Galaxy | (sandbox for testing) |

Changeset 0:82e0af566160 (2013-06-12)

Next changeset 1:34cbb9e749da (2013-06-12)

Commit message:
Uploaded

added:
rgedgeR/datatypes_conf_xml.sample
rgedgeR/installBioC.R
rgedgeR/rgGSEA.py
rgedgeR/rgGSEAcolumns.xml
rgedgeR/rgToolFactory.py
rgedgeR/rgedgeR.xml
rgedgeR/rgedgeRglm.xml
rgedgeR/rgedgeRpaired.xml
rgedgeR/rgedgeRpaired.xml.iaas1
rgedgeR/test-data/edgeRtest1out.html
rgedgeR/test-data/edgeRtest1out.xls
rgedgeR/test-data/gentestdata.sh
rgedgeR/test-data/gentestdata.sh~
rgedgeR/test-data/test_bams2mx.xls
rgedgeR/tool_dependencies.xml

diff -r 000000000000 -r 82e0af566160 rgedgeR/datatypes_conf_xml.sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/datatypes_conf_xml.sample Wed Jun 12 02:58:43 2013 -0400

@@ -0,0 +1,2 @@
+<datatype extension="gseagmt" type="galaxy.datatypes.tabular:Tabular" subclass="True" display_in_upload="true" mimetype="application/vnd.ms-excel"/>
+<datatype extension="gseachip" type="galaxy.datatypes.tabular:Tabular" subclass="True" display_in_upload="true" mimetype="application/vnd.ms-excel"/>

diff -r 000000000000 -r 82e0af566160 rgedgeR/installBioC.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/installBioC.R Wed Jun 12 02:58:43 2013 -0400

@@ -0,0 +1,4 @@
+source('http://bioconductor.org/biocLite.R')
+installme=c('edgeR','limma','DESeq','DEXSeq','ggplot2','gplots')
+biocLite()
+biocLite(installme)

diff -r 000000000000 -r 82e0af566160 rgedgeR/rgGSEA.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/rgGSEA.py Wed Jun 12 02:58:43 2013 -0400

[

b'@@ -0,0 +1,494 @@\n+"""\n+April 2013\n+eeesh GSEA does NOT respect the mode flag!\n+\n+Now realise that the creation of the input rank file for gsea needs to take the lowest p value for duplicate \n+feature names. To make Ish\'s life easier, remove duplicate gene ids from any gene set to stop GSEA from \n+barfing.\n+\n+October 14 2012\n+Amazingly long time to figure out that GSEA fails with useless error message if any filename contains a dash "-"\n+eesh.\n+\n+Added history .gmt source - requires passing a faked name to gsea \n+Wrapper for GSEA http://www.broadinstitute.org/gsea/index.jsp\n+Started Feb 22 \n+Copyright 2012 Ross Lazarus\n+All rights reserved\n+Licensed under the LGPL\n+\n+called eg as\n+\n+#!/bin/sh\n+GALAXY_LIB="/data/extended/galaxy/lib"\n+if [ "$GALAXY_LIB" != "None" ]; then\n+ if [ -n "$PYTHONPATH" ]; then\n+ PYTHONPATH="$GALAXY_LIB:$PYTHONPATH"\n+ else\n+ PYTHONPATH="$GALAXY_LIB"\n+ fi\n+ export PYTHONPATH\n+fi\n+\n+cd /data/extended/galaxy/database/job_working_directory/027/27311\n+python /data/extended/galaxy/tools/rgenetics/rgGSEA.py --input_tab "/data/extended/galaxy/database/files/033/dataset_33806.dat" --adjpvalcol "5" --signcol "2"\n+--idcol "1" --outhtml "/data/extended/galaxy/database/files/034/dataset_34455.dat" --input_name "actaearly-Controlearly-actalate-Controllate_topTable.xls"\n+--setMax "500" --setMin "15" --nPerm "1000" --plotTop "20"\n+--gsea_jar "/data/extended/galaxy/tool-data/shared/jars/gsea2-2.0.12.jar"\n+--output_dir "/data/extended/galaxy/database/job_working_directory/027/27311/dataset_34455_files" --mode "Max_probe"\n+ --title " actaearly-Controlearly-actalate-Controllate_interpro_GSEA" --builtin_gmt "/data/genomes/gsea/3.1/IPR_DOMAIN.gmt"\n+\n+\n+"""\n+import optparse\n+import tempfile\n+import os\n+import sys\n+import subprocess\n+import time\n+import shutil\n+import glob\n+import math\n+import re\n+\n+KEEPSELECTION = False # detailed records for selection of multiple probes\n+\n+def timenow():\n+ """return current time as a string\n+ """\n+ return time.strftime(\'%d/%m/%Y %H:%M:%S\', time.localtime(time.time()))\n+\n+\n+\n+def fix_subdir(adir,destdir):\n+ """ Galaxy wants everything in the same files_dir\n+ if os.path.exists(adir):\n+ for (d,dirs,files) in os.path.walk(adir):\n+ for f in files:\n+ sauce = os.path.join(d,f) \n+ shutil.copy(sauce,destdir) \n+ """\n+\n+ def fixAffycrap(apath=\'\'):\n+ """class=\'richTable\'>RUNNING ES</th><th class=\'richTable\'>CORE ENRICHMENT</th><tr><td class=\'lessen\'>1</td>\n+ <td><a href=\'https://www.affymetrix.com/LinkServlet?probeset=LBR\'>LBR</a></td><td></td><td></td><td>1113</td>\n+ <td>0.194</td><td>-0.1065</td><td>No</td></tr><tr><td class=\'lessen\'>2</td><td>\n+ <a href=\'https://www.affymetrix.com/LinkServlet?probeset=GGPS1\'>GGPS1</a></td><td></td><td></td><td>4309</td><td>0.014</td><td>-0.4328</td>\n+ <td>No</td></tr>\n+ """\n+ html = []\n+ try:\n+ html = open(apath,\'r\').readlines() \n+ except:\n+ return html\n+ for i,row in enumerate(html):\n+ row = re.sub(\'https\\:\\/\\/www.affymetrix.com\\/LinkServlet\\?probeset=\',"http://www.genecards.org/index.php?path=/Search/keyword/",row)\n+ html[i] = row\n+ return html\n+\n+ cleanup = False\n+ if os.path.exists(adir):\n+ flist = os.listdir(adir) # get all files created\n+ for f in flist:\n+ apath = os.path.join(adir,f)\n+ dest = os.path.join(destdir,f)\n+ if not os.path.isdir(apath):\n+ if os.path.splitext(f)[1].lower() == \'.html\':\n+ html = fixAffycrap(apath)\n+ fixed = open(apath,\'w\')\n+ fixed.write(\'\\n\'.join(html))\n+ fixed.write(\'\\n\')\n+ fixed.close()\n+ if not os.path.isfile(dest):\n+ shutil.copy(apath,dest)\n+ else:\n+ fix_subdir(apath,destdir)\n+ if cleanup:\n+ '..b'n(html))\n+ htmlf.write(\'\\n\')\n+ htmlf.close()\n+ os.unlink(self.fakeRanks)\n+ os.unlink(self.fakeGMT)\n+ if opts.outtab_neg:\n+ tabs = glob.glob(os.path.join(opts.output_dir,"gsea_report_for_*.xls"))\n+ if len(tabs) > 0:\n+ for tabi,t in enumerate(tabs):\n+ tkind = os.path.basename(t).split(\'_\')[4].lower()\n+ if tkind == \'neg\':\n+ outtab = opts.outtab_neg\n+ elif tkind == \'pos\':\n+ outtab = opts.outtab_pos\n+ else:\n+ print >> sys.stderr, \'## tab file matched %s which is not "neg" or "pos" in 4th segment %s\' % (t,tkind)\n+ sys.exit()\n+ content = open(t).readlines()\n+ tabf = open(outtab,\'w\')\n+ tabf.write(\'\'.join(content))\n+ tabf.close()\n+ else:\n+ print >> sys.stdout, \'Odd, maketab = %s but no matches - tabs = %s\' % (makeTab,tabs)\n+ return retval\n+ \n+\n+if __name__ == "__main__":\n+ """ \n+ called as:\n+ <command interpreter="python">rgGSEA.py --input_ranks "$input1" --outhtml "$html_file"\n+ --setMax "$setMax" --setMin "$setMin" --nPerm "$nPerm" --plotTop "$plotTop" --gsea_jar "$GALAXY_DATA_INDEX_DIR/shared/jars/gsea2-2.07.jar" \n+ --output_dir "$html_file.files_path" --use_gmt ""${use_gmt.fields.path}"" --chip "${use_chip.fields.path}"\n+ </command>\n+ """\n+ op = optparse.OptionParser()\n+ a = op.add_option\n+ a(\'--input_ranks\',default=None)\n+ a(\'--input_tab\',default=None)\n+ a(\'--input_name\',default=None)\n+ a(\'--use_gmt\',default=None)\n+ a(\'--history_gmt\',default=None)\n+ a(\'--builtin_gmt\',default=None)\n+ a(\'--history_gmt_name\',default=None)\n+ a(\'--setMax\',default="500")\n+ a(\'--setMin\',default="15")\n+ a(\'--nPerm\',default="1000") \n+ a(\'--title\',default="GSEA report") \n+ a(\'--chip\',default=\'\')\n+ a(\'--plotTop\',default=\'20\')\n+ a(\'--outhtml\',default=None)\n+ a(\'--makeTab\',default=None)\n+ a(\'--output_dir\',default=None)\n+ a(\'--outtab_neg\',default=None)\n+ a(\'--outtab_pos\',default=None)\n+ a(\'--adjpvalcol\',default=None)\n+ a(\'--signcol\',default=None)\n+ a(\'--idcol\',default=None)\n+ a(\'--mode\',default=\'Max_probe\')\n+ a(\'-j\',\'--gsea_jar\',default=\'/usr/local/bin/gsea2-2.07.jar\')\n+ opts, args = op.parse_args() \n+ assert os.path.isfile(opts.gsea_jar),\'## GSEA runner unable to find supplied gsea java desktop executable file %s\' % opts.gsea_jar\n+ if opts.input_ranks:\n+ inpf = opts.input_ranks\n+ else:\n+ inpf = opts.input_tab\n+ assert opts.idcol <> None, \'## GSEA runner needs an id column if a tabular file provided\'\n+ assert opts.signcol <> None, \'## GSEA runner needs a sign column if a tabular file provided\'\n+ assert opts.adjpvalcol <> None, \'## GSEA runner needs an adjusted p value column if a tabular file provided\'\n+ assert os.path.isfile(inpf),\'## GSEA runner unable to open supplied input file %s\' % inpf\n+ if opts.chip > \'\':\n+ assert os.path.isfile(opts.chip),\'## GSEA runner unable to open supplied chip file %s\' % opts.chip\n+ some = None\n+ if opts.history_gmt <> None:\n+ some = 1\n+ assert os.path.isfile(opts.history_gmt),\'## GSEA runner unable to open supplied history gene set matrix (.gmt) file %s\' % opts.history_gmt\n+ if opts.builtin_gmt <> None:\n+ some = 1\n+ assert os.path.isfile(opts.builtin_gmt),\'## GSEA runner unable to open supplied history gene set matrix (.gmt) file %s\' % opts.builtin_gmt\n+ assert some, \'## GSEA runner needs a gene set matrix file - none chosen?\'\n+ opts.title = re.sub(\'[^a-zA-Z0-9_]+\', \'\', opts.title)\n+ myName=os.path.split(sys.argv[0])[-1]\n+ gse = gsea_wrapper(myName, opts=opts)\n+ retcode = gse.run()\n+ if retcode <> 0:\n+ sys.exit(retcode) # indicate failure to job runner\n+ \n+ \n'

