%s_test1_output.xls' % self.toolname + self.test1HTML = '%s_test1_output.html' % self.toolname + + def makeXML(self): + """ + Create a Galaxy xml tool wrapper for the new script as a string to write out + fixme - use templating or something less fugly than this example of what we produce + + + a tabular file + + reverse.py --script_path "$runMe" --interpreter "python" + --tool_name "reverse" --input_tab "$input1" --output_tab "$tab_file" + + + + + + + + + + + +**What it Does** + +Reverse the columns in a tabular file + + + + + +# reverse order of columns in a tabular file +import sys +inp = sys.argv[1] +outp = sys.argv[2] +i = open(inp,'r') +o = open(outp,'w') +for row in i: + rs = row.rstrip().split('\t') + rs.reverse() + o.write('\t'.join(rs)) + o.write('\n') +i.close() +o.close() + + + + + + + """ + newXML=""" + %(tooldesc)s + %(command)s + + %(inputs)s + + + %(outputs)s + + + + %(script)s + + + %(tooltests)s + + %(help)s + + """ # needs a dict with toolname, toolid, interpreter, scriptname, command, inputs as a multi line string ready to write, outputs ditto, help ditto + + newCommand=""" + %(toolname)s.py --script_path "$runMe" --interpreter "%(interpreter)s" + --tool_name "%(toolname)s" %(command_inputs)s %(command_outputs)s + """ # may NOT be an input or htmlout + tooltestsTabOnly = """ + + + + + """ + tooltestsHTMLOnly = """ + + + + + """ + tooltestsBoth = """ + + + + + + """ + xdict = {} + xdict['tool_version'] = self.opts.tool_version + xdict['test1Input'] = self.test1Input + xdict['test1HTML'] = self.test1HTML + xdict['test1Output'] = self.test1Output + if self.opts.make_HTML and self.opts.output_tab <> 'None': + xdict['tooltests'] = tooltestsBoth % xdict + elif self.opts.make_HTML: + xdict['tooltests'] = tooltestsHTMLOnly % xdict + else: + xdict['tooltests'] = tooltestsTabOnly % xdict + xdict['script'] = self.escapedScript + # configfile is least painful way to embed script to avoid external dependencies + # but requires escaping of <, > and $ to avoid Mako parsing + if self.opts.help_text: + xdict['help'] = open(self.opts.help_text,'r').read() + else: + xdict['help'] = 'Please ask the tool author for help as none was supplied at tool generation' + coda = ['**Script**','Pressing execute will run the following code over your input file and generate some outputs in your history::'] + coda.append(self.indentedScript) + coda.append('**Attribution** This Galaxy tool was created by %s at %s\nusing the Galaxy Tool Factory.' % (self.opts.user_email,timenow())) + coda.append('See %s for details of that project' % (toolFactoryURL)) + coda.append('Please cite: Creating re-usable tools from scripts: The Galaxy Tool Factory. Ross Lazarus; Antony Kaspi; Mark Ziemann; The Galaxy Team. ') + coda.append('Bioinformatics 2012; doi: 10.1093/bioinformatics/bts573') + xdict['help'] = '%s\n%s' % (xdict['help'],'\n'.join(coda)) + if self.opts.tool_desc: + xdict['tooldesc'] = '%s' % self.opts.tool_desc + else: + xdict['tooldesc'] = '' + xdict['command_outputs'] = '' + xdict['outputs'] = '' + if self.opts.input_tab <> 'None': + xdict['command_inputs'] = '--input_tab "$input1" ' # the space may matter a lot if we append something + xdict['inputs'] = ' \n' % self.inputFormats + else: + xdict['command_inputs'] = '' # assume no input - eg a random data generator + xdict['inputs'] = '' + xdict['inputs'] += ' \n' % self.toolname + xdict['toolname'] = self.toolname + xdict['toolid'] = self.toolid + xdict['interpreter'] = self.opts.interpreter + xdict['scriptname'] = self.sfile + if self.opts.make_HTML: + xdict['command_outputs'] += ' --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" ' + xdict['outputs'] += ' \n' + if self.opts.output_tab <> 'None': + xdict['command_outputs'] += ' --output_tab "$tab_file"' + xdict['outputs'] += ' \n' % self.outFormats + xdict['command'] = newCommand % xdict + xmls = newXML % xdict + xf = open(self.xmlfile,'w') + xf.write(xmls) + xf.write('\n') + xf.close() + # ready for the tarball + + + def makeTooltar(self): + """ + a tool is a gz tarball with eg + /toolname/tool.xml /toolname/tool.py /toolname/test-data/test1_in.foo ... + """ + retval = self.run() + if retval: + print >> sys.stderr,'## Run failed. Cannot build yet. Please fix and retry' + sys.exit(1) + self.makeXML() + tdir = self.toolname + os.mkdir(tdir) + if self.opts.input_tab <> 'None': # no reproducible test otherwise? TODO: maybe.. + testdir = os.path.join(tdir,'test-data') + os.mkdir(testdir) # make tests directory + shutil.copyfile(self.opts.input_tab,os.path.join(testdir,self.test1Input)) + if self.opts.output_tab <> 'None': + shutil.copyfile(self.opts.output_tab,os.path.join(testdir,self.test1Output)) + if self.opts.make_HTML: + shutil.copyfile(self.opts.output_html,os.path.join(testdir,self.test1HTML)) + if self.opts.output_dir: + shutil.copyfile(self.tlog,os.path.join(testdir,'test1_out.log')) + op = '%s.py' % self.toolname # new name + outpiname = os.path.join(tdir,op) # path for the tool tarball + pyin = os.path.basename(self.pyfile) # our name - we rewrite ourselves (TM) + notes = ['# %s - a self annotated version of %s generated by running %s\n' % (op,pyin,pyin),] + notes.append('# to make a new Galaxy tool called %s\n' % self.toolname) + notes.append('# User %s at %s\n' % (self.opts.user_email,timenow())) + pi = open(self.pyfile,'r').readlines() # our code becomes new tool wrapper (!) - first Galaxy worm + notes += pi + outpi = open(outpiname,'w') + outpi.write(''.join(notes)) + outpi.write('\n') + outpi.close() + stname = os.path.join(tdir,self.sfile) + if not os.path.exists(stname): + shutil.copyfile(self.sfile, stname) + xtname = os.path.join(tdir,self.xmlfile) + if not os.path.exists(xtname): + shutil.copyfile(self.xmlfile,xtname) + tarpath = "%s.gz" % self.toolname + tar = tarfile.open(tarpath, "w:gz") + tar.add(tdir,arcname=self.toolname) + tar.close() + shutil.copyfile(tarpath,self.opts.new_tool) + shutil.rmtree(tdir) + ## TODO: replace with optional direct upload to local toolshed? + return retval + + + def compressPDF(self,inpdf=None,thumbformat='png'): + """need absolute path to pdf + note that GS gets confoozled if no $TMP or $TEMP + so we set it + """ + assert os.path.isfile(inpdf), "## Input %s supplied to %s compressPDF not found" % (inpdf,self.myName) + our_env = os.environ.copy() + if not (our_env.get('TMP',None) or our_env.get('TEMP',None)): + our_env['TMP'] = '/tmp' + if not self.temp_warned: + print >> sys.stdout,'## WARNING - no $TMP or $TEMP!!! Please fix - using /tmp temporarily' + self.temp_warned = True + hlog = os.path.join(self.opts.output_dir,"compress_%s.txt" % os.path.basename(inpdf)) + 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,env=our_env) + retval1 = x.wait() + sto.close() + if retval1 == 0: + os.unlink(inpdf) + shutil.move(outpdf,inpdf) + os.unlink(hlog) + else: + x = open(hlog,'r').readlines() + print >> sys.stdout,x + hlog = os.path.join(self.opts.output_dir,"thumbnail_%s.txt" % os.path.basename(inpdf)) + sto = open(hlog,'w') + outpng = '%s.%s' % (os.path.splitext(inpdf)[0],thumbformat) + if self.useGM: + cl2 = ['gm', 'convert', inpdf, outpng] + else: # assume imagemagick + cl2 = ['convert', inpdf, outpng] + x = subprocess.Popen(cl2,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env) + retval2 = x.wait() + sto.close() + if retval2 <> 0: + x = open(hlog,'r').readlines() + print >> sys.stdout,x + else: + os.unlink(hlog) + retval = retval1 or retval2 + return retval + + + def getfSize(self,fpath,outpath): + """ + format a nice file size string + """ + size = '' + fp = os.path.join(outpath,fpath) + if os.path.isfile(fp): + size = '0 B' + n = float(os.path.getsize(fp)) + if n > 2**20: + size = '%1.1f MB' % (n/2**20) + elif n > 2**10: + size = '%1.1f KB' % (n/2**10) + elif n > 0: + size = '%d B' % (int(n)) + return size + + def makeHtml(self): + """ Create an HTML file content to list all the artifacts found in the output_dir + """ + + galhtmlprefix = """ + + + + + + + +
+ """ + galhtmlattr = """
This tool (%s) was generated by the Galaxy Tool Factory

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

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

') + html.append('
All output files available for downloading
\n') + html += fhtml # add all non-pdf files to the end of the display + else: + html.append('
### Error - %s returned no files - please confirm that parameters are sane
' % self.opts.interpreter) + html.append(galhtmlpostfix) + htmlf = file(self.opts.output_html,'w') + htmlf.write('\n'.join(html)) + htmlf.write('\n') + htmlf.close() + self.html = html + + + def run(self): + """ + scripts must be small enough not to fill the pipe! + """ + if self.treatbashSpecial and self.opts.interpreter in ['bash','sh']: + retval = self.runBash() + else: + if self.opts.output_dir: + ste = open(self.elog,'w') + sto = open(self.tlog,'w') + sto.write('## Toolfactory generated command line = %s\n' % ' '.join(self.cl)) + sto.flush() + p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=ste,stdin=subprocess.PIPE,cwd=self.opts.output_dir) + else: + p = subprocess.Popen(self.cl,shell=False,stdin=subprocess.PIPE) + p.stdin.write(self.script) + p.stdin.close() + retval = p.wait() + if self.opts.output_dir: + sto.close() + ste.close() + err = open(self.elog,'r').readlines() + if retval <> 0 and err: # problem + print >> sys.stderr,err + if self.opts.make_HTML: + self.makeHtml() + return retval + + def runBash(self): + """ + cannot use - for bash so use self.sfile + """ + if self.opts.output_dir: + s = '## Toolfactory generated command line = %s\n' % ' '.join(self.cl) + sto = open(self.tlog,'w') + sto.write(s) + sto.flush() + p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=sto,cwd=self.opts.output_dir) + else: + p = subprocess.Popen(self.cl,shell=False) + retval = p.wait() + if self.opts.output_dir: + sto.close() + if self.opts.make_HTML: + self.makeHtml() + return retval + + +def main(): + u = """ + This is a Galaxy wrapper. It expects to be called by a special purpose tool.xml as: + rgBaseScriptWrapper.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript" + + """ + op = optparse.OptionParser() + a = op.add_option + a('--script_path',default=None) + a('--tool_name',default=None) + a('--interpreter',default=None) + a('--output_dir',default=None) + a('--output_html',default=None) + a('--input_tab',default="None") + a('--output_tab',default="None") + a('--user_email',default='Unknown') + a('--bad_user',default=None) + a('--make_Tool',default=None) + a('--make_HTML',default=None) + a('--help_text',default=None) + a('--tool_desc',default=None) + a('--new_tool',default=None) + a('--tool_version',default=None) + opts, args = op.parse_args() + assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user) + assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq' + assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript' + assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R' + if opts.output_dir: + try: + os.makedirs(opts.output_dir) + except: + pass + r = ScriptRunner(opts) + if opts.make_Tool: + retcode = r.makeTooltar() + else: + retcode = r.run() + os.unlink(r.sfile) + if retcode: + sys.exit(retcode) # indicate failure to job runner + + +if __name__ == "__main__": + main() + + diff -r 48d71bd383a1 -r c0fa3dde02d9 rgedgeRpaired.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeRpaired.xml Wed Aug 07 02:09:35 2013 -0400 @@ -0,0 +1,1084 @@ + + models using BioConductor packages + + biocbasics + r3 + graphicsmagick + ghostscript + + + + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts" + --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + edgeR['doedgeR'] == "T" + + + DESeq2['doDESeq2'] == "T" + + + doVoom == "T" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + nsamp) { + dm =dm[1:nsamp,] + #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total') + } + newcolnames = substr(colnames(dm),1,20) + colnames(dm) = newcolnames + pdf(outpdfname) + heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none', + Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5) + dev.off() +} + +hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here") +{ + # for 2 groups only was + #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"} + #pcols = unlist(lapply(group,col.map)) + gu = unique(group) + colours = rainbow(length(gu),start=0.3,end=0.6) + pcols = colours[match(group,gu)] + nrows = nrow(cmat) + mtitle = paste(myTitle,'Heatmap: n contigs =',nrows) + if (nrows > nsamp) { + cmat = cmat[c(1:nsamp),] + mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='') + } + newcolnames = substr(colnames(cmat),1,20) + colnames(cmat) = newcolnames + pdf(outpdfname) + heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols) + dev.off() +} + +qqPlot = function(descr='qqplot',pvector, outpdf='qqplot.pdf',...) +# stolen from https://gist.github.com/703512 +{ + o = -log10(sort(pvector,decreasing=F)) + e = -log10( 1:length(o)/length(o) ) + o[o==-Inf] = reallysmall + o[o==Inf] = reallybig + maint = descr + pdf(outpdf) + plot(e,o,pch=19,cex=1, main=maint, ..., + xlab=expression(Expected~~-log[10](italic(p))), + ylab=expression(Observed~~-log[10](italic(p))), + xlim=c(0,max(e)), ylim=c(0,max(o))) + lines(e,e,col="red") + grid(col = "lightgray", lty = "dotted") + dev.off() +} + +smearPlot = function(DGEList,deTags, outSmear, outMain) + { + pdf(outSmear) + plotSmear(DGEList,de.tags=deTags,main=outMain) + grid(col="lightgray", lty="dotted") + dev.off() + } + +boxPlot = function(rawrs,cleanrs,maint,myTitle,pdfname) +{ # + nc = ncol(rawrs) + for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA} + fullnames = colnames(rawrs) + newcolnames = substr(colnames(rawrs),1,20) + colnames(rawrs) = newcolnames + newcolnames = substr(colnames(cleanrs),1,20) + colnames(cleanrs) = newcolnames + defpar = par(no.readonly=T) + print.noquote('raw contig counts by sample:') + print.noquote(summary(rawrs)) + print.noquote('normalised contig counts by sample:') + print.noquote(summary(cleanrs)) + pdf(pdfname) + par(mfrow=c(1,2)) + boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('Raw:',maint)) + grid(col="lightgray",lty="dotted") + boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('After ',maint)) + grid(col="lightgray",lty="dotted") + dev.off() + pdfname = "sample_counts_histogram.pdf" + nc = ncol(rawrs) + print.noquote(paste('Using ncol rawrs=',nc)) + ncroot = round(sqrt(nc)) + if (ncroot*ncroot < nc) { ncroot = ncroot + 1 } + m = c() + for (i in c(1:nc)) { + rhist = hist(rawrs[,i],breaks=100,plot=F) + m = append(m,max(rhist\$counts)) + } + ymax = max(m) + ncols = length(fullnames) + if (ncols > 20) + { + scale = 7*ncols/20 + pdf(pdfname,width=scale,height=scale) + } else { + pdf(pdfname) + } + par(mfrow=c(ncroot,ncroot)) + for (i in c(1:nc)) { + hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon", + breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax)) + } + dev.off() + par(defpar) + +} + +cumPlot = function(rawrs,cleanrs,maint,myTitle) +{ # updated to use ecdf + pdfname = "Filtering_rowsum_bar_charts.pdf" + defpar = par(no.readonly=T) + lrs = log(rawrs,10) + lim = max(lrs) + pdf(pdfname) + par(mfrow=c(2,1)) + hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)", + ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1) + grid(col="lightgray", lty="dotted") + lrs = log(cleanrs,10) + hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)", + ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1) + grid(col="lightgray", lty="dotted") + dev.off() + par(defpar) +} + +cumPlot1 = function(rawrs,cleanrs,maint,myTitle) +{ # updated to use ecdf + pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_') + pdf(pdfname) + par(mfrow=c(2,1)) + lastx = max(rawrs) + rawe = knots(ecdf(rawrs)) + cleane = knots(ecdf(cleanrs)) + cy = 1:length(cleane)/length(cleane) + ry = 1:length(rawe)/length(rawe) + plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads", + ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) + grid(col="blue") + plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads", + ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) + grid(col="blue") + dev.off() +} + + + +doGSEAold = function(y=NULL,design=NULL,histgmt="", + bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") +{ + sink('Camera.log') + genesets = c() + if (bigmt > "") + { + bigenesets = readLines(bigmt) + genesets = bigenesets + } + if (histgmt > "") + { + hgenesets = readLines(histgmt) + if (bigmt > "") { + genesets = rbind(genesets,hgenesets) + } else { + genesets = hgenesets + } # use only history if no bi + } + print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) + genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n + outf = outfname + head=paste(myTitle,'edgeR GSEA') + write(head,file=outfname,append=F) + ntest=length(genesets) + urownames = toupper(rownames(y)) + upcam = c() + downcam = c() + for (i in 1:ntest) { + gs = unlist(genesets[i]) + g = gs[1] # geneset_id + u = gs[2] + if (u > "") { u = paste("",u,"",sep="") } + glist = gs[3:length(gs)] # member gene symbols + glist = toupper(glist) + inglist = urownames %in% glist + nin = sum(inglist) + if ((nin > minnin) && (nin < maxnin)) { + ### print(paste('@@found',sum(inglist),'genes in glist')) + camres = camera(y=y,index=inglist,design=design) + if (! is.null(camres)) { + rownames(camres) = g # gene set name + camres = cbind(GeneSet=g,URL=u,camres) + if (camres\$Direction == "Up") + { + upcam = rbind(upcam,camres) } else { + downcam = rbind(downcam,camres) + } + } + } + } + uscam = upcam[order(upcam\$PValue),] + unadjp = uscam\$PValue + uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + nup = max(10,sum((uscam\$adjPValue < fdrthresh))) + dscam = downcam[order(downcam\$PValue),] + unadjp = dscam\$PValue + dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) + write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) + write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) + write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) + write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) + sink() +} + + + + +doGSEA = function(y=NULL,design=NULL,histgmt="", + bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") +{ + sink('Camera.log') + genesets = c() + if (bigmt > "") + { + bigenesets = readLines(bigmt) + genesets = bigenesets + } + if (histgmt > "") + { + hgenesets = readLines(histgmt) + if (bigmt > "") { + genesets = rbind(genesets,hgenesets) + } else { + genesets = hgenesets + } # use only history if no bi + } + print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) + genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n + outf = outfname + head=paste(myTitle,'edgeR GSEA') + write(head,file=outfname,append=F) + ntest=length(genesets) + urownames = toupper(rownames(y)) + upcam = c() + downcam = c() + incam = c() + urls = c() + gsids = c() + for (i in 1:ntest) { + gs = unlist(genesets[i]) + gsid = gs[1] # geneset_id + url = gs[2] + if (url > "") { url = paste("",url,"",sep="") } + glist = gs[3:length(gs)] # member gene symbols + glist = toupper(glist) + inglist = urownames %in% glist + nin = sum(inglist) + if ((nin > minnin) && (nin < maxnin)) { + incam = c(incam,inglist) + gsids = c(gsids,gsid) + urls = c(urls,url) + } + } + incam = as.list(incam) + names(incam) = gsids + allcam = camera(y=y,index=incam,design=design) + allcamres = cbind(geneset=gsids,allcam,URL=urls) + for (i in 1:ntest) { + camres = allcamres[i] + res = try(test = (camres\$Direction == "Up")) + if ("try-error" %in% class(res)) { + cat("test failed, camres = :") + print.noquote(camres) + } else { if (camres\$Direction == "Up") + { upcam = rbind(upcam,camres) + } else { downcam = rbind(downcam,camres) + } + + } + } + uscam = upcam[order(upcam\$PValue),] + unadjp = uscam\$PValue + uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + nup = max(10,sum((uscam\$adjPValue < fdrthresh))) + dscam = downcam[order(downcam\$PValue),] + unadjp = dscam\$PValue + dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) + write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) + write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) + write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) + write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) + sink() + } + + +edgeIt = function (Count_Matrix=c(),group=c(),out_edgeR=F,out_VOOM=F,out_DESeq2=F,fdrtype='fdr',priordf=5, + fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F, + filterquantile=0.2, subjects=c(),mydesign=NULL, + doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19', + histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + doCook=F,DESeq_fitType="parameteric") +{ + # Error handling + if (length(unique(group))!=2){ + print("Number of conditions identified in experiment does not equal 2") + q() + } + require(edgeR) + options(width = 512) + mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ") + allN = nrow(Count_Matrix) + nscut = round(ncol(Count_Matrix)/2) + colTotmillionreads = colSums(Count_Matrix)/1e6 + counts.dataframe = as.data.frame(c()) + rawrs = rowSums(Count_Matrix) + nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes + nzN = nrow(nonzerod) + nzrs = rowSums(nonzerod) + zN = allN - nzN + print('# Quantiles for non-zero row counts:',quote=F) + print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F) + if (useNDF == T) + { + gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut + lo = colSums(Count_Matrix[!gt1rpin3,]) + workCM = Count_Matrix[gt1rpin3,] + cleanrs = rowSums(workCM) + cleanN = length(cleanrs) + meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="") + print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F) + maint = paste('Filter >=1/million reads in >=',nscut,'samples') + } else { + useme = (nzrs > quantile(nzrs,filterquantile)) + workCM = nonzerod[useme,] + lo = colSums(nonzerod[!useme,]) + cleanrs = rowSums(workCM) + cleanN = length(cleanrs) + meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="") + print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F) + maint = paste('Filter below',filterquantile,'quantile') + } + cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle) + allgenes = rownames(workCM) + reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" + genecards=" 0.8) # is ucsc style string + { + print("@@ using ucsc substitution for urls") + contigurls = paste0(ucsc,"&position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"") + } else { + print("@@ using genecards substitution for urls") + contigurls = paste0(genecards,allgenes,"\'>",allgenes,"") + } + print.noquote("# urls") + print.noquote(head(contigurls)) + print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F) + cmrowsums = rowSums(workCM) + TName=unique(group)[1] + CName=unique(group)[2] + if (is.null(mydesign)) { + if (length(subjects) == 0) + { + mydesign = model.matrix(~group) + } + else { + subjf = factor(subjects) + mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it + } + } + print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=','))) + print.noquote('Using design matrix:') + print.noquote(mydesign) + if (doedgeR) { + sink('edgeR.log') + #### Setup DGEList object + DGEList = DGEList(counts=workCM, group = group) + DGEList = calcNormFactors(DGEList) + + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + comdisp = DGEList\$common.dispersion + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + if (edgeR_priordf > 0) { + print.noquote(paste("prior.df =",edgeR_priordf)) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = edgeR_priordf) + } else { + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + } + DGLM = glmFit(DGEList,design=mydesign) + DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed + efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors + normData = (1e+06*DGEList\$counts/efflib) + uoutput = cbind( + Name=as.character(rownames(DGEList\$counts)), + DE\$table, + adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), + Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums,normData, + DGEList\$counts + ) + soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable + goodness = gof(DGLM, pcutoff=fdrthresh) + if (sum(goodness\$outlier) > 0) { + print.noquote('GLM outliers:') + print(paste(rownames(DGLM)[(goodness\$outlier)],collapse=','),quote=F) + } else { + print('No GLM fit outlier genes found\n') + } + z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) + pdf("edgeR_GoodnessofFit.pdf") + qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") + abline(0,1,lwd=3) + points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon") + dev.off() + estpriorn = getPriorN(DGEList) + print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F) + efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors + normData = (1e+06*DGEList\$counts/efflib) + uniqueg = unique(group) + #### Plot MDS + sample_colors = match(group,levels(group)) + sampleTypes = levels(factor(group)) + print.noquote(sampleTypes) + pdf("edgeR_MDSplot.pdf") + plotMDS.DGEList(DGEList,main=paste("edgeR MDS for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors) + legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19) + grid(col="blue") + dev.off() + colnames(normData) = paste( colnames(normData),'N',sep="_") + print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=','))) + nzd = data.frame(log(nonzerod + 1e-2,10)) + try( boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle,pdfname="edgeR_raw_norm_counts_box.pdf") ) + write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F) + tt = cbind( + Name=as.character(rownames(DGEList\$counts)), + DE\$table, + adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), + Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums + ) + print.noquote("# edgeR Top tags\n") + tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely + tt = tt[order(DE\$table\$PValue),] + print.noquote(tt[1:50,]) + deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,]) + nsig = length(deTags) + print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F) + deColours = ifelse(deTags,'red','black') + pdf("edgeR_BCV_vs_abundance.pdf") + plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance") + dev.off() + dg = DGEList[order(DE\$table\$PValue),] + #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg))) + efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors + normData = (1e+06*dg\$counts/efflib) + outpdfname="edgeR_top_100_heatmap.pdf" + hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('edgeR Heatmap',myTitle)) + outSmear = "edgeR_smearplot.pdf" + outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') + smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain) + qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf='edgeR_qqplot.pdf') + norm.factor = DGEList\$samples\$norm.factors + topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ] + edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR)) + edgeRcounts = rep(0, length(allgenes)) + edgeRcounts[edgeRcountsindex] = 1 # Create venn diagram of hits + sink() + } ### doedgeR + if (doDESeq2 == T) + { + sink("DESeq2.log") + # DESeq2 + require('DESeq2') + library('RColorBrewer') + if (length(subjects) == 0) + { + pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM)) + deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ Rx)) + } else { + pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM)) + deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ subjects + Rx)) + } + #DESeq2 = DESeq(deSEQds,fitType='local',pAdjustMethod=fdrtype) + #rDESeq = results(DESeq2) + #newCountDataSet(workCM, group) + deSeqDatsizefac = estimateSizeFactors(deSEQds) + deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType) + resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype) + rDESeq = as.data.frame(results(resDESeq)) + rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls) + srDESeq = rDESeq[order(rDESeq\$pvalue),] + qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf') + cat("# DESeq top 50\n") + print.noquote(srDESeq[1:50,]) + write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F) + topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ] + DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq)) + DESeqcounts = rep(0, length(allgenes)) + DESeqcounts[DESeqcountsindex] = 1 + pdf("DESeq2_dispersion_estimates.pdf") + plotDispEsts(resDESeq) + dev.off() + ysmall = abs(min(rDESeq\$log2FoldChange)) + ybig = abs(max(rDESeq\$log2FoldChange)) + ylimit = min(4,ysmall,ybig) + pdf("DESeq2_MA_plot.pdf") + plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit)) + dev.off() + rlogres = rlogTransformation(resDESeq) + sampledists = dist( t( assay(rlogres) ) ) + sdmat = as.matrix(sampledists) + pdf("DESeq2_sample_distance_plot.pdf") + heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"), + col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255)) + dev.off() + ###outpdfname="DESeq2_top50_heatmap.pdf" + ###hmap2(sresDESeq,nsamp=50,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('DESeq2 vst rlog Heatmap',myTitle)) + sink() + result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) ) + if ("try-error" %in% class(result)) { + print.noquote('DESeq2 plotPCA failed.') + } else { + pdf("DESeq2_PCA_plot.pdf") + #### wtf - print? Seems needed to get this to work + print(ppca) + dev.off() + } + } + + if (doVoom == T) { + sink('VOOM.log') + if (doedgeR == F) { + #### Setup DGEList object + DGEList = DGEList(counts=workCM, group = group) + DGEList = calcNormFactors(DGEList) + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + norm.factor = DGEList\$samples\$norm.factors + } + pdf("VOOM_mean_variance_plot.pdf") + dat.voomed = voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor) + dev.off() + # Use limma to fit data + fit = lmFit(dat.voomed, mydesign) + fit = eBayes(fit) + rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none") + qqPlot(descr=paste(myTitle,'VOOM-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf='VOOM_qqplot.pdf') + rownames(rvoom) = rownames(workCM) + rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls) + srvoom = rvoom[order(rvoom\$P.Value),] + cat("# VOOM top 50\n") + print(srvoom[1:50,]) + write.table(srvoom,file=out_VOOM, quote=FALSE, sep="\t",row.names=F) + # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma + topresults.voom = rvoom[which(rvoom\$adj.P.Val < fdrthresh), ] + voomcountsindex = which(allgenes %in% topresults.voom\$ID) + voomcounts = rep(0, length(allgenes)) + voomcounts[voomcountsindex] = 1 + sink() + } + + if (doCamera) { + doGSEA(y=DGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle, + outfname=paste(mt,"GSEA.