changeset 136:635f944c2499 draft

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/rgToolFactory.py	Tue Jan 06 19:36:41 2015 -0500
@@ -0,0 +1,638 @@
+# rgToolFactory.py
+# see https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home
+# 
+# copyright ross lazarus (ross stop lazarus at gmail stop com) May 2012
+# 
+# all rights reserved
+# Licensed under the LGPL
+# suggestions for improvement and bug fixes welcome at https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home
+#
+# march 2014
+# added ghostscript and graphicsmagick as dependencies 
+# fixed a wierd problem where gs was trying to use the new_files_path from universe (database/tmp) as ./database/tmp
+# errors ensued
+#
+# august 2013
+# found a problem with GS if $TMP or $TEMP missing - now inject /tmp and warn
+#
+# july 2013
+# added ability to combine images and individual log files into html output
+# just make sure there's a log file foo.log and it will be output
+# together with all images named like "foo_*.pdf
+# otherwise old format for html
+#
+# January 2013
+# problem pointed out by Carlos Borroto
+# added escaping for <>$ - thought I did that ages ago...
+#
+# August 11 2012 
+# changed to use shell=False and cl as a sequence
+
+# This is a Galaxy tool factory for simple scripts in python, R or whatever ails ye.
+# It also serves as the wrapper for the new tool.
+# 
+# you paste and run your script
+# Only works for simple scripts that read one input from the history.
+# Optionally can write one new history dataset,
+# and optionally collect any number of outputs into links on an autogenerated HTML page.
+
+# DO NOT install on a public or important site - please.
+
+# installed generated tools are fine if the script is safe.
+# They just run normally and their user cannot do anything unusually insecure
+# but please, practice safe toolshed.
+# Read the fucking code before you install any tool 
+# especially this one
+
+# After you get the script working on some test data, you can
+# optionally generate a toolshed compatible gzip file
+# containing your script safely wrapped as an ordinary Galaxy script in your local toolshed for
+# safe and largely automated installation in a production Galaxy.
+
+# If you opt for an HTML output, you get all the script outputs arranged
+# as a single Html history item - all output files are linked, thumbnails for all the pdfs.
+# Ugly but really inexpensive.
+# 
+# Patches appreciated please. 
+#
+#
+# long route to June 2012 product
+# Behold the awesome power of Galaxy and the toolshed with the tool factory to bind them
+# derived from an integrated script model  
+# called rgBaseScriptWrapper.py
+# Note to the unwary:
+#   This tool allows arbitrary scripting on your Galaxy as the Galaxy user
+#   There is nothing stopping a malicious user doing whatever they choose
+#   Extremely dangerous!!
+#   Totally insecure. So, trusted users only
+#
+# preferred model is a developer using their throw away workstation instance - ie a private site.
+# no real risk. The universe_wsgi.ini admin_users string is checked - only admin users are permitted to run this tool.
+#
+
+import sys 
+import shutil 
+import subprocess 
+import os 
+import time 
+import tempfile 
+import optparse
+import tarfile
+import re
+import shutil
+import math
+
+progname = os.path.split(sys.argv[0])[1] 
+myversion = 'V001.1 March 2014' 
+verbose = False 
+debug = False
+toolFactoryURL = 'https://bitbucket.org/fubar/galaxytoolfactory'
+
+def timenow():
+    """return current time as a string
+    """
+    return time.strftime('%d/%m/%Y %H:%M:%S', time.localtime(time.time()))
+
+html_escape_table = {
+     "&": "&amp;",
+     ">": "&gt;",
+     "<": "&lt;",
+     "$": "\$"
+     }
+
+def html_escape(text):
+     """Produce entities within text."""
+     return "".join(html_escape_table.get(c,c) for c in text)
+
+def cmd_exists(cmd):
+     return subprocess.call("type " + cmd, shell=True, 
+           stdout=subprocess.PIPE, stderr=subprocess.PIPE) == 0
+
+
+class ScriptRunner:
+    """class is a wrapper for an arbitrary script
+    """
+
+    def __init__(self,opts=None,treatbashSpecial=True):
+        """
+        cleanup inputs, setup some outputs
+        
+        """
+        self.useGM = cmd_exists('gm')
+        self.useIM = cmd_exists('convert')
+        self.useGS = cmd_exists('gs')
+        self.temp_warned = False # we want only one warning if $TMP not set
+        self.treatbashSpecial = treatbashSpecial
+        if opts.output_dir: # simplify for the tool tarball
+            os.chdir(opts.output_dir)
+        self.thumbformat = 'png'
+        self.opts = opts
+        self.toolname = re.sub('[^a-zA-Z0-9_]+', '', opts.tool_name) # a sanitizer now does this but..
+        self.toolid = self.toolname
+        self.myname = sys.argv[0] # get our name because we write ourselves out as a tool later
+        self.pyfile = self.myname # crude but efficient - the cruft won't hurt much
+        self.xmlfile = '%s.xml' % self.toolname
+        s = open(self.opts.script_path,'r').readlines()
+        s = [x.rstrip() for x in s] # remove pesky dos line endings if needed
+        self.script = '\n'.join(s)
+        fhandle,self.sfile = tempfile.mkstemp(prefix=self.toolname,suffix=".%s" % (opts.interpreter))
+        tscript = open(self.sfile,'w') # use self.sfile as script source for Popen
+        tscript.write(self.script)
+        tscript.close()
+        self.indentedScript = '\n'.join([' %s' % x for x in s]) # for restructured text in help
+        self.escapedScript = '\n'.join([html_escape(x) for x in s])
+        self.elog = os.path.join(self.opts.output_dir,"%s_error.log" % self.toolname)
+        if opts.output_dir: # may not want these complexities 
+            self.tlog = os.path.join(self.opts.output_dir,"%s_runner.log" % self.toolname)
+            art = '%s.%s' % (self.toolname,opts.interpreter)
+            artpath = os.path.join(self.opts.output_dir,art) # need full path
+            artifact = open(artpath,'w') # use self.sfile as script source for Popen
+            artifact.write(self.script)
+            artifact.close()
+        self.cl = []
+        self.html = []
+        a = self.cl.append
+        a(opts.interpreter)
+        if self.treatbashSpecial and opts.interpreter in ['bash','sh']:
+            a(self.sfile)
+        else:
+            a('-') # stdin
+        a(opts.input_tab)
+        a(opts.output_tab)
+        self.outFormats = 'tabular' # TODO make this an option at tool generation time
+        self.inputFormats = 'tabular' # TODO make this an option at tool generation time
+        self.test1Input = '%s_test1_input.xls' % self.toolname
+        self.test1Output = '%s_test1_output.xls' % self.toolname
+        self.test1HTML = '%s_test1_output.html' % self.toolname
+
+    def makeXML(self):
+        """
+        Create a Galaxy xml tool wrapper for the new script as a string to write out
+        fixme - use templating or something less fugly than this example of what we produce
+
+        <tool id="reverse" name="reverse" version="0.01">
+            <description>a tabular file</description>
+            <command interpreter="python">
+            reverse.py --script_path "$runMe" --interpreter "python" 
+            --tool_name "reverse" --input_tab "$input1" --output_tab "$tab_file" 
+            </command>
+            <inputs>
+            <param name="input1"  type="data" format="tabular" label="Select a suitable input file from your history"/><param name="job_name" type="text" label="Supply a name for the outputs to remind you what they contain" value="reverse"/>
+
+            </inputs>
+            <outputs>
+            <data format="tabular" name="tab_file" label="${job_name}"/>
+
+            </outputs>
+            <help>
+            
+**What it Does**
+
+Reverse the columns in a tabular file
+
+            </help>
+            <configfiles>
+            <configfile name="runMe">
+            
+# reverse order of columns in a tabular file
+import sys
+inp = sys.argv[1]
+outp = sys.argv[2]
+i = open(inp,'r')
+o = open(outp,'w')
+for row in i:
+     rs = row.rstrip().split('\t')
+     rs.reverse()
+     o.write('\t'.join(rs))
+     o.write('\n')
+i.close()
+o.close()
+ 
+
+            </configfile>
+            </configfiles>
+            </tool>
+        
+        """ 
+        newXML="""<tool id="%(toolid)s" name="%(toolname)s" version="%(tool_version)s">
+            %(tooldesc)s
+            %(command)s
+            <inputs>
+            %(inputs)s
+            </inputs>
+            <outputs>
+            %(outputs)s
+            </outputs>
+            <configfiles>
+            <configfile name="runMe">
+            %(script)s
+            </configfile>
+            </configfiles>
+            %(tooltests)s
+            <help>
+            %(help)s
+            </help>
+            </tool>""" # needs a dict with toolname, toolid, interpreter, scriptname, command, inputs as a multi line string ready to write, outputs ditto, help ditto
+               
+        newCommand="""<command interpreter="python">
+            %(toolname)s.py --script_path "$runMe" --interpreter "%(interpreter)s" 
+            --tool_name "%(toolname)s" %(command_inputs)s %(command_outputs)s 
+            </command>""" # may NOT be an input or htmlout
+        tooltestsTabOnly = """<tests><test>
+        <param name="input1" value="%(test1Input)s" ftype="tabular"/>
+        <param name="job_name" value="test1"/>
+        <param name="runMe" value="$runMe"/>
+        <output name="tab_file" file="%(test1Output)s" ftype="tabular"/>
+        </test></tests>"""
+        tooltestsHTMLOnly = """<tests><test>
+        <param name="input1" value="%(test1Input)s" ftype="tabular"/>
+        <param name="job_name" value="test1"/>
+        <param name="runMe" value="$runMe"/>
+        <output name="html_file" file="%(test1HTML)s" ftype="html" lines_diff="5"/>
+        </test></tests>"""
+        tooltestsBoth = """<tests><test>
+        <param name="input1" value="%(test1Input)s" ftype="tabular"/>
+        <param name="job_name" value="test1"/>
+        <param name="runMe" value="$runMe"/>
+        <output name="tab_file" file="%(test1Output)s" ftype="tabular" />
+        <output name="html_file" file="%(test1HTML)s" ftype="html" lines_diff="10"/>
+        </test></tests>"""
+        xdict = {}
+        xdict['tool_version'] = self.opts.tool_version
+        xdict['test1Input'] = self.test1Input
+        xdict['test1HTML'] = self.test1HTML
+        xdict['test1Output'] = self.test1Output   
+        if self.opts.make_HTML and self.opts.output_tab <> 'None':
+            xdict['tooltests'] = tooltestsBoth % xdict
+        elif self.opts.make_HTML:
+            xdict['tooltests'] = tooltestsHTMLOnly % xdict
+        else:
+            xdict['tooltests'] = tooltestsTabOnly % xdict
+        xdict['script'] = self.escapedScript 
+        # configfile is least painful way to embed script to avoid external dependencies
+        # but requires escaping of <, > and $ to avoid Mako parsing
+        if self.opts.help_text:
+            xdict['help'] = open(self.opts.help_text,'r').read()
+        else:
+            xdict['help'] = 'Please ask the tool author for help as none was supplied at tool generation'
+        coda = ['**Script**','Pressing execute will run the following code over your input file and generate some outputs in your history::']
+        coda.append(self.indentedScript)
+        coda.append('**Attribution** This Galaxy tool was created by %s at %s\nusing the Galaxy Tool Factory.' % (self.opts.user_email,timenow()))
+        coda.append('See %s for details of that project' % (toolFactoryURL))
+        coda.append('Please cite: Creating re-usable tools from scripts: The Galaxy Tool Factory. Ross Lazarus; Antony Kaspi; Mark Ziemann; The Galaxy Team. ')
+        coda.append('Bioinformatics 2012; doi: 10.1093/bioinformatics/bts573')
+        xdict['help'] = '%s\n%s' % (xdict['help'],'\n'.join(coda))
+        if self.opts.tool_desc:
+            xdict['tooldesc'] = '<description>%s</description>' % self.opts.tool_desc
+        else:
+            xdict['tooldesc'] = ''
+        xdict['command_outputs'] = '' 
+        xdict['outputs'] = '' 
+        if self.opts.input_tab <> 'None':
+            xdict['command_inputs'] = '--input_tab "$input1" ' # the space may matter a lot if we append something
+            xdict['inputs'] = '<param name="input1"  type="data" format="%s" label="Select a suitable input file from your history"/> \n' % self.inputFormats
+        else:
+            xdict['command_inputs'] = '' # assume no input - eg a random data generator       
+            xdict['inputs'] = ''
+        xdict['inputs'] += '<param name="job_name" type="text" label="Supply a name for the outputs to remind you what they contain" value="%s"/> \n' % self.toolname
+        xdict['toolname'] = self.toolname
+        xdict['toolid'] = self.toolid
+        xdict['interpreter'] = self.opts.interpreter
+        xdict['scriptname'] = self.sfile
+        if self.opts.make_HTML:
+            xdict['command_outputs'] += ' --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" '
+            xdict['outputs'] +=  ' <data format="html" name="html_file" label="${job_name}.html"/>\n'
+        if self.opts.output_tab <> 'None':
+            xdict['command_outputs'] += ' --output_tab "$tab_file"'
+            xdict['outputs'] += ' <data format="%s" name="tab_file" label="${job_name}"/>\n' % self.outFormats
+        xdict['command'] = newCommand % xdict
+        xmls = newXML % xdict
+        xf = open(self.xmlfile,'w')
+        xf.write(xmls)
+        xf.write('\n')
+        xf.close()
+        # ready for the tarball
+
+
+    def makeTooltar(self):
+        """
+        a tool is a gz tarball with eg
+        /toolname/tool.xml /toolname/tool.py /toolname/test-data/test1_in.foo ...
+        """
+        retval = self.run()
+        if retval:
+            print >> sys.stderr,'## Run failed. Cannot build yet. Please fix and retry'
+            sys.exit(1)
+        self.makeXML()
+        tdir = self.toolname
+        os.mkdir(tdir)
+        if self.opts.input_tab <> 'None': # no reproducible test otherwise? TODO: maybe..
+            testdir = os.path.join(tdir,'test-data')
+            os.mkdir(testdir) # make tests directory
+            shutil.copyfile(self.opts.input_tab,os.path.join(testdir,self.test1Input))
+            if self.opts.output_tab <> 'None':
+                shutil.copyfile(self.opts.output_tab,os.path.join(testdir,self.test1Output))
+            if self.opts.make_HTML:
+                shutil.copyfile(self.opts.output_html,os.path.join(testdir,self.test1HTML))
+            if self.opts.output_dir:
+                shutil.copyfile(self.tlog,os.path.join(testdir,'test1_out.log'))
+        op = '%s.py' % self.toolname # new name
+        outpiname = os.path.join(tdir,op) # path for the tool tarball
+        pyin = os.path.basename(self.pyfile) # our name - we rewrite ourselves (TM)
+        notes = ['# %s - a self annotated version of %s generated by running %s\n' % (op,pyin,pyin),]
+        notes.append('# to make a new Galaxy tool called %s\n' % self.toolname)
+        notes.append('# User %s at %s\n' % (self.opts.user_email,timenow()))
+        pi = open(self.pyfile,'r').readlines() # our code becomes new tool wrapper (!) - first Galaxy worm
+        notes += pi
+        outpi = open(outpiname,'w')
+        outpi.write(''.join(notes))
+        outpi.write('\n')
+        outpi.close()
+        stname = os.path.join(tdir,self.sfile)
+        if not os.path.exists(stname):
+            shutil.copyfile(self.sfile, stname)
+        xtname = os.path.join(tdir,self.xmlfile)
+        if not os.path.exists(xtname):
+            shutil.copyfile(self.xmlfile,xtname)
+        tarpath = "%s.gz" % self.toolname
+        tar = tarfile.open(tarpath, "w:gz")
+        tar.add(tdir,arcname=self.toolname)
+        tar.close()
+        shutil.copyfile(tarpath,self.opts.new_tool)
+        shutil.rmtree(tdir)
+        ## TODO: replace with optional direct upload to local toolshed?