diff -r 000000000000 -r 82e0af566160 rgedgeR/rgGSEAcolumns.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/rgGSEAcolumns.xml Wed Jun 12 02:58:43 2013 -0400

b'@@ -0,0 +1,834 @@\n+<tool id="rgGSEAcolumns" name="Gene set enrichment" version="0.05">\n+ <description>using a generic tabular file</description>\n+ <requirements>\n+ <requirement type="package" version="2.0.12">gsea_jar</requirement>\n+ <requirement type="set_environment">GSEAJAR_PATH</requirement>\n+ </requirements>\n+ <command interpreter="python">rgGSEA.py --input_tab "$input1" --adjpvalcol "$adjpvalcol" --signcol "$signcol" \n+ --idcol "$idcol" --outhtml "$html_file" --input_name "${input1.name}"\n+ --setMax "$setMax" --setMin "$setMin" --nPerm "$nPerm" --plotTop "$plotTop" \n+ --gsea_jar "\\$GSEAJAR_PATH" \n+ --output_dir "$html_file.files_path" --mode "$mode" --title "$title"\n+#if $makeTab.value==\'Yes\'\n+ --outtab_pos "$outtab_pos" --outtab_neg "$outtab_neg"\n+#end if\n+#if $gmtSource.refgmtSource == "indexed" or $gmtSource.refgmtSource == "both":\n+--builtin_gmt "${gmtSource.builtinGMT.fields.path}"\n+#end if\n+#if $gmtSource.refgmtSource == "history" or $gmtSource.refgmtSource == "both":\n+--history_gmt "${gmtSource.ownGMT}" --history_gmt_name "${gmtSource.ownGMT.name}"\n+#end if\n+#if $chipSource.refchipSource=="builtin"\n+ --chip "${chipSource.builtinChip.fields.path}"\n+#end if\n+#if $chipSource.refchipSource=="history"\n+ --chip "${chipSource.ownChip}"\n+#end if\n+</command>\n+ <inputs>\n+ <param name="input1" type="data" format="tabular" label="Select a tab delimited file with a probe id, adjusted p value and a signed weight (eg t-test statistic) on each row"\n+ help=""/>\n+ <param name="adjpvalcol" label="Column containing a p value for the DEG statistical test" \n+ help = "Use RAW p-values - not FDR adjusted as these have a non GSEA friendly non-uniform distribution"\n+ type="data_column" data_ref="input1" numerical="True" \n+ multiple="false" use_header_names="true" size="5">\n+ <validator type="no_options" message="Please select a p value column."/>\n+ </param>\n+ <param name="signcol" label="Column containing the DE sign - eg log fold change so positive/negative values = upregulated/downregulated in treatment" \n+ type="data_column" data_ref="input1" numerical="True" \n+ multiple="false" use_header_names="true" size="5">\n+ <validator type="no_options" message="Please select a sign column."/>\n+ </param>\n+ <param name="idcol" label="Column containing a gene id (refseq, symbol or Entrez" \n+ type="data_column" data_ref="input1" numerical="False" \n+ multiple="false" use_header_names="true" size="5">\n+ <validator type="no_options" message="Please select an id column."/>\n+ </param>\n+\n+ <param name="title" type="text" value="GSEA" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain"/>\n+ <param name="setMin" type="integer" label="Minimum gene set size to prune (default=15)" size="5" value="15"/>\n+ <param name="setMax" type="integer" label="Maximum gene set size to prune (default=500)" size="5" value="500"/>\n+ <param name="nPerm" type="integer" label="Number of permutations (default=1000)" size="7" value="1000"/>\n+ <param name="plotTop" type="integer" label="Number of top gene sets to plot and present in detailed reports(default=20)" size="10" value="20"/>\n+ <param name="mode" type="select" label="Mode for dealing with duplicated gene ids" >\n+ <option value="Max_probe" selected="true">Use the most extreme value</option>\n+ <option value="Median_of_probes">Use the median of all supplied values</option>\n+ </param>\n+ <param name="makeTab" type="select" label="Create a tabular report containing ALL gene sets for downstream analysis" >\n+ <option value="Yes">Yes</option>\n+ <option value="No" selected="true">No</option>\n+ </param>\n+ <conditional name="chipSource" >\n+ <param name="refchipSource" type="select" label="Translate the rank file IDs (first column) using a GSEA \'chip'..b"-regulated 4-like\n+\n+ Rosetta.chip \n+ Probe Set ID Gene Symbol Gene Title\n+ NM_000504 F10 coagulation factor X\n+ Contig32955_RC ARL6IP6 ADP-ribosylation-like factor 6 interacting protein 6\n+ AK000455 MGC16733 hypothetical gene MGC16733 similar to CG12113\n+\n+ RT_U34.chip \n+ Probe Set ID Gene Symbol Gene Title\n+ AA108277_at HSPH1 heat shock 105kDa/110kDa protein 1\n+ AA108308_i_at MDM2_PREDICTED Transformed mouse 3T3 cell double minute 2 homolog (mouse) (predicted)\n+ AA108308_s_at --- ---\n+\n+ RZPD_Human_Ensembl1.1.chip \n+ Probe Set ID Gene Symbol Gene Title\n+ RZPDp203A011001D SARDH sarcosine dehydrogenase\n+ RZPDp203A011002D ARHGAP22 Rho GTPase activating protein 22\n+ RZPDp203A011003D CNGA3 cyclic nucleotide gated channel alpha 3\n+\n+ RZPD_Human_ORF_Clones_Gateway.chip \n+ Probe Set ID Gene Symbol Gene Title\n+ RZPDo834A0110D - Gateway (closed) PTD015 PTD015 protein\n+ RZPDo834A0114D - Gateway (closed) TRIB3 tribbles homolog 3 (Drosophila)\n+ RZPDo834A0116D - Gateway (closed) ORM2 orosomucoid 2\n+\n+ RZPD_Human_Unigene3.1.chip \n+ Probe Set ID Gene Symbol Gene Title\n+ HU3_p983A011001D SARDH sarcosine dehydrogenase\n+ HU3_p983A011002D ARHGAP22 Rho GTPase activating protein 22\n+ HU3_p983A011003D CNGA3 cyclic nucleotide gated channel alpha 3\n+\n+ Seq_Accession.chip \n+ Probe Set ID Gene Symbol\n+ AA017197 C21ORF36\n+ AA191116 MTVR2\n+ AA280701 CXYORF7\n+\n+ Stanford.chip \n+ Probe Set ID Gene Symbol Gene Title\n+ IMAGE:703849 DDB2 damage-specific DNA binding protein 2, 48kDa\n+ IMAGE:1301778 ZFY zinc finger protein, Y-linked\n+ IMAGE:795810 HS.99503HS.520681 Homo sapiens transcribed sequenceHomo sapiens, clone IMAGE:4823270, mRNA\n+\n+ Stanford_Source_Accessions.chip \n+ Probe Set ID Gene Symbol Gene Title\n+ AI848107 0610010K14RIK RIKEN cDNA 0610010K14 gene\n+ AK002491 0610010K14RIK RIKEN cDNA 0610010K14 gene\n+ AK003842 0610010K14RIK RIKEN cDNA 0610010K14 gene\n+\n+ TIGR_31K_Human_Set.chip \n+ Probe Set ID Gene Symbol Gene Title\n+ 1-1 NULL NULL\n+ 1-10 WNT2 wingless-type MMTV integration site family member 2\n+ 1-11 VHL von Hippel-Lindau tumor suppressor\n+\n+ TIGR_40K_Human_Set.chip \n+ Probe Set ID Gene Symbol Gene Title\n+ 10 NULL NULL\n+ 100 TEX27 testis expressed sequence 27\n+ 1000 HOXA1 homeo box A1\n+\n+ U133_X3P.chip \n+ Probe Set ID Gene Symbol Gene Title\n+ 1053_3p_at RFC2 replication factor C (activator 1) 2, 40kDa\n+ 117_3p_at HSPA6 /// LOC652878 heat shock 70kDa protein 6 (HSP70B') /// similar to heat shock 70kDa protein 6 (HSP70B)\n+ 1494_3p_f_at CYP2A6 cytochrome P450, family 2, subfamily A, polypeptide 6\n+\n+ UCLA_NIH_33K.chip \n+ Probe Set ID Gene Symbol Gene Title\n+ 1020181 NULL NULL\n+ 1020315 VAV2 vav 2 oncogene\n+ 1020478 AP1GBP1 AP1 gamma subunit binding protein 1\n+\n+ Zebrafish.chip \n+ Probe Set ID Gene Symbol Gene Title\n+ AFFX-BioB-3_at --- ---\n+ AFFX-BioB-5_at --- ---\n+ AFFX-BioB-M_at --- ---\n+\n+\n+ .. _LGPL: http://www.gnu.org/copyleft/lesser.html\n+ .. _GSEA: http://www.broadinstitute.org/gsea\n+ .. _GUIDE: http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html?_Interpreting_GSEA_Results\n+ .. _MSigDB: http://www.broadinstitute.org/gsea/msigdb/index.jsp\n+ .. _2005Paper: http://www.pnas.org/content/102/43/15545.full\n+\n+</help>\n+\n+</tool>\n+\n+\n"

diff -r 000000000000 -r 82e0af566160 rgedgeR/rgToolFactory.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/rgToolFactory.py Wed Jun 12 02:58:43 2013 -0400