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype) + } + + if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) { + if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) { + vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, + VOOM_limma = voomcounts, row.names = allgenes) + } else if ((doDESeq2==T) && (doedgeR==T)) { + vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes) + } else if ((doVoom==T) && (doedgeR==T)) { + vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes) + } + + if (nrow(counts.dataframe > 1)) { + counts.venn = vennCounts(counts.dataframe) + vennf = "Venn_significant_genes_overlap.pdf" + pdf(vennf) + vennDiagram(counts.venn,main=vennmain,col="maroon") + dev.off() + } + } #### doDESeq2 or doVoom + +} +#### Done + +###sink(stdout(),append=T,type="message") +builtin_gmt = "" +history_gmt = "" +history_gmt_name = "" +out_edgeR = F +out_DESeq2 = F +out_VOOM = "$out_VOOM" +doDESeq2 = $DESeq2.doDESeq2 # make these T or F +doVoom = $doVoom +doCamera = F +doedgeR = $edgeR.doedgeR +edgeR_priordf = 0 + + +#if $doVoom == "T": + out_VOOM = "$out_VOOM" +#end if + +#if $DESeq2.doDESeq2 == "T": + out_DESeq2 = "$out_DESeq2" + DESeq_fitType = "$DESeq2.DESeq_fitType" +#end if + +#if $edgeR.doedgeR == "T": + out_edgeR = "$out_edgeR" + edgeR_priordf = $edgeR.edgeR_priordf +#end if + + + +if (sum(c(doedgeR,doVoom,doDESeq2)) == 0) +{ +write("No methods chosen - nothing to do! Please try again after choosing one or more methods", stderr()) +quit(save="no",status=2) +} + +Out_Dir = "$html_file.files_path" +Input = "$input1" +TreatmentName = "$treatment_name" +TreatmentCols = "$Treat_cols" +ControlName = "$control_name" +ControlCols= "$Control_cols" +org = "$input1.dbkey" +if (org == "") { org = "hg19"} +fdrtype = "$fdrtype" +fdrthresh = $fdrthresh +useNDF = $useNDF +fQ = $fQ # non-differential centile cutoff +myTitle = "$title" +sids = strsplit("$subjectids",',') +subjects = unlist(sids) +nsubj = length(subjects) +TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1 +CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1 +cat('Got TCols=') +cat(TCols) +cat('; CCols=') +cat(CCols) +cat('\n') +useCols = c(TCols,CCols) +if (file.exists(Out_Dir) == F) dir.create(Out_Dir) +Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header +snames = colnames(Count_Matrix) +nsamples = length(snames) +if (nsubj > 0 & nsubj != nsamples) { +options("show.error.messages"=T) +mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','), + 'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=',')) +write(mess, stderr()) +quit(save="no",status=4) +} +if (length(subjects) != 0) {subjects = subjects[useCols]} +Count_Matrix = Count_Matrix[,useCols] ### reorder columns +rn = rownames(Count_Matrix) +islib = rn %in% c('librarySize','NotInBedRegions') +LibSizes = Count_Matrix[subset(rn,islib),][1] # take first +Count_Matrix = Count_Matrix[subset(rn,! islib),] +group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) ) #Build a group descriptor +group = factor(group, levels=c(ControlName,TreatmentName)) +colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_") #Relable columns +results = edgeIt(Count_Matrix=Count_Matrix,group=group, out_edgeR=out_edgeR, out_VOOM=out_VOOM, out_DESeq2=out_DESeq2, + fdrtype='BH',mydesign=NULL,priordf=edgeR_priordf,fdrthresh=fdrthresh,outputdir='.', + myTitle=myTitle,useNDF=F,libSize=c(),filterquantile=fQ,subjects=subjects, + doDESeq2=doDESeq2,doVoom=doVoom,doCamera=doCamera,doedgeR=doedgeR,org=org, + histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fitType=DESeq_fitType) +sessionInfo() +]]> + + + + +**What it does** + +Allows short read sequence counts from controlled experiments to be analysed for differentially expressed genes. +Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design. + +**Input** + +Requires a count matrix as a tabular file. These are best made using the companion HTSeq_ based counter Galaxy wrapper +and your fave gene model to generate inputs. Each row is a genomic feature (gene or exon eg) and each column the +non-negative integer count of reads from one sample overlapping the feature. +The matrix must have a header row uniquely identifying the source samples, and unique row names in +the first column. Typically the row names are gene symbols or probe ids for downstream use in GSEA and other methods. + +**Specifying comparisons** + +This is basically dumbed down for two factors - case vs control. + +More complex interfaces are possible but painful at present. +Probably need to specify a phenotype file to do this better. +Work in progress. Send code. + +If you have (eg) paired samples and wish to include a term in the GLM to account for some other factor (subject in the case of paired samples), +put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or +A list of integers, one for each subject or an empty string if samples are all independent. +If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix. +Integers for samples that are not in the analysis *must* be present in the string as filler even if not used. + +So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones +eg if you had 6 samples with the first two independent but the second and third pairs each being from independent subjects. you might use +8,9,1,1,2,2 +as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6 + +**Methods available** + +You can run 3 popular Bioconductor packages available for count data. + +edgeR - see edgeR_ for details + +VOOM/limma - see limma_VOOM_ for details + +DESeq2 - see DESeq2_ for details + +and optionally camera in edgeR which works better if MSigDB is installed. + +**Outputs** + +Some helpful plots and analysis results. Note that most of these are produced using R code +suggested by the excellent documentation and vignettes for the Bioconductor +packages invoked. The Tool Factory is used to automatically lay these out for you to enjoy. + +**Note on Voom** + +The voom from limma version 3.16.6 help in R includes this from the authors - but you should read the paper to interpret this method. + +This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma. + +voom is an acronym for mean-variance modelling at the observational level. +The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation. +Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend. +This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance. +The weights are then used in the linear modelling process to adjust for heteroscedasticity. + +In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess. +The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag. +The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag. +Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays. +Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation. +This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data. + + +Author(s) + +Charity Law and Gordon Smyth + +References + +Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia. + +Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts. +Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia. +http://www.statsci.org/smyth/pubs/VoomPreprint.pdf + +See Also + +A voom case study is given in the edgeR User's Guide. + +vooma is a similar function but for microarrays instead of RNA-seq. + + +***old rant on changes to Bioconductor package variable names between versions*** + +The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue) +breaking this and all other code that assumed the old name for this variable, +between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). +This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing +to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly +when their old scripts break. This tool currently now works with 2.4.6. + +**Note on prior.N** + +http://seqanswers.com/forums/showthread.php?t=5591 says: + +*prior.n* + +The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. +You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood +in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your +tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the +common likelihood the weight of one observation. + +In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, +or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that +you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation +(squeezing) of the tagwise dispersions. How many samples do you have in your experiment? +What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10. +If you have more samples, then the tagwise dispersion estimates will be more reliable, +so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. + + +From Bioconductor Digest, Vol 118, Issue 5, Gordon writes: + +Dear Dorota, + +The important settings are prior.df and trend. + +prior.n and prior.df are related through prior.df = prior.n * residual.df, +and your experiment has residual.df = 36 - 12 = 24. So the old setting of +prior.n=10 is equivalent for your data to prior.df = 240, a very large +value. Going the other way, the new setting of prior.df=10 is equivalent +to prior.n=10/24. + +To recover old results with the current software you would use + + estimateTagwiseDisp(object, prior.df=240, trend="none") + +To get the new default from old software you would use + + estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE) + +Actually the old trend method is equivalent to trend="loess" in the new +software. You should use plotBCV(object) to see whether a trend is +required. + +Note you could also use + + prior.n = getPriorN(object, prior.df=10) + +to map between prior.df and prior.n. + +---- + +**Attributions** + +edgeR - edgeR_ + +VOOM/limma - limma_VOOM_ + +DESeq2 - DESeq2_ for details + +See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package. + +Galaxy_ (that's what you are using right now!) for gluing everything together + +Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is +licensed to you under the LGPL_ like other rgenetics artefacts + +.. _LGPL: http://www.gnu.org/copyleft/lesser.html +.. _HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/index.html +.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html +.. _DESeq2: http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html +.. _limma_VOOM: http://www.bioconductor.org/packages/release/bioc/html/limma.html +.. _Galaxy: http://getgalaxy.org + + + + + diff -r 48d71bd383a1 -r c0fa3dde02d9 rgedgeRpaired_nocamera.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgedgeRpaired_nocamera.xml Wed Aug 07 02:09:35 2013 -0400 @@ -0,0 +1,1072 @@ + + models using BioConductor packages + + biocbasics + r3 + graphicsmagick + ghostscript + + + + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts" + --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + edgeR['doedgeR'] == "T" + + + DESeq2['doDESeq2'] == "T" + + + doVoom == "T" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + nsamp) { + dm =dm[1:nsamp,] + #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total') + } + newcolnames = substr(colnames(dm),1,20) + colnames(dm) = newcolnames + pdf(outpdfname) + heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none', + Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5) + dev.off() +} + +hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here") +{ + # for 2 groups only was + #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"} + #pcols = unlist(lapply(group,col.map)) + gu = unique(group) + colours = rainbow(length(gu),start=0.3,end=0.6) + pcols = colours[match(group,gu)] + nrows = nrow(cmat) + mtitle = paste(myTitle,'Heatmap: n contigs =',nrows) + if (nrows > nsamp) { + cmat = cmat[c(1:nsamp),] + mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='') + } + newcolnames = substr(colnames(cmat),1,20) + colnames(cmat) = newcolnames + pdf(outpdfname) + heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols) + dev.off() +} + +qqPlot = function(descr='qqplot',pvector, outpdf='qqplot.pdf',...) +# stolen from https://gist.github.com/703512 +{ + o = -log10(sort(pvector,decreasing=F)) + e = -log10( 1:length(o)/length(o) ) + o[o==-Inf] = reallysmall + o[o==Inf] = reallybig + maint = descr + pdf(outpdf) + plot(e,o,pch=19,cex=1, main=maint, ..., + xlab=expression(Expected~~-log[10](italic(p))), + ylab=expression(Observed~~-log[10](italic(p))), + xlim=c(0,max(e)), ylim=c(0,max(o))) + lines(e,e,col="red") + grid(col = "lightgray", lty = "dotted") + dev.off() +} + +smearPlot = function(DGEList,deTags, outSmear, outMain) + { + pdf(outSmear) + plotSmear(DGEList,de.tags=deTags,main=outMain) + grid(col="lightgray", lty="dotted") + dev.off() + } + +boxPlot = function(rawrs,cleanrs,maint,myTitle,pdfname) +{ # + nc = ncol(rawrs) + for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA} + fullnames = colnames(rawrs) + newcolnames = substr(colnames(rawrs),1,20) + colnames(rawrs) = newcolnames + newcolnames = substr(colnames(cleanrs),1,20) + colnames(cleanrs) = newcolnames + defpar = par(no.readonly=T) + print.noquote('raw contig counts by sample:') + print.noquote(summary(rawrs)) + print.noquote('normalised contig counts by sample:') + print.noquote(summary(cleanrs)) + pdf(pdfname) + par(mfrow=c(1,2)) + boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('Raw:',maint)) + grid(col="lightgray",lty="dotted") + boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('After ',maint)) + grid(col="lightgray",lty="dotted") + dev.off() + pdfname = "sample_counts_histogram.pdf" + nc = ncol(rawrs) + print.noquote(paste('Using ncol rawrs=',nc)) + ncroot = round(sqrt(nc)) + if (ncroot*ncroot < nc) { ncroot = ncroot + 1 } + m = c() + for (i in c(1:nc)) { + rhist = hist(rawrs[,i],breaks=100,plot=F) + m = append(m,max(rhist\$counts)) + } + ymax = max(m) + ncols = length(fullnames) + if (ncols > 20) + { + scale = 7*ncols/20 + pdf(pdfname,width=scale,height=scale) + } else { + pdf(pdfname) + } + par(mfrow=c(ncroot,ncroot)) + for (i in c(1:nc)) { + hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon", + breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax)) + } + dev.off() + par(defpar) + +} + +cumPlot = function(rawrs,cleanrs,maint,myTitle) +{ # updated to use ecdf + pdfname = "Filtering_rowsum_bar_charts.pdf" + defpar = par(no.readonly=T) + lrs = log(rawrs,10) + lim = max(lrs) + pdf(pdfname) + par(mfrow=c(2,1)) + hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)", + ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1) + grid(col="lightgray", lty="dotted") + lrs = log(cleanrs,10) + hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)", + ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1) + grid(col="lightgray", lty="dotted") + dev.off() + par(defpar) +} + +cumPlot1 = function(rawrs,cleanrs,maint,myTitle) +{ # updated to use ecdf + pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_') + pdf(pdfname) + par(mfrow=c(2,1)) + lastx = max(rawrs) + rawe = knots(ecdf(rawrs)) + cleane = knots(ecdf(cleanrs)) + cy = 1:length(cleane)/length(cleane) + ry = 1:length(rawe)/length(rawe) + plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads", + ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) + grid(col="blue") + plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads", + ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) + grid(col="blue") + dev.off() +} + + + +doGSEAold = function(y=NULL,design=NULL,histgmt="", + bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") +{ + sink('Camera.log') + genesets = c() + if (bigmt > "") + { + bigenesets = readLines(bigmt) + genesets = bigenesets + } + if (histgmt > "") + { + hgenesets = readLines(histgmt) + if (bigmt > "") { + genesets = rbind(genesets,hgenesets) + } else { + genesets = hgenesets + } # use only history if no bi + } + print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) + genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n + outf = outfname + head=paste(myTitle,'edgeR GSEA') + write(head,file=outfname,append=F) + ntest=length(genesets) + urownames = toupper(rownames(y)) + upcam = c() + downcam = c() + for (i in 1:ntest) { + gs = unlist(genesets[i]) + g = gs[1] # geneset_id + u = gs[2] + if (u > "") { u = paste("",u,"",sep="") } + glist = gs[3:length(gs)] # member gene symbols + glist = toupper(glist) + inglist = urownames %in% glist + nin = sum(inglist) + if ((nin > minnin) && (nin < maxnin)) { + ### print(paste('@@found',sum(inglist),'genes in glist')) + camres = camera(y=y,index=inglist,design=design) + if (! is.null(camres)) { + rownames(camres) = g # gene set name + camres = cbind(GeneSet=g,URL=u,camres) + if (camres\$Direction == "Up") + { + upcam = rbind(upcam,camres) } else { + downcam = rbind(downcam,camres) + } + } + } + } + uscam = upcam[order(upcam\$PValue),] + unadjp = uscam\$PValue + uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + nup = max(10,sum((uscam\$adjPValue < fdrthresh))) + dscam = downcam[order(downcam\$PValue),] + unadjp = dscam\$PValue + dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) + write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) + write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) + write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) + write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) + sink() +} + + + + +doGSEA = function(y=NULL,design=NULL,histgmt="", + bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") +{ + sink('Camera.log') + genesets = c() + if (bigmt > "") + { + bigenesets = readLines(bigmt) + genesets = bigenesets + } + if (histgmt > "") + { + hgenesets = readLines(histgmt) + if (bigmt > "") { + genesets = rbind(genesets,hgenesets) + } else { + genesets = hgenesets + } # use only history if no bi + } + print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) + genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n + outf = outfname + head=paste(myTitle,'edgeR GSEA') + write(head,file=outfname,append=F) + ntest=length(genesets) + urownames = toupper(rownames(y)) + upcam = c() + downcam = c() + incam = c() + urls = c() + gsids = c() + for (i in 1:ntest) { + gs = unlist(genesets[i]) + gsid = gs[1] # geneset_id + url = gs[2] + if (url > "") { url = paste("",url,"",sep="") } + glist = gs[3:length(gs)] # member gene symbols + glist = toupper(glist) + inglist = urownames %in% glist + nin = sum(inglist) + if ((nin > minnin) && (nin < maxnin)) { + incam = c(incam,inglist) + gsids = c(gsids,gsid) + urls = c(urls,url) + } + } + incam = as.list(incam) + names(incam) = gsids + allcam = camera(y=y,index=incam,design=design) + allcamres = cbind(geneset=gsids,allcam,URL=urls) + for (i in 1:ntest) { + camres = allcamres[i] + res = try(test = (camres\$Direction == "Up")) + if ("try-error" %in% class(res)) { + cat("test failed, camres = :") + print.noquote(camres) + } else { if (camres\$Direction == "Up") + { upcam = rbind(upcam,camres) + } else { downcam = rbind(downcam,camres) + } + + } + } + uscam = upcam[order(upcam\$PValue),] + unadjp = uscam\$PValue + uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + nup = max(10,sum((uscam\$adjPValue < fdrthresh))) + dscam = downcam[order(downcam\$PValue),] + unadjp = dscam\$PValue + dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) + ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) + write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) + write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) + write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) + print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) + write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) + sink() + } + + +edgeIt = function (Count_Matrix=c(),group=c(),out_edgeR=F,out_VOOM=F,out_DESeq2=F,fdrtype='fdr',priordf=5, + fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F, + filterquantile=0.2, subjects=c(),mydesign=NULL, + doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19', + histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", + doCook=F,DESeq_fitType="parameteric") +{ + # Error handling + if (length(unique(group))!=2){ + print("Number of conditions identified in experiment does not equal 2") + q() + } + require(edgeR) + options(width = 512) + mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ") + allN = nrow(Count_Matrix) + nscut = round(ncol(Count_Matrix)/2) + colTotmillionreads = colSums(Count_Matrix)/1e6 + counts.dataframe = as.data.frame(c()) + rawrs = rowSums(Count_Matrix) + nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes + nzN = nrow(nonzerod) + nzrs = rowSums(nonzerod) + zN = allN - nzN + print('# Quantiles for non-zero row counts:',quote=F) + print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F) + if (useNDF == T) + { + gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut + lo = colSums(Count_Matrix[!gt1rpin3,]) + workCM = Count_Matrix[gt1rpin3,] + cleanrs = rowSums(workCM) + cleanN = length(cleanrs) + meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="") + print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F) + maint = paste('Filter >=1/million reads in >=',nscut,'samples') + } else { + useme = (nzrs > quantile(nzrs,filterquantile)) + workCM = nonzerod[useme,] + lo = colSums(nonzerod[!useme,]) + cleanrs = rowSums(workCM) + cleanN = length(cleanrs) + meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="") + print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F) + maint = paste('Filter below',filterquantile,'quantile') + } + cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle) + allgenes = rownames(workCM) + reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" + genecards=" 0.8) # is ucsc style string + { + print("@@ using ucsc substitution for urls") + contigurls = paste0(ucsc,"&position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"") + } else { + print("@@ using genecards substitution for urls") + contigurls = paste0(genecards,allgenes,"\'>",allgenes,"") + } + print.noquote("# urls") + print.noquote(head(contigurls)) + print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F) + cmrowsums = rowSums(workCM) + TName=unique(group)[1] + CName=unique(group)[2] + if (is.null(mydesign)) { + if (length(subjects) == 0) + { + mydesign = model.matrix(~group) + } + else { + subjf = factor(subjects) + mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it + } + } + print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=','))) + print.noquote('Using design matrix:') + print.noquote(mydesign) + if (doedgeR) { + sink('edgeR.log') + #### Setup DGEList object + DGEList = DGEList(counts=workCM, group = group) + DGEList = calcNormFactors(DGEList) + + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + comdisp = DGEList\$common.dispersion + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + if (edgeR_priordf > 0) { + print.noquote(paste("prior.df =",edgeR_priordf)) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = edgeR_priordf) + } else { + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + } + DGLM = glmFit(DGEList,design=mydesign) + DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed + efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors + normData = (1e+06*DGEList\$counts/efflib) + uoutput = cbind( + Name=as.character(rownames(DGEList\$counts)), + DE\$table, + adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), + Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums,normData, + DGEList\$counts + ) + soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable + goodness = gof(DGLM, pcutoff=fdrthresh) + if (sum(goodness\$outlier) > 0) { + print.noquote('GLM outliers:') + print(paste(rownames(DGLM)[(goodness\$outlier)],collapse=','),quote=F) + } else { + print('No GLM fit outlier genes found\n') + } + z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) + pdf("edgeR_GoodnessofFit.pdf") + qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") + abline(0,1,lwd=3) + points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon") + dev.off() + estpriorn = getPriorN(DGEList) + print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F) + efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors + normData = (1e+06*DGEList\$counts/efflib) + uniqueg = unique(group) + #### Plot MDS + sample_colors = match(group,levels(group)) + sampleTypes = levels(factor(group)) + print.noquote(sampleTypes) + pdf("edgeR_MDSplot.pdf") + plotMDS.DGEList(DGEList,main=paste("edgeR MDS for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors) + legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19) + grid(col="blue") + dev.off() + colnames(normData) = paste( colnames(normData),'N',sep="_") + print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=','))) + nzd = data.frame(log(nonzerod + 1e-2,10)) + try( boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle,pdfname="edgeR_raw_norm_counts_box.pdf") ) + write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F) + tt = cbind( + Name=as.character(rownames(DGEList\$counts)), + DE\$table, + adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), + Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums + ) + print.noquote("# edgeR Top tags\n") + tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely + tt = tt[order(DE\$table\$PValue),] + print.noquote(tt[1:50,]) + deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,]) + nsig = length(deTags) + print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F) + deColours = ifelse(deTags,'red','black') + pdf("edgeR_BCV_vs_abundance.pdf") + plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance") + dev.off() + dg = DGEList[order(DE\$table\$PValue),] + #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg))) + efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors + normData = (1e+06*dg\$counts/efflib) + outpdfname="edgeR_top_100_heatmap.pdf" + hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('edgeR Heatmap',myTitle)) + outSmear = "edgeR_smearplot.pdf" + outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') + smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain) + qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf='edgeR_qqplot.pdf') + norm.factor = DGEList\$samples\$norm.factors + topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ] + edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR)) + edgeRcounts = rep(0, length(allgenes)) + edgeRcounts[edgeRcountsindex] = 1 # Create venn diagram of hits + sink() + } ### doedgeR + if (doDESeq2 == T) + { + sink("DESeq2.log") + # DESeq2 + require('DESeq2') + library('RColorBrewer') + if (length(subjects) == 0) + { + pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM)) + deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ Rx)) + } else { + pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM)) + deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ subjects + Rx)) + } + #DESeq2 = DESeq(deSEQds,fitType='local',pAdjustMethod=fdrtype) + #rDESeq = results(DESeq2) + #newCountDataSet(workCM, group) + deSeqDatsizefac = estimateSizeFactors(deSEQds) + deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType) + resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype) + rDESeq = as.data.frame(results(resDESeq)) + rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls) + srDESeq = rDESeq[order(rDESeq\$pvalue),] + qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf') + cat("# DESeq top 50\n") + print.noquote(srDESeq[1:50,]) + write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F) + topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ] + DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq)) + DESeqcounts = rep(0, length(allgenes)) + DESeqcounts[DESeqcountsindex] = 1 + pdf("DESeq2_dispersion_estimates.pdf") + plotDispEsts(resDESeq) + dev.off() + ysmall = abs(min(rDESeq\$log2FoldChange)) + ybig = abs(max(rDESeq\$log2FoldChange)) + ylimit = min(4,ysmall,ybig) + pdf("DESeq2_MA_plot.pdf") + plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit)) + dev.off() + rlogres = rlogTransformation(resDESeq) + sampledists = dist( t( assay(rlogres) ) ) + sdmat = as.matrix(sampledists) + pdf("DESeq2_sample_distance_plot.pdf") + heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"), + col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255)) + dev.off() + ###outpdfname="DESeq2_top50_heatmap.pdf" + ###hmap2(sresDESeq,nsamp=50,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('DESeq2 vst rlog Heatmap',myTitle)) + sink() + result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) ) + if ("try-error" %in% class(result)) { + print.noquote('DESeq2 plotPCA failed.') + } else { + pdf("DESeq2_PCA_plot.pdf") + #### wtf - print? Seems needed to get this to work + print(ppca) + dev.off() + } + } + + if (doVoom == T) { + sink('VOOM.log') + if (doedgeR == F) { + #### Setup DGEList object + DGEList = DGEList(counts=workCM, group = group) + DGEList = calcNormFactors(DGEList) + DGEList = estimateGLMCommonDisp(DGEList,mydesign) + DGEList = estimateGLMTrendedDisp(DGEList,mydesign) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) + norm.factor = DGEList\$samples\$norm.factors + } + pdf("VOOM_mean_variance_plot.pdf") + dat.voomed = voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor) + dev.off() + # Use limma to fit data + fit = lmFit(dat.voomed, mydesign) + fit = eBayes(fit) + rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none") + qqPlot(descr=paste(myTitle,'VOOM-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf='VOOM_qqplot.pdf') + rownames(rvoom) = rownames(workCM) + rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls) + srvoom = rvoom[order(rvoom\$P.Value),] + cat("# VOOM top 50\n") + print(srvoom[1:50,]) + write.table(srvoom,file=out_VOOM, quote=FALSE, sep="\t",row.names=F) + # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma + topresults.voom = rvoom[which(rvoom\$adj.P.Val < fdrthresh), ] + voomcountsindex = which(allgenes %in% topresults.voom\$ID) + voomcounts = rep(0, length(allgenes)) + voomcounts[voomcountsindex] = 1 + sink() + } + + if (doCamera) { + doGSEA(y=DGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle, + outfname=paste(mt,"GSEA.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype) + } + + if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) { + if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) { + vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, + VOOM_limma = voomcounts, row.names = allgenes) + } else if ((doDESeq2==T) && (doedgeR==T)) { + vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes) + } else if ((doVoom==T) && (doedgeR==T)) { + vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh) + counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes) + } + + if (nrow(counts.dataframe > 1)) { + counts.venn = vennCounts(counts.dataframe) + vennf = "Venn_significant_genes_overlap.pdf" + pdf(vennf) + vennDiagram(counts.venn,main=vennmain,col="maroon") + dev.off() + } + } #### doDESeq2 or doVoom + +} +#### Done + +###sink(stdout(),append=T,type="message") +builtin_gmt = "" +history_gmt = "" +history_gmt_name = "" +out_edgeR = F +out_DESeq2 = F +out_VOOM = "$out_VOOM" +doDESeq2 = $DESeq2.doDESeq2 # make these T or F +doVoom = $doVoom +doCamera = F +doedgeR = $edgeR.doedgeR +edgeR_priordf = 0 + + +#if $doVoom == "T": + out_VOOM = "$out_VOOM" +#end if + +#if $DESeq2.doDESeq2 == "T": + out_DESeq2 = "$out_DESeq2" + DESeq_fitType = "$DESeq2.DESeq_fitType" +#end if + +#if $edgeR.doedgeR == "T": + out_edgeR = "$out_edgeR" + edgeR_priordf = $edgeR.edgeR_priordf +#end if + + +if (sum(c(doedgeR,doVoom,doDESeq2)) == 0) +{ +write("No methods chosen - nothing to do! Please try again after choosing one or more methods", stderr()) +quit(save="no",status=2) +} + +Out_Dir = "$html_file.files_path" +Input = "$input1" +TreatmentName = "$treatment_name" +TreatmentCols = "$Treat_cols" +ControlName = "$control_name" +ControlCols= "$Control_cols" +org = "$input1.dbkey" +if (org == "") { org = "hg19"} +fdrtype = "$fdrtype" +fdrthresh = $fdrthresh +useNDF = $useNDF +fQ = $fQ # non-differential centile cutoff +myTitle = "$title" +sids = strsplit("$subjectids",',') +subjects = unlist(sids) +nsubj = length(subjects) +TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1 +CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1 +cat('Got TCols=') +cat(TCols) +cat('; CCols=') +cat(CCols) +cat('\n') +useCols = c(TCols,CCols) +if (file.exists(Out_Dir) == F) dir.create(Out_Dir) +Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header +snames = colnames(Count_Matrix) +nsamples = length(snames) +if (nsubj > 0 & nsubj != nsamples) { +options("show.error.messages"=T) +mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','), + 'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=',')) +write(mess, stderr()) +quit(save="no",status=4) +} +if (length(subjects) != 0) {subjects = subjects[useCols]} +Count_Matrix = Count_Matrix[,useCols] ### reorder columns +rn = rownames(Count_Matrix) +islib = rn %in% c('librarySize','NotInBedRegions') +LibSizes = Count_Matrix[subset(rn,islib),][1] # take first +Count_Matrix = Count_Matrix[subset(rn,! islib),] +group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) ) #Build a group descriptor +group = factor(group, levels=c(ControlName,TreatmentName)) +colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_") #Relable columns +results = edgeIt(Count_Matrix=Count_Matrix,group=group, out_edgeR=out_edgeR, out_VOOM=out_VOOM, out_DESeq2=out_DESeq2, + fdrtype='BH',mydesign=NULL,priordf=edgeR_priordf,fdrthresh=fdrthresh,outputdir='.', + myTitle=myTitle,useNDF=F,libSize=c(),filterquantile=fQ,subjects=subjects, + doDESeq2=doDESeq2,doVoom=doVoom,doCamera=doCamera,doedgeR=doedgeR,org=org, + histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fitType=DESeq_fitType) +sessionInfo() +]]> + + + + +**What