# HG changeset patch
# User fubar
# Date 1387693949 18000
# Node ID 8051d14cf81947ba3040bf7ddd7601f64c519905
# Parent 5b6af671d9853adca32c473d2bb4d997150b2f5d
update with better filtering
diff -r 5b6af671d985 -r 8051d14cf819 differential_count_models/rgToolFactory.py
--- a/differential_count_models/rgToolFactory.py Thu Aug 08 20:04:16 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 = """
"""
- 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('
\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,'Output File Name (click to view) Size \n')
- fhtml.append('
')
- 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 5b6af671d985 -r 8051d14cf819 differential_count_models/rgedgeRpaired_nocamera.xml
--- a/differential_count_models/rgedgeRpaired_nocamera.xml Thu Aug 08 20:04:16 2013 -0400
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,1074 +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"
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Do not run edgeR
- Run edgeR
-
-
-
-
-
-
-
-
- Do not run DESeq2
- Run DESeq2
-
-
-
- Parametric (default) fit for dispersions
- Local fit - this will automagically be used if parametric fit fails
- Mean dispersion fit- use this if you really understand what you're doing - read the fine manual linked below in the documentation
-
-
-
-
-
- Do not run VOOM
- Run VOOM
-
-
-
-
- fdr
- Benjamini Hochberg
- Benjamini Yukateli
- Bonferroni
- Hochberg
- Holm
- Hommel
- no control for multiple tests
-
-
-
-
- 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(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)
- srDESeq = rDESeq[order(rDESeq\$pvalue),]
- write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F)
- qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf')
- cat("# DESeq top 50\n")
- rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls)
- srDESeq = rDESeq[order(rDESeq\$pvalue),]
- print.noquote(srDESeq[1:50,])
- 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)
- srvoom = rvoom[order(rvoom\$P.Value),]
- write.table(srvoom,file=out_VOOM, quote=FALSE, sep="\t",row.names=F)
- rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls)
- srvoom = rvoom[order(rvoom\$P.Value),]
- cat("# Voom top 50\n")
- print(srvoom[1:50,])
- # 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 5b6af671d985 -r 8051d14cf819 differential_count_models/test-data/edgeRtest1out.html
--- a/differential_count_models/test-data/edgeRtest1out.html Thu Aug 08 20:04:16 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)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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
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-Mir143 Mir143 179219.35960 -2.170303 0.1696199 1.746403e-37 6.966899e-36 1399819 Mir143
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-Mir155 Mir155 57.66182 -3.788079 0.3056585 2.845488e-35 1.096004e-33 463 Mir155
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-Mir322 Mir322 899.53469 -3.126011 0.2622595 9.363374e-33 3.486296e-31 7074 Mir322
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-Mir378 Mir378 483.21548 -2.994300 0.2577321 3.343457e-31 1.204723e-29 4075 Mir378
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-Mir24-2 Mir24-2 424.48288 -2.712674 0.2361028 1.491830e-30 5.049617e-29 3470 Mir24-2
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-Mir3074-2 Mir3074-2 424.48288 -2.712674 0.2361028 1.491830e-30 5.049617e-29 3470 Mir3074-2
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-Mir199b Mir199b 47.84725 -5.294373 0.4644474 4.215162e-30 1.384805e-28 370 Mir199b
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-Mir802 Mir802 166.83414 8.816580 0.7782636 9.478527e-30 3.025004e-28 1514 Mir802
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-Mir125b-2 Mir125b-2 493.08516 -2.919341 0.2631193 1.324797e-28 4.110551e-27 3837 Mir125b-2
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-Mir301 Mir301 260.53406 -1.676984 0.1526772 4.570133e-28 1.379686e-26 2119 Mir301
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-Snord104 Snord104 3851.90119 2.386573 0.2173857 4.847914e-28 1.425032e-26 33458 Snord104
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-Mir150 Mir150 553.20599 -2.836881 0.2595088 8.127991e-28 2.327940e-26 4229 Mir150
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-Mir148a Mir148a 118994.46955 2.678852 0.2481801 3.675045e-27 1.026256e-25 1002397 Mir148a
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-5430416N02Rik 5430416N02Rik 62.15966 3.089960 0.2941123 8.101331e-26 2.207119e-24 564 5430416N02Rik
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-Mir193 Mir193 45.70861 4.991530 0.4814098 3.446492e-25 9.166027e-24 421 Mir193
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-Mir3073 Mir3073 98.93199 8.208709 0.7944742 5.036320e-25 1.308272e-23 904 Mir3073
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-Mir125b-1 Mir125b-1 79.01988 -3.020660 0.2937360 8.355633e-25 2.121191e-23 609 Mir125b-1
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-2610203C20Rik 2610203C20Rik 79.17666 -3.023491 0.2948614 1.136165e-24 2.820214e-23 610 2610203C20Rik
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-Mir181a-1 Mir181a-1 59.53826 -3.151487 0.3211628 9.923707e-23 2.409735e-21 506 Mir181a-1
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-Mir184 Mir184 32.23796 -4.865023 0.4962776 1.092606e-22 2.596683e-21 247 Mir184
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-Mir199a-2 Mir199a-2 44.84878 -3.422216 0.3545647 4.826269e-22 1.123113e-20 352 Mir199a-2
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-Snord91a Snord91a 168.95251 2.700421 0.2835464 1.670595e-21 3.808275e-20 1437 Snord91a
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-Mir200b Mir200b 87.13638 5.940702 0.6338554 7.094881e-21 1.584996e-19 888 Mir200b
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DifferentialCounts log output
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-Loading required package: gtools
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-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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- 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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-Loading required package: methods
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-Loading required package: limma
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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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- combine
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- 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
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-Loading required package: IRanges
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-Attaching package: ‘IRanges’
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- space
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- trim
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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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-Loading required package: lattice
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-Loading required package: Rcpp
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-Loading required package: RcppArmadillo