[

b'@@ -0,0 +1,575 @@\n+# rgToolFactory.py\n+# see https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home\n+# \n+# copyright ross lazarus (ross stop lazarus at gmail stop com) May 2012\n+# \n+# all rights reserved\n+# Licensed under the LGPL\n+# suggestions for improvement and bug fixes welcome at https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home\n+#\n+# January 2013\n+# problem pointed out by Carlos Borroto\n+# added escaping for <>$ - thought I did that ages ago...\n+#\n+# August 11 2012 \n+# changed to use shell=False and cl as a sequence\n+\n+# This is a Galaxy tool factory for simple scripts in python, R or whatever ails ye.\n+# It also serves as the wrapper for the new tool.\n+# \n+# you paste and run your script\n+# Only works for simple scripts that read one input from the history.\n+# Optionally can write one new history dataset,\n+# and optionally collect any number of outputs into links on an autogenerated HTML page.\n+\n+# DO NOT install on a public or important site - please.\n+\n+# installed generated tools are fine if the script is safe.\n+# They just run normally and their user cannot do anything unusually insecure\n+# but please, practice safe toolshed.\n+# Read the fucking code before you install any tool \n+# especially this one\n+\n+# After you get the script working on some test data, you can\n+# optionally generate a toolshed compatible gzip file\n+# containing your script safely wrapped as an ordinary Galaxy script in your local toolshed for\n+# safe and largely automated installation in a production Galaxy.\n+\n+# If you opt for an HTML output, you get all the script outputs arranged\n+# as a single Html history item - all output files are linked, thumbnails for all the pdfs.\n+# Ugly but really inexpensive.\n+# \n+# Patches appreciated please. \n+#\n+#\n+# long route to June 2012 product\n+# Behold the awesome power of Galaxy and the toolshed with the tool factory to bind them\n+# derived from an integrated script model \n+# called rgBaseScriptWrapper.py\n+# Note to the unwary:\n+# This tool allows arbitrary scripting on your Galaxy as the Galaxy user\n+# There is nothing stopping a malicious user doing whatever they choose\n+# Extremely dangerous!!\n+# Totally insecure. So, trusted users only\n+#\n+# preferred model is a developer using their throw away workstation instance - ie a private site.\n+# no real risk. The universe_wsgi.ini admin_users string is checked - only admin users are permitted to run this tool.\n+#\n+\n+import sys \n+import shutil \n+import subprocess \n+import os \n+import time \n+import tempfile \n+import optparse\n+import tarfile\n+import re\n+import shutil\n+import math\n+\n+progname = os.path.split(sys.argv[0])[1] \n+myversion = \'V000.2 June 2012\' \n+verbose = False \n+debug = False\n+toolFactoryURL = \'https://bitbucket.org/fubar/galaxytoolfactory\'\n+\n+def timenow():\n+ """return current time as a string\n+ """\n+ return time.strftime(\'%d/%m/%Y %H:%M:%S\', time.localtime(time.time()))\n+\n+cheetah_escape_table = {\n+ "$": "\\$"\n+ }\n+\n+cheetah_unescape_table = {\n+ "\\$": "$"\n+ }\n+\n+def html_escape(t):\n+ """Unescape \\$ first in case already done\n+ cheetah barfs if any $ without \\\n+ xml parsing is controlled with <![CDATA[...]]>\n+ """\n+ text = t\n+ for k in cheetah_unescape_table.keys():\n+ text = text.replace(k,cheetah_unescape_table[k])\n+ for k in cheetah_escape_table.keys():\n+ text = text.replace(k,cheetah_escape_table[k])\n+ return text\n+\n+\n+class ScriptRunner:\n+ """class is a wrapper for an arbitrary script\n+ """\n+\n+ def __init__(self,opts=None,treatbashSpecial=True):\n+ """\n+ cleanup inputs, setup some outputs\n+ \n+ """\n+ self.treatbashSpecial = treatbashSpecial\n+ if opts.output_dir: # simplify for the tool tarball\n+ os.chdir(opts.output_dir)\n+ self.thumbformat = \'jpg\'\n+ self.opts = opts\n+ self.toolname = re.sub(\'[^a-zA-Z0-9_]+\', \'\', opts.tool_name) # a sanitizer now does this but..\n+ sel'..b'- please confirm that parameters are sane</div>\' % self.opts.interpreter)\n+ rlog = open(self.tlog,\'r\').readlines()\n+ rlog = [x for x in rlog if x.strip() > \'\']\n+ if len(rlog) > 1:\n+ html.append(\'<div class="toolFormTitle">%s log</div><pre>\\n\' % self.opts.interpreter)\n+ html += rlog\n+ html.append(\'</pre>\\n\')\n+ html.append(galhtmlattr % (self.toolname))\n+ html.append(galhtmlpostfix)\n+ htmlf = file(self.opts.output_html,\'w\')\n+ htmlf.write(\'\\n\'.join(html))\n+ htmlf.write(\'\\n\')\n+ htmlf.close()\n+ self.html = html\n+\n+\n+ def run(self):\n+ """\n+ scripts must be small enough not to fill the pipe!\n+ """\n+ if self.treatbashSpecial and self.opts.interpreter in [\'bash\',\'sh\']:\n+ retval = self.runBash()\n+ else:\n+ if self.opts.output_dir:\n+ sto = open(self.tlog,\'w\')\n+ sto.write(\'## Toolfactory generated command line = %s\\n\' % \' \'.join(self.cl))\n+ sto.flush()\n+ p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=sto,stdin=subprocess.PIPE,cwd=self.opts.output_dir)\n+ else:\n+ p = subprocess.Popen(self.cl,shell=False,stdin=subprocess.PIPE)\n+ p.stdin.write(self.script)\n+ p.stdin.close()\n+ retval = p.wait()\n+ if self.opts.output_dir:\n+ sto.close()\n+ if self.opts.make_HTML:\n+ self.makeHtml()\n+ return retval\n+\n+ def runBash(self):\n+ """\n+ cannot use - for bash so use self.sfile\n+ """\n+ if self.opts.output_dir:\n+ s = \'## Toolfactory generated command line = %s\\n\' % \' \'.join(self.cl)\n+ sto = open(self.tlog,\'w\')\n+ sto.write(s)\n+ sto.flush()\n+ p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=sto,cwd=self.opts.output_dir)\n+ else:\n+ p = subprocess.Popen(self.cl,shell=False) \n+ retval = p.wait()\n+ if self.opts.output_dir:\n+ sto.close()\n+ if self.opts.make_HTML:\n+ self.makeHtml()\n+ return retval\n+ \n+\n+def main():\n+ u = """\n+ This is a Galaxy wrapper. It expects to be called by a special purpose tool.xml as:\n+ <command interpreter="python">rgBaseScriptWrapper.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript"\n+ </command>\n+ """\n+ op = optparse.OptionParser()\n+ a = op.add_option\n+ a(\'--script_path\',default=None)\n+ a(\'--tool_name\',default=None)\n+ a(\'--interpreter\',default=None)\n+ a(\'--output_dir\',default=None)\n+ a(\'--output_html\',default=None)\n+ a(\'--input_tab\',default="None")\n+ a(\'--output_tab\',default="None")\n+ a(\'--user_email\',default=\'Unknown\')\n+ a(\'--bad_user\',default=None)\n+ a(\'--make_Tool\',default=None)\n+ a(\'--make_HTML\',default=None)\n+ a(\'--help_text\',default=None)\n+ a(\'--tool_desc\',default=None)\n+ a(\'--new_tool\',default=None)\n+ a(\'--tool_version\',default=None)\n+ opts, args = op.parse_args()\n+ 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)\n+ assert opts.tool_name,\'## Tool Factory expects a tool name - eg --tool_name=DESeq\'\n+ assert opts.interpreter,\'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript\'\n+ assert os.path.isfile(opts.script_path),\'## Tool Factory wrapper expects a script path - eg --script_path=foo.R\'\n+ if opts.output_dir:\n+ try:\n+ os.makedirs(opts.output_dir)\n+ except:\n+ pass\n+ r = ScriptRunner(opts)\n+ if opts.make_Tool:\n+ retcode = r.makeTooltar()\n+ else:\n+ retcode = r.run()\n+ os.unlink(r.sfile)\n+ if retcode:\n+ sys.exit(retcode) # indicate failure to job runner\n+\n+\n+if __name__ == "__main__":\n+ main()\n+\n+\n'

diff -r 000000000000 -r 82e0af566160 rgedgeR/rgedgeR.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/rgedgeR.xml Wed Jun 12 02:58:43 2013 -0400

b'@@ -0,0 +1,504 @@\n+<tool id="rgedgeR" name="edgeR" version="0.18">\n+ <description>digital DGE between two groups of replicates</description>\n+ <command interpreter="python">\n+ rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "edgeR" \n+ --output_dir "$html_file.files_path" --output_html "$html_file" --output_tab "$outtab" --make_HTML "yes"\n+ </command>\n+ <inputs>\n+ <param name="input1" type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample"\n+ help="Use the HTSeq based count matrix preparation tool to create these count matrices from BAM files and a GTF file"/>\n+ <param name="title" type="text" value="DGE" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain">\n+ <sanitizer invalid_char="">\n+ <valid initial="string.letters,string.digits"><add value="_" /> </valid>\n+ </sanitizer>\n+ </param>\n+ <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/>\n+ <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True" \n+ multiple="true" use_header_names="true" size="120" display="checkboxes">\n+ <validator type="no_options" message="Please select at least one column."/>\n+ </param>\n+ <param name="control_name" type="text" value="Control" size="50" label="Control Name"/>\n+ <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True" \n+ multiple="true" use_header_names="true" size="120" display="checkboxes" optional="true">\n+ </param>\n+ <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"\n+ 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"/>\n+ <param name="useQuantile" type="boolean" truevalue="T" checked=\'false\' falsevalue="" size="1" label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples"\n+ 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"/>\n+ <param name="priorn" type="integer" value="4" size="3" label="prior.df for tagwise dispersion - lower value = more emphasis on each tag\'s variance - note this used to be prior.n"\n+ help="Zero = auto-estimate. 1 to force high variance tags out. Use a small value to \'smooth\' small samples. See edgeR docs and note below"/>\n+ <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control"\n+ help="Conventional default value of 0.05 recommended"/>\n+ <param name="fdrtype" type="select" label="FDR (Type II error) control method" \n+ help="Use fdr or bh typically to control for the number of tests in a reliable way">\n+ <option value="fdr" selected="true">fdr</option>\n+ <option value="BH">Benjamini Hochberg</option>\n+ <option value="BY">Benjamini Yukateli</option>\n+ <option value="bonferroni">Bonferroni</option>\n+ <option value="hochberg">Hochberg</option>\n+ <option value="holm">Holm</option>\n+ <option value="hommel">Hommel</option>\n+ <option value="none">no control for multiple tests</option>\n+ </param>\n+ </inputs>\n+ <outputs>\n+ <data format="tabular" name="outtab" label="${title}.xls"/>\n+ <data format="html" name="html_file" label="${title}.html"/>\n+ <data format="gsearank" name="outgsea" label="${title}.gsearank">\n+ <filter> makeRank == \'Yes\' </filter>\n+ </data>\n+ </outputs>\n+<configfiles>\n+<configfile name="runme">\n+\n+# edgeR.Rscript\n+#'..b'names(Count_Matrix),sep="_") #Relable columns\n+if (priorn <= 0) {priorn = ceiling(20/(length(group)-1))} # estimate prior.n if not provided\n+# see http://comments.gmane.org/gmane.comp.lang.r.sequencing/2009 \n+results = edgeIt(Count_Matrix=Count_Matrix,group=group,outputfilename=outputfilename,fdrtype=fdrtype,priorn=priorn,fdrthresh=fdrthresh,\n+ outputdir=Out_Dir,myTitle=myTitle,libSize=c(),useQuantile=useQuantile,filterquantile=fQ) #Run the main function\n+# for the log\n+\n+\n+sessionInfo()\n+\n+\n+</configfile>\n+</configfiles>\n+<tests>\n+<test>\n+<param name=\'input1\' value=\'DGEtest.xls\' ftype=\'tabular\' />\n+ <param name=\'treatment_name\' value=\'case\' />\n+ <param name=\'title\' value=\'DGEtest\' />\n+ <param name=\'fdrtype\' value=\'fdr\' />\n+ <param name=\'priorn\' value="5" />\n+ <param name=\'fdrthresh\' value="0.05" />\n+ <param name=\'control_name\' value=\'control\' />\n+ <param name=\'Treat_cols\' value=\'c3,c6,c9\' />\n+ <param name=\'Control_cols\' value=\'c2,c5,c8\' />\n+ <output name=\'outtab\' file=\'DGEtest1out.xls\' ftype=\'tabular\' compare=\'diff\' />\n+ <output name=\'html_file\' file=\'DGEtest1out.html\' ftype=\'html\' compare=\'diff\' lines_diff=\'20\' />\n+</test>\n+</tests>\n+<help>\n+**What it does**\n+\n+Performs digital gene expression analysis between a treatment and control on a matrix.\n+\n+**Documentation** Please see documentation_ for methods and parameter details \n+\n+**Input**\n+\n+A matrix consisting of non-negative integers. The matrix must have a unique header row identifiying the samples, as well as a unique set of row names \n+as the first column.\n+\n+**Output**\n+\n+A matrix which consists the original data and relative expression levels and some helpful plots\n+\n+**Note on edgeR versions**\n+\n+The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue) \n+breaking this and all other code that assumed the old name for this variable, \n+between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). \n+This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing \n+to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly\n+when their old scripts break. This tool currently now works with 2.4.6.\n+\n+**Note on prior.N**\n+\n+http://seqanswers.com/forums/showthread.php?t=5591 says:\n+\n+*prior.n*\n+\n+The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. \n+You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood \n+in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your \n+tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the \n+common likelihood the weight of one observation.\n+\n+In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, \n+or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that \n+you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation \n+(squeezing) of the tagwise dispersions. How many samples do you have in your experiment? \n+What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10. \n+If you have more samples, then the tagwise dispersion estimates will be more reliable, \n+so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. \n+\n+**Attribution** Copyright Ross Lazarus (ross period lazarus at gmail period com) May 2012\n+Derived from the implementation by Antony Kaspi and Sebastian Lunke at the BakerIDI\n+\n+All rights reserved.\n+\n+Licensed under the LGPL_\n+\n+.. _LGPL: http://www.gnu.org/copyleft/lesser.html\n+.. _documentation: http://bioconductor.org/packages/release/bioc/html/edgeR.html\n+</help>\n+\n+</tool>\n+\n+\n'