it does** + +Allows short read sequence counts from controlled experiments to be analysed for differentially expressed genes. +Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design. + +**Input** + +Requires a count matrix as a tabular file. These are best made using the companion HTSeq_ based counter Galaxy wrapper +and your fave gene model to generate inputs. Each row is a genomic feature (gene or exon eg) and each column the +non-negative integer count of reads from one sample overlapping the feature. +The matrix must have a header row uniquely identifying the source samples, and unique row names in +the first column. Typically the row names are gene symbols or probe ids for downstream use in GSEA and other methods. + +**Specifying comparisons** + +This is basically dumbed down for two factors - case vs control. + +More complex interfaces are possible but painful at present. +Probably need to specify a phenotype file to do this better. +Work in progress. Send code. + +If you have (eg) paired samples and wish to include a term in the GLM to account for some other factor (subject in the case of paired samples), +put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or +A list of integers, one for each subject or an empty string if samples are all independent. +If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix. +Integers for samples that are not in the analysis *must* be present in the string as filler even if not used. + +So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones +eg if you had 6 samples with the first two independent but the second and third pairs each being from independent subjects. you might use +8,9,1,1,2,2 +as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6 + +**Methods available** + +You can run 3 popular Bioconductor packages available for count data. + +edgeR - see edgeR_ for details + +VOOM/limma - see limma_VOOM_ for details + +DESeq2 - see DESeq2_ for details + +and optionally camera in edgeR which works better if MSigDB is installed. + +**Outputs** + +Some helpful plots and analysis results. Note that most of these are produced using R code +suggested by the excellent documentation and vignettes for the Bioconductor +packages invoked. The Tool Factory is used to automatically lay these out for you to enjoy. + +**Note on Voom** + +The voom from limma version 3.16.6 help in R includes this from the authors - but you should read the paper to interpret this method. + +This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma. + +voom is an acronym for mean-variance modelling at the observational level. +The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation. +Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend. +This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance. +The weights are then used in the linear modelling process to adjust for heteroscedasticity. + +In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess. +The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag. +The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag. +Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays. +Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation. +This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data. + + +Author(s) + +Charity Law and Gordon Smyth + +References + +Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia. + +Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts. +Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia. +http://www.statsci.org/smyth/pubs/VoomPreprint.pdf + +See Also + +A voom case study is given in the edgeR User's Guide. + +vooma is a similar function but for microarrays instead of RNA-seq. + + +***old rant on changes to Bioconductor package variable names between versions*** + +The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue) +breaking this and all other code that assumed the old name for this variable, +between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). +This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing +to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly +when their old scripts break. This tool currently now works with 2.4.6. + +**Note on prior.N** + +http://seqanswers.com/forums/showthread.php?t=5591 says: + +*prior.n* + +The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. +You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood +in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your +tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the +common likelihood the weight of one observation. + +In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, +or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that +you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation +(squeezing) of the tagwise dispersions. How many samples do you have in your experiment? +What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10. +If you have more samples, then the tagwise dispersion estimates will be more reliable, +so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. + + +From Bioconductor Digest, Vol 118, Issue 5, Gordon writes: + +Dear Dorota, + +The important settings are prior.df and trend. + +prior.n and prior.df are related through prior.df = prior.n * residual.df, +and your experiment has residual.df = 36 - 12 = 24. So the old setting of +prior.n=10 is equivalent for your data to prior.df = 240, a very large +value. Going the other way, the new setting of prior.df=10 is equivalent +to prior.n=10/24. + +To recover old results with the current software you would use + + estimateTagwiseDisp(object, prior.df=240, trend="none") + +To get the new default from old software you would use + + estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE) + +Actually the old trend method is equivalent to trend="loess" in the new +software. You should use plotBCV(object) to see whether a trend is +required. + +Note you could also use + + prior.n = getPriorN(object, prior.df=10) + +to map between prior.df and prior.n. + +---- + +**Attributions** + +edgeR - edgeR_ + +VOOM/limma - limma_VOOM_ + +DESeq2 - DESeq2_ for details + +See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package. + +Galaxy_ (that's what you are using right now!) for gluing everything together + +Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is +licensed to you under the LGPL_ like other rgenetics artefacts + +.. _LGPL: http://www.gnu.org/copyleft/lesser.html +.. _HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/index.html +.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html +.. _DESeq2: http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html +.. _limma_VOOM: http://www.bioconductor.org/packages/release/bioc/html/limma.html +.. _Galaxy: http://getgalaxy.org + + + + + diff -r 48d71bd383a1 -r c0fa3dde02d9 test-data/edgeRtest1out.html --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/edgeRtest1out.html Wed Aug 07 02:09:35 2013 -0400 @@ -0,0 +1,733 @@ + + + + + + + + +
+ +
Galaxy Tool "DifferentialCounts" run at 07/08/2013 15:46:55

+
DESeq2 images and outputs
+(Click on a thumbnail image to download the corresponding original PDF image)
+
+ + + + + + + + + + + + + +
Image called DESeq2_MA_plot.pdfImage called DESeq2_PCA_plot.pdfImage called DESeq2_dispersion_estimates.pdf
Image called DESeq2_qqplot.pdfImage called DESeq2_sample_distance_plot.pdf 
+ +
DESeq2 log output
+ +
+
+# DESeq top 50
+
+                     Contig     baseMean log2FoldChange     lfcSE        pvalue          padj  NReads                                                                                               URL
+
+Mir192               Mir192 271352.97636       6.965264 0.2150593 4.096936e-230 4.576278e-227 2325567               Mir192
+
+Mir122a             Mir122a  10112.31117      10.312083 0.3292695 2.649323e-215 1.479647e-212   90428             Mir122a
+
+Mir149               Mir149    810.35429      -6.911118 0.2341392 1.735537e-191 6.461982e-189    6164               Mir149
+
+Mir23a               Mir23a   1289.18043      -3.104086 0.1191688 1.424246e-149 3.977206e-147   10118               Mir23a
+
+Mir181d             Mir181d    275.22797      -3.581172 0.1778187  3.329371e-90  7.437816e-88    2139             Mir181d
+
+Mir204               Mir204    347.57397      -7.284200 0.3771119  3.959336e-83  7.370965e-81    2601               Mir204
+
+Mir23b               Mir23b   2028.55377      -2.065110 0.1085802  1.182361e-80  1.886711e-78   16387               Mir23b
+
+Mir27a               Mir27a   2788.72629      -3.016676 0.1688167  2.036708e-71  2.843754e-69   21886               Mir27a
+
+Mir195               Mir195    519.86200      -3.152795 0.1784796  7.838123e-70  9.727982e-68    3962               Mir195
+
+Mir194-2           Mir194-2    391.65678       5.222911 0.3099275  1.013490e-63  1.132068e-61    3570           Mir194-2
+
+Mir208b             Mir208b   1649.77924     -11.396172 0.6771238  1.464479e-63  1.487112e-61   14756             Mir208b
+
+Mir10b               Mir10b  27820.40551      -5.071453 0.3044889  2.754493e-62  2.563974e-60  197340               Mir10b
+
+Mir181c             Mir181c   2765.96510      -3.660964 0.2275711  3.141153e-58  2.698975e-56   23605             Mir181c
+
+Mir208a             Mir208a    616.76981     -10.356524 0.6559217  3.688385e-56  2.942804e-54    4638             Mir208a
+
+Mir490               Mir490    220.99790      -8.059660 0.5142876  2.369067e-55  1.764165e-53    1741               Mir490
+
+Mir203               Mir203    772.92882       1.990849 0.1274099  4.877239e-55  3.404923e-53    6739               Mir203
+
+Mir215               Mir215    152.78082      -3.004380 0.1939090  3.822339e-54  2.511502e-52    1182               Mir215
+
+Dnm3os               Dnm3os    179.61643      -3.278392 0.2166491  9.922020e-52  6.157165e-50    1401               Dnm3os
+
+Mir214               Mir214    134.69038      -3.216444 0.2154916  2.230148e-50  1.311093e-48    1048               Mir214
+
+Mir21                 Mir21  26121.31011       2.963903 0.2008617  2.817434e-49  1.573537e-47  229120                 Mir21
+
+Mir1948             Mir1948    263.89527       7.074045 0.4867225  7.374030e-48  3.922282e-46    2404             Mir1948
+
+Mir27b               Mir27b  76478.05753      -1.904653 0.1312889  1.088626e-47  5.527251e-46  625308               Mir27b
+
+Rabggtb             Rabggtb   2257.19195       1.988368 0.1401741  1.134862e-45  5.511484e-44   19535             Rabggtb
+
+Mir499               Mir499    712.45950     -10.577061 0.7528467  7.766408e-45  3.614616e-43    6527               Mir499
+
+Mir101b             Mir101b   6846.19683       3.791681 0.2809666  1.670548e-41  7.464007e-40   59019             Mir101b
+
+Mir132               Mir132    106.46062      -2.797928 0.2083376  4.046163e-41  1.738294e-39     857               Mir132
+
+Mir143hg           Mir143hg 180217.77425      -2.169143 0.1685614  6.764675e-38  2.798571e-36 1407364           Mir143hg
+
+Mir143               Mir143 179219.35960      -2.170303 0.1696199  1.746403e-37  6.966899e-36 1399819               Mir143
+
+Mir155               Mir155     57.66182      -3.788079 0.3056585  2.845488e-35  1.096004e-33     463               Mir155
+
+Mir322               Mir322    899.53469      -3.126011 0.2622595  9.363374e-33  3.486296e-31    7074               Mir322
+
+Mir378               Mir378    483.21548      -2.994300 0.2577321  3.343457e-31  1.204723e-29    4075               Mir378
+
+Mir24-2             Mir24-2    424.48288      -2.712674 0.2361028  1.491830e-30  5.049617e-29    3470             Mir24-2
+
+Mir3074-2         Mir3074-2    424.48288      -2.712674 0.2361028  1.491830e-30  5.049617e-29    3470         Mir3074-2
+
+Mir199b             Mir199b     47.84725      -5.294373 0.4644474  4.215162e-30  1.384805e-28     370             Mir199b
+
+Mir802               Mir802    166.83414       8.816580 0.7782636  9.478527e-30  3.025004e-28    1514               Mir802
+
+Mir125b-2         Mir125b-2    493.08516      -2.919341 0.2631193  1.324797e-28  4.110551e-27    3837         Mir125b-2
+
+Mir301               Mir301    260.53406      -1.676984 0.1526772  4.570133e-28  1.379686e-26    2119               Mir301
+
+Snord104           Snord104   3851.90119       2.386573 0.2173857  4.847914e-28  1.425032e-26   33458           Snord104
+
+Mir150               Mir150    553.20599      -2.836881 0.2595088  8.127991e-28  2.327940e-26    4229               Mir150
+
+Mir148a             Mir148a 118994.46955       2.678852 0.2481801  3.675045e-27  1.026256e-25 1002397             Mir148a
+
+5430416N02Rik 5430416N02Rik     62.15966       3.089960 0.2941123  8.101331e-26  2.207119e-24     564 5430416N02Rik
+
+Mir193               Mir193     45.70861       4.991530 0.4814098  3.446492e-25  9.166027e-24     421               Mir193
+
+Mir3073             Mir3073     98.93199       8.208709 0.7944742  5.036320e-25  1.308272e-23     904             Mir3073
+
+Mir125b-1         Mir125b-1     79.01988      -3.020660 0.2937360  8.355633e-25  2.121191e-23     609         Mir125b-1
+
+2610203C20Rik 2610203C20Rik     79.17666      -3.023491 0.2948614  1.136165e-24  2.820214e-23     610 2610203C20Rik
+
+Mir181a-1         Mir181a-1     59.53826      -3.151487 0.3211628  9.923707e-23  2.409735e-21     506         Mir181a-1
+
+Mir184               Mir184     32.23796      -4.865023 0.4962776  1.092606e-22  2.596683e-21     247               Mir184
+
+Mir199a-2         Mir199a-2     44.84878      -3.422216 0.3545647  4.826269e-22  1.123113e-20     352         Mir199a-2
+
+Snord91a           Snord91a    168.95251       2.700421 0.2835464  1.670595e-21  3.808275e-20    1437           Snord91a
+
+Mir200b             Mir200b     87.13638       5.940702 0.6338554  7.094881e-21  1.584996e-19     888             Mir200b
+
+
+
+ +
DifferentialCounts log output
+ +
+
+Loading required package: gtools
+
+Loading required package: gdata
+
+gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
+
+gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
+
+Attaching package: ‘gdata’
+
+The following object is masked from ‘package:stats’:
+
+    nobs
+
+The following object is masked from ‘package:utils’:
+
+    object.size
+
+Loading required package: caTools
+
+Loading required package: grid
+
+Loading required package: KernSmooth
+
+KernSmooth 2.23 loaded
+
+Copyright M. P. Wand 1997-2009
+
+Loading required package: MASS
+
+Attaching package: ‘gplots’
+
+The following object is masked from ‘package:stats’:
+
+    lowess
+
+Loading required package: methods
+
+Loading required package: limma
+
+Loading required package: splines
+
+Loading required package: DESeq2
+
+Loading required package: GenomicRanges
+
+Loading required package: BiocGenerics
+
+Loading required package: parallel
+
+Attaching package: ‘BiocGenerics’
+
+The following object is masked from ‘package:parallel’:
+
+    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB
+
+The following object is masked from ‘package:gdata’:
+
+    combine
+
+The following object is masked from ‘package:stats’:
+
+    xtabs
+
+The following object is masked from ‘package:base’:
+
+    anyDuplicated, as.data.frame, cbind, colnames, duplicated, eval, Filter, Find, get, intersect, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rep.int, rownames, sapply, setdiff, sort, table, tapply, union, unique, unlist
+
+Loading required package: IRanges
+
+Attaching package: ‘IRanges’
+
+The following object is masked from ‘package:gplots’:
+
+    space
+
+The following object is masked from ‘package:caTools’:
+
+    runmean
+
+The following object is masked from ‘package:gdata’:
+
+    trim
+
+Loading required package: Biobase
+
+Welcome to Bioconductor
+
+    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'.