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-Attaching package: ‘DESeq2’
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- plotMA
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-gene-wise dispersion estimates
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-mean-dispersion relationship
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-final dispersion estimates
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-you had estimated dispersions, replacing these
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-gene-wise dispersion estimates
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-mean-dispersion relationship
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-final dispersion estimates
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-you had estimated dispersions, replacing these
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-gene-wise dispersion estimates
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-mean-dispersion relationship
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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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- Outlier (-Inf) in boxplot 3 is not drawn
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-3: In bxp(list(stats = c(-0.430723026286372, -0.127900608036896, 0.474159383291067, :
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-4: In par(defpar) : calling par(new=TRUE) with no plot
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VOOM images and outputs
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VOOM log output
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-# VOOM top 50
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- ID logFC AveExpr t P.Value adj.P.Val B NReads URL
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-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
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-Mir181d Mir181d -3.636211 6.3713218 -14.317073 8.157508e-10 4.898798e-08 12.956333 2139 Mir181d
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-Mir133b Mir133b -6.493549 1.2544862 -13.969968 1.129934e-09 6.446275e-08 11.982684 159 Mir133b
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-Mir27a Mir27a -3.106935 9.9255796 -13.838251 1.281011e-09 6.960160e-08 12.513086 21886 Mir27a
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-Mir194-2 Mir194-2 5.264136 6.0897615 13.044012 2.792884e-09 1.448491e-07 11.715753 3570 Mir194-2
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-Mir195 Mir195 -3.216595 7.4509350 -12.869478 3.332788e-09 1.653353e-07 11.587523 3962 Mir195
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-Mir27b Mir27b -1.976376 15.0957731 -11.756036 1.082197e-08 5.144946e-07 10.127719 625308 Mir27b
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-Mir378 Mir378 -3.097393 7.3832049 -11.684163 1.171371e-08 5.346138e-07 10.329692 4075 Mir378
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-Snord104 Snord104 2.337374 10.6109024 11.495675 1.444482e-08 6.339052e-07 10.023395 33458 Snord104
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-Mir1983 Mir1983 -5.895500 0.9931851 -11.445812 1.527548e-08 6.441605e-07 9.749260 101 Mir1983
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-Mir322 Mir322 -3.296618 8.2153415 -11.415362 1.580762e-08 6.441605e-07 10.008472 7074 Mir322
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-Mir200a Mir200a 6.191561 1.7981309 11.322172 1.756229e-08 6.909853e-07 9.662295 264 Mir200a
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-Mir215 Mir215 -3.045873 5.7544234 -11.148134 2.141822e-08 8.082459e-07 9.753268 1182 Mir215
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-Dnm3os Dnm3os -3.363344 5.8607432 -11.092261 2.283960e-08 8.082459e-07 9.689496 1401 Dnm3os
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-Mir182 Mir182 4.903995 7.1511683 11.074468 2.331304e-08 8.082459e-07 9.658842 7189 Mir182
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-Mir181a-2 Mir181a-2 -3.048298 6.9414651 -11.072128 2.337609e-08 8.082459e-07 9.644017 2817 Mir181a-2
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-Mir1948 Mir1948 7.195525 4.5513493 11.005492 2.524936e-08 8.473388e-07 9.341794 2404 Mir1948
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-Mir214 Mir214 -3.280874 5.4784451 -10.768257 3.332555e-08 1.086413e-06 9.318504 1048 Mir214
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-Mir153 Mir153 -5.963803 1.4386315 -10.727082 3.498742e-08 1.093990e-06 9.035569 140 Mir153
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-Cyp3a25 Cyp3a25 6.318200 1.4888933 10.698226 3.620443e-08 1.093990e-06 9.024973 226 Cyp3a25
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-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
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-Mir133a-1 Mir133a-1 -5.144671 0.5903264 -10.358205 5.447229e-08 1.553822e-06 8.575535 60 Mir133a-1
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-1110038B12Rik 1110038B12Rik 2.226702 10.8487089 10.194609 6.655312e-08 1.852125e-06 8.439308 37066 1110038B12Rik
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-Mir132 Mir132 -2.847559 5.3211839 -10.110952 7.380297e-08 2.004981e-06 8.531491 857 Mir132
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-Rabggtb Rabggtb 1.935779 9.9874171 9.928995 9.262821e-08 2.457879e-06 8.133384 19535 Rabggtb
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-Mir504 Mir504 -5.256127 0.6221088 -9.892894 9.693595e-08 2.513725e-06 8.068853 69 Mir504
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-Mir150 Mir150 -2.938531 7.6297870 -9.842102 1.033602e-07 2.620755e-06 8.116464 4229 Mir150
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-Mir199b Mir199b -5.752816 2.8805143 -9.823920 1.057683e-07 2.623514e-06 7.979387 370 Mir199b
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-Mir23b Mir23b -2.124129 9.8141190 -9.806316 1.081569e-07 2.625681e-06 7.979464 16387 Mir23b
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-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
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-Mir155 Mir155 -3.906600 3.9899000 -9.732173 1.188627e-07 2.712448e-06 8.046518 463 Mir155
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edgeR images and outputs
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edgeR log output
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-[1] prior.df = 8
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-[1] "No GLM fit outlier genes found\n"
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-[1] Common Dispersion = 0.228651460998105 CV = 0.478175136323613 getPriorN = 3.33333333333333
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-[1] heart liver
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-[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
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- 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
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- Mean :1.0519 Mean :1.0343 Mean :0.9855 Mean :0.9966 Mean :0.9210 Mean :0.9428 Mean :1.0205 Mean :0.9753
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- 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