+        return retval
+
+
+    def compressPDF(self,inpdf=None,thumbformat='png'):
+        """need absolute path to pdf
+           note that GS gets confoozled if no $TMP or $TEMP
+           so we set it
+        """
+        assert os.path.isfile(inpdf), "## Input %s supplied to %s compressPDF not found" % (inpdf,self.myName)
+        hlog = os.path.join(self.opts.output_dir,"compress_%s.txt" % os.path.basename(inpdf))
+        sto = open(hlog,'a')
+        our_env = os.environ.copy()
+        our_tmp = our_env.get('TMP',None)
+        if not our_tmp:
+            our_tmp = our_env.get('TEMP',None)
+        if not (our_tmp and os.path.exists(our_tmp)):
+            newtmp = os.path.join(self.opts.output_dir,'tmp')
+            try:
+                os.mkdir(newtmp)
+            except:
+                sto.write('## WARNING - cannot make %s - it may exist or permissions need fixing\n' % newtmp)
+            our_env['TEMP'] = newtmp
+            if not self.temp_warned:
+               sto.write('## WARNING - no $TMP or $TEMP!!! Please fix - using %s temporarily\n' % newtmp)
+               self.temp_warned = True          
+        outpdf = '%s_compressed' % inpdf
+        cl = ["gs", "-sDEVICE=pdfwrite", "-dNOPAUSE", "-dUseCIEColor", "-dBATCH","-dPDFSETTINGS=/printer", "-sOutputFile=%s" % outpdf,inpdf]
+        x = subprocess.Popen(cl,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env)
+        retval1 = x.wait()
+        sto.close()
+        if retval1 == 0:
+            os.unlink(inpdf)
+            shutil.move(outpdf,inpdf)
+            os.unlink(hlog)
+        hlog = os.path.join(self.opts.output_dir,"thumbnail_%s.txt" % os.path.basename(inpdf))
+        sto = open(hlog,'w')
+        outpng = '%s.%s' % (os.path.splitext(inpdf)[0],thumbformat)
+        if self.useGM:        
+            cl2 = ['gm', 'convert', inpdf, outpng]
+        else: # assume imagemagick
+            cl2 = ['convert', inpdf, outpng]
+        x = subprocess.Popen(cl2,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env)
+        retval2 = x.wait()
+        sto.close()
+        if retval2 == 0:
+             os.unlink(hlog)
+        retval = retval1 or retval2
+        return retval
+
+
+    def getfSize(self,fpath,outpath):
+        """
+        format a nice file size string
+        """
+        size = ''
+        fp = os.path.join(outpath,fpath)
+        if os.path.isfile(fp):
+            size = '0 B'
+            n = float(os.path.getsize(fp))
+            if n > 2**20:
+                size = '%1.1f MB' % (n/2**20)
+            elif n > 2**10:
+                size = '%1.1f KB' % (n/2**10)
+            elif n > 0:
+                size = '%d B' % (int(n))
+        return size
+
+    def makeHtml(self):
+        """ Create an HTML file content to list all the artifacts found in the output_dir
+        """
+
+        galhtmlprefix = """<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> 
+        <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> 
+        <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> 
+        <meta name="generator" content="Galaxy %s tool output - see http://getgalaxy.org/" /> 
+        <title></title> 
+        <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> 
+        </head> 
+        <body> 
+        <div class="toolFormBody"> 
+        """ 
+        galhtmlattr = """<hr/><div class="infomessage">This tool (%s) was generated by the <a href="https://bitbucket.org/fubar/galaxytoolfactory/overview">Galaxy Tool Factory</a></div><br/>""" 
+        galhtmlpostfix = """</div></body></html>\n"""
+
+        flist = os.listdir(self.opts.output_dir)
+        flist = [x for x in flist if x <> 'Rplots.pdf']
+        flist.sort()
+        html = []
+        html.append(galhtmlprefix % progname)
+        html.append('<div class="infomessage">Galaxy Tool "%s" run at %s</div><br/>' % (self.toolname,timenow()))
+        fhtml = []
+        if len(flist) > 0:
+            logfiles = [x for x in flist if x.lower().endswith('.log')] # log file names determine sections
+            logfiles.sort()
+            logfiles = [x for x in logfiles if os.path.abspath(x) <> os.path.abspath(self.tlog)]
+            logfiles.append(os.path.abspath(self.tlog)) # make it the last one
+            pdflist = []
+            npdf = len([x for x in flist if os.path.splitext(x)[-1].lower() == '.pdf'])
+            for rownum,fname in enumerate(flist):
+                dname,e = os.path.splitext(fname)
+                sfsize = self.getfSize(fname,self.opts.output_dir)
+                if e.lower() == '.pdf' : # compress and make a thumbnail
+                    thumb = '%s.%s' % (dname,self.thumbformat)
+                    pdff = os.path.join(self.opts.output_dir,fname)
+                    retval = self.compressPDF(inpdf=pdff,thumbformat=self.thumbformat)
+                    if retval == 0:
+                        pdflist.append((fname,thumb))
+                    else:
+                        pdflist.append((fname,fname))
+                if (rownum+1) % 2 == 0:
+                    fhtml.append('<tr class="odd_row"><td><a href="%s">%s</a></td><td>%s</td></tr>' % (fname,fname,sfsize))
+                else:
+                    fhtml.append('<tr><td><a href="%s">%s</a></td><td>%s</td></tr>' % (fname,fname,sfsize))
+            for logfname in logfiles: # expect at least tlog - if more
+                if os.path.abspath(logfname) == os.path.abspath(self.tlog): # handled later
+                    sectionname = 'All tool run'
+                    if (len(logfiles) > 1):
+                        sectionname = 'Other'
+                    ourpdfs = pdflist
+                else:
+                    realname = os.path.basename(logfname)
+                    sectionname = os.path.splitext(realname)[0].split('_')[0] # break in case _ added to log
+                    ourpdfs = [x for x in pdflist if os.path.basename(x[0]).split('_')[0] == sectionname]
+                    pdflist = [x for x in pdflist if os.path.basename(x[0]).split('_')[0] <> sectionname] # remove
+                nacross = 1
+                npdf = len(ourpdfs)
+
+                if npdf > 0:
+                    nacross = math.sqrt(npdf) ## int(round(math.log(npdf,2)))
+                    if int(nacross)**2 != npdf:
+                        nacross += 1
+                    nacross = int(nacross)
+                    width = min(400,int(1200/nacross))
+                    html.append('<div class="toolFormTitle">%s images and outputs</div>' % sectionname)
+                    html.append('(Click on a thumbnail image to download the corresponding original PDF image)<br/>')
+                    ntogo = nacross # counter for table row padding with empty cells
+                    html.append('<div><table class="simple" cellpadding="2" cellspacing="2">\n<tr>')
+                    for i,paths in enumerate(ourpdfs): 
+                        fname,thumb = paths
+                        s= """<td><a href="%s"><img src="%s" title="Click to download a PDF of %s" hspace="5" width="%d" 
+                           alt="Image called %s"/></a></td>\n""" % (fname,thumb,fname,width,fname)
+                        if ((i+1) % nacross == 0):
+                            s += '</tr>\n'
+                            ntogo = 0
+                            if i < (npdf - 1): # more to come
+                               s += '<tr>'
+                               ntogo = nacross
+                        else:
+                            ntogo -= 1
+                        html.append(s)
+                    if html[-1].strip().endswith('</tr>'):
+                        html.append('</table></div>\n')
+                    else:
+                        if ntogo > 0: # pad
+                           html.append('<td>&nbsp;</td>'*ntogo)
+                        html.append('</tr></table></div>\n')
+                logt = open(logfname,'r').readlines()
+                logtext = [x for x in logt if x.strip() > '']
+                html.append('<div class="toolFormTitle">%s log output</div>' % sectionname)
+                if len(logtext) > 1:
+                    html.append('\n<pre>\n')
+                    html += logtext
+                    html.append('\n</pre>\n')
+                else:
+                    html.append('%s is empty<br/>' % logfname)
+        if len(fhtml) > 0:
+           fhtml.insert(0,'<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr>\n')
+           fhtml.append('</table></div><br/>')
+           html.append('<div class="toolFormTitle">All output files available for downloading</div>\n')
+           html += fhtml # add all non-pdf files to the end of the display
+        else:
+            html.append('<div class="warningmessagelarge">### Error - %s returned no files - please confirm that parameters are sane</div>' % self.opts.interpreter)
+        html.append(galhtmlpostfix)
+        htmlf = file(self.opts.output_html,'w')
+        htmlf.write('\n'.join(html))
+        htmlf.write('\n')
+        htmlf.close()
+        self.html = html
+
+
+    def run(self):
+        """
+        scripts must be small enough not to fill the pipe!
+        """
+        if self.treatbashSpecial and self.opts.interpreter in ['bash','sh']:
+          retval = self.runBash()
+        else:
+            if self.opts.output_dir:
+                ste = open(self.elog,'w')
+                sto = open(self.tlog,'w')
+                sto.write('## Toolfactory generated command line = %s\n' % ' '.join(self.cl))
+                sto.flush()
+                p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=ste,stdin=subprocess.PIPE,cwd=self.opts.output_dir)
+            else:
+                p = subprocess.Popen(self.cl,shell=False,stdin=subprocess.PIPE)
+            p.stdin.write(self.script)
+            p.stdin.close()
+            retval = p.wait()
+            if self.opts.output_dir:
+                sto.close()
+                ste.close()
+                err = open(self.elog,'r').read()
+                if retval <> 0 and err: # problem
+                    print >> sys.stderr,err
+            if self.opts.make_HTML:
+                self.makeHtml()
+        return retval
+
+    def runBash(self):
+        """
+        cannot use - for bash so use self.sfile
+        """
+        if self.opts.output_dir:
+            s = '## Toolfactory generated command line = %s\n' % ' '.join(self.cl)
+            sto = open(self.tlog,'w')
+            sto.write(s)
+            sto.flush()
+            p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=sto,cwd=self.opts.output_dir)
+        else:
+            p = subprocess.Popen(self.cl,shell=False)            
+        retval = p.wait()
+        if self.opts.output_dir:
+            sto.close()
+        if self.opts.make_HTML:
+            self.makeHtml()
+        return retval
+  
+
+def main():
+    u = """
+    This is a Galaxy wrapper. It expects to be called by a special purpose tool.xml as:
+    <command interpreter="python">rgToolFactory.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript"
+    </command>
+    """
+    op = optparse.OptionParser()
+    a = op.add_option
+    a('--script_path',default=None)
+    a('--tool_name',default=None)
+    a('--interpreter',default=None)
+    a('--output_dir',default=None)
+    a('--output_html',default=None)
+    a('--input_tab',default="None")
+    a('--output_tab',default="None")
+    a('--user_email',default='Unknown')
+    a('--bad_user',default=None)
+    a('--make_Tool',default=None)
+    a('--make_HTML',default=None)
+    a('--help_text',default=None)
+    a('--tool_desc',default=None)
+    a('--new_tool',default=None)
+    a('--tool_version',default=None)
+    opts, args = op.parse_args()
+    assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user)
+    assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq'
+    assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript'
+    assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R'
+    if opts.output_dir:
+        try:
+            os.makedirs(opts.output_dir)
+        except:
+            pass
+    r = ScriptRunner(opts)
+    if opts.make_Tool:
+        retcode = r.makeTooltar()
+    else:
+        retcode = r.run()
+    os.unlink(r.sfile)
+    if retcode:
+        sys.exit(retcode) # indicate failure to job runner
+
+
+if __name__ == "__main__":
+    main()
+
+

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeRpaired.xml.camera	Tue Jan 06 19:36:41 2015 -0500
@@ -0,0 +1,1084 @@
+<tool id="rgDifferentialCount" name="Differential_Count" version="0.30">
+  <description>models using BioConductor packages</description>
+  <requirements>
+      <requirement type="package" version="2.14">biocbasics</requirement>
+      <requirement type="package" version="3.0.2">r302</requirement>
+      <requirement type="package" version="1.3.18">graphicsmagick</requirement>
+      <requirement type="package" version="9.10">ghostscript</requirement>
+  </requirements>
+  
+  <command interpreter="python">
+     rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts" 
+    --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"
+  </command>
+  <inputs>
+    <param name="input1"  type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample"
+       help="Use the HTSeq based count matrix preparation tool to create these matrices from BAM/SAM files and a GTF file of genomic features"/>
+    <param name="title" type="text" value="Differential Counts" size="80" label="Title for job outputs" 
+           help="Supply a meaningful name here to remind you what the outputs contain">
+      <sanitizer invalid_char="">
+        <valid initial="string.letters,string.digits"><add value="_" /> </valid>
+      </sanitizer>
+    </param>
+    <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/>
+    <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True" 
+         multiple="true" use_header_names="true" size="120" display="checkboxes">
+        <validator type="no_options" message="Please select at least one column."/>
+    </param>
+    <param name="control_name" type="text" value="Control" size="50" label="Control Name"/>
+    <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True" 
+         multiple="true" use_header_names="true" size="120" display="checkboxes" optional="true">
+    </param>
+    <param name="subjectids" type="text" optional="true" size="120" value = ""
+       label="IF SUBJECTS NOT ALL INDEPENDENT! Enter comma separated strings to indicate sample labels for (eg) pairing - must be one for every column in input"
+       help="Leave blank if no pairing, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter 'A99,C21,A99,C21'">
+      <sanitizer>
+        <valid initial="string.letters,string.digits"><add value="," /> </valid>
+      </sanitizer>
+    </param>
+    <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs"
+     help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/>
+    <param name="useNDF" type="boolean" truevalue="T" falsevalue="F" checked="false" size="1" 
+              label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples"
+     help="May be a good or a bad idea depending on the biology and the question. This was the old default. Quantile based is available as an alternative"/>
+
+    <conditional name="edgeR">
+        <param name="doedgeR" type="select" 
+           label="Run this model using edgeR"
+           help="edgeR uses a negative binomial model and seems to be powerful, even with few replicates">
+          <option value="F">Do not run edgeR</option>
+          <option value="T" selected="true">Run edgeR</option>
+         </param>
+         <when value="T">
+          <param name="edgeR_priordf" type="integer" value="20" size="3" 
+           label="prior.df for tagwise dispersion - lower value = more emphasis on each tag's variance. Replaces prior.n  and prior.df = prior.n * residual.df"
+           help="0 = Use edgeR default. Use a small value to 'smooth' small samples. See edgeR docs and note below"/>
+         </when>
+         <when value="F"></when>
+    </conditional>
+    <conditional name="DESeq2">
+    <param name="doDESeq2" type="select" 
+       label="Run the same model with DESeq2 and compare findings"
+       help="DESeq2 is an update to the DESeq package. It uses different assumptions and methods to edgeR">
+      <option value="F" selected="true">Do not run DESeq2</option>
+      <option value="T">Run DESeq2</option>
+     </param>
+     <when value="T">
+         <param name="DESeq_fitType" type="select">
+            <option value="parametric" selected="true">Parametric (default) fit for dispersions</option>
+            <option value="local">Local fit - this will automagically be used if parametric fit fails</option>
+            <option value="mean">Mean dispersion fit- use this if you really understand what you're doing - read the fine manual linked below in the documentation</option>
+         </param> 
+     </when>
+     <when value="F"> </when>
+    </conditional>
+    <param name="doVoom" type="select" 
+       label="Run the same model with Voom/limma and compare findings"
+       help="Voom uses counts per million and a precise transformation of variance so count data can be analysed using limma">
+      <option value="F" selected="true">Do not run VOOM</option>
+      <option value="T">Run VOOM</option>
+     </param>
+    <!--
+    <conditional name="camera">
+        <param name="doCamera" type="select" label="Run the edgeR implementation of Camera GSEA for up/down gene sets" 
+            help="If yes, you can choose a set of genesets to test and/or supply a gmt format geneset collection from your history">
+        <option value="F" selected="true">Do not run GSEA tests with the Camera algorithm</option>
+        <option value="T">Run GSEA tests with the Camera algorithm</option>
+        </param>
+         <when value="T">
+         <conditional name="gmtSource">
+          <param name="refgmtSource" type="select" 
+             label="Use a gene set (.gmt) from your history and/or use a built-in (MSigDB etc) gene set">
+            <option value="indexed" selected="true">Use a built-in gene set</option>
+            <option value="history">Use a gene set from my history</option>
+            <option value="both">Add a gene set from my history to a built in gene set</option>
+          </param>
+          <when value="indexed">
+            <param name="builtinGMT" type="select" label="Select a gene set matrix (.gmt) file to use for the analysis">
+              <options from_data_table="gseaGMT_3.1">
+                <filter type="sort_by" column="2" />
+                <validator type="no_options" message="No GMT v3.1 files are available - please install them"/>
+              </options>
+            </param>
+          </when>
+          <when value="history">
+            <param name="ownGMT" type="data" format="gmt" label="Select a Gene Set from your history" />
+          </when>
+          <when value="both">
+            <param name="ownGMT" type="data" format="gseagmt" label="Select a Gene Set from your history" />
+            <param name="builtinGMT" type="select" label="Select a gene set matrix (.gmt) file to use for the analysis">
+              <options from_data_table="gseaGMT_4">
+                <filter type="sort_by" column="2" />
+                <validator type="no_options" message="No GMT v4 files are available - please fix tool_data_table and loc files"/>
+              </options>
+            </param>
+           </when>
+         </conditional>
+         </when>
+         <when value="F"> 
+         </when>
+    </conditional>
+    -->
+    <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control"
+     help="Conventional default value of 0.05 recommended"/>
+    <param name="fdrtype" type="select" label="FDR (Type II error) control method" 
+         help="Use fdr or bh typically to control for the number of tests in a reliable way">
+            <option value="fdr" selected="true">fdr</option>
+            <option value="BH">Benjamini Hochberg</option>
+            <option value="BY">Benjamini Yukateli</option>
+            <option value="bonferroni">Bonferroni</option>
+            <option value="hochberg">Hochberg</option>
+            <option value="holm">Holm</option>
+            <option value="hommel">Hommel</option>
+            <option value="none">no control for multiple tests</option>
+    </param>
+  </inputs>
+  <outputs>
+    <data format="tabular" name="out_edgeR" label="${title}_topTable_edgeR.xls">
+         <filter>edgeR['doedgeR'] == "T"</filter>
+    </data>
+    <data format="tabular" name="out_DESeq2" label="${title}_topTable_DESeq2.xls">
+         <filter>DESeq2['doDESeq2'] == "T"</filter>
+    </data>
+    <data format="tabular" name="out_VOOM" label="${title}_topTable_VOOM.xls">
+         <filter>doVoom == "T"</filter>
+    </data>
+    <data format="html" name="html_file" label="${title}.html"/>
+  </outputs>
+ <stdio>
+     <exit_code range="4"   level="fatal"   description="Number of subject ids must match total number of samples in the input matrix" />
+ </stdio>
+ <tests>
+<test>
+<param name='input1' value='test_bams2mx.xls' ftype='tabular' />
+ <param name='treatment_name' value='liver' />
+ <param name='title' value='edgeRtest' />
+ <param name='useNDF' value='' />
+ <param name='doedgeR' value='T' />
+ <param name='doVoom' value='T' />
+ <param name='doDESeq2' value='T' />
+ <param name='fdrtype' value='fdr' />
+ <param name='edgeR_priordf' value="8" />
+ <param name='fdrthresh' value="0.05" />
+ <param name='control_name' value='heart' />
+ <param name='subjectids' value='' />
+ <param name='Control_cols' value='3,4,5,9' />
+ <param name='Treat_cols' value='2,6,7,8' />
+ <output name='out_edgeR' file='edgeRtest1out.xls' compare='diff' />
+ <output name='html_file' file='edgeRtest1out.html'  compare='diff' lines_diff='20' />
+</test>
+</tests>
+
+<configfiles>
+<configfile name="runme">
+<![CDATA[
+# 
+# edgeR.Rscript
+# updated npv 2011 for R 2.14.0 and edgeR 2.4.0 by ross
+# Performs DGE on a count table containing n replicates of two conditions
+#
+# Parameters
+#
+# 1 - Output Dir
+
+# Original edgeR code by: S.Lunke and A.Kaspi
+reallybig = log10(.Machine\$double.xmax)
+reallysmall = log10(.Machine\$double.xmin)
+library('stringr')
+library('gplots')
+library('edgeR')
+hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here')
+{
+# Perform clustering for significant pvalues after controlling FWER
+    samples = colnames(cmat)
+    gu = unique(group)
+    gn = rownames(cmat)
+    if (length(gu) == 2) {
+        col.map = function(g) {if (g==gu[1]) "#FF0000" else "#0000FF"}
+        pcols = unlist(lapply(group,col.map))        
+        } else {
+        colours = rainbow(length(gu),start=0,end=4/6)
+        pcols = colours[match(group,gu)]        }
+    dm = cmat[(! is.na(gn)),] 
+    # remove unlabelled hm rows
+    nprobes = nrow(dm)
+    # sub = paste('Showing',nprobes,'contigs ranked for evidence of differential abundance')
+    if (nprobes > nsamp) {
+      dm =dm[1:nsamp,]
+      #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total')
+    }
+    newcolnames = substr(colnames(dm),1,20)
+    colnames(dm) = newcolnames
+    pdf(outpdfname)
+    heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none',
+         Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5)
+    dev.off()
+}
+
+hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here")
+{
+    # for 2 groups only was
+    #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"}
+    #pcols = unlist(lapply(group,col.map))
+    gu = unique(group)
+    colours = rainbow(length(gu),start=0.3,end=0.6)
+    pcols = colours[match(group,gu)]
+    nrows = nrow(cmat)
+    mtitle = paste(myTitle,'Heatmap: n contigs =',nrows)
+    if (nrows > nsamp)  {
+               cmat = cmat[c(1:nsamp),]
+               mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='')
+          }
+    newcolnames = substr(colnames(cmat),1,20)
+    colnames(cmat) = newcolnames
+    pdf(outpdfname)
+    heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols)
+    dev.off()
+}
+
+qqPlot = function(descr='qqplot',pvector, outpdf='qqplot.pdf',...)