diff -r 000000000000 -r 82e0af566160 rgedgeR/rgedgeRglm.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/rgedgeRglm.xml Wed Jun 12 02:58:43 2013 -0400

b'@@ -0,0 +1,537 @@\n+\n+<tool id="rgedgeRglm" name="edgeRglm" version="0.18">\n+ <description>digital DGE glm</description>\n+ <command interpreter="python">\n+ rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "edgeRglm" \n+ --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"\n+ </command>\n+ <inputs>\n+ <param name="input1" type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample"\n+ help="Use the DGE matrix preparation tool to create these matrices from BAM files and a BED file of contigs"/>\n+ <param name="title" type="text" value="Factorial DGE" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain">\n+ <sanitizer invalid_char="">\n+ <valid initial="string.letters,string.digits"><add value="_" /> </valid>\n+ </sanitizer>\n+ </param>\n+ <param name="factor1name" type="text" value="Factor 1" size="80" label="Factor 1 name" help="Supply a meaningful name here to remind you when looking at the results">\n+ <sanitizer invalid_char="">\n+ <valid initial="string.letters,string.digits"><add value="_" /> </valid>\n+ </sanitizer>\n+ </param>\n+ <param name="factor1" type="text" optional="false" size="120"\n+ label="Enter comma separated values to indicate the factor level for all input file count columns"\n+ help="EG if there are 4 columns of counts from 2 treatment replicates (2,4) and 2 control replicates (3,5) then enter \'treat,control,treat,control\'">\n+ <sanitizer>\n+ <valid initial="string.digits,string.letters"><add value="," /> </valid>\n+ </sanitizer>\n+ </param>\n+ <param name="factor2name" type="text" value="Factor 2" size="80" label="Factor 2 name" help="Supply a meaningful name here to remind you when looking at the results">\n+ <sanitizer invalid_char="">\n+ <valid initial="string.letters,string.digits"><add value="_" /> </valid>\n+ </sanitizer>\n+ </param>\n+ <param name="factor2" type="text" optional="false" size="120"\n+ label="Enter comma separated values to indicate factor 2 level for all input file count columns"\n+ help="Leave blank if no factor 2, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter \'1,2,1,2\'">\n+ <sanitizer>\n+ <valid initial="string.digits,string.letters"><add value="," /> </valid>\n+ </sanitizer>\n+ </param>\n+ <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"\n+ 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"/>\n+ <param name="useNDfilt" type="boolean" truevalue="T" checked=\'false\' falsevalue="" size="1" label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples"\n+ 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"/>\n+ <param name="priorn" type="integer" value="4" size="3" label="prior.df for tagwise dispersion - higher value = more emphasis on each tag\'s variance - note this used to be prior.n"\n+ help="Zero = auto-estimate. 1 to force high variance tags out. Use a small value to \'smooth\' small samples. See edgeR docs and note below"/>\n+ <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for family wise error rate control"\n+ help="Conventional default value of 0.05 recommended"/>\n+ <param name="fdrtype" type="select" label="FDR (Type II error) control method" \n+ help="Use fdr or bh typically to control for the number of tests in a reliable way">\n+ <option value="fdr" selected="true">fdr</option>\n+ <op'..b'parison\n+best thought of as control and treatment (whatever that means) for each of the main comparisons. \n+\n+The interaction is defined as the difference between those two comparisons and is reported as a topTable as are the \n+primary comparisons.\n+\n+All comparisons are reported as separate tabular spreadsheets ordered by p value and a comprehensive summary is \n+provided in the html output.\n+\n+This code essentially embelishes the code described by Gordon Smythe in the limma documentation for a factorial \n+analysis.\n+\n+**Input**\n+\n+A matrix consisting of non-negative integers. The matrix must have a unique header row identifiying the samples, as well as a unique set of row names \n+as the first column.\n+\n+**Output**\n+\n+Tabular files which contain the statistical results and the raw and transformed counts and some colourful\n+and helpful plots\n+\n+**Note on edgeR versions**\n+\n+The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue) \n+breaking this and all other code that assumed the old name for this variable, \n+between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). \n+This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing \n+to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly\n+when their old scripts break. This tool currently now works with 2.4.6.\n+\n+**Note on prior.N - now replaced with prior.df**\n+\n+http://seqanswers.com/forums/showthread.php?t=5591 says:\n+\n+*prior.n*\n+\n+The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. \n+You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood \n+in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your \n+tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the \n+common likelihood the weight of one observation.\n+\n+In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, \n+or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that \n+you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation \n+(squeezing) of the tagwise dispersions. How many samples do you have in your experiment? \n+What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10. \n+If you have more samples, then the tagwise dispersion estimates will be more reliable, \n+so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. \n+\n+From Bioconductor Digest, Vol 118, Issue 5, Gordon writes:\n+\n+Dear Dorota,\n+\n+The important settings are prior.df and trend.\n+\n+prior.n and prior.df are related through prior.df = prior.n * residual.df,\n+and your experiment has residual.df = 36 - 12 = 24. So the old setting of\n+prior.n=10 is equivalent for your data to prior.df = 240, a very large\n+value. Going the other way, the new setting of prior.df=10 is equivalent\n+to prior.n=10/24.\n+\n+To recover old results with the current software you would use\n+\n+ estimateTagwiseDisp(object, prior.df=240, trend="none")\n+\n+To get the new default from old software you would use\n+\n+ estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE)\n+\n+Actually the old trend method is equivalent to trend="loess" in the new\n+software. You should use plotBCV(object) to see whether a trend is\n+required.\n+\n+Note you could also use\n+\n+ prior.n = getPriorN(object, prior.df=10)\n+\n+to map between prior.df and prior.n.\n+\n+\n+ .. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html\n+ .. _edgeR_Manual: http://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf\n+\n+</help>\n+\n+</tool>\n+\n+\n'

diff -r 000000000000 -r 82e0af566160 rgedgeR/rgedgeRpaired.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/rgedgeRpaired.xml Wed Jun 12 02:58:43 2013 -0400

b'@@ -0,0 +1,627 @@\n+<tool id="rgedgeRpaired" name="edgeR paired" version="0.18">\n+ <description>2 level Anova for counts</description>\n+ <command interpreter="python">\n+ rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "edgeR" \n+ --output_dir "$html_file.files_path" --output_html "$html_file" --output_tab "$outtab" --make_HTML "yes"\n+ </command>\n+ <inputs>\n+ <param name="input1" type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample"\n+ help="Use the HTSeq based count matrix preparation tool to create these matrices from BAM/SAM files and a GTF file of genomic features"/>\n+ <param name="title" type="text" value="edgeR" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain">\n+ <sanitizer invalid_char="">\n+ <valid initial="string.letters,string.digits"><add value="_" /> </valid>\n+ </sanitizer>\n+ </param>\n+ <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/>\n+ <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True" \n+ multiple="true" use_header_names="true" size="120" display="checkboxes">\n+ <validator type="no_options" message="Please select at least one column."/>\n+ </param>\n+ <param name="control_name" type="text" value="Control" size="50" label="Control Name"/>\n+ <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True" \n+ multiple="true" use_header_names="true" size="120" display="checkboxes" optional="true">\n+ </param>\n+ <param name="subjectids" type="text" optional="true" size="120"\n+ label="IF SUBJECTS NOT ALL INDEPENDENT! Enter integers to indicate sample pairing for every column in input"\n+ help="Leave blank if no pairing, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter \'1,2,1,2\'">\n+ <sanitizer>\n+ <valid initial="string.digits"><add value="," /> </valid>\n+ </sanitizer>\n+ </param>\n+ <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"\n+ 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"/>\n+ <param name="useNDF" type="boolean" truevalue="T" checked=\'false\' falsevalue="" size="1" label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples"\n+ 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"/>\n+ <param name="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"\n+ help="Zero = Use edgeR default. Use a small value to \'smooth\' small samples. See edgeR docs and note below"/>\n+ <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control"\n+ help="Conventional default value of 0.05 recommended"/>\n+ <param name="fdrtype" type="select" label="FDR (Type II error) control method" \n+ help="Use fdr or bh typically to control for the number of tests in a reliable way">\n+ <option value="fdr" selected="true">fdr</option>\n+ <option value="BH">Benjamini Hochberg</option>\n+ <option value="BY">Benjamini Yukateli</option>\n+ <option value="bonferroni">Bonferroni</option>\n+ <option value="hochberg">Hochberg</option>\n+ <option value="holm">Holm</option>\n+ <option value="hommel">Hommel</option>\n+ '..b'ethods.\n+\n+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),\n+put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or \n+A list of integers, one for each subject or an empty string if samples are all independent.\n+If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix.\n+Integers for samples that are not in the analysis *must* be present in the string as filler even if not used.\n+\n+So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones\n+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\n+8,9,1,1,2,2 \n+as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6\n+\n+**Output**\n+\n+A matrix which consists the original data and relative expression levels and some helpful plots\n+\n+**Note on edgeR versions**\n+\n+The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue) \n+breaking this and all other code that assumed the old name for this variable, \n+between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). \n+This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing \n+to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly\n+when their old scripts break. This tool currently now works with 2.4.6.\n+\n+**Note on prior.N**\n+\n+http://seqanswers.com/forums/showthread.php?t=5591 says:\n+\n+*prior.n*\n+\n+The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. \n+You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood \n+in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your \n+tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the \n+common likelihood the weight of one observation.\n+\n+In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, \n+or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that \n+you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation \n+(squeezing) of the tagwise dispersions. How many samples do you have in your experiment? \n+What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10. \n+If you have more samples, then the tagwise dispersion estimates will be more reliable, \n+so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. \n+\n+\n+From Bioconductor Digest, Vol 118, Issue 5, Gordon writes:\n+\n+Dear Dorota,\n+\n+The important settings are prior.df and trend.\n+\n+prior.n and prior.df are related through prior.df = prior.n * residual.df,\n+and your experiment has residual.df = 36 - 12 = 24. So the old setting of\n+prior.n=10 is equivalent for your data to prior.df = 240, a very large\n+value. Going the other way, the new setting of prior.df=10 is equivalent\n+to prior.n=10/24.\n+\n+To recover old results with the current software you would use\n+\n+ estimateTagwiseDisp(object, prior.df=240, trend="none")\n+\n+To get the new default from old software you would use\n+\n+ estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE)\n+\n+Actually the old trend method is equivalent to trend="loess" in the new\n+software. You should use plotBCV(object) to see whether a trend is\n+required.\n+\n+Note you could also use\n+\n+ prior.n = getPriorN(object, prior.df=10)\n+\n+to map between prior.df and prior.n.\n+\n+</help>\n+\n+</tool>\n+\n+\n'

diff -r 000000000000 -r 82e0af566160 rgedgeR/rgedgeRpaired.xml.iaas1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/rgedgeRpaired.xml.iaas1 Wed Jun 12 02:58:43 2013 -0400

diff -r 000000000000 -r 82e0af566160 rgedgeR/test-data/edgeRtest1out.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/test-data/edgeRtest1out.html Wed Jun 12 02:58:43 2013 -0400