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- Max. :5.8209 Max. :5.6905 Max. :5.7999 Max. :5.7215 Max. :5.3609 Max. :5.3589 Max. :5.6967 Max. :5.3702
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- NA's :650 NA's :969 NA's :664 NA's :886 NA's :902 NA's :957 NA's :821 NA's :950
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-[1] normalised 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. :-Inf Min. :-Inf Min. :-Inf Min. :-Inf Min. :-Inf Min. :-Inf Min. :-Inf Min. :-Inf
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- 1st Qu.: 0 1st Qu.:-Inf 1st Qu.: 0 1st Qu.:-Inf 1st Qu.:-Inf 1st Qu.:-Inf 1st Qu.:-Inf 1st Qu.:-Inf
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- Median : 0 Median : 0 Median : 0 Median : 0 Median : 0 Median : 0 Median : 0 Median : 0
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- Mean :-Inf Mean :-Inf Mean :-Inf Mean :-Inf Mean :-Inf Mean :-Inf Mean :-Inf Mean :-Inf
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- 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1
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- Max. : 6 Max. : 6 Max. : 6 Max. : 5 Max. : 5 Max. : 5 Max. : 6 Max. : 5
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-[1] Using ncol rawrs= 8
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-[1] # edgeR Top tags\n
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- Name logFC logCPM LR PValue adj.p.value Dispersion totreads URL
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-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
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-Mir208b Mir208b -13.291635 9.905945 417.69758 7.737463e-93 2.942815e-90 0.10508096 14756 Mir208b
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-Mir122a Mir122a 10.514683 12.478088 415.17429 2.740525e-92 7.817349e-90 0.10803882 90428 Mir122a
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-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
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-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
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-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
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-Mir195 Mir195 -3.203683 8.215089 150.20548 1.563314e-34 1.114838e-32 0.05235020 3962 Mir195
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-Mir10b Mir10b -5.182616 13.946466 147.24793 6.926819e-34 4.649118e-32 0.12268790 197340 Mir10b
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-Mir101b Mir101b 3.759962 11.863187 136.31359 1.703812e-31 1.080028e-29 0.07961343 59019 Mir101b
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-Mir378 Mir378 -3.115599 8.119617 126.76408 2.092233e-29 1.256441e-27 0.05942391 4075 Mir378
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-Mir27a Mir27a -3.064687 10.642480 124.98911 5.117477e-29 2.919520e-27 0.06113852 21886 Mir27a
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-Mir182 Mir182 5.057509 8.846381 123.17765 1.275060e-28 6.927826e-27 0.13653707 7189 Mir182
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-Mir322 Mir322 -3.194159 9.012888 107.34926 3.732413e-25 1.935765e-23 0.07536483 7074 Mir322
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-Mir199b Mir199b -5.520119 4.792610 102.10724 5.259607e-24 2.609223e-22 0.13417024 370 Mir199b
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-Mir181a-2 Mir181a-2 -3.000177 7.637692 101.38361 7.578821e-24 3.603098e-22 0.06896654 2817 Mir181a-2
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-Mir125b-2 Mir125b-2 -2.987759 8.144514 91.72544 9.957640e-22 4.488356e-20 0.07737381 3837 Mir125b-2
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-Dnm3os Dnm3os -3.331215 6.686950 91.67250 1.022763e-21 4.488356e-20 0.08810497 1401 Dnm3os
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-Mir184 Mir184 -5.111350 4.234160 84.35542 4.133639e-20 1.686711e-18 0.13502324 247 Mir184
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-Mir215 Mir215 -3.058208 6.447966 84.35278 4.139167e-20 1.686711e-18 0.08138517 1182 Mir215
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-Mir133b Mir133b -8.383611 3.584760 83.96681 5.031517e-20 1.960318e-18 0.17482280 159 Mir133b
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-Mir150 Mir150 -2.883446 8.307765 83.91918 5.154210e-20 1.960318e-18 0.08008123 4229 Mir150
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-Mir3074-2 Mir3074-2 -2.778308 7.935651 83.74839 5.619282e-20 2.040616e-18 0.07424646 3470 Mir3074-2
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-Mir24-2 Mir24-2 -2.778307 7.935651 83.71222 5.723024e-20 2.040616e-18 0.07427992 3470 Mir24-2
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-Mir193 Mir193 5.176579 4.801090 83.19222 7.445011e-20 2.574169e-18 0.14794861 421 Mir193
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-Scarna17 Scarna17 2.182159 9.244479 81.91330 1.421894e-19 4.771710e-18 0.04982909 9224 Scarna17
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-Mir214 Mir214 -3.271172 6.271755 80.43948 2.997458e-19 9.771712e-18 0.09566584 1048 Mir214
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-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)
-
-
-
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 5b6af671d985 -r 8051d14cf819 differential_count_models/test-data/edgeRtest1out.xls
--- a/differential_count_models/test-data/edgeRtest1out.xls Thu Aug 08 20:04:16 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
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-Mir878 2.41936085459412 -0.447944854953861 3.82652263259005 0.00181799260036803 0.00947182446127819 -1.16702401112308 13 Mir878
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-Surf1 2.36126838851735 0.0872159088448913 3.80305260835126 0.00190467958178741 0.00978936667936682 -1.23097217655323 20 Surf1
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-0610007N19Rik -1.90120438734532 2.49555567542219 -3.77758577340756 0.00200349441161053 0.0102052996591412 -1.41487533930946 104 0610007N19Rik
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-Mir3096b -1.20474634794453 4.70053095823646 -3.5153757202842 0.00337831126227612 0.0153571838655659 -2.25812559332021 435 Mir3096b
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-Snord35a 1.64122011441228 2.9111550248254 3.43698444459166 0.00395112336041969 0.0171175138601232 -2.13032503281622 131 Snord35a
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-Gm10069 5.68607477678649 3.08099524867498 3.3046846231052 0.00514741011835455 0.0212796918298643 -2.27892670543982 1309 Gm10069
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-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
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-G630025P09Rik 2.04699188377197 -0.244188556172649 3.10780445430196 0.00762812710513572 0.0292070235804022 -2.51605087870259 15 G630025P09Rik
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-Snord2 0.691526499495304 8.00175923757187 3.08142494474571 0.008040321980135 0.030265949526277 -3.40026204758547 3954 Snord2
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-Mir185 -1.40702024721662 4.08694622913669 -3.05877067430607 0.008411855287644 0.0313657741281105 -3.06664608794292 331 Mir185
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-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