+# stolen from https://gist.github.com/703512
+{
+    o = -log10(sort(pvector,decreasing=F))
+    e = -log10( 1:length(o)/length(o) )
+    o[o==-Inf] = reallysmall
+    o[o==Inf] = reallybig
+    maint = descr
+    pdf(outpdf)
+    plot(e,o,pch=19,cex=1, main=maint, ...,
+        xlab=expression(Expected~~-log[10](italic(p))),
+        ylab=expression(Observed~~-log[10](italic(p))),
+        xlim=c(0,max(e)), ylim=c(0,max(o)))
+    lines(e,e,col="red")
+    grid(col = "lightgray", lty = "dotted")
+    dev.off()
+}
+
+smearPlot = function(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("<a href=\'",u,"\'>",u,"</a>",sep="") }
+    glist = gs[3:length(gs)] # member gene symbols
+    glist = toupper(glist)
+    inglist = urownames %in% glist
+    nin = sum(inglist)
+    if ((nin > minnin) && (nin < maxnin)) {
+      ### print(paste('@@found',sum(inglist),'genes in glist'))
+      camres = camera(y=y,index=inglist,design=design)
+      if (! is.null(camres)) {
+      rownames(camres) = g # gene set name
+      camres = cbind(GeneSet=g,URL=u,camres)
+      if (camres\$Direction == "Up") 
+        {
+        upcam = rbind(upcam,camres) } else {
+          downcam = rbind(downcam,camres)
+        }
+      }
+   }
+  }
+  uscam = upcam[order(upcam\$PValue),]
+  unadjp = uscam\$PValue
+  uscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
+  nup = max(10,sum((uscam\$adjPValue < fdrthresh)))
+  dscam = downcam[order(downcam\$PValue),]
+  unadjp = dscam\$PValue
+  dscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
+  ndown = max(10,sum((dscam\$adjPValue < fdrthresh)))
+  write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F)
+  write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F)
+  print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:'))
+  write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F)
+  print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:'))
+  write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F)
+  sink()
+}
+
+
+
+
+doGSEA = function(y=NULL,design=NULL,histgmt="",
+                  bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
+                  ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH")
+{
+  sink('Camera.log')
+  genesets = c()
+  if (bigmt > "")
+  {
+    bigenesets = readLines(bigmt)
+    genesets = bigenesets
+  }
+  if (histgmt > "")
+  {
+    hgenesets = readLines(histgmt)
+    if (bigmt > "") {
+      genesets = rbind(genesets,hgenesets)
+    } else {
+      genesets = hgenesets
+    } # use only history if no bi
+  }
+  print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt))
+  genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n
+  outf = outfname
+  head=paste(myTitle,'edgeR GSEA')
+  write(head,file=outfname,append=F)
+  ntest=length(genesets)
+  urownames = toupper(rownames(y))
+  upcam = c()
+  downcam = c()
+  incam = c()
+  urls = c()
+  gsids = c()
+  for (i in 1:ntest) {
+    gs = unlist(genesets[i])
+    gsid = gs[1] # geneset_id
+    url = gs[2]
+    if (url > "") { url = paste("<a href=\'",url,"\'>",url,"</a>",sep="") }
+    glist = gs[3:length(gs)] # member gene symbols
+    glist = toupper(glist)
+    inglist = urownames %in% glist
+    nin = sum(inglist)
+    if ((nin > minnin) && (nin < maxnin)) {
+      incam = c(incam,inglist)
+      gsids = c(gsids,gsid)
+      urls = c(urls,url)
+    }
+  }
+  incam = as.list(incam)
+  names(incam) = gsids
+  allcam = camera(y=y,index=incam,design=design)
+  allcamres = cbind(geneset=gsids,allcam,URL=urls)
+  for (i in 1:ntest) {
+    camres = allcamres[i]
+    res = try(test = (camres\$Direction == "Up"))
+    if ("try-error" %in% class(res)) {
+      cat("test failed, camres = :")
+      print.noquote(camres)
+    } else  { if (camres\$Direction == "Up")
+    {  upcam = rbind(upcam,camres)
+    } else { downcam = rbind(downcam,camres)
+    }
+              
+    }
+  }
+  uscam = upcam[order(upcam\$PValue),]
+  unadjp = uscam\$PValue
+  uscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
+  nup = max(10,sum((uscam\$adjPValue < fdrthresh)))
+  dscam = downcam[order(downcam\$PValue),]
+  unadjp = dscam\$PValue
+  dscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
+  ndown = max(10,sum((dscam\$adjPValue < fdrthresh)))
+  write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F)
+  write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F)
+  print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:'))
+  write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F)
+  print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:'))
+  write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F)
+  sink()
+  }
+ 
+
+edgeIt = function (Count_Matrix=c(),group=c(),out_edgeR=F,out_VOOM=F,out_DESeq2=F,fdrtype='fdr',priordf=5, 
+        fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F,
+        filterquantile=0.2, subjects=c(),mydesign=NULL,
+        doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19',
+        histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
+        doCook=F,DESeq_fitType="parameteric") 
+{
+  # Error handling
+  if (length(unique(group))!=2){
+    print("Number of conditions identified in experiment does not equal 2")
+    q()
+    }
+  require(edgeR)
+  options(width = 512) 
+  mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ")
+  allN = nrow(Count_Matrix)
+  nscut = round(ncol(Count_Matrix)/2)
+  colTotmillionreads = colSums(Count_Matrix)/1e6
+  counts.dataframe = as.data.frame(c()) 
+  rawrs = rowSums(Count_Matrix)
+  nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes
+  nzN = nrow(nonzerod)
+  nzrs = rowSums(nonzerod)
+  zN = allN - nzN
+  print('# Quantiles for non-zero row counts:',quote=F)
+  print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F)
+  if (useNDF == T)
+  {
+    gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut
+    lo = colSums(Count_Matrix[!gt1rpin3,])
+    workCM = Count_Matrix[gt1rpin3,]
+    cleanrs = rowSums(workCM)
+    cleanN = length(cleanrs)
+    meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="")
+    print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F)
+    maint = paste('Filter >=1/million reads in >=',nscut,'samples')
+  }   else {        
+    useme = (nzrs > quantile(nzrs,filterquantile))
+    workCM = nonzerod[useme,]
+    lo = colSums(nonzerod[!useme,])
+    cleanrs = rowSums(workCM)
+    cleanN = length(cleanrs)
+    meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="")
+    print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F)
+    maint = paste('Filter below',filterquantile,'quantile')
+  }
+  cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle)
+  allgenes = rownames(workCM)
+  reg = "^chr([0-9]+):([0-9]+)-([0-9]+)"
+  genecards="<a href=\'http://www.genecards.org/index.php?path=/Search/keyword/"
+  ucsc = paste("<a href=\'http://genome.ucsc.edu/cgi-bin/hgTracks?db=",org,sep='')
+  testreg = str_match(allgenes,reg)
+  if (sum(!is.na(testreg[,1]))/length(testreg[,1]) > 0.8) # is ucsc style string
+  {
+    print("@@ using ucsc substitution for urls")
+    contigurls = paste0(ucsc,"&amp;position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"</a>")
+  } else {
+    print("@@ using genecards substitution for urls")
+    contigurls = paste0(genecards,allgenes,"\'>",allgenes,"</a>")
+  }
+  print.noquote("# urls")
+  print.noquote(head(contigurls))
+  print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F) 
+  cmrowsums = rowSums(workCM)
+  TName=unique(group)[1]
+  CName=unique(group)[2]
+  if (is.null(mydesign)) {
+    if (length(subjects) == 0) 
+    {
+      mydesign = model.matrix(~group)
+    } 
+    else { 
+      subjf = factor(subjects)
+      mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it
+    }
+  } 
+  print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=',')))
+  print.noquote('Using design matrix:')
+  print.noquote(mydesign)
+  if (doedgeR) {
+  sink('edgeR.log')
+  #### Setup DGEList object
+  DGEList = DGEList(counts=workCM, group = group)
+  DGEList = calcNormFactors(DGEList)
+
+  DGEList = estimateGLMCommonDisp(DGEList,mydesign)
+  comdisp = DGEList\$common.dispersion
+  DGEList = estimateGLMTrendedDisp(DGEList,mydesign)
+  if (edgeR_priordf > 0) {
+    print.noquote(paste("prior.df =",edgeR_priordf))
+    DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = edgeR_priordf)
+  } else {
+    DGEList = estimateGLMTagwiseDisp(DGEList,mydesign)
+  }
+  DGLM = glmFit(DGEList,design=mydesign)
+  DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed
+  efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors
+  normData = (1e+06*DGEList\$counts/efflib)
+  uoutput = cbind( 
+    Name=as.character(rownames(DGEList\$counts)),
+    DE\$table,
+    adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype),
+    Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums,normData,
+    DGEList\$counts
+  )
+  soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable
+  goodness = gof(DGLM, pcutoff=fdrthresh)
+  if (sum(goodness\$outlier) > 0) {
+    print.noquote('GLM outliers:')
+    print(paste(rownames(DGLM)[(goodness\$outlier)],collapse=','),quote=F)
+    } else { 
+      print('No GLM fit outlier genes found\n')
+    }
+  z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2)
+  pdf("edgeR_GoodnessofFit.pdf")
+  qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion")
+  abline(0,1,lwd=3)
+  points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon")
+  dev.off()
+  estpriorn = getPriorN(DGEList)
+  print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F)
+  efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors
+  normData = (1e+06*DGEList\$counts/efflib)
+  uniqueg = unique(group)
+  #### Plot MDS
+  sample_colors =  match(group,levels(group))
+  sampleTypes = levels(factor(group))
+  print.noquote(sampleTypes)
+  pdf("edgeR_MDSplot.pdf")
+  plotMDS.DGEList(DGEList,main=paste("edgeR MDS for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors)
+  legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19)
+  grid(col="blue")
+  dev.off()
+  colnames(normData) = paste( colnames(normData),'N',sep="_")
+  print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=',')))
+  nzd = data.frame(log(nonzerod + 1e-2,10))
+  try( boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle,pdfname="edgeR_raw_norm_counts_box.pdf") )
+  write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F)
+  tt = cbind( 
+    Name=as.character(rownames(DGEList\$counts)),
+    DE\$table,
+    adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype),
+    Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums
+  )
+  print.noquote("# edgeR Top tags\n")
+  tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely
+  tt = tt[order(DE\$table\$PValue),] 
+  print.noquote(tt[1:50,])
+  deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,])
+  nsig = length(deTags)
+  print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F)
+  deColours = ifelse(deTags,'red','black')
+  pdf("edgeR_BCV_vs_abundance.pdf")
+  plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance")
+  dev.off()
+  dg = DGEList[order(DE\$table\$PValue),]
+  #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg)))
+  efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors
+  normData = (1e+06*dg\$counts/efflib)
+  outpdfname="edgeR_top_100_heatmap.pdf"
+  hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('edgeR Heatmap',myTitle))
+  outSmear = "edgeR_smearplot.pdf"
+  outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='')
+  smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain)
+  qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf='edgeR_qqplot.pdf')
+  norm.factor = DGEList\$samples\$norm.factors
+  topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ]
+  edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR))
+  edgeRcounts = rep(0, length(allgenes))
+  edgeRcounts[edgeRcountsindex] = 1  # Create venn diagram of hits
+  sink()
+  } ### doedgeR
+  if (doDESeq2 == T)
+  {
+    sink("DESeq2.log")
+    # DESeq2
+    require('DESeq2')
+    library('RColorBrewer')
+    if (length(subjects) == 0)
+        {
+        pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM))
+        deSEQds = DESeqDataSetFromMatrix(countData = workCM,  colData = pdata, design = formula(~ Rx))
+        } else {
+        pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM))
+        deSEQds = DESeqDataSetFromMatrix(countData = workCM,  colData = pdata, design = formula(~ subjects + Rx))
+        }
+    #DESeq2 = DESeq(deSEQds,fitType='local',pAdjustMethod=fdrtype)
+    #rDESeq = results(DESeq2)
+    #newCountDataSet(workCM, group)
+    deSeqDatsizefac = estimateSizeFactors(deSEQds)
+    deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType)
+    resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype)
+    rDESeq = as.data.frame(results(resDESeq))
+    rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls)
+    srDESeq = rDESeq[order(rDESeq\$pvalue),]
+    qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf')
+    cat("# DESeq top 50\n")
+    print.noquote(srDESeq[1:50,])
+    write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F)
+    topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ]
+    DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq))
+    DESeqcounts = rep(0, length(allgenes))
+    DESeqcounts[DESeqcountsindex] = 1
+    pdf("DESeq2_dispersion_estimates.pdf")
+    plotDispEsts(resDESeq)
+    dev.off()
+    ysmall = abs(min(rDESeq\$log2FoldChange))
+    ybig = abs(max(rDESeq\$log2FoldChange))
+    ylimit = min(4,ysmall,ybig)
+    pdf("DESeq2_MA_plot.pdf")
+    plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit))
+    dev.off()
+    rlogres = rlogTransformation(resDESeq)
+    sampledists = dist( t( assay(rlogres) ) )
+    sdmat = as.matrix(sampledists)
+    pdf("DESeq2_sample_distance_plot.pdf")
+    heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"),
+         col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255))
+    dev.off()
+    ###outpdfname="DESeq2_top50_heatmap.pdf"
+    ###hmap2(sresDESeq,nsamp=50,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('DESeq2 vst rlog Heatmap',myTitle))
+    sink()
+    result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) )
+    if ("try-error" %in% class(result)) {
+         print.noquote('DESeq2 plotPCA failed.')
+         } else {
+         pdf("DESeq2_PCA_plot.pdf")
+         #### wtf - print? Seems needed to get this to work
+         print(ppca)
+         dev.off()
+        }
+  }
+
+  if (doVoom == T) {
+      sink('VOOM.log')
+      if (doedgeR == F) {
+         #### Setup DGEList object
+         DGEList = DGEList(counts=workCM, group = group)
+         DGEList = calcNormFactors(DGEList)
+         DGEList = estimateGLMCommonDisp(DGEList,mydesign)
+         DGEList = estimateGLMTrendedDisp(DGEList,mydesign)
+         DGEList = estimateGLMTagwiseDisp(DGEList,mydesign)
+         DGEList = estimateGLMTagwiseDisp(DGEList,mydesign)
+         norm.factor = DGEList\$samples\$norm.factors
+         }
+      pdf("VOOM_mean_variance_plot.pdf")
+      dat.voomed = voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor)
+      dev.off()
+      # Use limma to fit data
+      fit = lmFit(dat.voomed, mydesign)
+      fit = eBayes(fit)
+      rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none")
+      qqPlot(descr=paste(myTitle,'VOOM-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf='VOOM_qqplot.pdf')
+      rownames(rvoom) = rownames(workCM)
+      rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls)
+      srvoom = rvoom[order(rvoom\$P.Value),]
+      cat("# VOOM top 50\n")
+      print(srvoom[1:50,])
+      write.table(srvoom,file=out_VOOM, quote=FALSE, sep="\t",row.names=F)
+      # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma
+      topresults.voom = rvoom[which(rvoom\$adj.P.Val < fdrthresh), ]
+      voomcountsindex = which(allgenes %in% topresults.voom\$ID)
+      voomcounts = rep(0, length(allgenes))
+      voomcounts[voomcountsindex] = 1
+      sink()
+  }
+
+  if (doCamera) {
+  doGSEA(y=DGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle,
+    outfname=paste(mt,"GSEA.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype)
+  }
+
+  if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) {
+    if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) {
+        vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh)
+        counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, 
+                                       VOOM_limma = voomcounts, row.names = allgenes)
+       } else if ((doDESeq2==T) && (doedgeR==T))  {
+         vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh)
+         counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes)
+       } else if ((doVoom==T) && (doedgeR==T)) {
+        vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh)
+        counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes)
+       }
+    
+    if (nrow(counts.dataframe > 1)) {
+      counts.venn = vennCounts(counts.dataframe)
+      vennf = "Venn_significant_genes_overlap.pdf" 
+      pdf(vennf)
+      vennDiagram(counts.venn,main=vennmain,col="maroon")
+      dev.off()
+    }
+  } #### doDESeq2 or doVoom
+  
+}
+#### Done
+
+###sink(stdout(),append=T,type="message")
+builtin_gmt = ""
+history_gmt = ""
+history_gmt_name = ""
+out_edgeR = F
+out_DESeq2 = F
+out_VOOM = "$out_VOOM"
+doDESeq2 = $DESeq2.doDESeq2 # make these T or F
+doVoom = $doVoom
+doCamera = F
+doedgeR = $edgeR.doedgeR
+edgeR_priordf = 0
+
+
+#if $doVoom == "T":
+  out_VOOM = "$out_VOOM"
+#end if
+
+#if $DESeq2.doDESeq2 == "T":
+  out_DESeq2 = "$out_DESeq2"
+  DESeq_fitType = "$DESeq2.DESeq_fitType"
+#end if
+
+#if $edgeR.doedgeR == "T":
+  out_edgeR = "$out_edgeR"
+  edgeR_priordf = $edgeR.edgeR_priordf  
+#end if
+
+<!--
+#if $camera.doCamera == 'T'
+ doCamera = $camera.doCamera
+ #if $camera.gmtSource.refgmtSource == "indexed" or $camera.gmtSource.refgmtSource == "both":
+  builtin_gmt = "${camera.gmtSource.builtinGMT.fields.path}"
+ #end if
+ #if $camera.gmtSource.refgmtSource == "history" or $camera.gmtSource.refgmtSource == "both":
+    history_gmt = "${camera.gmtSource.ownGMT}"
+    history_gmt_name = "${camera.gmtSource.ownGMT.name}"
+  #end if
+#end if
+-->
+
+if (sum(c(doedgeR,doVoom,doDESeq2)) == 0)
+{
+write("No methods chosen - nothing to do! Please try again after choosing one or more methods", stderr())
+quit(save="no",status=2)
+}
+
+Out_Dir = "$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()
+]]>
+</configfile>
+</configfiles>
+<help>
+
+**What it does**
+
+Allows short read sequence counts from controlled experiments to be analysed for differentially expressed genes.
+Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design.
+
+**Input**
+
+Requires a count matrix as a tabular file. These are best made using the companion HTSeq_ based counter Galaxy wrapper
+and your fave gene model to generate inputs. Each row is a genomic feature (gene or exon eg) and each column the 
+non-negative integer count of reads from one sample overlapping the feature. 
+The matrix must have a header row uniquely identifying the source samples, and unique row names in 
+the first column. Typically the row names are gene symbols or probe ids for downstream use in GSEA and other methods.