[

b'@@ -0,0 +1,813 @@\n+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> \n+ <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> \n+ <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> \n+ <meta name="generator" content="Galaxy rgToolFactory.py tool output - see http://g2.trac.bx.psu.edu/" /> \n+ <title></title> \n+ <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> \n+ </head> \n+ <body> \n+ <div class="toolFormBody"> \n+ \n+<div class="infomessage">Galaxy Tool "edgeR" run at 12/06/2013 16:42:51</div><br/>\n+<div><table class="simple" cellpadding="2" cellspacing="2">\n+<tr>\n+<td><a href="edgeRtest_BCV_vs_abundance.pdf"><img src="edgeRtest_BCV_vs_abundance.jpg" title="Click to download a PDF of edgeRtest_BCV_vs_abundance.pdf" hspace="5" width="400" \n+ alt="Image called edgeRtest_BCV_vs_abundance.pdf"/></a></td>\n+\n+<td><a href="edgeRtest_MDSplot.pdf"><img src="edgeRtest_MDSplot.jpg" title="Click to download a PDF of edgeRtest_MDSplot.pdf" hspace="5" width="400" \n+ alt="Image called edgeRtest_MDSplot.pdf"/></a></td>\n+\n+<td><a href="edgeRtest_RowsumCum.pdf"><img src="edgeRtest_RowsumCum.jpg" title="Click to download a PDF of edgeRtest_RowsumCum.pdf" hspace="5" width="400" \n+ alt="Image called edgeRtest_RowsumCum.pdf"/></a></td>\n+</tr>\n+<tr>\n+<td><a href="edgeRtest_Smearplot.pdf"><img src="edgeRtest_Smearplot.jpg" title="Click to download a PDF of edgeRtest_Smearplot.pdf" hspace="5" width="400" \n+ alt="Image called edgeRtest_Smearplot.pdf"/></a></td>\n+\n+<td><a href="edgeRtest_heatmap.pdf"><img src="edgeRtest_heatmap.jpg" title="Click to download a PDF of edgeRtest_heatmap.pdf" hspace="5" width="400" \n+ alt="Image called edgeRtest_heatmap.pdf"/></a></td>\n+\n+<td><a href="edgeRtest_pval_qq.pdf"><img src="edgeRtest_pval_qq.jpg" title="Click to download a PDF of edgeRtest_pval_qq.pdf" hspace="5" width="400" \n+ alt="Image called edgeRtest_pval_qq.pdf"/></a></td>\n+</tr>\n+<tr>\n+<td><a href="edgeRtest_sampleBoxplot.pdf"><img src="edgeRtest_sampleBoxplot.jpg" title="Click to download a PDF of edgeRtest_sampleBoxplot.pdf" hspace="5" width="400" \n+ alt="Image called edgeRtest_sampleBoxplot.pdf"/></a></td>\n+\n+<td><a href="edgeRtest_samplehistplot.pdf"><img src="edgeRtest_samplehistplot.jpg" title="Click to download a PDF of edgeRtest_samplehistplot.pdf" hspace="5" width="400" \n+ alt="Image called edgeRtest_samplehistplot.pdf"/></a></td>\n+\n+<td><a href="edgeRtest_venn.pdf"><img src="edgeRtest_venn.jpg" title="Click to download a PDF of edgeRtest_venn.pdf" hspace="5" width="400" \n+ alt="Image called edgeRtest_venn.pdf"/></a></td>\n+</tr>\n+<tr>\n+<td><a href="edgeRtest_voomplot.pdf"><img src="edgeRtest_voomplot.jpg" title="Click to download a PDF of edgeRtest_voomplot.pdf" hspace="5" width="400" \n+ alt="Image called edgeRtest_voomplot.pdf"/></a></td>\n+\n+<td> </td><td> </td>\n+</tr></table></div>\n+\n+<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr>\n+\n+<tr><td><a href="edgeR.Rscript">edgeR.Rscript</a></td><td>19.7 KB)</td></tr>\n+<tr class="odd_row"><td><a href="edgeR_runner.log">edgeR_runner.log</a></td><td>79.2 KB)</td></tr>\n+<tr><td><a href="edgeRtest_BCV_vs_abundance.pdf">edgeRtest_BCV_vs_abundance.pdf</a></td><td>16.4 KB)</td></tr>\n+<tr class="odd_row"><td><a href="edgeRtest_DESeq_TopTable.xls">edgeRtest_DESeq_TopTable.xls</a></td><td>135.1 KB)</td></tr>\n+<tr><td><a href="edgeRtest_MDSplot.pdf">edgeRtest_MDSplot.pdf</a></td><td>4.9 KB)</td></tr>\n+<tr class="odd_row"><td><a href="edgeRtest_RowsumCum.pdf">edgeRtest_RowsumCum.pdf</a></td><td>6.3 KB)</td></tr>\n+<tr><td><a hr'..b'+\n+655 Mir215 3.045873 5.7544234 11.148134 2.141822e-08 8.082459e-07 9.753268\n+\n+628 Mir1983 5.895500 0.9931851 11.445812 1.527548e-08 6.441605e-07 9.749260\n+\n+344 Dnm3os 3.363344 5.8607432 11.092261 2.283960e-08 8.082459e-07 9.689496\n+\n+637 Mir200a -6.191561 1.7981309 -11.322172 1.756229e-08 6.909853e-07 9.662295\n+\n+587 Mir182 -4.903995 7.1511683 -11.074468 2.331304e-08 8.082459e-07 9.658842\n+\n+582 Mir181a-2 3.048298 6.9414651 11.072128 2.337609e-08 8.082459e-07 9.644017\n+\n+614 Mir1948 -7.195525 4.5513493 -11.005492 2.524936e-08 8.473388e-07 9.341794\n+\n+654 Mir214 3.280874 5.4784451 10.768257 3.332555e-08 1.086413e-06 9.318504\n+\n+571 Mir153 5.963803 1.4386315 10.727082 3.498742e-08 1.093990e-06 9.035569\n+\n+318 Cyp3a25 -6.318200 1.4888933 -10.698226 3.620443e-08 1.093990e-06 9.024973\n+\n+464 Gm5441 5.982176 1.4484953 10.692891 3.643436e-08 1.093990e-06 9.000362\n+\n+541 Mir125b-2 3.077678 7.4316058 10.446668 4.893073e-08 1.431538e-06 8.884250\n+\n+551 Mir133a-1 5.144671 0.5903264 10.358205 5.447229e-08 1.553822e-06 8.575535\n+\n+550 Mir132 2.847559 5.3211839 10.110952 7.380297e-08 2.004981e-06 8.531491\n+\n+13 1110038B12Rik -2.226702 10.8487089 -10.194609 6.655312e-08 1.852125e-06 8.439308\n+\n+922 Rabggtb -1.935779 9.9874171 -9.928995 9.262821e-08 2.457879e-06 8.133384\n+\n+569 Mir150 2.938531 7.6297870 9.842102 1.033602e-07 2.620755e-06 8.116464\n+\n+800 Mir504 5.256127 0.6221088 9.892894 9.693595e-08 2.513725e-06 8.068853\n+\n+573 Mir155 3.906600 3.9899000 9.732173 1.188627e-07 2.712448e-06 8.046518\n+\n+666 Mir24-2 2.833979 7.3083691 9.767192 1.136724e-07 2.646944e-06 8.030550\n+\n+700 Mir3074-2 2.833979 7.3083691 9.767192 1.136724e-07 2.646944e-06 8.030550\n+\n+664 Mir23b 2.124129 9.8141190 9.806316 1.081569e-07 2.625681e-06 7.979464\n+\n+631 Mir199b 5.752816 2.8805143 9.823920 1.057683e-07 2.623514e-06 7.979387\n+\n+Warning messages:\n+\n+1: In bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group == :\n+\n+ Outlier (-Inf) in boxplot 5 is not drawn\n+\n+2: In bplt(at[i], wid = width[i], stats = z$stats[, i], out = z$out[z$group == :\n+\n+ Outlier (-Inf) in boxplot 7 is not drawn\n+\n+3: In bxp(list(stats = c(-Inf, -Inf, -0.158608221176912, 0.826586442285183, :\n+\n+ some notches went outside hinges (\'box\'): maybe set notch=FALSE\n+\n+4: In par(defpar) : calling par(new=TRUE) with no plot\n+\n+R version 2.15.1 (2012-06-22)\n+\n+Platform: x86_64-unknown-linux-gnu (64-bit)\n+\n+locale:\n+\n+ [1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8 LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8 LC_PAPER=C LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C \n+\n+attached base packages:\n+\n+[1] methods grid stats graphics grDevices utils datasets base \n+\n+other attached packages:\n+\n+ [1] edgeR_3.0.8 limma_3.14.4 DESeq_1.10.1 lattice_0.20-15 locfit_1.5-9.1 Biobase_2.18.0 BiocGenerics_0.4.0 gplots_2.11.0 MASS_7.3-23 KernSmooth_2.23-10 caTools_1.14 gdata_2.12.0.2 gtools_2.7.1 stringr_0.6.2 \n+\n+loaded via a namespace (and not attached):\n+\n+ [1] annotate_1.36.0 AnnotationDbi_1.20.7 bitops_1.0-5 DBI_0.2-7 genefilter_1.40.0 geneplotter_1.36.0 IRanges_1.16.6 parallel_2.15.1 RColorBrewer_1.0-5 RSQLite_0.11.3 splines_2.15.1 stats4_2.15.1 survival_2.37-4 XML_3.96-1.1 xtable_1.7-1 \n+\n+</pre>\n+\n+<hr/><div class="infomessage">This tool (edgeR) was generated by the <a href="https://bitbucket.org/fubar/galaxytoolfactory/overview">Galaxy Tool Factory</a></div><br/>\n+</div></body></html>\n+\n'

diff -r 000000000000 -r 82e0af566160 rgedgeR/test-data/edgeRtest1out.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/test-data/edgeRtest1out.xls Wed Jun 12 02:58:43 2013 -0400