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-Mirlet7c-1 -0.837889959835657 5.12475158025001 -2.96511158812865 0.0101364526370322 0.0366002925913093 -3.39152169935695 536 Mirlet7c-1
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-Mir5121 1.92484055344077 0.963995516493567 2.91681375354519 0.0111574945019998 0.0397834413336931 -2.90528656811773 37 Mir5121
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-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
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-Mir337 -2.00512882554678 1.46245475734951 -2.790176090596 0.0143381997019222 0.0484912300051582 -3.15685281038144 50 Mir337
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-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
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-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
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-Snord55 0.601021636432394 6.97866331241363 2.08546096371218 0.0556040229210925 0.132208091094402 -5.17041448286456 2070 Snord55
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-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
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-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
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-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
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-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
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-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
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-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
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-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
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-Gm7854 1.41521331892746 0.409161384184755 1.87132675965834 0.0821161885893659 0.176117615000877 -4.66097795086114 23 Gm7854
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-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
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-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
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-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
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-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
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-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
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-Mir1247 0.717962491463309 4.29587920045115 1.77899645447695 0.09671837092938 0.196712408610379 -5.37990996340849 300 Mir1247
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-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
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-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
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-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
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-Mir425 0.033427373456698 6.66465089052376 0.101272505699857 0.920754905156141 0.950345151621533 -7.15835044873542 1583 Mir425
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-Chkb 0.0776691859878007 0.62903919856468 0.0899505655256887 0.929587100348321 0.956253428651282 -6.22872128912026 28 Chkb
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-Mipep 0.0164012673799433 -0.280376883322022 0.0179445082376201 0.985933647271923 0.991145631310365 -6.21618933242714 14 Mipep
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-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 5b6af671d985 -r 8051d14cf819 differential_count_models/test-data/gentestdata.sh
--- a/differential_count_models/test-data/gentestdata.sh Thu Aug 08 20:04:16 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 5b6af671d985 -r 8051d14cf819 differential_count_models/test-data/test_bams2mx.xls
--- a/differential_count_models/test-data/test_bams2mx.xls Thu Aug 08 20:04:16 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
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diff -r 5b6af671d985 -r 8051d14cf819 differential_count_models/tool_dependencies.xml
--- a/differential_count_models/tool_dependencies.xml Thu Aug 08 20:04:16 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 $INSTALL_DIR/runme.R
-
-
- Installs some basic bioc packages for the edgeR wrapper and dependencies graphicsmagick (replaces imagemagick) and ghostscript for compressing R's bloated pdfs
- It's clunky but this is the most convenient way I could get anything installed into the package_r3
- Note we use cran at fred hutch since no fastest mirror thingy
-
-
-
diff -r 5b6af671d985 -r 8051d14cf819 rgToolFactory.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgToolFactory.py Sun Dec 22 01:32:29 2013 -0500
@@ -0,0 +1,575 @@
+# rgToolFactory.py
+# see https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home
+#
+# copyright ross lazarus (ross stop lazarus at gmail stop com) May 2012
+#
+# all rights reserved
+# Licensed under the LGPL
+# suggestions for improvement and bug fixes welcome at https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home
+#
+# January 2013
+# problem pointed out by Carlos Borroto
+# added escaping for <>$ - thought I did that ages ago...
+#
+# August 11 2012
+# changed to use shell=False and cl as a sequence
+
+# This is a Galaxy tool factory for simple scripts in python, R or whatever ails ye.
+# It also serves as the wrapper for the new tool.
+#
+# you paste and run your script
+# Only works for simple scripts that read one input from the history.
+# Optionally can write one new history dataset,
+# and optionally collect any number of outputs into links on an autogenerated HTML page.
+
+# DO NOT install on a public or important site - please.
+
+# installed generated tools are fine if the script is safe.
+# They just run normally and their user cannot do anything unusually insecure
+# but please, practice safe toolshed.
+# Read the fucking code before you install any tool
+# especially this one
+
+# After you get the script working on some test data, you can
+# optionally generate a toolshed compatible gzip file
+# containing your script safely wrapped as an ordinary Galaxy script in your local toolshed for
+# safe and largely automated installation in a production Galaxy.
+
+# If you opt for an HTML output, you get all the script outputs arranged
+# as a single Html history item - all output files are linked, thumbnails for all the pdfs.
+# Ugly but really inexpensive.
+#
+# Patches appreciated please.
+#
+#
+# long route to June 2012 product
+# Behold the awesome power of Galaxy and the toolshed with the tool factory to bind them
+# derived from an integrated script model
+# called rgBaseScriptWrapper.py
+# Note to the unwary:
+# This tool allows arbitrary scripting on your Galaxy as the Galaxy user
+# There is nothing stopping a malicious user doing whatever they choose
+# Extremely dangerous!!
+# Totally insecure. So, trusted users only
+#
+# preferred model is a developer using their throw away workstation instance - ie a private site.
+# no real risk. The universe_wsgi.ini admin_users string is checked - only admin users are permitted to run this tool.