+
+**Specifying comparisons**
+
+This is basically dumbed down for two factors - case vs control.
+
+More complex interfaces are possible but painful at present. 
+Probably need to specify a phenotype file to do this better.
+Work in progress. Send code.
+
+If you have (eg) paired samples and wish to include a term in the GLM to account for some other factor (subject in the case of paired samples),
+put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or 
+A list of integers, one for each subject or an empty string if samples are all independent.
+If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix.
+Integers for samples that are not in the analysis *must* be present in the string as filler even if not used.
+
+So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones
+eg if you had 6 samples with the first two independent but the second and third pairs each being from independent subjects. you might use
+8,9,1,1,2,2 
+as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6
+
+**Methods available**
+
+You can run 3 popular Bioconductor packages available for count data.
+
+edgeR - see edgeR_ for details
+
+VOOM/limma - see limma_VOOM_ for details
+
+DESeq2 - see DESeq2_ for details
+
+and optionally camera in edgeR which works better if MSigDB is installed.
+
+**Outputs**
+
+Some helpful plots and analysis results. Note that most of these are produced using R code 
+suggested by the excellent documentation and vignettes for the Bioconductor
+packages invoked. The Tool Factory is used to automatically lay these out for you to enjoy.
+
+**Note on Voom**
+
+The voom from limma version 3.16.6 help in R includes this from the authors - but you should read the paper to interpret this method.
+
+This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma.
+
+voom is an acronym for mean-variance modelling at the observational level.
+The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation.
+Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend.
+This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance.
+The weights are then used in the linear modelling process to adjust for heteroscedasticity.
+
+In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess.
+The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag.
+The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag.
+Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays.
+Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation.
+This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data.
+
+
+Author(s)
+
+Charity Law and Gordon Smyth
+
+References
+
+Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia.
+
+Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts.
+Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia.
+http://www.statsci.org/smyth/pubs/VoomPreprint.pdf
+
+See Also
+
+A voom case study is given in the edgeR User's Guide.
+
+vooma is a similar function but for microarrays instead of RNA-seq.
+
+
+***old rant on changes to Bioconductor package variable names between versions***
+
+The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue) 
+breaking this and all other code that assumed the old name for this variable, 
+between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). 
+This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing 
+to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly
+when their old scripts break. This tool currently now works with 2.4.6.
+
+**Note on prior.N**
+
+http://seqanswers.com/forums/showthread.php?t=5591 says:
+
+*prior.n*
+
+The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. 
+You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood 
+in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your 
+tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the 
+common likelihood the weight of one observation.
+
+In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, 
+or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that 
+you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation 
+(squeezing) of the tagwise dispersions. How many samples do you have in your experiment? 
+What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10. 
+If you have more samples, then the tagwise dispersion estimates will be more reliable, 
+so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. 
+
+
+From Bioconductor Digest, Vol 118, Issue 5, Gordon writes:
+
+Dear Dorota,
+
+The important settings are prior.df and trend.
+
+prior.n and prior.df are related through prior.df = prior.n * residual.df,
+and your experiment has residual.df = 36 - 12 = 24.  So the old setting of
+prior.n=10 is equivalent for your data to prior.df = 240, a very large
+value.  Going the other way, the new setting of prior.df=10 is equivalent
+to prior.n=10/24.
+
+To recover old results with the current software you would use
+
+  estimateTagwiseDisp(object, prior.df=240, trend="none")
+
+To get the new default from old software you would use
+
+  estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE)
+
+Actually the old trend method is equivalent to trend="loess" in the new
+software. You should use plotBCV(object) to see whether a trend is
+required.
+
+Note you could also use
+
+  prior.n = getPriorN(object, prior.df=10)
+
+to map between prior.df and prior.n.
+
+----
+
+**Attributions**
+
+edgeR - edgeR_ 
+
+VOOM/limma - limma_VOOM_ 
+
+DESeq2 - DESeq2_ for details
+
+See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package.
+
+Galaxy_ (that's what you are using right now!) for gluing everything together 
+
+Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is 
+licensed to you under the LGPL_ like other rgenetics artefacts
+
+.. _LGPL: http://www.gnu.org/copyleft/lesser.html
+.. _HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/index.html
+.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
+.. _DESeq2: http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
+.. _limma_VOOM: http://www.bioconductor.org/packages/release/bioc/html/limma.html
+.. _Galaxy: http://getgalaxy.org
+</help>
+
+</tool>
+
+

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeRpaired_nocamera.xml	Tue Jan 06 19:36:41 2015 -0500
@@ -0,0 +1,1069 @@
+<tool id="rgdifferentialcount" name="Differential_Count" version="0.28">
+  <description>models using BioConductor packages</description>
+  <requirements>
+      <requirement type="package" version="3.1.2">R</requirement>
+      <requirement type="package" version="1.3.18">graphicsmagick</requirement>
+      <requirement type="package" version="9.10">ghostscript</requirement> 
+      <requirement type="package" version="2.14">biocbasics</requirement>
+  </requirements>
+  
+  <command interpreter="python">
+     rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "Differential_Counts" 
+    --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"
+  </command>
+  <inputs>
+    <param name="input1"  type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample"
+       help="Use the HTSeq based count matrix preparation tool to create these matrices from BAM/SAM files and a GTF file of genomic features"/>
+    <param name="title" type="text" value="Differential Counts" size="80" label="Title for job outputs" 
+           help="Supply a meaningful name here to remind you what the outputs contain">
+      <sanitizer invalid_char="">
+        <valid initial="string.letters,string.digits"><add value="_" /> </valid>
+      </sanitizer>
+    </param>
+    <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/>
+    <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True" 
+         multiple="true" use_header_names="true" size="120" display="checkboxes"  force_select="True">
+        <validator type="no_options" message="Please select at least one column."/>
+    </param>
+    <param name="control_name" type="text" value="Control" size="50" label="Control Name"/>
+    <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True" 
+         multiple="true" use_header_names="true" size="120" display="checkboxes"  force_select="True">
+    </param>
+    <param name="subjectids" type="text" optional="true" size="120" value = ""
+       label="IF SUBJECTS NOT ALL INDEPENDENT! Enter comma separated strings to indicate sample labels for (eg) pairing - must be one for every column in input"
+       help="Leave blank if no pairing, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter 'A99,C21,A99,C21'">
+      <sanitizer>
+        <valid initial="string.letters,string.digits"><add value="," /> </valid>
+      </sanitizer>
+    </param>
+    <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs"
+     help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/>
+    <param name="useNDF" type="boolean" truevalue="T" falsevalue="F" checked="false" size="1" 
+              label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples"
+     help="May be a good or a bad idea depending on the biology and the question. This was the old default. Quantile based is available as an alternative"/>
+
+    <conditional name="edgeR">
+        <param name="doedgeR" type="select" 
+           label="Run this model using edgeR"
+           help="edgeR uses a negative binomial model and seems to be powerful, even with few replicates">
+          <option value="F">Do not run edgeR</option>
+          <option value="T" selected="true">Run edgeR</option>
+         </param>
+         <when value="T">
+          <param name="edgeR_priordf" type="integer" value="10" size="3" 
+           label="prior.df for tagwise dispersion - larger value = more squeezing of tag dispersions to common dispersion. Replaces prior.n  and prior.df = prior.n * residual.df"
+           help="10 = edgeR default. Use a larger value to 'smooth' small samples. See edgeR docs and note below"/>
+          <param name="edgeR_robust_method" type="select" value="20" size="3" 
+           label="Use robust dispersion method"
+           help="Use ordinary, anscombe or deviance robust deviance estimates">
+             <option value="ordinary" selected="true">Use ordinary deviance estimates</option>
+             <option value="deviance">Use robust deviance estimates</option>
+             <option value="anscombe">use Anscombe robust deviance estimates</option>
+          </param>
+         </when>
+         <when value="F"></when>
+    </conditional>
+    <conditional name="DESeq2">
+    <param name="doDESeq2" type="select" 
+       label="Run the same model with DESeq2 and compare findings"
+       help="DESeq2 is an update to the DESeq package. It uses different assumptions and methods to edgeR">
+      <option value="F" selected="true">Do not run DESeq2</option>
+      <option value="T">Run DESeq2</option>
+     </param>
+     <when value="T">
+         <param name="DESeq_fitType" type="select">
+            <option value="parametric" selected="true">Parametric (default) fit for dispersions</option>
+            <option value="local">Local fit - this will automagically be used if parametric fit fails</option>
+            <option value="mean">Mean dispersion fit- use this if you really understand what you're doing - read the fine manual linked below in the documentation</option>
+         </param> 
+     </when>
+     <when value="F"> </when>
+    </conditional>
+    <param name="doVoom" type="select" 
+       label="Run the same model with Voom/limma and compare findings"
+       help="Voom uses counts per million and a precise transformation of variance so count data can be analysed using limma">
+      <option value="F" selected="true">Do not run VOOM</option>
+      <option value="T">Run VOOM</option>
+     </param>
+    <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control"
+     help="Conventional default value of 0.05 recommended"/>
+    <param name="fdrtype" type="select" label="FDR (Type II error) control method" 
+         help="Use fdr or bh typically to control for the number of tests in a reliable way">
+            <option value="fdr" selected="true">fdr</option>
+            <option value="BH">Benjamini Hochberg</option>
+            <option value="BY">Benjamini Yukateli</option>
+            <option value="bonferroni">Bonferroni</option>
+            <option value="hochberg">Hochberg</option>
+            <option value="holm">Holm</option>
+            <option value="hommel">Hommel</option>
+            <option value="none">no control for multiple tests</option>
+    </param>
+  </inputs>
+  <outputs>
+    <data format="tabular" name="out_edgeR" label="${title}_topTable_edgeR.xls">
+         <filter>edgeR['doedgeR'] == "T"</filter>
+    </data>
+    <data format="tabular" name="out_DESeq2" label="${title}_topTable_DESeq2.xls">
+         <filter>DESeq2['doDESeq2'] == "T"</filter>
+    </data>
+    <data format="tabular" name="out_VOOM" label="${title}_topTable_VOOM.xls">
+         <filter>doVoom == "T"</filter>
+    </data>
+    <data format="html" name="html_file" label="${title}.html"/>
+  </outputs>
+ <stdio>
+     <exit_code range="4"   level="fatal"   description="Number of subject ids must match total number of samples in the input matrix" />
+ </stdio>
+ <tests>
+<test>
+<param name='input1' value='test_bams2mx.xls' ftype='tabular' />
+ <param name='treatment_name' value='liver' />
+ <param name='title' value='edgeRtest' />
+ <param name='useNDF' value='' />
+ <param name='doedgeR' value='T' />
+ <param name='doVoom' value='T' />
+ <param name='doDESeq2' value='T' />
+ <param name='fdrtype' value='fdr' />
+ <param name='edgeR_priordf' value="8" />
+ <param name='edgeR_robust' value="ordinary" />
+ <param name='fdrthresh' value="0.05" />
+ <param name='control_name' value='heart' />
+ <param name='subjectids' value='' />
+ <param name='Control_cols' value='3,4,5,9' />
+ <param name='Treat_cols' value='2,6,7,8' />
+ <output name='out_edgeR' file='edgeRtest1out.xls' compare='diff' />
+ <output name='html_file' file='edgeRtest1out.html'  compare='diff' lines_diff='20' />
+</test>
+</tests>
+
+<configfiles>
+<configfile name="runme">
+<![CDATA[
+# 
+# edgeR.Rscript
+# updated feb 2014 adding outlier-robust deviance estimate options by ross for R 3.0.2/bioc 2.13
+# updated npv 2011 for R 2.14.0 and edgeR 2.4.0 by ross
+# Performs DGE on a count table containing n replicates of two conditions
+#
+# Parameters
+#
+# 1 - Output Dir
+
+# Original edgeR code by: S.Lunke and A.Kaspi
+reallybig = log10(.Machine\$double.xmax)
+reallysmall = log10(.Machine\$double.xmin)
+library("stringr")
+library("gplots")
+library("edgeR")
+hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here')
+{
+# Perform clustering for significant pvalues after controlling FWER
+    samples = colnames(cmat)
+    gu = unique(group)
+    gn = rownames(cmat)
+    if (length(gu) == 2) {
+        col.map = function(g) {if (g==gu[1]) "#FF0000" else "#0000FF"}
+        pcols = unlist(lapply(group,col.map))        
+        } else {
+        colours = rainbow(length(gu),start=0,end=4/6)
+        pcols = colours[match(group,gu)]        }
+    dm = cmat[(! is.na(gn)),] 
+    # remove unlabelled hm rows
+    nprobes = nrow(dm)
+    # sub = paste('Showing',nprobes,'contigs ranked for evidence of differential abundance')
+    if (nprobes > nsamp) {
+      dm =dm[1:nsamp,]
+      #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total')
+    }
+    newcolnames = substr(colnames(dm),1,20)
+    colnames(dm) = newcolnames
+    pdf(outpdfname)
+    heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none',
+         Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5)
+    dev.off()
+}
+
+hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here")
+{
+    # for 2 groups only was
+    #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"}
+    #pcols = unlist(lapply(group,col.map))
+    gu = unique(group)
+    colours = rainbow(length(gu),start=0.3,end=0.6)
+    pcols = colours[match(group,gu)]
+    nrows = nrow(cmat)
+    mtitle = paste(myTitle,'Heatmap: n contigs =',nrows)
+    if (nrows > nsamp)  {
+               cmat = cmat[c(1:nsamp),]
+               mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='')
+          }
+    newcolnames = substr(colnames(cmat),1,20)
+    colnames(cmat) = newcolnames
+    pdf(outpdfname)
+    heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols)
+    dev.off()
+}
+
+qqPlot = function(descr='qqplot',pvector, outpdf='qqplot.pdf',...)