b'@@ -0,0 +1,1142 @@\n+Name\tlogFC\tlogCPM\tLR\tPValue\tadj.p.value\tDispersion\ttotreads\tcase_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam\tcase_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam\tcase_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam\tcase_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam\tcontrol_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam\tcontrol_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam\tcontrol_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam\tcontrol_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam\tcase_X11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam\tcase_X11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam\tcase_X11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam\tcase_X11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam\tcontrol_X11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam\tcontrol_X11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam\tcontrol_X11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam\tcontrol_X11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam\n+Mir122a\t-10.6613482198735\t12.5379487184339\t445.912028851371\t5.59474310450852e-99\t6.38360188224422e-96\t0.102055096028295\t90428\t6.64466912039531\t2.86464823551992\t20.1236936368881\t4.75652253016081\t20841.6623198401\t16818.9531619257\t7242.55663282834\t8468.31297344994\t12\t7\t27\t8\t29767\t24233\t11911\t24463\n+Mir192\t-7.10901851688288\t17.2474145508888\t366.131659138235\t1.3015583263717e-81\t7.42539025195057e-79\t0.0776548700185199\t2325567\t1779.66387941254\t1294.41176699279\t4493.54625691845\t1933.52640851037\t463599.032378899\t340312.253828562\t383591.60517699\t182292.180510107\t3214\t3163\t6029\t3252\t662133\t490327\t630849\t526600\n+Mir208b\t13.4237202575152\t9.82233816515324\t355.417815392181\t2.80129611458365e-79\t1.06542628891331e-76\t0.124440587141633\t14756\t1245.90702213628\t570.139045385768\t4930.34448733332\t881.902421077918\t0.74532198655141\t0\t0\t0\t2049\t1647\t8904\t2155\t1\t0\t0\t0\n+Mir208a\t11.9775863683342\t8.38828977985358\t348.815401742963\t7.675450754893e-78\t2.18942232783323e-75\t0.0993679816515115\t4638\t433.789589950159\t712.527819143148\t1006.59908044528\t649.748467524829\t0\t0\t0\t0.553722426699609\t1060\t956\t1693\t928\t0\t0\t0\t1\n+Mir499\t14.4882160574998\t8.61277040381698\t291.127944921827\t2.82350748745294e-65\t6.4432440863676e-63\t0.134816198797062\t6527\t608.439028317971\t506.657690265579\t2521.60879492395\t270.357373677161\t0\t0\t0\t0\t869\t730\t4147\t781\t0\t0\t0\t0\n+Mir149\t6.86973108218652\t8.78566474384905\t253.019861795716\t5.70293016120625e-57\t1.08450721898939e-54\t0.0983468375157501\t6164\t774.657674952754\t459.571424069838\t1710.51395913549\t766.394692672161\t17.5039996639232\t6.24646467450714\t4.86444908593961\t5.5386913941544\t1399\t1123\t2295\t1289\t25\t9\t8\t16\n+Mir490\t8.4539298899245\t6.90959613094456\t251.056095815646\t1.52828085290668e-56\t2.4910977902379e-54\t0.0974500488770791\t1741\t209.881556643346\t217.749755819205\t520.538722875595\t195.794075709069\t1.03850463640395\t0.553722426699609\t0\t0.74532198655141\t353\t311\t750\t322\t3\t1\t0\t1\n+Mir802\t-12.4996776940066\t6.62939088571757\t237.561606597444\t1.33781241656995e-53\t1.90805495913289e-51\t0.10939980863872\t1514\t0\t0\t0\t0\t305.654779538184\t164.10342034907\t155.772295189245\t209.286991327076\t0\t0\t0\t0\t552\t401\t209\t352\n+Mir204\t7.37117920479641\t7.55899555710097\t237.12302218121\t1.66736458270285e-53\t2.11384776540439e-51\t0.10292005004102\t2601\t425.578854320855\t326.416358632286\t646.247667592045\t375.481932100929\t3.04028067871226\t0.6923364242693\t0.553722426699609\t3.68311915995418\t571\t549\t923\t541\t5\t2\t1\t9\n+Mir1948\t-7.58332571104735\t7.29345576009458\t204.796406017043\t1.87589722530299e-46\t2.14039873407072e-44\t0.118339985035871\t2404\t1.2161122714849\t0.6923364242693\t1.66116728009883\t1.63694184886853\t647.684806313175\t385.278324943026\t181.341436518244\t428.229856018989\t2\t2\t3\t4\t869\t648\t259\t617\n+Mir3073\t-11.7485555893732\t5.88146234349958\t200.033500954155\t2.05362547312175e-45\t2.13016969530174e-43\t0.110685621278186\t904\t0\t0\t0\t0\t207.347142288176\t75.4646702453536\t72.5376378976488\t87.5763889144661\t0\t0\t0\t0\t341\t218\t131\t214\n+Mir194-2\t-5.43982669183809\t7.86004832737241\t165.57'..b'0.00330101054142751\t0.954183212529461\t0.967174636846159\t0.252311230579068\t8\t0\t0.34616821213465\t1.10744485339922\t0.409235462217131\t0.74532198655141\t0.594565316270102\t0.700159986556928\t0.694051630500793\t0\t1\t2\t1\t1\t1\t1\t1\n+Gm15545\t0.0849589739751836\t-1.89448493270386\t0.0032612312701028\t0.954459808140908\t0.967174636846159\t0.638146949527696\t4\t0\t0.594565316270102\t0.700159986556928\t0\t0.608056135742452\t0\t0.553722426699609\t0\t0\t1\t1\t0\t1\t0\t1\t0\n+Plscr3\t0.0814004336200907\t-1.89261335490638\t0.00301642056831319\t0.956200644868514\t0.968047332759487\t0.638146949527696\t4\t0\t0.74532198655141\t0.594565316270102\t0\t0.694051630500793\t0.608056135742452\t0\t0\t0\t1\t1\t0\t1\t1\t0\t0\n+5430402O13Rik\t-0.0927467442781055\t-1.94142512096775\t0.00279115036404498\t0.957866291573585\t0.968047332759487\t0.638146949527696\t4\t0\t0\t1.2161122714849\t0\t0\t0\t0\t1.1891306325402\t0\t0\t2\t0\t0\t0\t0\t2\n+Gm6297\t-0.0927467442781055\t-1.94142512096775\t0.00279115036404498\t0.957866291573585\t0.968047332759487\t0.638146949527696\t4\t0\t0\t0.818470924434263\t0\t0\t0\t0\t1.2161122714849\t0\t0\t2\t0\t0\t0\t0\t2\n+Gm5095\t0.0741497349563809\t-1.89982855309554\t0.00255353302993466\t0.959698055373646\t0.969040248833035\t0.638146949527696\t4\t0.409235462217131\t0\t0.594565316270102\t0\t0.694051630500793\t0\t0.34616821213465\t0\t1\t0\t1\t0\t1\t0\t1\t0\n+Mir18\t-0.0321144919271105\t2.13829995459641\t0.00222894649578897\t0.96234448681064\t0.970853279797471\t0.319997109480565\t62\t4.9011199058985\t4.85836141350555\t4.86444908593961\t2.42317748494255\t3.87605698689727\t3.68311915995418\t2.98128794620564\t7.72934911151132\t7\t7\t8\t7\t7\t9\t4\t13\n+4930547E14Rik\t0.0332717629837295\t-1.87681159162927\t0.000628960971966741\t0.979991875252521\t0.987622213297045\t0.638146949527696\t4\t0\t0\t0.608056135742452\t0.34616821213465\t0\t0\t0\t1.1891306325402\t0\t0\t1\t1\t0\t0\t0\t2\n+Mir30a\t-0.00674515544774862\t15.2305157438047\t0.000585365330948662\t0.980697605315997\t0.987622213297045\t0.0746707420324099\t565907\t22165.4203400665\t38322.2205825139\t68810.8277478877\t37443.855921078\t63416.8855821185\t38709.4616575002\t22770.252657793\t38928.9014866893\t54163\t51417\t115733\t53479\t91372\t63661\t65778\t70304\n+2610002J02Rik\t0.018998702253143\t-1.31967503888113\t0.000345631121349221\t0.985167257251054\t0.991248536616801\t0.365725430522663\t6\t0\t0\t1.38810326100159\t0.608056135742452\t0.34616821213465\t0.553722426699609\t0.409235462217131\t0\t0\t0\t2\t1\t1\t1\t1\t0\n+Mir1306\t0.010990964703492\t0.387899924622603\t0.000182949792714382\t0.989208227531673\t0.993710996805651\t0.374307689020903\t20\t0.34616821213465\t0.553722426699609\t2.45541277330279\t1.49064397310282\t2.37826126508041\t1.40031997311386\t1.38810326100159\t1.2161122714849\t1\t1\t6\t2\t4\t2\t2\t2\n+2810001G20Rik\t0.0139659525352028\t-1.99687003375001\t0.000177958964401981\t0.989356435031744\t0.993710996805651\t0.638146949527696\t4\t0\t0\t1.40031997311386\t0\t1.2161122714849\t0\t0\t0\t0\t0\t2\t0\t2\t0\t0\t0\n+Snora31\t-0.00608992937342303\t4.51111691273084\t0.000131880843156296\t0.990837344181505\t0.994323139587597\t0.245986767999803\t320\t26.6060794891633\t26.3739619590301\t21.8900208867283\t12.1158874247127\t31.5621783218777\t4.09235462217131\t60.3710809106642\t14.8641329067525\t38\t38\t36\t35\t57\t10\t81\t25\n+2010320M18Rik\t-0.0135816064033237\t-0.577688977281378\t9.37664770823687e-05\t0.992273957831848\t0.994889794276045\t0.436705588816395\t10\t0\t0\t1.03850463640395\t1.10744485339922\t0.409235462217131\t1.49064397310282\t0.594565316270102\t0.700159986556928\t0\t0\t3\t2\t1\t2\t1\t1\n+2310001K24Rik\t0.00612441798475525\t-1.86264950933858\t6.95515125732626e-05\t0.993345915420738\t0.995090157590046\t0.638146949527696\t4\t0\t0\t0.594565316270102\t0.700159986556928\t0\t0.608056135742452\t0.34616821213465\t0\t0\t0\t1\t1\t0\t1\t1\t0\n+Mir1966\t0.000918355090882732\t-0.94191710317151\t1.38947096530728e-05\t0.997025843661245\t0.997900427734632\t0.449008936327196\t8\t0\t0.34616821213465\t1.66116728009883\t0\t1.49064397310282\t0.594565316270102\t0.700159986556928\t0\t0\t1\t3\t0\t2\t1\t1\t0\n+Mir376b\t-0.00136357255892604\t1.39148339025352\t2.78221370209053e-06\t0.998669131632702\t0.998669131632702\t0.592374698826143\t42\t1.49064397310282\t2.37826126508041\t7.70175985212621\t2.08215489150238\t9.72889817187923\t1.73084106067325\t0.553722426699609\t0\t2\t4\t11\t3\t16\t5\t1\t0\n'

diff -r 000000000000 -r 82e0af566160 rgedgeR/test-data/gentestdata.sh
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/test-data/gentestdata.sh Wed Jun 12 02:58:43 2013 -0400