+#
+
+import sys
+import shutil
+import subprocess
+import os
+import time
+import tempfile
+import optparse
+import tarfile
+import re
+import shutil
+import math
+
+progname = os.path.split(sys.argv[0])[1]
+myversion = 'V000.2 June 2012'
+verbose = False
+debug = False
+toolFactoryURL = 'https://bitbucket.org/fubar/galaxytoolfactory'
+
+def timenow():
+ """return current time as a string
+ """
+ return time.strftime('%d/%m/%Y %H:%M:%S', time.localtime(time.time()))
+
+cheetah_escape_table = {
+ "$": "\$"
+ }
+
+cheetah_unescape_table = {
+ "\$": "$"
+ }
+
+def html_escape(t):
+ """Unescape \$ first in case already done
+ cheetah barfs if any $ without \
+ xml parsing is controlled with
+ """
+ text = t
+ for k in cheetah_unescape_table.keys():
+ text = text.replace(k,cheetah_unescape_table[k])
+ for k in cheetah_escape_table.keys():
+ text = text.replace(k,cheetah_escape_table[k])
+ return text
+
+
+class ScriptRunner:
+ """class is a wrapper for an arbitrary script
+ """
+
+ def __init__(self,opts=None,treatbashSpecial=True):
+ """
+ cleanup inputs, setup some outputs
+
+ """
+ self.treatbashSpecial = treatbashSpecial
+ if opts.output_dir: # simplify for the tool tarball
+ os.chdir(opts.output_dir)
+ self.thumbformat = 'jpg'
+ self.opts = opts
+ self.toolname = re.sub('[^a-zA-Z0-9_]+', '', opts.tool_name) # a sanitizer now does this but..
+ self.toolid = self.toolname
+ self.myname = sys.argv[0] # get our name because we write ourselves out as a tool later
+ self.pyfile = self.myname # crude but efficient - the cruft won't hurt much
+ self.xmlfile = '%s.xml' % self.toolname
+ s = open(self.opts.script_path,'r').readlines()
+ s = [x.rstrip() for x in s] # remove pesky dos line endings if needed
+ self.script = '\n'.join(s)
+ fhandle,self.sfile = tempfile.mkstemp(prefix=self.toolname,suffix=".%s" % (opts.interpreter))
+ tscript = open(self.sfile,'w') # use self.sfile as script source for Popen
+ tscript.write(self.script)
+ tscript.close()
+ self.escapedS = [html_escape(x) for x in s] # for restructured text in help
+ self.escapedScript = '\n'.join(self.escapedS)
+ self.indentedScript = '\n'.join([' %s' % x for x in s]) # for restructured text in help
+ if opts.output_dir: # may not want these complexities
+ self.tlog = os.path.join(opts.output_dir,"%s_runner.log" % self.toolname)
+ art = '%s.%s' % (self.toolname,opts.interpreter)
+ artpath = os.path.join(self.opts.output_dir,art) # need full path
+ artifact = open(artpath,'w') # use self.sfile as script source for Popen
+ artifact.write(self.script)
+ artifact.close()
+ self.cl = []
+ self.html = []
+ a = self.cl.append
+ a(opts.interpreter)
+ if self.treatbashSpecial and opts.interpreter in ['bash','sh']:
+ a(self.sfile)
+ else:
+ a('-') # stdin
+ a(opts.input_tab)
+ a(opts.output_tab)
+ self.outFormats = 'tabular' # TODO make this an option at tool generation time
+ self.inputFormats = 'tabular' # TODO make this an option at tool generation time
+ self.test1Input = '%s_test1_input.xls' % self.toolname
+ self.test1Output = '%s_test1_output.xls' % self.toolname
+ self.test1HTML = '%s_test1_output.html' % self.toolname
+
+ def makeXML(self):
+ """
+ Create a Galaxy xml tool wrapper for the new script as a string to write out
+ fixme - use templating or something less fugly than this example of what we produce
+
+
+ a tabular file
+
+ reverse.py --script_path "$runMe" --interpreter "python"
+ --tool_name "reverse" --input_tab "$input1" --output_tab "$tab_file"
+
+
+
+
+
+
+
+
+
+
+
+**What it Does**
+
+Reverse the columns in a tabular file
+
+
+
+
+
+# reverse order of columns in a tabular file
+import sys
+inp = sys.argv[1]
+outp = sys.argv[2]
+i = open(inp,'r')
+o = open(outp,'w')
+for row in i:
+ rs = row.rstrip().split('\t')
+ rs.reverse()
+ o.write('\t'.join(rs))
+ o.write('\n')
+i.close()
+o.close()
+
+
+
+
+
+
+ """
+ newXML="""
+%(tooldesc)s
+%(command)s
+
+%(inputs)s
+
+
+%(outputs)s
+
+
+
+
+
+
+%(tooltests)s
+
+
+
+ """
+# needs a dict with toolname, toolid, interpreter, scriptname, command, inputs as a multi line string ready to write, outputs ditto, help ditto
+
+ newCommand="""
+ %(toolname)s.py --script_path "$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::\n']
+ coda.append(self.indentedScript)
+ coda.append('\n\n')
+ coda.append('**Attribution** This Galaxy tool was created by %s at %s\nusing the Galaxy Tool Factory.' % (self.opts.user_email,timenow()))
+ coda.append('See %s for details of that project' % (toolFactoryURL))
+ coda.append('Please cite: Creating re-usable tools from scripts: The Galaxy Tool Factory. Ross Lazarus; Antony Kaspi; Mark Ziemann; The Galaxy Team. ')
+ coda.append('Bioinformatics 2012; doi: 10.1093/bioinformatics/bts573')
+ xdict['help'] = '%s\n%s' % (xdict['help'],'\n'.join(coda))
+ if self.opts.tool_desc:
+ xdict['tooldesc'] = '%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
+ """
+ assert os.path.isfile(inpdf), "## Input %s supplied to %s compressPDF not found" % (inpdf,self.myName)
+ hf,hlog = tempfile.mkstemp(suffix="%s.log" % self.toolname)
+ sto = open(hlog,'w')
+ outpdf = '%s_compressed' % inpdf
+ cl = ["gs", "-sDEVICE=pdfwrite", "-dNOPAUSE", "-dBATCH", "-sOutputFile=%s" % outpdf,inpdf]
+ x = subprocess.Popen(cl,stdout=sto,stderr=sto,cwd=self.opts.output_dir)
+ retval1 = x.wait()
+ if retval1 == 0:
+ os.unlink(inpdf)
+ shutil.move(outpdf,inpdf)
+ outpng = '%s.%s' % (os.path.splitext(inpdf)[0],thumbformat)
+ cl2 = ['convert', inpdf, outpng]
+ x = subprocess.Popen(cl2,stdout=sto,stderr=sto,cwd=self.opts.output_dir)
+ retval2 = x.wait()
+ sto.close()
+ retval = retval1 or retval2
+ return retval
+
+
+ def getfSize(self,fpath,outpath):
+ """
+ format a nice file size string
+ """
+ size = ''
+ fp = os.path.join(outpath,fpath)