+# stolen from https://gist.github.com/703512
+{
+    o = -log10(sort(pvector,decreasing=F))
+    e = -log10( 1:length(o)/length(o) )
+    o[o==-Inf] = reallysmall
+    o[o==Inf] = reallybig
+    maint = descr
+    pdf(outpdf)
+    plot(e,o,pch=19,cex=1, main=maint, ...,
+        xlab=expression(Expected~~-log[10](italic(p))),
+        ylab=expression(Observed~~-log[10](italic(p))),
+        xlim=c(0,max(e)), ylim=c(0,max(o)))
+    lines(e,e,col="red")
+    grid(col = "lightgray", lty = "dotted")
+    dev.off()
+}
+
+smearPlot = function(myDGEList,deTags, outSmear, outMain)
+        {
+        pdf(outSmear)
+        plotSmear(myDGEList,de.tags=deTags,main=outMain)
+        grid(col="lightgray", lty="dotted")
+        dev.off()
+        }
+        
+boxPlot = function(rawrs,cleanrs,maint,myTitle,pdfname)
+{    
+        nc = ncol(rawrs)
+        ##### for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA}
+        fullnames = colnames(rawrs)
+        newcolnames = substr(colnames(rawrs),1,20)
+        colnames(rawrs) = newcolnames
+        newcolnames = substr(colnames(cleanrs),1,20)
+        colnames(cleanrs) = newcolnames
+        defpar = par(no.readonly=T)
+        print.noquote('@@@ Raw contig counts by sample:')
+        print.noquote(summary(rawrs))
+        print.noquote('@@@ Library size contig counts by sample:')
+        print.noquote(summary(cleanrs))
+        pdf(pdfname)
+        par(mfrow=c(1,2))
+        boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main='log2 raw counts')
+        grid(col="lightgray",lty="dotted")
+        boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('log2 counts after ',maint))
+        grid(col="lightgray",lty="dotted")
+        dev.off()
+        pdfname = "sample_counts_histogram.pdf" 
+        nc = ncol(rawrs)
+        print.noquote(paste('Using ncol rawrs=',nc))
+        ncroot = round(sqrt(nc))
+        if (ncroot*ncroot < nc) { ncroot = ncroot + 1 }
+        m = c()
+        for (i in c(1:nc)) {
+              rhist = hist(rawrs[,i],breaks=100,plot=F)
+              m = append(m,max(rhist\$counts))
+             }
+        ymax = max(m)
+        ncols = length(fullnames)
+        if (ncols > 20) 
+        {
+           scale = 7*ncols/20
+           pdf(pdfname,width=scale,height=scale)
+        } else { 
+           pdf(pdfname)
+        }
+        par(mfrow=c(ncroot,ncroot))
+        for (i in c(1:nc)) {
+                 hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon", 
+                 breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax))
+             }
+        dev.off()
+        par(defpar)
+      
+}
+
+cumPlot = function(rawrs,cleanrs,maint,myTitle)
+{   # updated to use ecdf
+        pdfname = "Differential_rowsum_bar_charts.pdf"
+        defpar = par(no.readonly=T)
+        lrs = log(rawrs,10) 
+        lim = max(lrs)
+        pdf(pdfname)
+        par(mfrow=c(2,1))
+        hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)",
+             ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1)
+        grid(col="lightgray", lty="dotted")
+        lrs = log(cleanrs,10) 
+        hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)",
+             ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1)
+        grid(col="lightgray", lty="dotted")
+        dev.off()
+        par(defpar)
+}
+
+cumPlot1 = function(rawrs,cleanrs,maint,myTitle)
+{   # updated to use ecdf
+        pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_')
+        pdf(pdfname)
+        par(mfrow=c(2,1))
+        lastx = max(rawrs)
+        rawe = knots(ecdf(rawrs))
+        cleane = knots(ecdf(cleanrs))
+        cy = 1:length(cleane)/length(cleane)
+        ry = 1:length(rawe)/length(rawe)
+        plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads",
+             ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle)
+        grid(col="blue")
+        plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads",
+             ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle)
+        grid(col="blue")
+        dev.off()
+}
+
+
+
+doGSEAold = function(y=NULL,design=NULL,histgmt="",
+                  bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
+                  ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH")
+{
+  sink('Camera.log')
+  genesets = c()
+  if (bigmt > "")
+  {
+    bigenesets = readLines(bigmt)
+    genesets = bigenesets
+  }
+  if (histgmt > "")
+  {
+    hgenesets = readLines(histgmt)
+    if (bigmt > "") {
+      genesets = rbind(genesets,hgenesets)
+    } else {
+      genesets = hgenesets
+    } # use only history if no bi
+  }
+  print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt))
+  genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n
+  outf = outfname
+  head=paste(myTitle,'edgeR GSEA')
+  write(head,file=outfname,append=F)
+  ntest=length(genesets)
+  urownames = toupper(rownames(y))
+  upcam = c()
+  downcam = c()
+  for (i in 1:ntest) {
+    gs = unlist(genesets[i])
+    g = gs[1] # geneset_id
+    u = gs[2]
+    if (u > "") { u = paste("<a href=\'",u,"\'>",u,"</a>",sep="") }
+    glist = gs[3:length(gs)] # member gene symbols
+    glist = toupper(glist)
+    inglist = urownames %in% glist
+    nin = sum(inglist)
+    if ((nin > minnin) && (nin < maxnin)) {
+      ### print(paste('@@found',sum(inglist),'genes in glist'))
+      camres = camera(y=y,index=inglist,design=design)
+      if (! is.null(camres)) {
+      rownames(camres) = g # gene set name
+      camres = cbind(GeneSet=g,URL=u,camres)
+      if (camres\$Direction == "Up") 
+        {
+        upcam = rbind(upcam,camres) } else {
+          downcam = rbind(downcam,camres)
+        }
+      }
+   }
+  }
+  uscam = upcam[order(upcam\$PValue),]
+  unadjp = uscam\$PValue
+  uscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
+  nup = max(10,sum((uscam\$adjPValue < fdrthresh)))
+  dscam = downcam[order(downcam\$PValue),]
+  unadjp = dscam\$PValue
+  dscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
+  ndown = max(10,sum((dscam\$adjPValue < fdrthresh)))
+  write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F)
+  write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F)
+  print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:'))
+  write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F)
+  print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:'))
+  write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F)
+  sink()
+}
+
+
+
+
+doGSEA = function(y=NULL,design=NULL,histgmt="",
+                  bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
+                  ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH")
+{
+  sink('Camera.log')
+  genesets = c()
+  if (bigmt > "")
+  {
+    bigenesets = readLines(bigmt)
+    genesets = bigenesets
+  }
+  if (histgmt > "")
+  {
+    hgenesets = readLines(histgmt)
+    if (bigmt > "") {
+      genesets = rbind(genesets,hgenesets)
+    } else {
+      genesets = hgenesets
+    } # use only history if no bi
+  }
+  print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt))
+  genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n
+  outf = outfname
+  head=paste(myTitle,'edgeR GSEA')
+  write(head,file=outfname,append=F)
+  ntest=length(genesets)
+  urownames = toupper(rownames(y))
+  upcam = c()
+  downcam = c()
+  incam = c()
+  urls = c()
+  gsids = c()
+  for (i in 1:ntest) {
+    gs = unlist(genesets[i])
+    gsid = gs[1] # geneset_id
+    url = gs[2]
+    if (url > "") { url = paste("<a href=\'",url,"\'>",url,"</a>",sep="") }
+    glist = gs[3:length(gs)] # member gene symbols
+    glist = toupper(glist)
+    inglist = urownames %in% glist
+    nin = sum(inglist)
+    if ((nin > minnin) && (nin < maxnin)) {
+      incam = c(incam,inglist)
+      gsids = c(gsids,gsid)
+      urls = c(urls,url)
+    }
+  }
+  incam = as.list(incam)
+  names(incam) = gsids
+  allcam = camera(y=y,index=incam,design=design)
+  allcamres = cbind(geneset=gsids,allcam,URL=urls)
+  for (i in 1:ntest) {
+    camres = allcamres[i]
+    res = try(test = (camres\$Direction == "Up"))
+    if ("try-error" %in% class(res)) {
+      cat("test failed, camres = :")
+      print.noquote(camres)
+    } else  { if (camres\$Direction == "Up")
+    {  upcam = rbind(upcam,camres)
+    } else { downcam = rbind(downcam,camres)
+    }
+              
+    }
+  }
+  uscam = upcam[order(upcam\$PValue),]
+  unadjp = uscam\$PValue
+  uscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
+  nup = max(10,sum((uscam\$adjPValue < fdrthresh)))
+  dscam = downcam[order(downcam\$PValue),]
+  unadjp = dscam\$PValue
+  dscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
+  ndown = max(10,sum((dscam\$adjPValue < fdrthresh)))
+  write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F)
+  write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F)
+  print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:'))
+  write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F)
+  print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:'))
+  write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F)
+  sink()
+  }
+
+
+edgeIt = function (Count_Matrix=c(),group=c(),out_edgeR=F,out_Voom=F,out_DESeq2=F,fdrtype='fdr',priordf=5, 
+        fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F,
+        filterquantile=0.2, subjects=c(),TreatmentName="Rx",ControlName="Ctrl",mydesign=NULL,
+        doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19',
+        histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
+        doCook=F,DESeq_fitType="parameteric",robust_meth='ordinary') 
+{
+
+logf = file('Differential.log', open = "a")
+sink(logf,type = c("output", "message"))
+
+
+run_edgeR = function(workCM,pdata,subjects,group,priordf,robust_meth,mydesign,mt,cmrowsums,out_edgeR,nonzerod)
+{
+  logf = file('edgeR.log', open = "a")
+  sink(logf,type = c("output", "message"))
+  #### Setup myDGEList object
+  myDGEList = DGEList(counts=workCM, group = group)
+  myDGEList = calcNormFactors(myDGEList)
+  if (robust_meth == 'ordinary') {
+       myDGEList = estimateGLMCommonDisp(myDGEList,mydesign)
+       myDGEList = estimateGLMTrendedDisp(myDGEList,mydesign)
+       if (priordf > 0) {  myDGEList = estimateGLMTagwiseDisp(myDGEList,mydesign,prior.df = priordf) 
+       } else { myDGEList = estimateGLMTagwiseDisp(myDGEList,mydesign) }
+       comdisp = myDGEList\$common.dispersion
+       estpriorn = getPriorN(myDGEList)
+       print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F)
+     } else { 
+       myDGEList = estimateGLMRobustDisp(myDGEList,design=mydesign, prior.df = priordf, maxit = 6, residual.type = robust_meth)
+          }
+    
+  
+  DGLM = glmFit(myDGEList,design=mydesign)
+  DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed
+  normData = cpm(myDGEList)
+  uoutput = cbind( 
+    Name=as.character(rownames(myDGEList\$counts)),
+    DE\$table,
+    adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype),
+    Dispersion=myDGEList\$tagwise.dispersion,totreads=cmrowsums,normData,
+    myDGEList\$counts
+  )
+  soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable
+  goodness = gof(DGLM, pcutoff=fdrthresh)
+  if (sum(goodness\$outlier) > 0) {
+    print.noquote('GLM outliers:')
+    print(paste(rownames(DGLM)[(goodness\$outlier)],collapse=','),quote=F)
+    } else { 
+      print('No GLM fit outlier genes found\n')
+    }
+  z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2)
+  pdf(paste("edgeR",mt,"GoodnessofFit.pdf",sep='_'))
+  qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion")
+  abline(0,1,lwd=3)
+  points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon")
+  dev.off()
+  uniqueg = unique(group)
+  write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F)
+  tt = cbind( 
+    Name=as.character(rownames(myDGEList)),
+    DE\$table,
+    adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype),
+    Dispersion=myDGEList\$tagwise.dispersion,totreads=cmrowsums
+  )
+  tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely
+  stt = tt[order(DE\$table\$PValue),]
+  print.noquote("@@ edgeR Top tags\n")
+  print.noquote(stt[1:50,])
+  deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,])
+  nsig = length(deTags)
+  print.noquote(paste('@@',nsig,'tags significant at adj p=',fdrthresh))
+  deColours = ifelse(deTags,'red','black')
+  pdf(paste("edgeR",mt,"BCV_vs_abundance.pdf",sep="_"))
+  plotBCV(myDGEList, cex=0.3, main="Biological CV vs abundance")
+  dev.off()
+  dg = myDGEList[order(DE\$table\$PValue),]
+  outpdfname= paste("edgeR",mt,"top_100_heatmap.pdf",sep="_")
+  ocpm = normData[order(DE\$table\$PValue),]
+  ocpm = ocpm[c(1:100),]
+  hmap2(ocpm,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste(myTitle,'Heatmap'))
+  outSmear = paste("edgeR",mt,"smearplot.pdf",sep="_")
+  outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='')
+  smearPlot(myDGEList=myDGEList,deTags=deTags, outSmear=outSmear, outMain = outMain)
+  qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf=paste('edgeR',mt,'qqplot.pdf',sep='_'))
+  topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ]
+  edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR))
+  edgeRcounts = rep(0, length(allgenes))
+  edgeRcounts[edgeRcountsindex] = 1  # Create venn diagram of hits
+  sink()
+  return(list(myDGEList=myDGEList,edgeRcounts=edgeRcounts))
+} ### run_edgeR
+
+
+run_DESeq2 = function(workCM,pdata,subjects,group,out_DESeq2,mt,DESeq_fitType)
+
+ {
+    logf = file("DESeq2.log", open = "a")
+    sink(logf,type = c("output", "message"))
+    # DESeq2
+    require('DESeq2')
+    library('RColorBrewer')
+    if (length(subjects) == 0)
+        {
+        pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM))
+        deSEQds = DESeqDataSetFromMatrix(countData = workCM,  colData = pdata, design = formula(~ Rx))
+        } else {
+        pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM))
+        deSEQds = DESeqDataSetFromMatrix(countData = workCM,  colData = pdata, design = formula(~ subjects + Rx))
+        }
+    deSeqDatsizefac = estimateSizeFactors(deSEQds)
+    deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType)
+    resDESeq = nbinomWaldTest(deSeqDatdisp)
+    rDESeq = as.data.frame(results(resDESeq))
+    rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls)
+    srDESeq = rDESeq[order(rDESeq\$pvalue),]
+    qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf=paste('DESeq2',mt,'qqplot.pdf',sep="_"))
+    cat("# DESeq top 50\n")
+    print.noquote(srDESeq[1:50,])
+    write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F)
+    topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ]
+    DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq))
+    DESeqcounts = rep(0, length(allgenes))
+    DESeqcounts[DESeqcountsindex] = 1
+    pdf(paste("DESeq2",mt,"dispersion_estimates.pdf",sep='_'))
+    plotDispEsts(resDESeq)
+    dev.off()
+    ysmall = abs(min(rDESeq\$log2FoldChange))
+    ybig = abs(max(rDESeq\$log2FoldChange))
+    ylimit = min(4,ysmall,ybig)
+    pdf(paste("DESeq2",mt,"MA_plot.pdf",sep="_"))
+    plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit))
+    dev.off()
+    rlogres = rlogTransformation(resDESeq)
+    sampledists = dist( t( assay(rlogres) ) )
+    sdmat = as.matrix(sampledists)
+    pdf(paste("DESeq2",mt,"sample_distance_plot.pdf",sep="_"))
+    heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"),
+         col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255))
+    dev.off()
+    result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) )
+    if ("try-error" %in% class(result)) {
+         print.noquote('DESeq2 plotPCA failed.')
+         } else {
+         pdf(paste("DESeq2",mt,"PCA_plot.pdf",sep="_"))
+         #### wtf - print? Seems needed to get this to work
+         print(ppca)
+         dev.off()
+        }
+    sink()
+    return(DESeqcounts)
+  }
+
+
+run_Voom = function(workCM,pdata,subjects,group,mydesign,mt,out_Voom)
+  {
+    logf = file('VOOM.log', open = "a")
+    sink(logf,type = c("output", "message")) 
+    if (doedgeR == F) {
+        #### Setup myDGEList object
+        myDGEList = DGEList(counts=workCM, group = group)
+        myDGEList = calcNormFactors(myDGEList)
+        myDGEList = estimateGLMCommonDisp(myDGEList,mydesign)
+        myDGEList = estimateGLMTrendedDisp(myDGEList,mydesign) 
+        myDGEList = estimateGLMTagwiseDisp(myDGEList,mydesign)
+    }
+    pdf(paste("VOOM",mt,"mean_variance_plot.pdf",sep='_'))
+    dat.voomed <- voom(myDGEList, mydesign, plot = TRUE, normalize.method="quantil", lib.size = NULL)
+    dev.off()
+    # Use limma to fit data
+    fit = lmFit(dat.voomed, mydesign)
+    fit = eBayes(fit)
+    rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none")
+    qqPlot(descr=paste(myTitle,'VOOM-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf=paste('VOOM',mt,'qqplot.pdf',sep='_'))
+    rownames(rvoom) = rownames(workCM)
+    rvoom = cbind(Contig=rownames(workCM),rvoom,NReads=cmrowsums,URL=contigurls)
+    srvoom = rvoom[order(rvoom\$P.Value),]
+    cat("# VOOM top 50\n")
+    print(srvoom[1:50,])
+    write.table(srvoom,file=out_Voom, quote=FALSE, sep="\t",row.names=F)
+    # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma
+    topresults.voom = rvoom[which(rvoom\$adj.P.Val < fdrthresh), ]
+    voomcountsindex <- which(allgenes %in% rownames(topresults.voom))
+    voomcounts = rep(0, length(allgenes))
+    voomcounts[voomcountsindex] = 1
+    sink()
+    return(voomcounts)
+    }
+
+
+#### data cleaning and analsis control starts here
+
+
+  # Error handling
+  nugroup = length(unique(group))
+  if (nugroup!=2){
+    print("Number of conditions identified in experiment does not equal 2")
+    q()
+    }
+  require(edgeR)
+  options(width = 512) 
+  mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ")
+  allN = nrow(Count_Matrix)
+  nscut = round(ncol(Count_Matrix)/2) # half samples
+  colTotmillionreads = colSums(Count_Matrix)/1e6
+  counts.dataframe = as.data.frame(c()) 
+  rawrs = rowSums(Count_Matrix)
+  nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes
+  nzN = nrow(nonzerod)
+  nzrs = rowSums(nonzerod)
+  zN = allN - nzN
+  print('@@@ Quantiles for non-zero row counts:',quote=F)
+  print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F)
+  if (useNDF == T)
+  {
+    gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut
+    lo = colSums(Count_Matrix[!gt1rpin3,])
+    workCM = Count_Matrix[gt1rpin3,]
+    cleanrs = rowSums(workCM)
+    cleanN = length(cleanrs)
+    meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="")
+    print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F)
+    maint = paste('Filter >=1/million reads in >=',nscut,'samples')
+  }   else {        
+    useme = (nzrs > quantile(nzrs,filterquantile))
+    workCM = nonzerod[useme,]
+    lo = colSums(nonzerod[!useme,])
+    cleanrs = rowSums(workCM)
+    cleanN = length(cleanrs)
+    meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="")
+    print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F)
+    maint = paste('Filter below',filterquantile,'quantile')
+  }
+  cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle)
+  allgenes = rownames(workCM)
+  reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" # ucsc chr:start-end regexp
+  genecards="<a href=\'http://www.genecards.org/index.php?path=/Search/keyword/"
+  ucsc = paste("<a href=\'http://genome.ucsc.edu/cgi-bin/hgTracks?db=",org,sep='')
+  testreg = str_match(allgenes,reg)
+  if (sum(!is.na(testreg[,1]))/length(testreg[,1]) > 0.8) # is ucsc style string
+  {
+    print("@@ using ucsc substitution for urls")
+    contigurls = paste0(ucsc,"&amp;position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"</a>")
+  } else {
+    print("@@ using genecards substitution for urls")
+    contigurls = paste0(genecards,allgenes,"\'>",allgenes,"</a>")
+  }
+  print.noquote(paste("@@ Total low count contigs per sample = ",paste(table(lo),collapse=','))) 
+  cmrowsums = rowSums(workCM)
+  TName=unique(group)[1]
+  CName=unique(group)[2]
+  if (is.null(mydesign)) {
+    if (length(subjects) == 0) 
+    {
+      mydesign = model.matrix(~group)
+    } 
+    else { 
+      subjf = factor(subjects)
+      mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it
+    }
+  } 
+  print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=',')))
+  print.noquote('Using design matrix:')
+  print.noquote(mydesign)
+  normData = cpm(workCM)*1e6
+  colnames(normData) = paste( colnames(workCM),'N',sep="_")
+  print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=',')))
+
+  if (doedgeR == T) {
+      eres = run_edgeR(workCM,pdata,subjects,group,priordf,robust_meth,mydesign,mt,cmrowsums,out_edgeR,nonzerod)
+      myDGEList = eres\$myDGEList
+      edgeRcounts = eres\$edgeRcounts
+      #### Plot MDS
+      sample_colors =  match(group,levels(group))
+      sampleTypes = levels(factor(group))
+      print.noquote(sampleTypes)
+      pdf(paste("edgeR",mt,"MDSplot.pdf",sep='_'))
+      plotMDS.DGEList(myDGEList,main=paste("MDS for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors)
+      legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19)
+      grid(col="blue")
+      dev.off()
+      scale <- myDGEList\$samples\$lib.size*myDGEList\$samples\$norm.factors
+      normCounts <- round(t(t(myDGEList\$counts)/scale)*mean(scale))
+      try({boxPlot(rawrs=nzd,cleanrs=log2(normCounts+1),maint='Effects of TMM size normalisation',myTitle=myTitle,pdfname=paste("edgeR",mt,"raw_norm_counts_box.pdf",sep='_'))},T)
+   }
+  if (doDESeq2 == T) {  DESeqcounts = run_DESeq2(workCM,pdata,subjects,group,out_DESeq2,mt,DESeq_fitType) }
+  if (doVoom == T) { voomcounts = run_Voom(workCM,pdata,subjects,group,mydesign,mt,out_Voom) }
+
+
+  if (doCamera) {
+  doGSEA(y=myDGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle,
+    outfname=paste("GSEA_Camera",mt,"table.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype)
+  }
+  counts.dataframe = c()
+  vennmain = 'no venn'
+  if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) {
+    if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) {
+        vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh)
+        counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, 
+                                       VOOM_limma = voomcounts, row.names = allgenes)
+       } else if ((doDESeq2==T) && (doedgeR==T))  {
+         vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh)
+         counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes)
+       } else if ((doVoom==T) && (doedgeR==T)) {
+        vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh)
+        counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes)
+       }
+    
+    if (nrow(counts.dataframe > 1)) {
+      counts.venn = vennCounts(counts.dataframe)
+      vennf = paste("Differential_venn",mt,"significant_genes_overlap.pdf",sep="_") 
+      pdf(vennf)
+      vennDiagram(counts.venn,main=vennmain,col="maroon")
+      dev.off()
+    }
+  } #### doDESeq2 or doVoom
+sink()
+}
+#### Done
+]]>
+builtin_gmt = ""
+history_gmt = ""
+history_gmt_name = ""
+out_edgeR = F
+out_DESeq2 = F
+out_Voom = "$out_VOOM"
+edgeR_robust_meth = "ordinary" 
+doDESeq2 = $DESeq2.doDESeq2
+doVoom = $doVoom
+doCamera = F
+doedgeR = $edgeR.doedgeR
+edgeR_priordf = 10
+
+
+#if $doVoom == "T":
+  out_Voom = "$out_VOOM"
+#end if
+
+#if $DESeq2.doDESeq2 == "T":
+  out_DESeq2 = "$out_DESeq2"
+  doDESeq2 = T
+  DESeq_fitType = "$DESeq2.DESeq_fitType"
+#end if
+
+#if $edgeR.doedgeR == "T":
+  out_edgeR = "$out_edgeR"
+  edgeR_priordf = $edgeR.edgeR_priordf  
+  edgeR_robust_meth = "$edgeR.edgeR_robust_method"
+#end if
+
+
+if (sum(c(doedgeR,doVoom,doDESeq2)) == 0)
+{
+write("No methods chosen - nothing to do! Please try again after choosing one or more methods", stderr())
+quit(save="no",status=2)
+}
+
+Out_Dir = "$html_file.files_path"
+Input =  "$input1"
+TreatmentName = "$treatment_name"
+TreatmentCols = "$Treat_cols"
+ControlName = "$control_name"
+ControlCols= "$Control_cols"
+org = "$input1.dbkey"
+if (org == "") { org = "hg19"}
+fdrtype = "$fdrtype"
+fdrthresh = $fdrthresh
+useNDF = $useNDF
+fQ = $fQ # non-differential centile cutoff
+myTitle = "$title"
+sids = strsplit("$subjectids",',')
+subjects = unlist(sids)
+nsubj = length(subjects)
+TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1
+CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1 
+cat('Got TCols=')
+cat(TCols)
+cat('; CCols=')
+cat(CCols)
+cat('\n')
+<![CDATA[
+useCols = c(TCols,CCols)
+if (file.exists(Out_Dir) == F) dir.create(Out_Dir)
+Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') 
+snames = colnames(Count_Matrix)
+nsamples = length(snames)
+if (nsubj >  0 & nsubj != nsamples) {
+options("show.error.messages"=T)
+mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','),
+   'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=','))
+write(mess, stderr())
+quit(save="no",status=4)
+}
+if (length(subjects) != 0) {subjects = subjects[useCols]}
+Count_Matrix = Count_Matrix[,useCols] ### reorder columns
+rn = rownames(Count_Matrix)
+islib = rn %in% c('librarySize','NotInBedRegions')
+LibSizes = Count_Matrix[subset(rn,islib),][1] # take first
+Count_Matrix = Count_Matrix[subset(rn,! islib),]
+group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) )             
+group = factor(group, levels=c(ControlName,TreatmentName))
+colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_")        
+results = edgeIt(Count_Matrix=Count_Matrix,group=group, out_edgeR=out_edgeR, out_Voom=out_Voom, out_DESeq2=out_DESeq2,
+                 fdrtype='BH',mydesign=NULL,priordf=edgeR_priordf,fdrthresh=fdrthresh,outputdir='.',
+                 myTitle=myTitle,useNDF=F,libSize=c(),filterquantile=fQ,subjects=subjects,TreatmentName=TreatmentName,ControlName=ControlName,
+                 doDESeq2=doDESeq2,doVoom=doVoom,doCamera=doCamera,doedgeR=doedgeR,org=org,
+                 histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fitType=DESeq_fitType,robust_meth=edgeR_robust_meth)
+sessionInfo()
+
+sink()
+]]>
+</configfile>
+</configfiles>
+<help>
+
+**What it does**
+
+Allows short read sequence counts from controlled experiments to be analysed for differentially expressed genes.
+Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design.
+
+**Input**
+
+Requires a count matrix as a tabular file. These are best made using the companion HTSeq_ based counter Galaxy wrapper
+and your fave gene model to generate inputs. Each row is a genomic feature (gene or exon eg) and each column the 
+non-negative integer count of reads from one sample overlapping the feature.
+
+The matrix must have a header row uniquely identifying the source samples, and unique row names in 
+the first column. Typically the row names are gene symbols or probe ids for downstream use in GSEA and other methods.
+They must be unique and R names or they will be mangled - please read the fine R docs for the rules on identifiers. 
+
+**Specifying comparisons**
+
+This is basically dumbed down for two factors - case vs control.
+
+More complex interfaces are possible but painful at present. 
+Probably need to specify a phenotype file to do this better.
+Work in progress. Send code.
+
+If you have (eg) paired samples and wish to include a term in the GLM to account for some other factor (subject in the case of paired samples),
+put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or 
+A list of integers, one for each subject or an empty string if samples are all independent.
+If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix.
+Integers for samples that are not in the analysis *must* be present in the string as filler even if not used.
+
+So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones
+eg if you had 6 samples with the first two independent but the second and third pairs each being from independent subjects. you might use
+8,9,1,1,2,2 
+as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6
+
+**Methods available**
+
+You can run 3 popular Bioconductor packages available for count data.
+
+edgeR - see edgeR_ for details
+
+VOOM/limma - see limma_VOOM_ for details
+
+DESeq2 - see DESeq2_ for details
+
+and optionally camera in edgeR which works better if MSigDB is installed.
+
+**Outputs**
+
+Some helpful plots and analysis results. Note that most of these are produced using R code 
+suggested by the excellent documentation and vignettes for the Bioconductor
+packages invoked. The Tool Factory is used to automatically lay these out for you to enjoy.
+
+**Note on Voom**
+
+The voom from limma version 3.16.6 help in R includes this from the authors - but you should read the paper to interpret this method.
+
+This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma.
+
+voom is an acronym for mean-variance modelling at the observational level.
+The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation.
+Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend.
+This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance.
+The weights are then used in the linear modelling process to adjust for heteroscedasticity.
+
+In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess.
+The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag.
+The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag.
+Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays.
+Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation.
+This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data.
+
+
+Author(s)
+
+Charity Law and Gordon Smyth
+
+References
+
+Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia.
+
+Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts.
+Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia.
+http://www.statsci.org/smyth/pubs/VoomPreprint.pdf
+
+See Also
+
+A voom case study is given in the edgeR User's Guide.
+
+vooma is a similar function but for microarrays instead of RNA-seq.
+
+
+***old rant on changes to Bioconductor package variable names between versions***
+
+The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue) 
+breaking this and all other code that assumed the old name for this variable, 
+between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). 
+This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing 
+to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly
+when their old scripts break. This tool currently now works with 2.4.6.
+
+**Note on prior.N**
+
+http://seqanswers.com/forums/showthread.php?t=5591 says:
+
+*prior.n*
+
+The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. 
+You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood 
+in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your 
+tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the 
+common likelihood the weight of one observation.
+
+In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, 
+or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that 
+you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation 
+(squeezing) of the tagwise dispersions. How many samples do you have in your experiment? 
+What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10. 
+If you have more samples, then the tagwise dispersion estimates will be more reliable, 
+so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. 
+
+
+From Bioconductor Digest, Vol 118, Issue 5, Gordon writes:
+
+Dear Dorota,
+
+The important settings are prior.df and trend.
+
+prior.n and prior.df are related through prior.df = prior.n * residual.df,
+and your experiment has residual.df = 36 - 12 = 24.  So the old setting of
+prior.n=10 is equivalent for your data to prior.df = 240, a very large
+value.  Going the other way, the new setting of prior.df=10 is equivalent
+to prior.n=10/24.
+
+To recover old results with the current software you would use
+
+  estimateTagwiseDisp(object, prior.df=240, trend="none")
+
+To get the new default from old software you would use
+
+  estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE)
+
+Actually the old trend method is equivalent to trend="loess" in the new
+software. You should use plotBCV(object) to see whether a trend is
+required.
+
+Note you could also use
+
+  prior.n = getPriorN(object, prior.df=10)
+
+to map between prior.df and prior.n.
+
+----
+
+**Attributions**
+
+edgeR - edgeR_ 
+
+VOOM/limma - limma_VOOM_ 
+
+DESeq2 - DESeq2_ for details
+
+See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package.
+
+Galaxy_ (that's what you are using right now!) for gluing everything together 
+
+Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is 
+licensed to you under the LGPL_ like other rgenetics artefacts
+
+.. _LGPL: http://www.gnu.org/copyleft/lesser.html
+.. _HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/index.html
+.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
+.. _DESeq2: http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
+.. _limma_VOOM: http://www.bioconductor.org/packages/release/bioc/html/limma.html
+.. _Galaxy: http://getgalaxy.org
+</help>
+
+</tool>
+
+

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/edgeRtest1out.html	Tue Jan 06 19:36:41 2015 -0500
@@ -0,0 +1,621 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> 
+        <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> 
+        <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> 
+        <meta name="generator" content="Galaxy rgToolFactory.py tool output - see http://getgalaxy.org/" /> 
+        <title></title> 
+        <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> 
+        </head> 
+        <body> 
+        <div class="toolFormBody"> 
+        
+<div class="infomessage">Galaxy Tool "Differential_Counts" run at 28/12/2014 21:02:37</div><br/>
+<div class="toolFormTitle">DESeq2 images and outputs</div>
+(Click on a thumbnail image to download the corresponding original PDF image)<br/>
+<div><table class="simple" cellpadding="2" cellspacing="2">
+<tr>
+<td><a href="DESeq2_edgeRtest_MA_plot.pdf"><img src="DESeq2_edgeRtest_MA_plot.png" title="Click to download a PDF of DESeq2_edgeRtest_MA_plot.pdf" hspace="5" width="400" 
+                           alt="Image called DESeq2_edgeRtest_MA_plot.pdf"/></a></td>
+
+<td><a href="DESeq2_edgeRtest_PCA_plot.pdf"><img src="DESeq2_edgeRtest_PCA_plot.png" title="Click to download a PDF of DESeq2_edgeRtest_PCA_plot.pdf" hspace="5" width="400" 
+                           alt="Image called DESeq2_edgeRtest_PCA_plot.pdf"/></a></td>
+
+<td><a href="DESeq2_edgeRtest_dispersion_estimates.pdf"><img src="DESeq2_edgeRtest_dispersion_estimates.png" title="Click to download a PDF of DESeq2_edgeRtest_dispersion_estimates.pdf" hspace="5" width="400" 
+                           alt="Image called DESeq2_edgeRtest_dispersion_estimates.pdf"/></a></td>
+</tr>
+<tr>
+<td><a href="DESeq2_edgeRtest_qqplot.pdf"><img src="DESeq2_edgeRtest_qqplot.png" title="Click to download a PDF of DESeq2_edgeRtest_qqplot.pdf" hspace="5" width="400" 
+                           alt="Image called DESeq2_edgeRtest_qqplot.pdf"/></a></td>
+
+<td><a href="DESeq2_edgeRtest_sample_distance_plot.pdf"><img src="DESeq2_edgeRtest_sample_distance_plot.png" title="Click to download a PDF of DESeq2_edgeRtest_sample_distance_plot.pdf" hspace="5" width="400" 
+                           alt="Image called DESeq2_edgeRtest_sample_distance_plot.pdf"/></a></td>
+
+<td>&nbsp;</td>
+</tr></table></div>
+
+<div class="toolFormTitle">DESeq2 log output</div>
+
+<pre>
+
+# DESeq top 50
+
+                     Contig     baseMean log2FoldChange     lfcSE       stat        pvalue          padj  NReads                                                                                               URL
+
+Mir192               Mir192 271352.97636       6.965264 0.2150593  32.387646 4.096935e-230 3.818343e-227 2325567               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir192'>Mir192</a>
+
+Mir122a             Mir122a  10112.31117      10.312083 0.3292695  31.318061 2.649329e-215 1.234587e-212   90428             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir122a'>Mir122a</a>
+
+Mir149               Mir149    810.35429      -6.911118 0.2341392 -29.517132 1.735536e-191 5.391733e-189    6164               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir149'>Mir149</a>
+
+Mir23a               Mir23a   1289.18043      -3.104086 0.1191688 -26.047815 1.424245e-149 3.318491e-147   10118               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23a'>Mir23a</a>
+
+Mir181d             Mir181d    275.22797      -3.581172 0.1778187 -20.139461  3.329373e-90  6.205952e-88    2139             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181d'>Mir181d</a>
+
+Mir204               Mir204    347.57397      -7.284200 0.3771119 -19.315751  3.959346e-83  6.150183e-81    2601               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir204'>Mir204</a>
+
+Mir23b               Mir23b   2028.55377      -2.065110 0.1085802 -19.019217  1.182361e-80  1.574229e-78   16387               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23b'>Mir23b</a>
+
+Mir27a               Mir27a   2788.72629      -3.016676 0.1688167 -17.869539  2.036708e-71  2.372765e-69   21886               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27a'>Mir27a</a>
+
+Mir195               Mir195    519.86200      -3.152795 0.1784796 -17.664734  7.838131e-70  8.116820e-68    3962               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir195'>Mir195</a>
+
+Mir194-2           Mir194-2    391.65678       5.222911 0.3099275  16.852045  1.013492e-63  9.445744e-62    3570           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-2'>Mir194-2</a>
+
+Mir208b             Mir208b   1649.77924     -11.396172 0.6771238 -16.830264  1.464482e-63  1.240816e-61   14756             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208b'>Mir208b</a>
+
+Mir10b               Mir10b  27820.40551      -5.071453 0.3044884 -16.655656  2.753110e-62  2.138249e-60  197340               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir10b'>Mir10b</a>
+
+Mir181c             Mir181c   2765.96510      -3.660964 0.2275711 -16.087120  3.141152e-58  2.251965e-56   23605             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181c'>Mir181c</a>
+
+Mir208a             Mir208a    616.76981     -10.356524 0.6559218 -15.789267  3.688391e-56  2.455415e-54    4638             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208a'>Mir208a</a>
+
+Mir490               Mir490    220.99790      -8.059660 0.5142876 -15.671504  2.369067e-55  1.471980e-53    1741               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir490'>Mir490</a>
+
+Mir203               Mir203    772.92882       1.990849 0.1274099  15.625546  4.877239e-55  2.840992e-53    6739               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir203'>Mir203</a>
+
+Mir215               Mir215    152.78082      -3.004380 0.1939090 -15.493765  3.822341e-54  2.095542e-52    1182               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir215'>Mir215</a>
+
+Dnm3os               Dnm3os    179.61643      -3.278392 0.2166491 -15.132265  9.922045e-52  5.137415e-50    1401               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Dnm3os'>Dnm3os</a>
+
+Mir214               Mir214    134.69038      -3.216444 0.2154916 -14.926074  2.230149e-50  1.093947e-48    1048               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir214'>Mir214</a>
+
+Mir21                 Mir21  26121.31011       2.963903 0.2008617  14.755939  2.817433e-49  1.312924e-47  229120                 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir21'>Mir21</a>
+
+Mir1948             Mir1948    263.89527       7.074045 0.4867226  14.534039  7.374076e-48  3.272685e-46    2404             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1948'>Mir1948</a>
+
+Mir27b               Mir27b  76478.05753      -1.904653 0.1312889 -14.507339  1.088626e-47  4.611815e-46  625308               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27b'>Mir27b</a>
+
+Rabggtb             Rabggtb   2257.19195       1.988368 0.1401741  14.184987  1.134862e-45  4.598659e-44   19535             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Rabggtb'>Rabggtb</a>
+
+Mir499               Mir499    712.45950     -10.577061 0.7528467 -14.049423  7.766426e-45  3.015962e-43    6527               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir499'>Mir499</a>
+
+Mir101b             Mir101b   6846.19683       3.791681 0.2809666  13.495132  1.670548e-41  6.227801e-40   59019             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir101b'>Mir101b</a>
+
+Mir132               Mir132    106.46062      -2.797928 0.2083376 -13.429779  4.046171e-41  1.450397e-39     857               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir132'>Mir132</a>
+
+Mir143hg           Mir143hg 180217.77425      -2.169143 0.1685614 -12.868566  6.764677e-38  2.335066e-36 1407364           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir143hg'>Mir143hg</a>
+
+Mir143               Mir143 179219.35960      -2.170303 0.1696199 -12.795094  1.746402e-37  5.813025e-36 1399819               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir143'>Mir143</a>
+
+Mir155               Mir155     57.66182      -3.788079 0.3056585 -12.393175  2.845516e-35  9.144898e-34     463               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir155'>Mir155</a>
+
+Mir322               Mir322    899.53469      -3.126011 0.2622596 -11.919531  9.363380e-33  2.908890e-31    7074               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir322'>Mir322</a>
+
+Mir378               Mir378    483.21548      -2.994300 0.2577321 -11.617876  3.343461e-31  1.005195e-29    4075               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir378'>Mir378</a>
+
+Mir24-2             Mir24-2    424.48288      -2.712674 0.2361028 -11.489378  1.491830e-30  4.213289e-29    3470             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir24-2'>Mir24-2</a>
+
+Mir3074-2         Mir3074-2    424.48288      -2.712674 0.2361028 -11.489378  1.491830e-30  4.213289e-29    3470         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3074-2'>Mir3074-2</a>
+
+Mir199b             Mir199b     47.84725      -5.294373 0.4644474 -11.399295  4.215163e-30  1.155451e-28     370             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199b'>Mir199b</a>
+
+Mir802               Mir802    166.83414       8.816580 0.7782636  11.328527  9.478530e-30  2.523997e-28    1514               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir802'>Mir802</a>
+
+Mir125b-2         Mir125b-2    493.08516      -2.919341 0.2631193 -11.095122  1.324798e-28  3.429754e-27    3837         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-2'>Mir125b-2</a>
+
+Mir301               Mir301    260.53406      -1.676984 0.1526772 -10.983852  4.570133e-28  1.151179e-26    2119               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir301'>Mir301</a>
+
+Snord104           Snord104   3851.90119       2.386573 0.2173857  10.978522  4.847915e-28  1.189015e-26   33458           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord104'>Snord104</a>
+
+Mir150               Mir150    553.20599      -2.836881 0.2595088 -10.931734  8.127991e-28  1.942381e-26    4229               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir150'>Mir150</a>
+
+Mir148a             Mir148a 118994.46955       2.678852 0.2481801  10.793984  3.675045e-27  8.562855e-26 1002397             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir148a'>Mir148a</a>
+
+5430416N02Rik 5430416N02Rik     62.15966       3.089960 0.2941123  10.506053  8.101331e-26  1.841571e-24     564 <a href='http://www.genecards.org/index.php?path=/Search/keyword/5430416N02Rik'>5430416N02Rik</a>
+
+Mir193               Mir193     45.70861       4.991530 0.4814098  10.368568  3.446495e-25  7.647936e-24     421               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir193'>Mir193</a>
+
+Mir3073             Mir3073     98.93199       8.208709 0.7944742  10.332254  5.036321e-25  1.091593e-23     904             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3073'>Mir3073</a>
+
+Mir125b-1         Mir125b-1     79.01988      -3.020660 0.2937360 -10.283590  8.355635e-25  1.769875e-23     609         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-1'>Mir125b-1</a>
+
+2610203C20Rik 2610203C20Rik     79.17666      -3.023491 0.2948614 -10.253939  1.136165e-24  2.353124e-23     610 <a href='http://www.genecards.org/index.php?path=/Search/keyword/2610203C20Rik'>2610203C20Rik</a>
+
+Mir181a-1         Mir181a-1     59.53826      -3.151487 0.3211628  -9.812740  9.923710e-23  2.010630e-21     506         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181a-1'>Mir181a-1</a>
+
+Mir184               Mir184     32.23796      -4.865023 0.4962776  -9.803028  1.092606e-22  2.166615e-21     247               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir184'>Mir184</a>
+
+Mir199a-2         Mir199a-2     44.84878      -3.422216 0.3545647  -9.651880  4.826276e-22  9.371019e-21     352         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199a-2'>Mir199a-2</a>
+
+Mir182               Mir182    886.79583       4.919630 0.5101689   9.643140  5.255515e-22  9.996204e-21    7189               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir182'>Mir182</a>
+
+Snord91a           Snord91a    168.95251       2.700421 0.2835464   9.523738  1.670595e-21  3.113990e-20    1437           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord91a'>Snord91a</a>
+
+
+</pre>
+