@@ -0,0 +1,9 @@
+#!/bin/bash
+# generate test data for rgGSEA
+# ross lazarus June 2013
+# adjust gseajar_path !
+GSEAJAR_PATH=/home/rlazarus/galaxy-central/tool_dependency_dir/gsea_jar/2.0.12/fubar/rg_gsea_test/8e291f464aa0/jars/gsea2-2.0.12.jar
+python ../rgGSEA.py --input_tab "gsea_test_DGE.xls" --adjpvalcol "5" --signcol "2" --idcol "1" --outhtml "gseatestout.html" --input_name "gsea_test" --setMax "500" --setMin "15" --nPerm "10" --plotTop "20" --gsea_jar "$GSEAJAR_PATH" --output_dir "gseatestout" --mode "Max_probe"
+--title "GSEA test" --builtin_gmt "gseatestdata.gmt"
+
+

diff -r 000000000000 -r 82e0af566160 rgedgeR/test-data/gentestdata.sh~
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/test-data/gentestdata.sh~ Wed Jun 12 02:58:43 2013 -0400

@@ -0,0 +1,8 @@
+#!/bin/bash
+# generate test data for rgGSEA
+# ross lazarus June 2013
+# adjust gseajar_path !
+GSEAJAR_PATH=/home/rlazarus/galaxy-central/tool_dependency_dir/gsea_jar/2.0.12/fubar/rg_gsea_test/8e291f464aa0/jars/gsea2-2.0.12.jar
+python ../rgGSEA.py --input_tab "gsea_test_DGE.xls" --adjpvalcol "5" --signcol "2" --idcol "1" --outhtml "gseatestout.html" --input_name "gsea_test" --setMax "500" --setMin "15" --nPerm "10" --plotTop "20" --gsea_jar "$GSEAJAR_PATH" --output_dir "gseatestout" --mode "Max_probe" --title "GSEA test" --builtin_gmt "gseatestdata.gmt"
+
+

diff -r 000000000000 -r 82e0af566160 rgedgeR/test-data/test_bams2mx.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/test-data/test_bams2mx.xls Wed Jun 12 02:58:43 2013 -0400

b'@@ -0,0 +1,3243 @@\n+Contigname\t11706Liv_CAAAAG_L003_R1_001_trimmed.fastq_bwa.sam.bam\t11706He_AGTTCC_L001_R1_001_trimmed.fastq_bwa.sam.bam\t11699He_GGCTAC_L001_R1_001_trimmed.fastq_bwa.sam.bam\t11698He_TAGCTT_L001_R1_001_trimmed.fastq_bwa.sam.bam\t11700Liv_ATTCCT_L003_R1_001_trimmed.fastq_bwa.sam.bam\t11698Liv_ACTGAT_L003_R1_001_trimmed.fastq_bwa.sam.bam\t11699Liv_ATGAGC_L003_R1_001_trimmed.fastq_bwa.sam.bam\t11700He_CTTGTA_L001_R1_001_trimmed.fastq_bwa.sam.bam\n+0610005C13Rik\t40\t0\t2\t0\t6\t70\t6\t2\n+0610007N19Rik\t10\t17\t11\t42\t2\t6\t6\t10\n+0610008F07Rik\t16\t0\t0\t0\t8\t5\t4\t1\n+0610009B14Rik\t0\t0\t0\t1\t0\t0\t0\t0\n+0610009L18Rik\t3\t2\t2\t11\t0\t1\t1\t1\n+0610012G03Rik\t6\t0\t0\t0\t4\t5\t2\t0\n+0610031O16Rik\t33\t0\t0\t0\t10\t25\t10\t0\n+0610038B21Rik\t0\t0\t0\t2\t0\t0\t0\t0\n+0610038L08Rik\t0\t0\t0\t0\t0\t3\t0\t0\n+0610039K10Rik\t9\t0\t0\t1\t3\t1\t4\t3\n+0610040B10Rik\t0\t0\t0\t0\t0\t0\t2\t0\n+0610040F04Rik\t2\t1\t0\t1\t2\t5\t2\t0\n+0610043K17Rik\t9\t2\t0\t4\t4\t12\t0\t1\n+1110002L01Rik\t3\t1\t0\t0\t0\t0\t0\t0\n+1110006O24Rik\t0\t0\t0\t1\t0\t0\t0\t0\n+1110015O18Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1110017D15Rik\t0\t0\t0\t0\t0\t2\t0\t0\n+1110019D14Rik\t1\t1\t1\t0\t0\t0\t2\t0\n+1110020A21Rik\t2\t0\t0\t2\t0\t2\t6\t0\n+1110028F11Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1110028F18Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1110035M17Rik\t0\t0\t0\t0\t0\t0\t0\t1\n+1110036E04Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1110038B12Rik\t9406\t1346\t1212\t1600\t7604\t6486\t8084\t1328\n+1110046J04Rik\t0\t0\t0\t0\t0\t0\t0\t1\n+1110054M08Rik\t6\t1\t0\t1\t0\t6\t1\t1\n+1190002F15Rik\t0\t0\t0\t0\t0\t4\t0\t0\n+1300002E11Rik\t10\t1\t0\t0\t0\t17\t0\t0\n+1300015D01Rik\t0\t0\t0\t0\t1\t2\t0\t0\n+1500002O10Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1500004A13Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1500009C09Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1500011B03Rik\t0\t0\t0\t0\t1\t0\t0\t0\n+1500011K16Rik\t0\t0\t0\t1\t0\t4\t2\t0\n+1500012K07Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1500015A07Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1500015L24Rik\t2\t0\t0\t0\t0\t2\t0\t0\n+1500016L03Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1500017E21Rik\t4\t1\t3\t0\t0\t7\t1\t4\n+1600002D24Rik\t0\t0\t1\t2\t0\t2\t0\t2\n+1600010M07Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1600019K03Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1600020E01Rik\t2\t0\t0\t0\t0\t0\t0\t2\n+1600023N17Rik\t0\t0\t0\t0\t0\t0\t1\t0\n+1600025M17Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1600029I14Rik\t1\t0\t0\t0\t0\t0\t0\t0\n+1600029O15Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700001D01Rik\t0\t0\t0\t0\t0\t0\t0\t1\n+1700001G11Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700001G17Rik\t1\t0\t0\t0\t0\t0\t0\t1\n+1700001J11Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700001K23Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700001L05Rik\t6\t0\t0\t0\t0\t6\t4\t0\n+1700001O22Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700003C15Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700003D09Rik\t1\t0\t0\t0\t0\t1\t0\t0\n+1700003F17Rik\t1\t2\t1\t4\t0\t2\t0\t6\n+1700003G13Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700003G18Rik\t2\t0\t0\t1\t0\t0\t0\t0\n+1700003H04Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700003L19Rik\t0\t5\t4\t12\t0\t0\t0\t2\n+1700003M07Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700003P14Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700006F04Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700006H21Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700007F19Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700007J10Rik\t0\t0\t0\t2\t1\t3\t0\t1\n+1700007L15Rik\t0\t0\t0\t0\t0\t0\t1\t0\n+1700007P06Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700008J07Rik\t4\t0\t0\t0\t1\t0\t3\t1\n+1700008K24Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700009C05Rik\t0\t0\t0\t1\t0\t0\t0\t0\n+1700009J07Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700010I02Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700010J16Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700010K23Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700011B04Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700011J10Rik\t1\t2\t0\t2\t9\t0\t7\t1\n+1700011M02Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700012B15Rik\t7\t0\t0\t0\t2\t8\t1\t1\n+1700012D01Rik\t1\t1\t1\t0\t1\t2\t1\t0\n+1700012D14Rik\t5\t0\t0\t5\t2\t2\t0\t1\n+1700012I11Rik\t0\t2\t0\t0\t0\t0\t0\t0\n+1700013G23Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700016G22Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700016L04Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700016L21Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700016P04Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700017G19Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700017J07Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700018A04Rik\t0\t0\t0\t0\t2\t1\t0\t0\n+1700018B24Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700018G05Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700018L02Rik\t8\t1\t0\t0\t4\t6\t0\t1\n+1700019B21Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700019E08Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700019G24Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700020G17Rik\t0\t1\t1\t1\t0\t0\t0\t0\n+1700020I14Rik\t6\t4\t0\t6\t0\t12\t2\t2\n+1700020M21Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700020N01Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700020N18Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700021N21Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700022A21Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700022A22Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700022E09Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700022H16Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700023C21Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700023F02Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700023L04Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700024B18Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700024F13Rik\t0\t0\t0\t0\t0\t0\t0\t0\n+1700024P03Rik\t0\t0\t0\t0\t0\t0'..b'13\n+Snord64\t24\t22\t19\t48\t13\t19\t35\t17\n+Snord65\t29\t74\t90\t8\t17\t19\t15\t68\n+Snord66\t884\t267\t281\t340\t693\t807\t696\t244\n+Snord67\t39\t4\t1\t17\t18\t13\t11\t5\n+Snord68\t1644\t460\t353\t1060\t1295\t805\t1310\t376\n+Snord69\t440\t157\t105\t401\t341\t407\t333\t117\n+Snord7\t6\t1\t0\t1\t0\t0\t0\t2\n+Snord70\t23\t6\t11\t16\t10\t6\t14\t6\n+Snord71\t93\t20\t20\t38\t51\t26\t64\t29\n+Snord72\t159\t21\t24\t57\t102\t123\t101\t16\n+Snord73a\t29\t3\t3\t7\t6\t9\t2\t5\n+Snord8\t10\t1\t3\t4\t4\t6\t2\t2\n+Snord82\t47\t17\t14\t19\t19\t40\t18\t17\n+Snord83b\t48\t22\t12\t35\t22\t34\t32\t16\n+Snord85\t3804\t911\t737\t1422\t3047\t1133\t3156\t882\n+Snord87\t390\t145\t138\t190\t235\t255\t215\t191\n+Snord88a\t96\t69\t73\t77\t62\t101\t59\t52\n+Snord88c\t74\t29\t22\t35\t41\t65\t52\t23\n+Snord89\t13\t2\t2\t3\t2\t8\t3\t3\n+Snord90\t43\t2\t3\t46\t14\t48\t10\t5\n+Snord91a\t363\t40\t29\t81\t256\t358\t285\t25\n+Snord92\t23\t9\t5\t17\t8\t29\t7\t8\n+Snord93\t45\t54\t50\t161\t29\t34\t29\t33\n+Snord95\t708\t217\t195\t326\t483\t675\t444\t210\n+Snord96a\t145\t16\t22\t56\t71\t106\t59\t12\n+Snord98\t74\t44\t24\t150\t77\t96\t56\t32\n+Snord99\t931\t126\t80\t847\t655\t668\t555\t108\n+Sox2ot\t0\t1\t0\t1\t0\t0\t0\t0\n+Speer1-ps1\t0\t0\t0\t0\t0\t0\t0\t0\n+Speer5-ps1\t0\t0\t0\t0\t0\t0\t0\t0\n+Speer6-ps1\t0\t0\t0\t0\t0\t0\t0\t0\n+Speer7-ps1\t0\t0\t0\t0\t0\t0\t0\t0\n+Speer8-ps1\t0\t0\t0\t0\t0\t0\t0\t0\n+Speer9-ps1\t1\t0\t0\t0\t0\t0\t0\t0\n+Spn-ps\t0\t0\t0\t0\t0\t0\t0\t0\n+Spns1\t5\t0\t0\t1\t1\t1\t0\t1\n+Sprr2g\t0\t0\t0\t0\t0\t0\t0\t0\n+Sprr2j-ps\t0\t0\t0\t0\t0\t0\t0\t0\n+Sqrdl\t35\t1\t0\t1\t4\t58\t3\t0\n+Sra1\t7\t0\t0\t0\t2\t4\t6\t0\n+Srsf3\t7\t2\t8\t2\t11\t9\t14\t4\n+Srsf7\t1\t1\t0\t0\t0\t5\t0\t1\n+Srsf9\t1\t2\t0\t1\t0\t7\t0\t3\n+St18\t0\t0\t0\t0\t0\t0\t0\t0\n+St5\t7\t3\t0\t1\t2\t7\t2\t2\n+St7l\t8\t1\t1\t1\t3\t12\t4\t2\n+Stap2\t10\t0\t2\t2\t1\t6\t4\t0\n+Stmn1-rs1\t0\t0\t0\t0\t0\t0\t0\t0\n+Stoml1\t0\t0\t0\t0\t0\t0\t0\t0\n+Stxbp3b\t0\t0\t0\t0\t0\t0\t0\t0\n+Surf1\t3\t0\t0\t2\t3\t7\t5\t0\n+Suv39h2\t0\t0\t0\t0\t0\t0\t0\t0\n+Svip\t1\t0\t2\t0\t0\t1\t0\t0\n+Syce2\t6\t0\t0\t0\t0\t19\t1\t0\n+Sycp1-ps1\t0\t0\t0\t0\t0\t0\t0\t0\n+Szrd1\t40\t4\t0\t2\t16\t40\t4\t2\n+Taf1b\t1\t1\t1\t0\t0\t0\t0\t0\n+Taf1d\t357\t53\t56\t156\t174\t341\t230\t54\n+Tardbp\t18\t0\t1\t3\t7\t20\t3\t0\n+Tbrg3\t1\t1\t0\t0\t0\t0\t0\t0\n+Tcp10a\t0\t0\t0\t0\t0\t0\t0\t0\n+Terc\t22\t1\t0\t0\t18\t10\t15\t0\n+Thap6\t2\t0\t2\t2\t1\t1\t0\t0\n+Tk2\t1\t2\t0\t2\t1\t1\t3\t0\n+Tmem161b\t0\t0\t0\t0\t2\t2\t4\t0\n+Tmem179b\t8\t0\t0\t0\t0\t8\t0\t0\n+Tmem181b-ps\t0\t0\t0\t0\t0\t0\t0\t0\n+Tmem181c-ps\t0\t0\t0\t0\t0\t0\t0\t0\n+Tmem194b\t0\t0\t1\t0\t1\t0\t0\t0\n+Tmem205\t35\t2\t0\t1\t4\t57\t6\t1\n+Tmem41a\t7\t0\t0\t0\t0\t5\t2\t0\n+Tmem51as1\t1\t0\t0\t0\t0\t2\t0\t0\n+Tmem80\t0\t0\t0\t1\t1\t0\t0\t0\n+Tor2a\t2\t0\t0\t0\t0\t11\t3\t0\n+Tpsab1\t0\t0\t0\t0\t0\t0\t0\t0\n+Trappc13\t1\t0\t0\t1\t0\t0\t3\t1\n+Trim30e-ps1\t0\t0\t0\t0\t0\t0\t0\t0\n+Trim52\t0\t0\t0\t0\t0\t0\t0\t0\n+Trmt61b\t11\t0\t0\t0\t2\t5\t0\t0\n+Trpc4\t1\t4\t1\t1\t0\t0\t1\t0\n+Trpt1\t2\t0\t0\t0\t0\t2\t0\t0\n+Trub2\t0\t0\t0\t0\t0\t0\t0\t0\n+Tsix\t0\t0\t0\t0\t0\t4\t0\t0\n+Tslp\t0\t0\t0\t1\t0\t0\t0\t0\n+Tspy-ps\t0\t0\t0\t0\t0\t0\t0\t0\n+Tsr2\t0\t0\t1\t0\t0\t2\t0\t0\n+Tubb2a-ps2\t0\t0\t0\t0\t0\t0\t0\t0\n+Tubgcp4\t2\t2\t0\t1\t0\t7\t1\t0\n+Tug1\t18\t3\t0\t16\t2\t27\t6\t6\n+Tyms\t0\t0\t0\t0\t0\t0\t1\t1\n+Tyms-ps\t0\t0\t0\t0\t0\t0\t0\t0\n+Tysnd1\t2\t0\t0\t0\t1\t7\t3\t0\n+Tyw5\t1\t1\t0\t0\t0\t0\t0\t1\n+U05342\t20\t0\t1\t3\t3\t24\t3\t0\n+U90926\t0\t0\t0\t0\t0\t0\t0\t0\n+Ube2w\t3\t1\t2\t1\t1\t3\t1\t0\n+Ubl4\t3\t1\t0\t1\t0\t5\t3\t2\n+Ubxn11\t0\t0\t0\t0\t0\t0\t0\t0\n+Uchl1as\t0\t0\t0\t0\t0\t0\t0\t1\n+Ufd1l\t0\t0\t1\t0\t0\t6\t0\t0\n+Uqcc\t5\t0\t2\t1\t0\t8\t1\t1\n+Vash2\t0\t0\t0\t0\t0\t0\t0\t0\n+Vaultrc5\t243\t258\t161\t383\t159\t262\t286\t270\n+Vax2os\t0\t0\t0\t0\t0\t0\t0\t0\n+Vmn1r-ps79\t0\t0\t0\t0\t0\t0\t0\t0\n+Vmn2r-ps11\t0\t0\t0\t1\t0\t0\t0\t0\n+Vmn2r-ps129\t0\t0\t0\t0\t0\t0\t0\t0\n+Vmn2r-ps159\t0\t0\t0\t0\t0\t0\t0\t0\n+Vmn2r-ps54\t0\t0\t1\t0\t0\t0\t0\t0\n+Vmn2r-ps60\t0\t0\t0\t0\t0\t0\t0\t0\n+Vmn2r29\t2\t0\t0\t0\t0\t0\t0\t0\n+Vps39\t3\t1\t0\t0\t0\t8\t0\t0\n+Vsig8\t194\t50\t35\t100\t99\t74\t109\t50\n+Wac\t15\t0\t0\t0\t2\t15\t2\t2\n+Wbscr25\t0\t2\t1\t0\t0\t1\t0\t0\n+Wdr13\t2\t2\t1\t3\t0\t1\t0\t3\n+Wdr73\t4\t0\t0\t0\t0\t2\t1\t0\n+Wiz\t10\t0\t1\t6\t1\t3\t3\t2\n+Wls\t1\t1\t0\t3\t2\t0\t0\t1\n+Wwp1\t40\t0\t2\t2\t4\t56\t10\t4\n+Xist\t0\t0\t0\t0\t0\t8\t0\t0\n+Yaf2\t2\t0\t0\t0\t0\t6\t0\t2\n+Yars2\t1\t0\t0\t0\t1\t1\t0\t0\n+Yipf2\t1\t0\t0\t1\t0\t10\t0\t0\n+Ythdf3\t16\t4\t1\t4\t3\t19\t3\t2\n+Zbtb24\t1\t0\t0\t1\t0\t0\t0\t0\n+Zc3h14\t11\t1\t0\t0\t1\t2\t2\t2\n+Zc3h7a\t336\t148\t60\t20\t17\t99\t14\t60\n+Zf12\t0\t0\t0\t0\t0\t0\t0\t0\n+Zfa-ps\t0\t0\t0\t0\t0\t0\t0\t0\n+Zfhx2as\t0\t0\t0\t0\t0\t1\t0\t0\n+Zfp133-ps\t0\t0\t0\t0\t0\t0\t0\t0\n+Zfp207\t5\t0\t2\t0\t0\t9\t1\t2\n+Zfp326\t7\t2\t1\t1\t0\t4\t4\t0\n+Zfp389\t0\t0\t0\t0\t0\t0\t0\t0\n+Zfp410\t10\t0\t2\t0\t0\t6\t2\t0\n+Zfp414\t1\t0\t0\t0\t0\t4\t0\t0\n+Zfp57\t0\t0\t0\t0\t0\t0\t0\t0\n+Zfp572\t0\t0\t0\t0\t0\t0\t0\t0\n+Zfp672\t0\t0\t0\t0\t0\t2\t0\t0\n+Zfp783\t0\t0\t0\t0\t0\t0\t0\t0\n+Zfp809\t7\t1\t0\t0\t0\t4\t2\t0\n+Zfp821\t0\t0\t0\t2\t2\t0\t0\t0\n+Zfp862\t4\t0\t0\t0\t0\t10\t2\t0\n+Zim3\t0\t0\t0\t0\t0\t0\t0\t0\n+Zmynd8\t3\t5\t4\t4\t3\t8\t2\t1\n+Znf41-ps\t0\t0\t0\t0\t0\t0\t0\t0\n+Zp4-ps\t0\t0\t0\t0\t0\t0\t0\t0\n+Zscan4a\t0\t0\t0\t0\t0\t0\t0\t0\n+Zxda\t0\t0\t0\t0\t0\t0\t0\t0\n'