+ if os.path.isfile(fp):
+ size = '0 B'
+ n = float(os.path.getsize(fp))
+ if n > 2**20:
+ size = '%1.1f MB' % (n/2**20)
+ elif n > 2**10:
+ size = '%1.1f KB)' % (n/2**10)
+ elif n > 0:
+ size = '%d B' % (int(n))
+ return size
+
+ def makeHtml(self):
+ """ Create an HTML file content to list all the artifacts found in the output_dir
+ """
+
+ galhtmlprefix = """
+
+
+
+
+
+
+
+
+ """
+ galhtmlattr = """
"""
+ 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:
+ pdflist = []
+ npdf = len([x for x in flist if os.path.splitext(x)[-1].lower() == '.pdf'])
+ nacross = 1
+ if npdf > 0:
+ nacross = int(round(math.log(npdf,2)))
+ nacross = max(1,nacross)
+ width = min(400,int(1200/nacross))
+ for rownum,fname in enumerate(flist):
+ dname,e = os.path.splitext(fname)
+ sfsize = self.getfSize(fname,self.opts.output_dir)
+ if e.lower() == '.pdf' : # compress and make a thumbnail
+ thumb = '%s.%s' % (dname,self.thumbformat)
+ pdff = os.path.join(self.opts.output_dir,fname)
+ retval = self.compressPDF(inpdf=pdff,thumbformat=self.thumbformat)
+ if retval == 0:
+ pdflist.append((fname,thumb))
+ if (rownum+1) % 2 == 0:
+ fhtml.append('%s %s ' % (fname,fname,sfsize))
+ else:
+ fhtml.append('%s %s ' % (fname,fname,sfsize))
+ ntogo = nacross # counter for table row padding with empty cells
+ if len(pdflist) > 0:
+ html.append('\n')
+ for i,paths in enumerate(pdflist):
+ 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('
\n')
+ else:
+ if ntogo > 0: # pad
+ html.append(' '*ntogo)
+ html.append('\n')
+ if len(fhtml) > 0:
+ fhtml.insert(0,'Output File Name (click to view) Size \n')
+ fhtml.append('
')
+ 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)
+ rlog = open(self.tlog,'r').readlines()
+ rlog = [x for x in rlog if x.strip() > '']
+ if len(rlog) > 1:
+ html.append('%s log
\n' % self.opts.interpreter)
+ html += rlog
+ html.append(' \n')
+ html.append(galhtmlattr % (self.toolname))
+ html.append(galhtmlpostfix)
+ htmlf = file(self.opts.output_html,'w')
+ htmlf.write('\n'.join(html))
+ htmlf.write('\n')
+ htmlf.close()
+ self.html = html
+
+
+ def run(self):
+ """
+ scripts must be small enough not to fill the pipe!
+ """
+ if self.treatbashSpecial and self.opts.interpreter in ['bash','sh']:
+ retval = self.runBash()
+ else:
+ if self.opts.output_dir:
+ sto = open(self.tlog,'w')
+ sto.write('## Toolfactory generated command line = %s\n' % ' '.join(self.cl))
+ sto.flush()
+ p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=sto,stdin=subprocess.PIPE,cwd=self.opts.output_dir)
+ else:
+ p = subprocess.Popen(self.cl,shell=False,stdin=subprocess.PIPE)
+ p.stdin.write(self.script)
+ p.stdin.close()
+ retval = p.wait()
+ if self.opts.output_dir:
+ sto.close()
+ if self.opts.make_HTML:
+ self.makeHtml()
+ return retval
+
+ def runBash(self):
+ """
+ cannot use - for bash so use self.sfile
+ """
+ if self.opts.output_dir:
+ s = '## Toolfactory generated command line = %s\n' % ' '.join(self.cl)
+ sto = open(self.tlog,'w')
+ sto.write(s)
+ sto.flush()
+ p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=sto,cwd=self.opts.output_dir)
+ else:
+ p = subprocess.Popen(self.cl,shell=False)
+ retval = p.wait()
+ if self.opts.output_dir:
+ sto.close()
+ if self.opts.make_HTML:
+ self.makeHtml()
+ return retval
+
+
+def main():
+ u = """
+ This is a Galaxy wrapper. It expects to be called by a special purpose tool.xml as:
+ rgBaseScriptWrapper.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript"
+
+ """
+ op = optparse.OptionParser()
+ a = op.add_option
+ a('--script_path',default=None)
+ a('--tool_name',default=None)
+ a('--interpreter',default=None)
+ a('--output_dir',default=None)
+ a('--output_html',default=None)
+ a('--input_tab',default="None")
+ a('--output_tab',default="None")
+ a('--user_email',default='Unknown')
+ a('--bad_user',default=None)
+ a('--make_Tool',default=None)
+ a('--make_HTML',default=None)
+ a('--help_text',default=None)
+ a('--tool_desc',default=None)
+ a('--new_tool',default=None)
+ a('--tool_version',default=None)
+ opts, args = op.parse_args()
+ assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user)
+ assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq'
+ assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript'
+ assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R'
+ if opts.output_dir:
+ try:
+ os.makedirs(opts.output_dir)
+ except:
+ pass
+ r = ScriptRunner(opts)
+ if opts.make_Tool:
+ retcode = r.makeTooltar()
+ else:
+ retcode = r.run()
+ os.unlink(r.sfile)
+ if retcode:
+ sys.exit(retcode) # indicate failure to job runner
+
+
+if __name__ == "__main__":
+ main()
+
+
diff -r 5b6af671d985 -r 8051d14cf819 rgedgeRpaired_nocamera.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeRpaired_nocamera.xml Sun Dec 22 01:32:29 2013 -0500
@@ -0,0 +1,1085 @@
+
+ models using BioConductor packages
+
+ r3
+ graphicsmagick
+ ghostscript
+ biocbasics
+
+
+
+ rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts"
+ --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Do not run edgeR
+ Run edgeR
+
+
+
+
+
+
+
+
+ Do not run DESeq2
+ Run DESeq2
+
+
+
+ Parametric (default) fit for dispersions
+ Local fit - this will automagically be used if parametric fit fails
+ Mean dispersion fit- use this if you really understand what you're doing - read the fine manual linked below in the documentation
+
+
+
+
+
+ Do not run VOOM
+ Run VOOM
+
+
+
+
+ fdr
+ Benjamini Hochberg
+ Benjamini Yukateli
+ Bonferroni
+ Hochberg
+ Holm
+ Hommel
+ no control for multiple tests
+
+
+
+
+ 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()
+}
+
+
+
+doGSEA = function(y=NULL,design=NULL,histgmt="",
+ bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
+ ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH")
+{
+ sink('Camera.log')