+<div class="toolFormTitle">Differential images and outputs</div>
+(Click on a thumbnail image to download the corresponding original PDF image)<br/>
+<div><table class="simple" cellpadding="2" cellspacing="2">
+<tr>
+<td><a href="Differential_rowsum_bar_charts.pdf"><img src="Differential_rowsum_bar_charts.png" title="Click to download a PDF of Differential_rowsum_bar_charts.pdf" hspace="5" width="400" 
+                           alt="Image called Differential_rowsum_bar_charts.pdf"/></a></td>
+
+<td><a href="Differential_venn_edgeRtest_significant_genes_overlap.pdf"><img src="Differential_venn_edgeRtest_significant_genes_overlap.png" title="Click to download a PDF of Differential_venn_edgeRtest_significant_genes_overlap.pdf" hspace="5" width="400" 
+                           alt="Image called Differential_venn_edgeRtest_significant_genes_overlap.pdf"/></a></td>
+</tr>
+
+</table></div>
+
+<div class="toolFormTitle">Differential log output</div>
+
+<pre>
+
+[1] @@@ Quantiles for non-zero row counts:
+
+       0%       10%       20%       30%       40%       50%       60%       70%       80%       90%      100% 
+
+      1.0       1.0       2.0       3.0       4.0       8.0      13.0      24.0      86.6     753.0 2325567.0 
+
+[1] Read 3242 contigs. Removed 1494 with no reads. After filtering at count quantile =0.3, there are 1141 contigs
+
+[1] "@@ using genecards substitution for urls"
+
+[1] @@ Total low count contigs per sample =  1,1,1,1,1,1,1,1
+
+[1] Using samples: liver_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam,liver_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam,heart_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam
+
+[1] Using design matrix:
+
+  (Intercept) groupliver
+
+1           1          1
+
+2           1          1
+
+3           1          1
+
+4           1          1
+
+5           1          0
+
+6           1          0
+
+7           1          0
+
+8           1          0
+
+attr(,"assign")
+
+[1] 0 1
+
+attr(,"contrasts")
+
+attr(,"contrasts")$group
+
+[1] contr.treatment
+
+[1] "Raw sample read totals 2443751,1644652,1682104,1806045,1440960,1341813,2888924,1428365"
+
+[1] heart liver
+
+
+</pre>
+
+<div class="toolFormTitle">Differential log output</div>
+
+<pre>
+
+Attaching package: ‘gplots’
+
+The following object is masked from ‘package:stats’:
+
+    lowess
+
+Loading required package: 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 objects are masked from ‘package:parallel’:
+
+    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB
+
+The following object is masked from ‘package:limma’:
+
+    plotMA
+
+The following object is masked from ‘package:stats’:
+
+    xtabs
+
+The following objects are masked from ‘package:base’:
+
+    anyDuplicated, append, as.data.frame, as.vector, cbind, colnames, duplicated, eval, evalq, Filter, Find, get, intersect, is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rep.int, rownames, sapply, setdiff, sort, table, tapply, union, unique, unlist
+
+Loading required package: IRanges
+
+Attaching package: ‘IRanges’
+
+The following object is masked from ‘package:gplots’:
+
+    space
+
+Loading required package: XVector
+
+Loading required package: Rcpp
+
+Loading required package: RcppArmadillo
+
+gene-wise dispersion estimates
+
+mean-dispersion relationship
+
+final dispersion estimates
+
+you had estimated gene-wise dispersions, removing these
+
+you had estimated fitted dispersions, removing these
+
+you had estimated gene-wise dispersions, removing these
+
+you had estimated fitted dispersions, removing these
+
+Warning message:
+
+closing unused connection 4 (edgeR.log) 
+
+Warning message:
+
+In sink() : no sink to remove
+
+
+</pre>
+
+<div class="toolFormTitle">VOOM images and outputs</div>
+(Click on a thumbnail image to download the corresponding original PDF image)<br/>
+<div><table class="simple" cellpadding="2" cellspacing="2">
+<tr>
+<td><a href="VOOM_edgeRtest_mean_variance_plot.pdf"><img src="VOOM_edgeRtest_mean_variance_plot.png" title="Click to download a PDF of VOOM_edgeRtest_mean_variance_plot.pdf" hspace="5" width="400" 
+                           alt="Image called VOOM_edgeRtest_mean_variance_plot.pdf"/></a></td>
+
+<td><a href="VOOM_edgeRtest_qqplot.pdf"><img src="VOOM_edgeRtest_qqplot.png" title="Click to download a PDF of VOOM_edgeRtest_qqplot.pdf" hspace="5" width="400" 
+                           alt="Image called VOOM_edgeRtest_qqplot.pdf"/></a></td>
+</tr>
+
+</table></div>
+
+<div class="toolFormTitle">VOOM log output</div>
+
+<pre>
+
+# VOOM top 50
+
+                     Contig      logFC    AveExpr          t      P.Value    adj.P.Val         B  NReads                                                                                               URL
+
+Mir192               Mir192   6.689950 14.4417888  50.335160 1.802287e-16 2.056409e-13 27.414844 2325567               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir192'>Mir192</a>
+
+Mir208a             Mir208a -10.458438  3.8918506 -29.183545 2.249812e-13 1.283518e-10 19.141041    4638             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208a'>Mir208a</a>
+
+Mir3073             Mir3073   8.318578  2.6485638  25.821264 1.102217e-12 4.192097e-10 18.063600     904             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3073'>Mir3073</a>
+
+Mir802               Mir802   8.992449  2.9857711  25.195575 1.514327e-12 4.319618e-10 17.906674    1514               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir802'>Mir802</a>
+
+Mir208b             Mir208b -12.256447  4.4678897 -22.360114 7.074494e-12 1.614400e-09 16.920424   14756             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208b'>Mir208b</a>
+
+Mir499               Mir499 -11.104485  3.8066799 -21.990054 8.769804e-12 1.667724e-09 16.728874    6527               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir499'>Mir499</a>
+
+Mir10b               Mir10b  -4.775768 12.4173688 -21.487387 1.180685e-11 1.924516e-09 17.249348  197340               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir10b'>Mir10b</a>
+
+Mir148a             Mir148a   2.751538 15.4237642  20.289553 2.464883e-11 3.515539e-09 16.455471 1002397             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir148a'>Mir148a</a>
+
+Mir490               Mir490  -8.497742  3.6613221 -18.336110 8.980482e-11 1.138526e-08 13.923237    1741               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir490'>Mir490</a>
+
+Mir122a             Mir122a  10.197963  8.1512374  17.467826 1.663427e-10 1.897970e-08 14.215445   90428             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir122a'>Mir122a</a>
+
+Mir133b             Mir133b  -6.172367  1.3497975 -17.274094 1.916064e-10 1.987481e-08 13.840201     159             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir133b'>Mir133b</a>
+
+Mir149               Mir149  -7.041176  6.0886889 -16.861286 2.602547e-10 2.474589e-08 13.714880    6164               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir149'>Mir149</a>
+
+Mir101b             Mir101b   3.837883 10.6216725  15.443054 7.873164e-10 6.910215e-08 13.054350   59019             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir101b'>Mir101b</a>
+
+Mir143               Mir143  -1.912927 16.0353646 -14.922755 1.209475e-09 9.857220e-08 12.374988 1399819               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir143'>Mir143</a>
+
+Mir194-2           Mir194-2   5.534694  6.2627211  14.703097 1.455682e-09 1.107289e-07 12.316769    3570           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-2'>Mir194-2</a>
+
+Mir23a               Mir23a  -2.905961  8.6431895 -14.558394 1.646894e-09 1.174441e-07 12.339306   10118               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23a'>Mir23a</a>
+
+Mir1983             Mir1983  -5.612359  1.1061384 -14.266537 2.119488e-09 1.422551e-07 11.743589     101             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1983'>Mir1983</a>
+
+Mir27a               Mir27a  -2.849084 10.0939084 -14.158498 2.329669e-09 1.476752e-07 11.960195   21886               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27a'>Mir27a</a>
+
+Cyp3a25             Cyp3a25   6.312461  1.6425308  13.845627 3.074630e-09 1.846396e-07 11.502713     226             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Cyp3a25'>Cyp3a25</a>
+
+Mir200a             Mir200a   6.129125  1.8320913  13.226966 5.410979e-09 3.086963e-07 10.979834     264             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200a'>Mir200a</a>
+
+Mir181d             Mir181d  -3.405544  6.3702152 -13.064584 6.300369e-09 3.423201e-07 11.006301    2139             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181d'>Mir181d</a>
+
+Mir153               Mir153  -5.698257  1.5328802 -12.832092 7.856705e-09 3.829623e-07 10.583835     140               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir153'>Mir153</a>
+
+Mir204               Mir204  -7.718081  4.5031856 -12.808496 8.036265e-09 3.829623e-07 10.353229    2601               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir204'>Mir204</a>
+
+Gm5441               Gm5441  -5.716851  1.5430406 -12.806028 8.055298e-09 3.829623e-07 10.562881     142               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Gm5441'>Gm5441</a>
+
+Rabggtb             Rabggtb   2.327908  9.9369857  12.760291 8.416902e-09 3.841474e-07 10.654006   19535             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Rabggtb'>Rabggtb</a>
+
+Mir504               Mir504  -5.122304  0.8161671 -12.391521 1.205304e-08 5.289430e-07 10.144865      69               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir504'>Mir504</a>
+
+Mir133a-1         Mir133a-1  -4.912497  0.7297882 -12.335045 1.274466e-08 5.385801e-07 10.076395      60         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir133a-1'>Mir133a-1</a>
+
+Mir195               Mir195  -2.954216  7.3530970 -12.081859 1.641098e-08 6.603731e-07 10.055879    3962               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir195'>Mir195</a>
+
+Mir27b               Mir27b  -1.496991 14.9464877 -12.059553 1.678424e-08 6.603731e-07  9.674850  625308               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27b'>Mir27b</a>
+
+Snord52             Snord52   2.631712  9.7652181  11.922618 1.928407e-08 7.334374e-07  9.811774   18059             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord52'>Snord52</a>
+
+Mir322               Mir322  -3.029558  8.1188344 -11.736839 2.333148e-08 8.587488e-07  9.679815    7074               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir322'>Mir322</a>
+
+Mir181c             Mir181c  -3.676262  9.6244506 -11.575598 2.758358e-08 9.835271e-07  9.453057   23605             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181c'>Mir181c</a>
+
+Mir1948             Mir1948   7.101780  4.7821564  11.471202 3.077353e-08 1.033009e-06  9.233632    2404             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1948'>Mir1948</a>
+
+0610031O16Rik 0610031O16Rik   4.519875  0.7388871  11.470939 3.078205e-08 1.033009e-06  9.284014      78 <a href='http://www.genecards.org/index.php?path=/Search/keyword/0610031O16Rik'>0610031O16Rik</a>
+
+Mir201               Mir201  -4.964105  0.7490919 -11.289794 3.729283e-08 1.210650e-06  9.114028      63               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir201'>Mir201</a>
+
+Mir21                 Mir21   2.746616 13.2835800  11.267331 3.819755e-08 1.210650e-06  8.925217  229120                 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir21'>Mir21</a>
+
+Mir184               Mir184  -5.569565  2.3521173 -11.190343 4.148052e-08 1.279170e-06  8.854599     247               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir184'>Mir184</a>
+
+1810019D21Rik 1810019D21Rik   5.164581  1.0784751  11.082009 4.662057e-08 1.399844e-06  8.951908     117 <a href='http://www.genecards.org/index.php?path=/Search/keyword/1810019D21Rik'>1810019D21Rik</a>
+
+Mir203               Mir203   2.216791  8.5426169  10.866758 5.896538e-08 1.725115e-06  8.715639    6739               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir203'>Mir203</a>
+
+1110038B12Rik 1110038B12Rik   2.383720 10.6847245  10.832157 6.125623e-08 1.744094e-06  8.573067   37066 <a href='http://www.genecards.org/index.php?path=/Search/keyword/1110038B12Rik'>1110038B12Rik</a>
+
+Snord104           Snord104   2.571210 10.4798167  10.811468 6.267120e-08 1.744094e-06  8.561110   33458           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord104'>Snord104</a>
+
+Mir182               Mir182   5.196800  7.2088299  10.640454 7.579543e-08 2.021320e-06  8.545839    7189               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir182'>Mir182</a>
+
+Mir547               Mir547  -4.542934  0.5799793 -10.635980 7.617593e-08 2.021320e-06  8.435473      42               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir547'>Mir547</a>
+
+Mir143hg           Mir143hg  -2.291921 16.3789153 -10.597275 7.955395e-08 2.062978e-06  7.952584 1407364           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir143hg'>Mir143hg</a>
+
+Scnn1b               Scnn1b  -4.541403  0.5700621 -10.243065 1.190487e-07 3.018546e-06  8.023327      45               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Scnn1b'>Scnn1b</a>
+
+Mir125b-2         Mir125b-2  -2.896115  7.2737925 -10.091091 1.420082e-07 3.522420e-06  7.876068    3837         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-2'>Mir125b-2</a>
+
+Mir1a-1             Mir1a-1  -4.402568  0.4498447  -9.950346 1.675164e-07 4.066729e-06  7.692790      42             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1a-1'>Mir1a-1</a>
+
+Mir378               Mir378  -2.733247  7.2964165  -9.922980 1.730212e-07 4.112858e-06  7.672216    4075               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir378'>Mir378</a>
+
+Mir199b             Mir199b  -5.651345  2.8029895  -9.883978 1.812024e-07 4.219426e-06  7.548022     370             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199b'>Mir199b</a>
+
+Mir155               Mir155  -4.158272  3.8002361  -9.845490 1.896814e-07 4.328530e-06  7.604112     463               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir155'>Mir155</a>
+
+
+</pre>
+
+<div class="toolFormTitle">edgeR images and outputs</div>
+(Click on a thumbnail image to download the corresponding original PDF image)<br/>
+<div><table class="simple" cellpadding="2" cellspacing="2">
+<tr>
+<td><a href="edgeR_edgeRtest_BCV_vs_abundance.pdf"><img src="edgeR_edgeRtest_BCV_vs_abundance.png" title="Click to download a PDF of edgeR_edgeRtest_BCV_vs_abundance.pdf" hspace="5" width="400" 
+                           alt="Image called edgeR_edgeRtest_BCV_vs_abundance.pdf"/></a></td>
+
+<td><a href="edgeR_edgeRtest_GoodnessofFit.pdf"><img src="edgeR_edgeRtest_GoodnessofFit.png" title="Click to download a PDF of edgeR_edgeRtest_GoodnessofFit.pdf" hspace="5" width="400" 
+                           alt="Image called edgeR_edgeRtest_GoodnessofFit.pdf"/></a></td>
+
+<td><a href="edgeR_edgeRtest_MDSplot.pdf"><img src="edgeR_edgeRtest_MDSplot.png" title="Click to download a PDF of edgeR_edgeRtest_MDSplot.pdf" hspace="5" width="400" 
+                           alt="Image called edgeR_edgeRtest_MDSplot.pdf"/></a></td>
+</tr>
+<tr>
+<td><a href="edgeR_edgeRtest_qqplot.pdf"><img src="edgeR_edgeRtest_qqplot.png" title="Click to download a PDF of edgeR_edgeRtest_qqplot.pdf" hspace="5" width="400" 
+                           alt="Image called edgeR_edgeRtest_qqplot.pdf"/></a></td>
+
+<td><a href="edgeR_edgeRtest_smearplot.pdf"><img src="edgeR_edgeRtest_smearplot.png" title="Click to download a PDF of edgeR_edgeRtest_smearplot.pdf" hspace="5" width="400" 
+                           alt="Image called edgeR_edgeRtest_smearplot.pdf"/></a></td>
+
+<td><a href="edgeR_edgeRtest_top_100_heatmap.pdf"><img src="edgeR_edgeRtest_top_100_heatmap.png" title="Click to download a PDF of edgeR_edgeRtest_top_100_heatmap.pdf" hspace="5" width="400" 
+                           alt="Image called edgeR_edgeRtest_top_100_heatmap.pdf"/></a></td>
+</tr>
+
+</table></div>
+
+<div class="toolFormTitle">edgeR log output</div>
+
+<pre>
+
+[1] Common Dispersion = 0.228651460998105 CV =  0.478175136323613 getPriorN =  3.33333333333333
+
+[1] "No GLM fit outlier genes found\n"
+
+[1] @@ edgeR Top tags\n
+
+                       Name      logFC    logCPM        LR        PValue   adj.p.value Dispersion totreads                                                                                               URL
+
+Mir208a             Mir208a -11.840751  8.465017 594.16946 3.104543e-131 3.542284e-128 0.05171220     4638             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208a'>Mir208a</a>
+
+Mir149               Mir149  -7.008984  8.861767 484.30321 2.473909e-107 1.411365e-104 0.04959937     6164               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir149'>Mir149</a>
+
+Mir208b             Mir208b -13.291635  9.905945 417.69758  7.737463e-93  2.942815e-90 0.10508096    14756             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir208b'>Mir208b</a>
+
+Mir122a             Mir122a  10.514683 12.478088 415.17429  2.740525e-92  7.817349e-90 0.10803882    90428             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir122a'>Mir122a</a>
+
+Mir204               Mir204  -7.498162  7.634507 341.30678  3.313430e-76  7.561247e-74 0.06907958     2601               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir204'>Mir204</a>
+
+Mir499               Mir499 -13.577454  8.700078 325.79199  7.930755e-73  1.508165e-70 0.12042284     6527               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir499'>Mir499</a>
+
+Mir490               Mir490  -8.534394  6.991023 303.17184  6.710366e-68  1.093790e-65 0.07949711     1741               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir490'>Mir490</a>
+
+Mir192               Mir192   6.953853 17.169364 217.22867  3.638307e-49  5.189135e-47 0.12700995  2325567               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir192'>Mir192</a>
+
+Mir802               Mir802  11.440805  6.593380 212.88059  3.231644e-48  4.097007e-46 0.12273671     1514               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir802'>Mir802</a>
+
+Mir1948             Mir1948   7.418142  7.252734 195.66958  1.840248e-44  2.099723e-42 0.12060221     2404             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir1948'>Mir1948</a>
+
+Mir194-2           Mir194-2   5.298950  7.811522 191.85588  1.250960e-43  1.297587e-41 0.08670751     3570           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir194-2'>Mir194-2</a>
+
+Mir23a               Mir23a  -3.153807  9.529402 177.53185  1.676248e-40  1.593833e-38 0.04442763    10118               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23a'>Mir23a</a>
+
+Mir181c             Mir181c  -3.767686 10.639598 169.87390  7.883295e-39  6.919107e-37 0.06368883    23605             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181c'>Mir181c</a>
+
+Mir3073             Mir3073  10.686337  5.859950 164.86740  9.778593e-38  7.969554e-36 0.14069249      904             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3073'>Mir3073</a>
+