+
+Loading required package: lattice
+
+Loading required package: Rcpp
+
+Loading required package: RcppArmadillo
+
+Attaching package: ‘DESeq2’
+
+The following object is masked from ‘package:limma’:
+
+    plotMA
+
+gene-wise dispersion estimates
+
+mean-dispersion relationship
+
+final dispersion estimates
+
+you had estimated dispersions, replacing these
+
+gene-wise dispersion estimates
+
+mean-dispersion relationship
+
+final dispersion estimates
+
+you had estimated dispersions, replacing these
+
+gene-wise dispersion estimates
+
+mean-dispersion relationship
+
+final dispersion estimates
+
+Warning messages:
+
+1: In bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group ==  :
+
+  Outlier (-Inf) in boxplot 1 is not drawn
+
+2: In bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group ==  :
+
+  Outlier (-Inf) in boxplot 3 is not drawn
+
+3: In bxp(list(stats = c(-0.430723026286372, -0.127900608036896, 0.474159383291067,  :
+
+  some notches went outside hinges ('box'): maybe set notch=FALSE
+
+4: In par(defpar) : calling par(new=TRUE) with no plot
+
+
+
+ +
VOOM images and outputs
+(Click on a thumbnail image to download the corresponding original PDF image)
+
+ + + + + + +
Image called VOOM_mean_variance_plot.pdfImage called VOOM_qqplot.pdf
+ +
VOOM log output
+ +
+
+# VOOM top 50
+
+                         ID      logFC    AveExpr          t      P.Value    adj.P.Val         B  NReads                                                                                               URL
+
+Mir192               Mir192   6.948883 14.6763803  42.722954 2.301199e-16 2.625668e-13 27.266471 2325567               Mir192
+
+Mir208a             Mir208a -11.015018  3.9395538 -23.252407 1.118938e-12 6.383542e-10 17.208662    4638             Mir208a
+
+Mir122a             Mir122a  10.426125  8.1698641  21.722912 2.859682e-12 1.087633e-09 17.760171   90428             Mir122a
+
+Mir149               Mir149  -7.030463  6.3160807 -20.883835 4.915491e-12 1.402144e-09 17.277609    6164               Mir149
+
+Mir208b             Mir208b -12.433228  4.6076218 -19.592458 1.179199e-11 2.690931e-09 15.683666   14756             Mir208b
+
+Mir10b               Mir10b  -5.130915 12.2628672 -18.242023 3.124991e-11 4.963978e-09 16.221503  197340               Mir10b
+
+Mir143hg           Mir143hg  -2.249221 16.2444825 -18.082481 3.521739e-11 4.963978e-09 16.026695 1407364           Mir143hg
+
+Mir143               Mir143  -2.251067 16.2358599 -18.081481 3.524391e-11 4.963978e-09 16.026084 1399819               Mir143
+
+Mir499               Mir499 -11.567529  3.7874598 -17.942086 3.915496e-11 4.963978e-09 14.821741    6527               Mir499
+
+Mir802               Mir802   9.158434  2.9157675  17.316522 6.338616e-11 7.232360e-09 14.381577    1514               Mir802
+
+Mir3073             Mir3073   8.420542  2.5457189  16.702657 1.033066e-10 1.036045e-08 13.985845     904             Mir3073
+
+Mir148a             Mir148a   2.638213 15.4435820  16.548188 1.171186e-10 1.036045e-08 14.814792 1002397             Mir148a
+
+Mir101b             Mir101b   3.765722 10.8508440  16.538566 1.180419e-10 1.036045e-08 14.900027   59019             Mir101b
+
+Mir490               Mir490  -8.474378  3.7506957 -16.259650 1.484816e-10 1.210125e-08 13.424617    1741               Mir490
+
+Mir21                 Mir21   2.938537 13.1642917  15.375404 3.148335e-10 2.394833e-08 13.867698  229120                 Mir21
+
+Mir181c             Mir181c  -3.742560  9.6295577 -15.242361 3.537063e-10 2.522368e-08 13.810405   23605             Mir181c
+
+Mir204               Mir204  -7.684425  4.7751735 -15.033484 4.254268e-10 2.855364e-08 12.822427    2601               Mir204
+
+Mir23a               Mir23a  -3.165768  8.7896592 -14.631179 6.110682e-10 3.873493e-08 13.269174   10118               Mir23a
+
+Mir181d             Mir181d  -3.636211  6.3713218 -14.317073 8.157508e-10 4.898798e-08 12.956333    2139             Mir181d
+
+Mir133b             Mir133b  -6.493549  1.2544862 -13.969968 1.129934e-09 6.446275e-08 11.982684     159             Mir133b
+
+Mir27a               Mir27a  -3.106935  9.9255796 -13.838251 1.281011e-09 6.960160e-08 12.513086   21886               Mir27a
+
+Mir194-2           Mir194-2   5.264136  6.0897615  13.044012 2.792884e-09 1.448491e-07 11.715753    3570           Mir194-2
+
+Mir195               Mir195  -3.216595  7.4509350 -12.869478 3.332788e-09 1.653353e-07 11.587523    3962               Mir195
+
+Mir27b               Mir27b  -1.976376 15.0957731 -11.756036 1.082197e-08 5.144946e-07 10.127719  625308               Mir27b
+
+Mir378               Mir378  -3.097393  7.3832049 -11.684163 1.171371e-08 5.346138e-07 10.329692    4075               Mir378
+
+Snord104           Snord104   2.337374 10.6109024  11.495675 1.444482e-08 6.339052e-07 10.023395   33458           Snord104
+
+Mir1983             Mir1983  -5.895500  0.9931851 -11.445812 1.527548e-08 6.441605e-07  9.749260     101             Mir1983
+
+Mir322               Mir322  -3.296618  8.2153415 -11.415362 1.580762e-08 6.441605e-07 10.008472    7074               Mir322
+
+Mir200a             Mir200a   6.191561  1.7981309  11.322172 1.756229e-08 6.909853e-07  9.662295     264             Mir200a
+
+Mir215               Mir215  -3.045873  5.7544234 -11.148134 2.141822e-08 8.082459e-07  9.753268    1182               Mir215
+
+Dnm3os               Dnm3os  -3.363344  5.8607432 -11.092261 2.283960e-08 8.082459e-07  9.689496    1401               Dnm3os
+
+Mir182               Mir182   4.903995  7.1511683  11.074468 2.331304e-08 8.082459e-07  9.658842    7189               Mir182
+
+Mir181a-2         Mir181a-2  -3.048298  6.9414651 -11.072128 2.337609e-08 8.082459e-07  9.644017    2817         Mir181a-2
+
+Mir1948             Mir1948   7.195525  4.5513493  11.005492 2.524936e-08 8.473388e-07  9.341794    2404             Mir1948
+
+Mir214               Mir214  -3.280874  5.4784451 -10.768257 3.332555e-08 1.086413e-06  9.318504    1048               Mir214
+
+Mir153               Mir153  -5.963803  1.4386315 -10.727082 3.498742e-08 1.093990e-06  9.035569     140               Mir153
+
+Cyp3a25             Cyp3a25   6.318200  1.4888933  10.698226 3.620443e-08 1.093990e-06  9.024973     226             Cyp3a25
+
+Gm5441               Gm5441  -5.982176  1.4484953 -10.692891 3.643436e-08 1.093990e-06  9.000362     142               Gm5441
+
+Mir125b-2         Mir125b-2  -3.077678  7.4316058 -10.446668 4.893073e-08 1.431538e-06  8.884250    3837         Mir125b-2
+
+Mir133a-1         Mir133a-1  -5.144671  0.5903264 -10.358205 5.447229e-08 1.553822e-06  8.575535      60         Mir133a-1
+
+1110038B12Rik 1110038B12Rik   2.226702 10.8487089  10.194609 6.655312e-08 1.852125e-06  8.439308   37066 1110038B12Rik
+
+Mir132               Mir132  -2.847559  5.3211839 -10.110952 7.380297e-08 2.004981e-06  8.531491     857               Mir132
+
+Rabggtb             Rabggtb   1.935779  9.9874171   9.928995 9.262821e-08 2.457879e-06  8.133384   19535             Rabggtb
+
+Mir504               Mir504  -5.256127  0.6221088  -9.892894 9.693595e-08 2.513725e-06  8.068853      69               Mir504
+
+Mir150               Mir150  -2.938531  7.6297870  -9.842102 1.033602e-07 2.620755e-06  8.116464    4229               Mir150
+
+Mir199b             Mir199b  -5.752816  2.8805143  -9.823920 1.057683e-07 2.623514e-06  7.979387     370             Mir199b
+
+Mir23b               Mir23b  -2.124129  9.8141190  -9.806316 1.081569e-07 2.625681e-06  7.979464   16387               Mir23b
+
+Mir24-2             Mir24-2  -2.833979  7.3083691  -9.767192 1.136724e-07 2.646944e-06  8.030550    3470             Mir24-2
+
+Mir3074-2         Mir3074-2  -2.833979  7.3083691  -9.767192 1.136724e-07 2.646944e-06  8.030550    3470         Mir3074-2
+
+Mir155               Mir155  -3.906600  3.9899000  -9.732173 1.188627e-07 2.712448e-06  8.046518     463               Mir155
+
+
+
+ +
edgeR images and outputs
+(Click on a thumbnail image to download the corresponding original PDF image)
+
+ + + + + + + + + + + + + + + + + + +
Image called edgeR_BCV_vs_abundance.pdfImage called edgeR_GoodnessofFit.pdfImage called edgeR_MDSplot.pdf
Image called edgeR_qqplot.pdfImage called edgeR_raw_norm_counts_box.pdfImage called edgeR_smearplot.pdf
Image called edgeR_top_100_heatmap.pdf  
+ +
edgeR log output
+ +
+
+[1] prior.df = 8
+
+[1] "No GLM fit outlier genes found\n"
+
+[1] Common Dispersion = 0.228651460998105 CV =  0.478175136323613 getPriorN =  3.33333333333333
+
+[1] heart liver
+
+[1] "Raw sample read totals 2443751,1644652,1682104,1806045,1440960,1341813,2888924,1428365"
+
+[1] raw contig counts by sample:
+
+ liver_X11706Liv_CAAA liver_X11700Liv_ATTC liver_X11698Liv_ACTG liver_X11699Liv_ATGA heart_X11706He_AGTTC heart_X11699He_GGCTA heart_X11698He_TAGCT heart_X11700He_CTTGT
+
+ Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043       Min.   :0.0043      
+
+ 1st Qu.:0.3032       1st Qu.:0.0043       1st Qu.:0.3032       1st Qu.:0.0043       1st Qu.:0.0043       1st Qu.:0.0043       1st Qu.:0.0043       1st Qu.:0.0043      
+
+ Median :0.7789       Median :0.6031       Median :0.6998       Median :0.6031       Median :0.4786       Median :0.4786       Median :0.4786       Median :0.4786      
+
+ Mean   :1.0519       Mean   :1.0343       Mean   :0.9855       Mean   :0.9966       Mean   :0.9210       Mean   :0.9428       Mean   :1.0205       Mean   :0.9753      
+
+ 3rd Qu.:1.5410       3rd Qu.:1.6335       3rd Qu.:1.4473       3rd Qu.:1.5534       3rd Qu.:1.5770       3rd Qu.:1.5855       3rd Qu.:1.7161       3rd Qu.:1.6022      
+
+ Max.   :5.8209       Max.   :5.6905       Max.   :5.7999       Max.   :5.7215       Max.   :5.3609       Max.   :5.3589       Max.   :5.6967       Max.   :5.3702      
+
+ NA's   :650          NA's   :969          NA's   :664          NA's   :886          NA's   :902          NA's   :957          NA's   :821          NA's   :950         
+
+[1] normalised contig counts by sample:
+
+ liver_X11706Liv_CAAA liver_X11700Liv_ATTC liver_X11698Liv_ACTG liver_X11699Liv_ATGA heart_X11706He_AGTTC heart_X11699He_GGCTA heart_X11698He_TAGCT heart_X11700He_CTTGT
+
+ Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf         Min.   :-Inf        
+
+ 1st Qu.:   0         1st Qu.:-Inf         1st Qu.:   0         1st Qu.:-Inf         1st Qu.:-Inf         1st Qu.:-Inf         1st Qu.:-Inf         1st Qu.:-Inf        
+
+ Median :   0         Median :   0         Median :   0         Median :   0         Median :   0         Median :   0         Median :   0         Median :   0        
+
+ Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf         Mean   :-Inf        
+
+ 3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1         3rd Qu.:   1        
+
+ Max.   :   6         Max.   :   6         Max.   :   6         Max.   :   5         Max.   :   5         Max.   :   5         Max.   :   6         Max.   :   5        
+
+[1] Using ncol rawrs= 8
+
+[1] # edgeR Top tags\n
+
+                       Name      logFC    logCPM        LR        PValue   adj.p.value Dispersion totreads                                                                                               URL
+
+Mir208a             Mir208a -11.840751  8.465017 594.16946 3.104543e-131 3.542284e-128 0.05171220     4638             Mir208a
+
+Mir149               Mir149  -7.008984  8.861767 484.30321 2.473909e-107 1.411365e-104 0.04959937     6164               Mir149
+
+Mir208b             Mir208b -13.291635  9.905945 417.69758  7.737463e-93  2.942815e-90 0.10508096    14756             Mir208b
+
+Mir122a             Mir122a  10.514683 12.478088 415.17429  2.740525e-92  7.817349e-90 0.10803882    90428             Mir122a
+
+Mir204               Mir204  -7.498162  7.634507 341.30678  3.313430e-76  7.561247e-74 0.06907958     2601               Mir204