+ genesets = c()
+ if (bigmt > "")
+ {
+ bigenesets = readLines(bigmt)
+ genesets = bigenesets
+ }
+ if (histgmt > "")
+ {
+ hgenesets = readLines(histgmt)
+ if (bigmt > "") {
+ genesets = rbind(genesets,hgenesets)
+ } else {
+ genesets = hgenesets
+ } # use only history if no bi
+ }
+ print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt))
+ genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n
+ outf = outfname
+ head=paste(myTitle,'edgeR GSEA')
+ write(head,file=outfname,append=F)
+ ntest=length(genesets)
+ urownames = toupper(rownames(y))
+ upcam = c()
+ downcam = c()
+ for (i in 1:ntest) {
+ gs = unlist(genesets[i])
+ g = gs[1] # geneset_id
+ u = gs[2]
+ if (u > "") { u = paste("",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()
+}
+
+
+
+
+doGSEAatonce = 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.noquote("@@ using genecards substitution for urls")
+ contigurls = paste0(genecards,allgenes,"\'>",allgenes,"")
+ }
+ 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)
+ srDESeq = rDESeq[order(rDESeq\$pvalue),]
+ write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F)
+ qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf')
+ cat("# DESeq top 50\n")
+ rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls)
+ srDESeq = rDESeq[order(rDESeq\$pvalue),]
+ print.noquote(srDESeq[1:50,])
+ 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)
+ srvoom = rvoom[order(rvoom\$P.Value),]
+ write.table(srvoom,file=out_VOOM, quote=FALSE, sep="\t",row.names=F)
+ rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls)
+ srvoom = rvoom[order(rvoom\$P.Value),]
+ cat("# Voom top 50\n")
+ print(srvoom[1:50,])
+ # 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 5b6af671d985 -r 8051d14cf819 rgedgeRpaired_nocamera.xml~
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeRpaired_nocamera.xml~ Sun Dec 22 01:32:29 2013 -0500
@@ -0,0 +1,1081 @@
+
+ models using BioConductor packages
+
+ r3
+ graphicsmagick
+ ghostscript
+ biocbasics
+
+
+
+ rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts"
+ --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Do not run edgeR
+ Run edgeR
+
+
+
+
+
+
+
+
+ Do not run DESeq2
+ Run DESeq2
+
+
+
+ Parametric (default) fit for dispersions
+ Local fit - this will automagically be used if parametric fit fails
+ Mean dispersion fit- use this if you really understand what you're doing - read the fine manual linked below in the documentation
+
+
+
+
+
+ Do not run VOOM
+ Run VOOM
+
+
+
+
+ fdr
+ Benjamini Hochberg
+ Benjamini Yukateli
+ Bonferroni
+ Hochberg
+ Holm
+ Hommel
+ no control for multiple tests
+
+
+
+
+ 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()
+}
+
+
+
+doGSEA = function(y=NULL,design=NULL,histgmt="",
+ bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
+ ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH")
+{
+ sink('Camera.log')
+ genesets = c()
+ if (bigmt > "")
+ {
+ bigenesets = readLines(bigmt)
+ genesets = bigenesets
+ }
+ if (histgmt > "")
+ {
+ hgenesets = readLines(histgmt)
+ if (bigmt > "") {
+ genesets = rbind(genesets,hgenesets)
+ } else {
+ genesets = hgenesets
+ } # use only history if no bi
+ }
+ print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt))
+ genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n
+ outf = outfname
+ head=paste(myTitle,'edgeR GSEA')
+ write(head,file=outfname,append=F)
+ ntest=length(genesets)
+ urownames = toupper(rownames(y))
+ upcam = c()
+ downcam = c()
+ for (i in 1:ntest) {
+ gs = unlist(genesets[i])
+ g = gs[1] # geneset_id
+ u = gs[2]
+ if (u > "") { u = paste("",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()
+}
+
+
+
+
+doGSEAatonce = 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.noquote("@@ using genecards substitution for urls")
+ contigurls = paste0(genecards,allgenes,"\'>",allgenes,"")
+ }
+ 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)
+ srDESeq = rDESeq[order(rDESeq\$pvalue),]
+ write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F)
+ qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf')
+ cat("# DESeq top 50\n")
+ rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls)
+ srDESeq = rDESeq[order(rDESeq\$pvalue),]
+ print.noquote(srDESeq[1:50,])
+ 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)
+ srvoom = rvoom[order(rvoom\$P.Value),]
+ write.table(srvoom,file=out_VOOM, quote=FALSE, sep="\t",row.names=F)
+ rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls)
+ srvoom = rvoom[order(rvoom\$P.Value),]
+ cat("# Voom top 50\n")
+ print(srvoom[1:50,])
+ # 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 5b6af671d985 -r 8051d14cf819 test-data/edgeRtest1out.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/edgeRtest1out.html Sun Dec 22 01:32:29 2013 -0500
@@ -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)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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)
+
+
+
+
+
+
+
+
+
+
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)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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)
+
+
+
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 5b6af671d985 -r 8051d14cf819 test-data/edgeRtest1out.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/edgeRtest1out.xls Sun Dec 22 01:32:29 2013 -0500
@@ -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
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+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 5b6af671d985 -r 8051d14cf819 test-data/gentestdata.sh
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gentestdata.sh Sun Dec 22 01:32:29 2013 -0500
@@ -0,0 +1,9 @@
+#!/bin/bash
+# generate test data for rgGSEA
+# ross lazarus June 2013
+# adjust gseajar_path !