diff -r 000000000000 -r 82e0af566160 rgedgeR/tool_dependencies.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rgedgeR/tool_dependencies.xml Wed Jun 12 02:58:43 2013 -0400

@@ -0,0 +1,47 @@
+
+<?xml version="1.0"?>
+
+<tool_dependency>
+    <package name="readline" version="6.2">
+        <repository  name="package_readline_6_2" owner="boris" prior_installation_required="True" toolshed="http://testtoolshed.g2.bx.psu.edu/" />
+    </package>
+    <package name="package_R" version="3.0.1">
+        <install version="1.0">
+            <actions>
+                <action type="download_by_url">http://cran.ms.unimelb.edu.au/src/base/R-3/R-3.0.1.tar.gz</action>
+                <action type="set_environment_for_install">
+                    <repository name="package_readline_6_2" owner="boris" toolshed="http://testtoolshed.g2.bx.psu.edu/">
+                        <package name="readline" version="6.2" />
+                    </repository>
+                </action>
+                <action type="make_directory">$INSTALL_DIR</action>
+                <action type="shell_command">./configure --with-blas --with-lapack --enable-R-shlib --with-readline=no --with-x=no --prefix=$INSTALL_DIR && make && make install</action>
+                <action type="set_environment">
+                    <environment_variable action="set_to" name="R_HOME">$INSTALL_DIR/lib/R</environment_variable>
+                    <environment_variable action="set_to" name="R_LIBS">$INSTALL_DIR/lib/R/library</environment_variable>
+                    <environment_variable action="prepend_to" name="PATH">$INSTALL_DIR/lib/R/bin</environment_variable>
+
+                </action>
+            </actions>
+        </install>
+        <readme>R is a free software environment for statistical computing and graphics
+                WARNING: See custom compilation options above
+                Modified from an older version of R by Boris by Ross Lazarus for R 3.0
+                Added Bioc basics too
+       </readme>
+    </package>
+    <package name="package_BioCBasics" version="2.12">
+        <install version="1.0">
+            <actions>
+                <action type="shell_command">$INSTALL_DIR/lib/R/bin/R CMD BATCH installBioC.R </action>
+            </actions>
+        </install>
+        <readme>R is a free software environment for statistical computing and graphics
+                WARNING: See custom compilation options above
+                Modified from an older version of R by Boris by Ross Lazarus for R 3.0
+                Added Bioc basics via this package installBioC.R script
+       </readme>
+    </package>
+</tool_dependency>