+
+Mir499               Mir499 -13.577454  8.700078 325.79199  7.930755e-73  1.508165e-70 0.12042284     6527               Mir499
+
+Mir490               Mir490  -8.534394  6.991023 303.17184  6.710366e-68  1.093790e-65 0.07949711     1741               Mir490
+
+Mir192               Mir192   6.953853 17.169364 217.22867  3.638307e-49  5.189135e-47 0.12700995  2325567               Mir192
+
+Mir802               Mir802  11.440805  6.593380 212.88059  3.231644e-48  4.097007e-46 0.12273671     1514               Mir802
+
+Mir1948             Mir1948   7.418142  7.252734 195.66958  1.840248e-44  2.099723e-42 0.12060221     2404             Mir1948
+
+Mir194-2           Mir194-2   5.298950  7.811522 191.85588  1.250960e-43  1.297587e-41 0.08670751     3570           Mir194-2
+
+Mir23a               Mir23a  -3.153807  9.529402 177.53185  1.676248e-40  1.593833e-38 0.04442763    10118               Mir23a
+
+Mir181c             Mir181c  -3.767686 10.639598 169.87390  7.883295e-39  6.919107e-37 0.06368883    23605             Mir181c
+
+Mir3073             Mir3073  10.686337  5.859950 164.86740  9.778593e-38  7.969554e-36 0.14069249      904             Mir3073
+
+Mir181d             Mir181d  -3.643963  7.300371 162.18591  3.767663e-37  2.865936e-35 0.05729574     2139             Mir181d
+
+Mir195               Mir195  -3.203683  8.215089 150.20548  1.563314e-34  1.114838e-32 0.05235020     3962               Mir195
+
+Mir10b               Mir10b  -5.182616 13.946466 147.24793  6.926819e-34  4.649118e-32 0.12268790   197340               Mir10b
+
+Mir101b             Mir101b   3.759962 11.863187 136.31359  1.703812e-31  1.080028e-29 0.07961343    59019             Mir101b
+
+Mir378               Mir378  -3.115599  8.119617 126.76408  2.092233e-29  1.256441e-27 0.05942391     4075               Mir378
+
+Mir27a               Mir27a  -3.064687 10.642480 124.98911  5.117477e-29  2.919520e-27 0.06113852    21886               Mir27a
+
+Mir182               Mir182   5.057509  8.846381 123.17765  1.275060e-28  6.927826e-27 0.13653707     7189               Mir182
+
+Mir322               Mir322  -3.194159  9.012888 107.34926  3.732413e-25  1.935765e-23 0.07536483     7074               Mir322
+
+Mir199b             Mir199b  -5.520119  4.792610 102.10724  5.259607e-24  2.609223e-22 0.13417024      370             Mir199b
+
+Mir181a-2         Mir181a-2  -3.000177  7.637692 101.38361  7.578821e-24  3.603098e-22 0.06896654     2817         Mir181a-2
+
+Mir125b-2         Mir125b-2  -2.987759  8.144514  91.72544  9.957640e-22  4.488356e-20 0.07737381     3837         Mir125b-2
+
+Dnm3os               Dnm3os  -3.331215  6.686950  91.67250  1.022763e-21  4.488356e-20 0.08810497     1401               Dnm3os
+
+Mir184               Mir184  -5.111350  4.234160  84.35542  4.133639e-20  1.686711e-18 0.13502324      247               Mir184
+
+Mir215               Mir215  -3.058208  6.447966  84.35278  4.139167e-20  1.686711e-18 0.08138517     1182               Mir215
+
+Mir133b             Mir133b  -8.383611  3.584760  83.96681  5.031517e-20  1.960318e-18 0.17482280      159             Mir133b
+
+Mir150               Mir150  -2.883446  8.307765  83.91918  5.154210e-20  1.960318e-18 0.08008123     4229               Mir150
+
+Mir3074-2         Mir3074-2  -2.778308  7.935651  83.74839  5.619282e-20  2.040616e-18 0.07424646     3470         Mir3074-2
+
+Mir24-2             Mir24-2  -2.778307  7.935651  83.71222  5.723024e-20  2.040616e-18 0.07427992     3470             Mir24-2
+
+Mir193               Mir193   5.176579  4.801090  83.19222  7.445011e-20  2.574169e-18 0.14794861      421               Mir193
+
+Scarna17           Scarna17   2.182159  9.244479  81.91330  1.421894e-19  4.771710e-18 0.04982909     9224           Scarna17
+
+Mir214               Mir214  -3.271172  6.271755  80.43948  2.997458e-19  9.771712e-18 0.09566584     1048               Mir214
+
+Snord104           Snord104   2.330488 11.053611  79.50529  4.809369e-19  1.524303e-17 0.05915990    33458           Snord104
+
+Mir200a             Mir200a   7.201555  4.139422  77.35503  1.428304e-18  4.365755e-17 0.19287764      264             Mir200a
+
+Mir200b             Mir200b   6.525423  5.752604  77.31985  1.453976e-18  4.365755e-17 0.26237966      888             Mir200b
+
+Mir21                 Mir21   2.923147 13.825255  75.51798  3.620938e-18  1.059357e-16 0.09395834   229120                 Mir21
+
+Mir203               Mir203   1.956427  8.767610  75.17870  4.299815e-18  1.226522e-16 0.04381710     6739               Mir203
+
+Mir155               Mir155  -3.886731  5.068563  73.81316  8.587210e-18  2.389758e-16 0.12522673      463               Mir155
+
+Cyp3a25             Cyp3a25   8.681501  3.972085  72.29680  1.851471e-17  5.029829e-16 0.23125383      226             Cyp3a25
+
+Rabggtb             Rabggtb   1.934093 10.298211  72.02043  2.129809e-17  5.651422e-16 0.04596646    19535             Rabggtb
+
+Mir23b               Mir23b  -2.100584 10.184110  71.44225  2.854935e-17  7.403367e-16 0.05416378    16387               Mir23b
+
+Snord52             Snord52   2.207491 10.217554  71.27974  3.100027e-17  7.860292e-16 0.05941483    18059             Snord52
+
+Gm5441               Gm5441  -6.881248  3.538457  70.05615  5.764004e-17  1.429724e-15 0.20097284      142               Gm5441
+
+Mir153               Mir153  -6.857671  3.517446  69.37600  8.137282e-17  1.975455e-15 0.20158808      140               Mir153
+
+Mir132               Mir132  -2.858294  5.938312  64.52507  9.531204e-16  2.265647e-14 0.09274248      857               Mir132
+
+1110038B12Rik 1110038B12Rik   2.195962 11.253090  62.92015  2.152583e-15  5.012443e-14 0.06712174    37066 1110038B12Rik
+
+Snord91a           Snord91a   2.654072  6.557504  62.40549  2.795431e-15  6.379174e-14 0.08637410     1437           Snord91a
+
+[1] # 416 tags significant at adj p= 0.05
+
+
+
+ +
Other images and outputs
+(Click on a thumbnail image to download the corresponding original PDF image)
+
+ + + + + + + + + +
Image called Filtering_rowsum_bar_charts.pdfImage called Venn_significant_genes_overlap.pdf
Image called sample_counts_histogram.pdf 
+ +
Other log output
+ +
+
+## Toolfactory generated command line = Rscript - None None
+
+Got TCols=1 5 6 7; CCols=2 3 4 8
+
+[1] # Quantiles for non-zero row counts:
+
+       0%       10%       20%       30%       40%       50%       60%       70%       80%       90%      100% 
+
+      1.0       1.0       2.0       3.0       4.0       8.0      13.0      24.0      86.6     753.0 2325567.0 
+
+[1] Read 3242 contigs. Removed 1494 with no reads. After filtering at count quantile =0.3, there are 1141 contigs
+
+[1] "@@ using genecards substitution for urls"
+
+[1] # urls
+
+[1] 0610005C13Rik 0610007N19Rik 0610008F07Rik 0610009L18Rik 0610012G03Rik
+
+[6] 0610031O16Rik
+
+[1] # Total low count contigs per sample =  170,67,203,86,145,111,155,120
+
+[1] Using samples: liver_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam
+
+[1] Using design matrix:
+
+  (Intercept) groupliver
+
+1           1          1
+
+2           1          1
+
+3           1          1
+
+4           1          1
+
+5           1          0
+
+6           1          0
+
+7           1          0
+
+8           1          0
+
+attr(,"assign")
+
+[1] 0 1
+
+attr(,"contrasts")
+
+attr(,"contrasts")$group
+
+[1] contr.treatment
+
+R version 3.0.1 (2013-05-16)
+
+Platform: x86_64-unknown-linux-gnu (64-bit)
+
+locale:
+
+ [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C               LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8     LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8    LC_PAPER=C                 LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       
+
+attached base packages:
+
+ [1] parallel  splines   methods   grid      stats     graphics  grDevices utils     datasets  base     
+
+other attached packages:
+
+ [1] RColorBrewer_1.0-5      DESeq2_1.0.18           RcppArmadillo_0.3.900.7 Rcpp_0.10.4             lattice_0.20-15         Biobase_2.20.1          GenomicRanges_1.12.4    IRanges_1.18.2          BiocGenerics_0.6.0      edgeR_3.2.4             limma_3.16.7            gplots_2.11.3           MASS_7.3-28             KernSmooth_2.23-10      caTools_1.14            gdata_2.13.2            gtools_3.0.0            stringr_0.6.2          
+
+loaded via a namespace (and not attached):
+
+ [1] annotate_1.38.0      AnnotationDbi_1.22.6 bitops_1.0-5         DBI_0.2-7            genefilter_1.42.0    locfit_1.5-9.1       RSQLite_0.11.4       stats4_3.0.1         survival_2.37-4      XML_3.98-1.1         xtable_1.7-1        
+
+
+
+ +
All output files available for downloading
+ +
+
+ diff -r 48d71bd383a1 -r c0fa3dde02d9 test-data/edgeRtest1out.xls --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/edgeRtest1out.xls Wed Aug 07 02:09:35 2013 -0400 @@ -0,0 +1,1142 @@ +ID logFC AveExpr t P.Value adj.P.Val B NReads URL +Mir192 6.94888256843679 14.6763802609023 42.7229535356942 2.30119906424271e-16 2.62566813230094e-13 27.2664713266936 2325567 Mir192 +Mir208a -11.0150177152075 3.93955375669227 -23.2524066836307 1.11893807599952e-12 6.38354172357727e-10 17.2086622097974 4638 Mir208a +Mir122a 10.4261254701779 8.16986409392255 21.7229119192922 2.85968233611017e-12 1.08763251516723e-09 17.760171141852 90428 Mir122a +Mir149 -7.03046258655617 6.31608073609863 -20.8838348040628 4.91549082404237e-12 1.40214375755809e-09 17.2776088871455 6164 Mir149 +Mir208b -12.4332279840446 4.60762179736006 -19.5924575126382 1.17919871718875e-11 2.69093147262473e-09 15.6836663826186 14756 Mir208b +Mir10b -5.1309149063532 12.2628671946242 -18.2420234752943 3.12499057505143e-11 4.96397841614262e-09 16.2215027882858 197340 Mir10b +Mir143hg -2.24922058313374 16.2444825488726 -18.0824813146443 3.52173903971276e-11 4.96397841614262e-09 16.0266951625541 1407364 Mir143hg +Mir143 -2.25106712131643 16.235859869169 -18.0814805993441 3.524391092512e-11 4.96397841614262e-09 16.0260836456534 1399819 Mir143 +Mir499 -11.5675289490546 3.78745976580796 -17.9420857279689 3.91549568319751e-11 4.96397841614262e-09 14.8217405828874 6527 Mir499 +Mir802 9.15843445824816 2.91576747878654 17.3165224121399 6.33861560587965e-11 7.23236040630868e-09 14.381577240531 1514 Mir802 +Mir3073 8.42054159318439 2.54571889776166 16.7026571721381 1.03306635740721e-10 1.03604453339228e-08 13.9858447292853 904 Mir3073 +Mir148a 2.63821345578617 15.4435819751152 16.5481882215215 1.17118649515038e-10 1.03604453339228e-08 14.8147917664862 1002397 Mir148a +Mir101b 3.76572195114225 10.8508440499081 16.5385659719288 1.1804188373444e-10 1.03604453339228e-08 14.9000274171241 59019 Mir101b +Mir490 -8.47437764634465 3.75069567634692 -16.2596504905533 1.48481644820999e-10 1.21012540529114e-08 13.4246171016517 1741 Mir490 +Mir21 2.93853744034991 13.1642916950886 15.3754036511693 3.14833456057776e-10 2.39483315574615e-08 13.8676979022068 229120 Mir21 +Mir181c -3.74256009957124 9.62955774646065 -15.2423608550805 3.53706264458683e-10 2.52236779842098e-08 13.8104046176901 23605 Mir181c +Mir204 -7.68442507149438 4.77517348536933 -15.0334839919296 4.2542677795722e-10 2.85536443323052e-08 12.8224274879526 2601 Mir204 +Mir23a -3.16576837850821 8.78965917558611 -14.6311785109623 6.11068192724496e-10 3.87349337721472e-08 13.2691736804205 10118 Mir23a +Mir181d -3.63621106402109 6.37132182424908 -14.3170733565449 8.15750840848868e-10 4.89879847057136e-08 12.9563328312209 2139 Mir181d +Mir133b -6.49354876170712 1.25448620431148 -13.969968060601 1.12993427319653e-09 6.44627502858619e-08 11.9826837063041 159 Mir133b +Mir27a -3.10693537246128 9.92557960348829 -13.8382510839158 1.28101104196848e-09 6.96015999469543e-08 12.5130856443239 21886 Mir27a +Mir194-2 5.26413595786074 6.08976151689046 13.0440120203829 2.79288399641768e-09 1.44849119996026e-07 11.7157527118771 3570 