+GSEAJAR_PATH=/home/rlazarus/galaxy-central/tool_dependency_dir/gsea_jar/2.0.12/fubar/rg_gsea_test/8e291f464aa0/jars/gsea2-2.0.12.jar
+python ../rgGSEA.py --input_tab "gsea_test_DGE.xls" --adjpvalcol "5" --signcol "2" --idcol "1" --outhtml "gseatestout.html" --input_name "gsea_test" --setMax "500" --setMin "15" --nPerm "10" --plotTop "20" --gsea_jar "$GSEAJAR_PATH" --output_dir "gseatestout" --mode "Max_probe"
+--title "GSEA test" --builtin_gmt "gseatestdata.gmt"
+
+
diff -r 5b6af671d985 -r 8051d14cf819 test-data/test_bams2mx.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/test_bams2mx.xls Sun Dec 22 01:32:29 2013 -0500
@@ -0,0 +1,3243 @@
+Contigname 11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam 11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam 11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam 11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam 11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam 11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam 11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam 11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam
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+Snhg1 8185 2277 1889 5095 5790 6478 5840 2283
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+Snhg12 1273 233 180 1043 811 922 743 198
+Snhg3 28 9 2 10 6 18 18 3
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+Snhg7 34 4 3 15 15 82 13 7
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+Snord66 884 267 281 340 693 807 696 244
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+Snord69 440 157 105 401 341 407 333 117
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+Snord72 159 21 24 57 102 123 101 16
+Snord73a 29 3 3 7 6 9 2 5
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+Snord88c 74 29 22 35 41 65 52 23
+Snord89 13 2 2 3 2 8 3 3
+Snord90 43 2 3 46 14 48 10 5
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+Snord92 23 9 5 17 8 29 7 8
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+Snord96a 145 16 22 56 71 106 59 12
+Snord98 74 44 24 150 77 96 56 32
+Snord99 931 126 80 847 655 668 555 108
+Sox2ot 0 1 0 1 0 0 0 0
+Speer1-ps1 0 0 0 0 0 0 0 0
+Speer5-ps1 0 0 0 0 0 0 0 0
+Speer6-ps1 0 0 0 0 0 0 0 0
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+Spns1 5 0 0 1 1 1 0 1
+Sprr2g 0 0 0 0 0 0 0 0
+Sprr2j-ps 0 0 0 0 0 0 0 0
+Sqrdl 35 1 0 1 4 58 3 0
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+Srsf3 7 2 8 2 11 9 14 4
+Srsf7 1 1 0 0 0 5 0 1
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+St18 0 0 0 0 0 0 0 0
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+St7l 8 1 1 1 3 12 4 2
+Stap2 10 0 2 2 1 6 4 0
+Stmn1-rs1 0 0 0 0 0 0 0 0
+Stoml1 0 0 0 0 0 0 0 0
+Stxbp3b 0 0 0 0 0 0 0 0
+Surf1 3 0 0 2 3 7 5 0
+Suv39h2 0 0 0 0 0 0 0 0
+Svip 1 0 2 0 0 1 0 0
+Syce2 6 0 0 0 0 19 1 0
+Sycp1-ps1 0 0 0 0 0 0 0 0
+Szrd1 40 4 0 2 16 40 4 2
+Taf1b 1 1 1 0 0 0 0 0
+Taf1d 357 53 56 156 174 341 230 54
+Tardbp 18 0 1 3 7 20 3 0
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+Terc 22 1 0 0 18 10 15 0
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+Vash2 0 0 0 0 0 0 0 0
+Vaultrc5 243 258 161 383 159 262 286 270
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+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
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+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 5b6af671d985 -r 8051d14cf819 tool_dependencies.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_dependencies.xml Sun Dec 22 01:32:29 2013 -0500
@@ -0,0 +1,29 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+ $INSTALL_DIR
+ echo "source('http://bioconductor.org/biocLite.R')" > $INSTALL_DIR/runme.R
+ echo "installme=c('edgeR','limma','DESeq','DEXSeq','ggplot2','gplots')" >> $INSTALL_DIR/runme.R
+ echo "biocLite()" >> $INSTALL_DIR/runme.R
+ echo "biocLite(installme)" >> $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 > $INSTALL_DIR/install.log
+ cat $INSTALL_DIR/install.log
+
+
+ Installs some basic bioc packages for the edgeR wrapper
+ It's clunky but this is the only way I could get anything installed into the package_r3 R
+
+
+