+Mir181d             Mir181d  -3.643963  7.300371 162.18591  3.767663e-37  2.865936e-35 0.05729574     2139             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181d'>Mir181d</a>
+
+Mir195               Mir195  -3.203683  8.215089 150.20548  1.563314e-34  1.114838e-32 0.05235020     3962               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir195'>Mir195</a>
+
+Mir10b               Mir10b  -5.182616 13.946466 147.24793  6.926819e-34  4.649118e-32 0.12268790   197340               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir10b'>Mir10b</a>
+
+Mir101b             Mir101b   3.759962 11.863187 136.31359  1.703812e-31  1.080028e-29 0.07961343    59019             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir101b'>Mir101b</a>
+
+Mir378               Mir378  -3.115599  8.119617 126.76408  2.092233e-29  1.256441e-27 0.05942391     4075               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir378'>Mir378</a>
+
+Mir27a               Mir27a  -3.064687 10.642480 124.98911  5.117477e-29  2.919520e-27 0.06113852    21886               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir27a'>Mir27a</a>
+
+Mir182               Mir182   5.057509  8.846381 123.17765  1.275060e-28  6.927826e-27 0.13653707     7189               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir182'>Mir182</a>
+
+Mir322               Mir322  -3.194159  9.012888 107.34926  3.732413e-25  1.935765e-23 0.07536483     7074               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir322'>Mir322</a>
+
+Mir199b             Mir199b  -5.520119  4.792610 102.10724  5.259607e-24  2.609223e-22 0.13417024      370             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir199b'>Mir199b</a>
+
+Mir181a-2         Mir181a-2  -3.000177  7.637692 101.38361  7.578821e-24  3.603098e-22 0.06896654     2817         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir181a-2'>Mir181a-2</a>
+
+Mir125b-2         Mir125b-2  -2.987759  8.144514  91.72544  9.957640e-22  4.488356e-20 0.07737381     3837         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir125b-2'>Mir125b-2</a>
+
+Dnm3os               Dnm3os  -3.331215  6.686950  91.67250  1.022763e-21  4.488356e-20 0.08810497     1401               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Dnm3os'>Dnm3os</a>
+
+Mir184               Mir184  -5.111350  4.234160  84.35542  4.133639e-20  1.686711e-18 0.13502324      247               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir184'>Mir184</a>
+
+Mir215               Mir215  -3.058208  6.447966  84.35278  4.139167e-20  1.686711e-18 0.08138517     1182               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir215'>Mir215</a>
+
+Mir133b             Mir133b  -8.383611  3.584760  83.96681  5.031517e-20  1.960318e-18 0.17482280      159             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir133b'>Mir133b</a>
+
+Mir150               Mir150  -2.883446  8.307765  83.91918  5.154210e-20  1.960318e-18 0.08008123     4229               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir150'>Mir150</a>
+
+Mir3074-2         Mir3074-2  -2.778308  7.935651  83.74839  5.619282e-20  2.040616e-18 0.07424646     3470         <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir3074-2'>Mir3074-2</a>
+
+Mir24-2             Mir24-2  -2.778307  7.935651  83.71222  5.723024e-20  2.040616e-18 0.07427992     3470             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir24-2'>Mir24-2</a>
+
+Mir193               Mir193   5.176579  4.801090  83.19222  7.445011e-20  2.574169e-18 0.14794861      421               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir193'>Mir193</a>
+
+Scarna17           Scarna17   2.182159  9.244479  81.91330  1.421894e-19  4.771710e-18 0.04982909     9224           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Scarna17'>Scarna17</a>
+
+Mir214               Mir214  -3.271172  6.271755  80.43948  2.997458e-19  9.771712e-18 0.09566584     1048               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir214'>Mir214</a>
+
+Snord104           Snord104   2.330488 11.053611  79.50529  4.809369e-19  1.524303e-17 0.05915990    33458           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord104'>Snord104</a>
+
+Mir200a             Mir200a   7.201555  4.139422  77.35503  1.428304e-18  4.365755e-17 0.19287764      264             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200a'>Mir200a</a>
+
+Mir200b             Mir200b   6.525423  5.752604  77.31985  1.453976e-18  4.365755e-17 0.26237966      888             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir200b'>Mir200b</a>
+
+Mir21                 Mir21   2.923147 13.825255  75.51798  3.620938e-18  1.059357e-16 0.09395834   229120                 <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir21'>Mir21</a>
+
+Mir203               Mir203   1.956427  8.767610  75.17870  4.299815e-18  1.226522e-16 0.04381710     6739               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir203'>Mir203</a>
+
+Mir155               Mir155  -3.886731  5.068563  73.81316  8.587210e-18  2.389758e-16 0.12522673      463               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir155'>Mir155</a>
+
+Cyp3a25             Cyp3a25   8.681501  3.972085  72.29680  1.851471e-17  5.029829e-16 0.23125383      226             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Cyp3a25'>Cyp3a25</a>
+
+Rabggtb             Rabggtb   1.934093 10.298211  72.02043  2.129809e-17  5.651422e-16 0.04596646    19535             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Rabggtb'>Rabggtb</a>
+
+Mir23b               Mir23b  -2.100584 10.184110  71.44225  2.854935e-17  7.403367e-16 0.05416378    16387               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir23b'>Mir23b</a>
+
+Snord52             Snord52   2.207491 10.217554  71.27974  3.100027e-17  7.860292e-16 0.05941483    18059             <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord52'>Snord52</a>
+
+Gm5441               Gm5441  -6.881248  3.538457  70.05615  5.764004e-17  1.429724e-15 0.20097284      142               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Gm5441'>Gm5441</a>
+
+Mir153               Mir153  -6.857671  3.517446  69.37600  8.137282e-17  1.975455e-15 0.20158808      140               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir153'>Mir153</a>
+
+Mir132               Mir132  -2.858294  5.938312  64.52507  9.531204e-16  2.265647e-14 0.09274248      857               <a href='http://www.genecards.org/index.php?path=/Search/keyword/Mir132'>Mir132</a>
+
+1110038B12Rik 1110038B12Rik   2.195962 11.253090  62.92015  2.152583e-15  5.012443e-14 0.06712174    37066 <a href='http://www.genecards.org/index.php?path=/Search/keyword/1110038B12Rik'>1110038B12Rik</a>
+
+Snord91a           Snord91a   2.654072  6.557504  62.40549  2.795431e-15  6.379174e-14 0.08637410     1437           <a href='http://www.genecards.org/index.php?path=/Search/keyword/Snord91a'>Snord91a</a>
+
+[1] @@ 416 tags significant at adj p= 0.05
+
+
+</pre>
+
+<div class="toolFormTitle">Other log output</div>
+
+<pre>
+
+## Toolfactory generated command line = Rscript - None None
+
+Got TCols=1 5 6 7; CCols=2 3 4 8
+
+R version 3.0.2 (2013-09-25)
+
+Platform: x86_64-pc-linux-gnu (64-bit)
+
+locale:
+
+ [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C               LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8     LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8    LC_PAPER=en_AU.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       
+
+attached base packages:
+
+[1] parallel  splines   methods   stats     graphics  grDevices utils     datasets  base     
+
+other attached packages:
+
+ [1] RColorBrewer_1.0-5      DESeq2_1.2.10           RcppArmadillo_0.4.400.0 Rcpp_0.11.2             GenomicRanges_1.14.4    XVector_0.2.0           IRanges_1.20.7          BiocGenerics_0.8.0      edgeR_3.4.2             limma_3.18.13           gplots_2.15.0           stringr_0.6.2          
+
+loaded via a namespace (and not attached):
+
+ [1] annotate_1.40.1      AnnotationDbi_1.24.0 Biobase_2.22.0       bitops_1.0-6         caTools_1.17.1       DBI_0.3.0            gdata_2.13.3         genefilter_1.44.0    grid_3.0.2           gtools_3.4.1         KernSmooth_2.23-13   lattice_0.20-29      locfit_1.5-9.1       RSQLite_0.11.4       stats4_3.0.2         survival_2.37-7      XML_3.98-1.1         xtable_1.7-4        
+
+
+</pre>
+
+<div class="toolFormTitle">All output files available for downloading</div>
+
+<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr>
+
+<tr><td><a href="DESeq2.log">DESeq2.log</a></td><td>10.5 KB</td></tr>
+<tr class="odd_row"><td><a href="DESeq2_edgeRtest_MA_plot.pdf">DESeq2_edgeRtest_MA_plot.pdf</a></td><td>15.0 KB</td></tr>
+<tr><td><a href="DESeq2_edgeRtest_PCA_plot.pdf">DESeq2_edgeRtest_PCA_plot.pdf</a></td><td>4.9 KB</td></tr>
+<tr class="odd_row"><td><a href="DESeq2_edgeRtest_dispersion_estimates.pdf">DESeq2_edgeRtest_dispersion_estimates.pdf</a></td><td>190.6 KB</td></tr>
+<tr><td><a href="DESeq2_edgeRtest_qqplot.pdf">DESeq2_edgeRtest_qqplot.pdf</a></td><td>13.3 KB</td></tr>
+<tr class="odd_row"><td><a href="DESeq2_edgeRtest_sample_distance_plot.pdf">DESeq2_edgeRtest_sample_distance_plot.pdf</a></td><td>9.5 KB</td></tr>
+<tr><td><a href="Differential.log">Differential.log</a></td><td>1.4 KB</td></tr>
+<tr class="odd_row"><td><a href="Differential_Counts.Rscript">Differential_Counts.Rscript</a></td><td>28.3 KB</td></tr>
+<tr><td><a href="Differential_Counts_error.log">Differential_Counts_error.log</a></td><td>1.8 KB</td></tr>
+<tr class="odd_row"><td><a href="Differential_Counts_runner.log">Differential_Counts_runner.log</a></td><td>1.3 KB</td></tr>
+<tr><td><a href="Differential_rowsum_bar_charts.pdf">Differential_rowsum_bar_charts.pdf</a></td><td>6.3 KB</td></tr>
+<tr class="odd_row"><td><a href="Differential_venn_edgeRtest_significant_genes_overlap.pdf">Differential_venn_edgeRtest_significant_genes_overlap.pdf</a></td><td>9.7 KB</td></tr>
+<tr><td><a href="VOOM.log">VOOM.log</a></td><td>10.1 KB</td></tr>
+<tr class="odd_row"><td><a href="VOOM_edgeRtest_mean_variance_plot.pdf">VOOM_edgeRtest_mean_variance_plot.pdf</a></td><td>18.3 KB</td></tr>
+<tr><td><a href="VOOM_edgeRtest_qqplot.pdf">VOOM_edgeRtest_qqplot.pdf</a></td><td>17.5 KB</td></tr>
+<tr class="odd_row"><td><a href="edgeR.log">edgeR.log</a></td><td>10.4 KB</td></tr>
+<tr><td><a href="edgeR_edgeRtest_BCV_vs_abundance.pdf">edgeR_edgeRtest_BCV_vs_abundance.pdf</a></td><td>17.4 KB</td></tr>
+<tr class="odd_row"><td><a href="edgeR_edgeRtest_GoodnessofFit.pdf">edgeR_edgeRtest_GoodnessofFit.pdf</a></td><td>13.1 KB</td></tr>
+<tr><td><a href="edgeR_edgeRtest_MDSplot.pdf">edgeR_edgeRtest_MDSplot.pdf</a></td><td>4.9 KB</td></tr>
+<tr class="odd_row"><td><a href="edgeR_edgeRtest_qqplot.pdf">edgeR_edgeRtest_qqplot.pdf</a></td><td>15.2 KB</td></tr>
+<tr><td><a href="edgeR_edgeRtest_smearplot.pdf">edgeR_edgeRtest_smearplot.pdf</a></td><td>16.7 KB</td></tr>
+<tr class="odd_row"><td><a href="edgeR_edgeRtest_top_100_heatmap.pdf">edgeR_edgeRtest_top_100_heatmap.pdf</a></td><td>11.2 KB</td></tr>
+</table></div><br/>
+</div></body></html>
+

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/edgeRtest1out.xls	Tue Jan 06 19:36:41 2015 -0500
@@ -0,0 +1,1142 @@
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+Gm16291	-0.0497984136905431	1.04049827004916	0.00165184486677905	0.96758058758877	0.979600222217201	0.760533853439203	14	0.752820037051481	0.612579053628728	2.66280975855643	0	2.23470729485323	1.54680939617797	0	0.725352245598938	2	1	5	0	3	2	0	1
+Gm4890	-0.0259416163380367	0.436081268353407	0.00151936462515234	0.968907109122493	0.98007359176309	0.663367644323302	4	0	0	1.06512390342257	0	0	0	0.741834401735823	0	0	0	2	0	0	0	2	0
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+Mir3103	-0.0011092316730465	0.437273490728585	0.00054275250380531	0.981413325432899	0.987471432380016	0.663329937374097	4	0	0.612579053628728	0	0.539638866040004	0	0	0.741834401735823	0	0	1	0	1	0	0	2	0
+Mir877	0.0169238059397901	2.07117778204749	0.000493226392977775	0.982281477037185	0.987474154448835	0.411290532451564	45	2.25846011115444	4.2880533754011	0.532561951711286	5.93602752644005	2.23470729485323	3.09361879235595	1.85458600433956	5.8028179647915	6	7	1	11	3	4	5	8
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+Zfp821	0.0194908209613808	0.438251386312134	0.000105716899237329	0.991796397330917	0.994410974828274	0.663299009834018	4	0	1.22515810725746	0	0	0	0	0.741834401735823	0	0	2	0	0	0	0	2	0
+Snord7	-0.00363412912961608	0.808396104681926	2.65747690626483e-05	0.99588686572452	0.997635569615169	1.23423123101037	10	2.25846011115444	0	0	0	0.744902431617745	0	0.370917200867911	1.45070449119788	6	0	0	0	1	0	1	2
+D330022K07Rik	8.83246306215062e-05	2.3059219632559	9.35429808279764e-07	0.999228305160102	0.999852120027929	0.587915295555401	56	4.89333024083463	0.612579053628728	9.05355317909186	1.07927773208001	4.46941458970647	7.73404698088987	1.11275160260373	2.90140898239575	13	1	17	2	6	10	3	4
+AA465934	-0.00551213095838715	1.05434721413031	3.43509380940077e-08	0.999852120027929	0.999852120027929	1.50064027311007	15	3.38769016673167	0	0	0	0.744902431617745	0	0.741834401735823	2.17605673679681	9	0	0	0	1	0	2	3

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gentestdata.sh	Tue Jan 06 19:36:41 2015 -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"
+
+

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/test_bams2mx.xls	Tue Jan 06 19:36:41 2015 -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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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_dependencies.xml	Tue Jan 06 19:36:41 2015 -0500
@@ -0,0 +1,90 @@
+<?xml version="1.0"?>
+<tool_dependency>
+    <package name="R" version="3.1.2">
+        <repository changeset_revision="f0626dac6765" name="package_r_3_1_2" owner="iuc" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" />
+    </package>
+    <package name="graphicsmagick" version="1.3.18">
+        <repository changeset_revision="bff3f66adff2" name="package_graphicsmagick_1_3" owner="iuc" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" />
+    </package>
+    <package name="ghostscript" version="9.10">
+        <repository changeset_revision="9345d2740f0c" name="package_ghostscript_9_10" owner="devteam" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" />
+    </package>
+
+    <package name="biocbasics" version="2.14">
+        <install version="1.0">
+            <actions>
+                <action type="setup_r_environment">
+                    <repository changeset_revision="f0626dac6765" name="package_r_3_1_2" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu">
+                        <package name="R" version="3.1.2" />
+                    </repository>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/stringr_0.6.2.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/gtools_3.4.1.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/gdata_2.13.3.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/bitops_1.0-6.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/caTools_1.17.1.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/KernSmooth_2.23-13.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/gplots_2.15.0.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/limma_3.22.1.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/edgeR_3.8.5.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/BiocGenerics_0.8.0.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/S4Vectors_0.4.0.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/IRanges_2.0.1.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/GenomeInfoDb_1.2.4.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/XVector_0.6.0.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/GenomicRanges_1.18.3.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/RcppArmadillo_0.4.600.0.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/Biobase_2.26.0.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/codetools_0.2-9.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/iterators_1.0.7.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/foreach_1.4.2.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/checkmate_1.5.1.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/BBmisc_1.8.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/brew_1.0-6.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/DBI_0.3.1.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/digest_0.6.8.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/fail_1.2.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/RSQLite_1.0.0.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/base64enc_0.1-2.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/sendmailR_1.2-1.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/BatchJobs_1.5.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/BiocParallel_1.0.0.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/AnnotationDbi_1.28.1.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/XML_3.98-1.1.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/xtable_1.7-4.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/annotate_1.44.0.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/survival_2.37-7.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/genefilter_1.48.1.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/latticeExtra_0.6-26.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/locfit_1.5-9.1.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/RColorBrewer_1.1-2.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/geneplotter_1.44.0.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/plyr_1.8.1.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/gtable_0.1.2.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/reshape2_1.4.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/dichromat_2.0-0.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/colorspace_1.2-4.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/munsell_0.4.2.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/labeling_0.3.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/scales_0.2.4.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/proto_0.3-10.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/MASS_7.3-35.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/ggplot2_1.0.0.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/Formula_1.1-2.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/cluster_1.15.3.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/rpart_4.1-8.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/nnet_7.3-8.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/acepack_1.3-3.3.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/foreign_0.8-61.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/Hmisc_3.14-6.tar.gz?raw=true</package>
+                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/DESeq2_1.6.3.tar.gz?raw=true</package>
+                    
+                </action>
+            </actions>
+        </install>
+        <readme>
+        Differential gene expression analysis
+        You may need libxml2-dev for XML to compile
+        Ubuntu has a bug with libgfortran. To fix that create a symlink like: sudo ln -s /usr/lib/x86_64-linux-gnu/libgfortran.so.3 /usr/lib/x86_64-linux-gnu/libgfortran.so
+        </readme>
+    </package>
+</tool_dependency>