Mir194-2 +Mir195 -3.21659545049586 7.4509349905835 -12.869478368273 3.33278798407795e-09 1.65335264775345e-07 11.5875227405737 3962 Mir195 +Mir27b -1.97637614533106 15.0957731023791 -11.75603589654 1.08219717999805e-08 5.14494575990741e-07 10.1277185662145 625308 Mir27b +Mir378 -3.09739319841142 7.38320489393809 -11.6841625470748 1.17137125863851e-08 5.34613842442616e-07 10.3296922348831 4075 Mir378 +Snord104 2.33737428989677 10.6109023861403 11.4956750870273 1.44448164322638e-08 6.33905213431269e-07 10.0233949189609 33458 Snord104 +Mir1983 -5.89550024150745 0.993185099223749 -11.4458119994178 1.52754786535047e-08 6.44160462853232e-07 9.74926029381244 101 Mir1983 +Mir322 -3.29661750880005 8.21534154356388 -11.4153616003567 1.58076187203247e-08 6.44160462853232e-07 10.0084716002011 7074 Mir322 +Mir200a 6.19156065085543 1.79813092499896 11.3221723123067 1.75622912046568e-08 6.9098531946598e-07 9.66229453831667 264 Mir200a +Mir215 -3.04587333807051 5.75442336214621 -11.1481336257529 2.14182153707674e-08 8.08245865886245e-07 9.75326755116029 1182 Mir215 +Dnm3os -3.36334357719079 5.86074322417943 -11.0922610835813 2.28395969947309e-08 8.08245865886245e-07 9.68949616901383 1401 Dnm3os +Mir182 4.90399541739044 7.1511683493624 11.0744681203078 2.33130367310143e-08 8.08245865886245e-07 9.65884218207857 7189 Mir182 +Mir181a-2 -3.04829832099813 6.94146510070354 -11.0721276255975 2.33760855164295e-08 8.08245865886245e-07 9.64401697815694 2817 Mir181a-2 +Mir1948 7.19552540631629 4.5513492833967 11.0054920626234 2.52493600829575e-08 8.47338819254543e-07 9.34179361673467 2404 Mir1948 +Mir214 -3.28087400431203 5.47844506177362 -10.7682572190636 3.3325545851092e-08 1.0864127947456e-06 9.3185039394887 1048 Mir214 +Mir153 -5.9638030672045 1.43863148956561 -10.7270821099311 3.49874201497232e-08 1.09398957489501e-06 9.03556928822473 140 Mir153 +Cyp3a25 6.3181999278218 1.48889330889732 10.698226339624 3.62044305804168e-08 1.09398957489501e-06 9.02497280953977 226 Cyp3a25 +Gm5441 -5.98217559924296 1.44849534030207 -10.6928905219357 3.64343591989574e-08 1.09398957489501e-06 9.00036161342169 142 Gm5441 +Mir125b-2 -3.07767777869034 7.43160584496732 -10.4466676018996 4.89307304174606e-08 1.43153752323904e-06 8.88425047860251 3837 Mir125b-2 +Mir133a-1 -5.1446707114636 0.590326422018506 -10.3582048674166 5.44722862183524e-08 1.5538219643785e-06 8.57553468686339 60 Mir133a-1 +1110038B12Rik 2.2267024913641 10.8487089345135 10.1946092089644 6.6553123680318e-08 1.85212473461568e-06 8.43930767784171 37066 1110038B12Rik +Mir132 -2.84755882904953 5.32118389129247 -10.1109518209472 7.38029688527407e-08 2.00498065383279e-06 8.53149092529729 857 Mir132 +Rabggtb 1.93577875106629 9.98741711657765 9.92899544482161 9.26282059254969e-08 2.45787867351144e-06 8.13338404178509 19535 Rabggtb +Mir504 -5.25612736011945 0.622108812393712 -9.89289391617729 9.69359452558378e-08 2.51372530765707e-06 8.06885345567596 69 Mir504 +Mir150 -2.93853062743745 7.62978695478889 -9.84210172881295 1.0336018288322e-07 2.62075485932786e-06 8.11646444295523 4229 Mir150 +Mir199b -5.75281586680568 2.88051428778536 -9.82392040215114 1.05768314120536e-07 2.62351405242461e-06 7.97938664656761 370 Mir199b +Mir23b -2.12412853458823 9.81411903779331 -9.80631552439508 1.08156895246613e-07 2.62568122290182e-06 7.97946421769546 16387 Mir23b +Mir24-2 -2.83397931671342 7.30836912335631 -9.76719188905173 1.13672435494913e-07 2.64694385509584e-06 8.03055035937719 3470 Mir24-2 +Mir3074-2 -2.83397931671342 7.30836912335631 -9.76719188905173 1.13672435494913e-07 2.64694385509584e-06 8.03055035937719 3470 Mir3074-2 +Mir155 -3.90660012470502 3.98990000150695 -9.73217278591154 1.18862734970705e-07 2.71244761203149e-06 8.04651834419983 463 Mir155 +Snord52 2.24297808473649 9.80695706696893 9.65508926768283 1.31194241930095e-07 2.93514960867135e-06 7.78317356265822 18059 Snord52 +Scarna17 2.22135685073443 8.84690751573681 9.60031550237377 1.4077767610154e-07 3.08898708522803e-06 7.75116302071276 9224 Scarna17 +Mir201 -5.13478218238105 0.574435171385817 -9.36375298852343 1.91556946938131e-07 4.12389578219637e-06 7.45101629313275 63 Mir201 +Mir184 -5.19740389169544 2.57741455998865 -9.28503096123923 2.12504578894189e-07 4.42570763295992e-06 7.34294491810118 247 Mir184 +Snord91a 2.64601724328653 5.9737770599439 9.28208610647144 2.1333384733812e-07 4.42570763295992e-06 7.45176303063652 1437 Snord91a +Mir193 4.93970055277848 3.14663535122407 9.21323530535893 2.33732739541295e-07 4.76230456815389e-06 7.28752724029761 421 Mir193 +Mir470 5.07947201153691 1.71052510073336 8.84181308472679 3.85901653714362e-07 7.72480327873838e-06 6.83971839651152 157 Mir470 +0610031O16Rik 4.84298796174609 0.739739561937192 8.8004727262221 4.08431072700958e-07 8.03482506813436e-06 6.77016207746292 78 0610031O16Rik 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Cnot2 +Gm16039 0.0860006893041729 0.989643407906595 0.141187247205111 0.889711963452007 0.932061058543482 -6.26618211598301 27 Gm16039 +1700007J10Rik -0.113205756535242 -0.788253108012747 -0.140564172695348 0.890195249337243 0.932061058543482 -6.24719705198026 7 1700007J10Rik +Nup88 -0.124975737193874 -0.239213430165402 -0.139113735537381 0.891320451556099 0.932061058543482 -6.20078805666017 15 Nup88 +Mir1946b -0.11008548363065 0.157027195014887 -0.13736714896008 0.892675718878866 0.932061058543482 -6.19984394272254 15 Mir1946b +Mirlet7g 0.0395844887395015 11.1837033910963 0.136145707336603 0.893623705801407 0.932061058543482 -7.47148737731621 38152 Mirlet7g +Gm10548 -0.0899680772947313 -0.34669122290937 -0.136099070118153 0.89365990529298 0.932061058543482 -6.20842396396051 8 Gm10548 +1810034E14Rik -0.075352723109679 2.06893973454929 -0.136089081740893 0.893667658235381 0.932061058543482 -6.48017984570887 64 1810034E14Rik +4931440J10Rik 0.0849667984841879 0.325911500083247 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-6.25776602096988 61 Chpt1 +Mir101a 0.0244432527205966 10.3082648606422 0.106757481268119 0.916479919716901 0.948054023931989 -7.4177003681481 19692 Mir101a +E130102H24Rik 0.0240283211557467 10.3090843068977 0.10496799166705 0.917874358796149 0.94863645234276 -7.4179565369203 19702 E130102H24Rik +Mir425 0.033427373456698 6.66465089052376 0.101272505699857 0.920754905156141 0.950345151621533 -7.15835044873542 1583 Mir425 +Mir362 -0.0544522631314578 3.35231486548691 -0.100710009948844 0.921193459854001 0.950345151621533 -6.73276860148804 169 Mir362 +Mir17 0.0294271489414047 5.12380283689553 0.0952058023213346 0.925486243109504 0.953911294840058 -7.01165582330098 534 Mir17 +E230016M11Rik -0.0696354231248524 -0.941052160679803 -0.0918911504840257 0.928072541539687 0.955713691242584 -6.26870805660384 5 E230016M11Rik +Chkb 0.0776691859878007 0.62903919856468 0.0899505655256887 0.929587100348321 0.956253428651282 -6.22872128912026 28 Chkb +Arhgap1 0.0966234721391633 -0.198705450261483 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0.944132233479521 0.964417975291077 -6.22141853049579 26 Mir4660 +Mir5113 0.0484940420860439 -0.452690836228738 0.0682297423584027 0.946557346616918 0.966030351064315 -6.22526905240972 8 Mir5113 +E530011L22Rik -0.0404713843408522 -1.27661443246381 -0.0613724386888617 0.951920883212834 0.970284520587158 -6.29857782441741 4 E530011L22Rik +Gm15441 0.0426104096369733 1.51577639605449 0.0604630248332405 0.952632381396158 0.970284520587158 -6.37051029075163 43 Gm15441 +Fam172a 0.0503754462959195 0.409161384184755 0.0596392630819191 0.953276904100091 0.970284520587158 -6.21618388102567 23 Fam172a +Rbm18 -0.0538668143348915 -0.299891783561682 -0.0557642901053547 0.956309184092742 0.972503368137093 -6.21475268708383 12 Rbm18 +Gm12191 -0.0354587706805078 -0.880373807283523 -0.0497068478436261 0.961050697355324 0.975480839012736 -6.26527518040501 6 Gm12191 +Rprl3 0.0408471478447264 -0.798864220211061 0.0488367850296659 0.961731876147215 0.975480839012736 -6.25649782741468 8 Rprl3 +H19 -0.0415019911233814 -0.880373807283523 -0.0480992443304892 0.962309326654761 0.975480839012736 -6.26535636348749 6 H19 +Ppifos -0.045813233148843 -0.325698143245111 -0.047655434486338 0.962656813959983 0.975480839012736 -6.21600758126971 15 Ppifos +Mirlet7a-2 -0.0146726577509692 3.37472927372453 -0.0386745178686727 0.969690141624003 0.981735981892624 -6.74139737131634 158 Mirlet7a-2 +9530082P21Rik -0.027891459987058 -1.27661443246381 -0.0346374807689275 0.972852600944678 0.983192929741255 -6.29990631175915 4 9530082P21Rik +Mir1196 -0.027891459987058 -1.27661443246381 -0.0346374807689275 0.972852600944678 0.983192929741255 -6.29990631175915 4 Mir1196 +Kcnk2 -0.0253403588431765 -0.835052547360434 -0.031369255395874 0.975413152136616 0.984908324414052 -6.26109829686273 7 Kcnk2 +A930011G23Rik -0.0241809579465951 -0.00965077170076215 -0.0291709235913898 0.977135635463562 0.985775207837245 -6.2074206456237 12 A930011G23Rik +Mir3103 0.0180714470554271 -1.13917247326995 0.0259355368025229 0.979670906552206 0.987459809519494 -6.2920042186438 4 Mir3103 +Snora75 -0.0172327656019052 -0.590132795422603 -0.021476287065286 0.983165572405378 0.989793060148976 -6.23767380603265 10 Snora75 +4930414L22Rik -0.0152743869054217 -0.484133182103234 -0.0207679670021376 0.983720710086713 0.989793060148976 -6.22874423282067 8 4930414L22Rik +Mipep 0.0164012673799433 -0.280376883322022 0.0179445082376201 0.985933647271923 0.991145631310365 -6.21618933242714 14 Mipep +Ippk -0.0101933209515463 0.0857377294202925 -0.0128542290662295 0.989923536727642 0.99288598158411 -6.20831119150974 15 Ippk +Mir598 -0.00787301950188535 1.52736379718861 -0.0127956250729132 0.989969473858512 0.99288598158411 -6.3747989571524 44 Mir598 +1500017E21Rik 0.0125149432592357 0.202348454937975 0.0124053255048405 0.990275413709656 0.99288598158411 -6.21004071500431 20 1500017E21Rik +Map1lc3a 0.00968300434227123 0.00717248334519302 0.0097742078067436 0.992337878036726 0.994080350166729 -6.20806203375241 21 Map1lc3a +Trappc13 -0.00465914834988075 -0.880373807283523 -0.00622703745231414 0.995118499154886 0.995991410119056 -6.26653097741332 6 Trappc13 +Mir1966 -0.00246570556544304 -0.590132795422603 -0.00337044327478241 0.997357828291427 0.997357828291427 -6.23790561933129 8 Mir1966 diff -r 48d71bd383a1 -r c0fa3dde02d9 test-data/gentestdata.sh --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/gentestdata.sh Wed Aug 07 02:09:35 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 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+Zfp57 0 0 0 0 0 0 0 0 +Zfp572 0 0 0 0 0 0 0 0 +Zfp672 0 0 0 0 0 2 0 0 +Zfp783 0 0 0 0 0 0 0 0 +Zfp809 7 1 0 0 0 4 2 0 +Zfp821 0 0 0 2 2 0 0 0 +Zfp862 4 0 0 0 0 10 2 0 +Zim3 0 0 0 0 0 0 0 0 +Zmynd8 3 5 4 4 3 8 2 1 +Znf41-ps 0 0 0 0 0 0 0 0 +Zp4-ps 0 0 0 0 0 0 0 0 +Zscan4a 0 0 0 0 0 0 0 0 +Zxda 0 0 0 0 0 0 0 0 diff -r 48d71bd383a1 -r c0fa3dde02d9 tool_dependencies.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_dependencies.xml Wed Aug 07 02:09:35 2013 -0400 @@ -0,0 +1,35 @@ + + + + + package_ghostscript_9_07 + + + + + + + + + + + + + + + $INSTALL_DIR + echo "source('http://bioconductor.org/biocLite.R')" > $INSTALL_DIR/runme.R + echo "installme=c('edgeR','limma','DESeq','DESeq2')" >> $INSTALL_DIR/runme.R + echo "biocLite()" >> $INSTALL_DIR/runme.R + echo "biocLite(installme)" >> $INSTALL_DIR/runme.R + echo "install.packages(c('stringr','gplots'),dependencies=T,repos='http://cran.us.r-project.org')" >> $INSTALL_DIR/runme.R + echo "quit(save='no')" >> $INSTALL_DIR/runme.R + export PATH=$PATH && export R_HOME=$R_HOME && export R_LIBS=$R_LIBS && R CMD BATCH $INSTALL_DIR/runme.R + + + Installs some basic bioc packages for the edgeR wrapper and dependencies graphicsmagick (replaces imagemagick) and ghostscript for compressing R's bloated pdfs + It's clunky but this is the most convenient way I could get anything installed into the package_r3 + Note we use cran at fred hutch since no fastest mirror thingy + + +