| Previous changeset 10:d951677a50d4 (2019-06-28) Next changeset 12:85f039f53414 (2020-01-24) |
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Commit message:
planemo upload commit 9664cb97c1ab9d21af2b302eb976015178089a72 |
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modified:
cluster_profiler.xml |
| b |
| diff -r d951677a50d4 -r cc2bd0d2afa2 cluster_profiler.xml --- a/cluster_profiler.xml Fri Jun 28 05:08:48 2019 -0400 +++ b/cluster_profiler.xml Fri Sep 27 05:48:17 2019 -0400 |
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| b'@@ -1,12 +1,12 @@\n-<tool id="cluter_profiler" name="GO terms classification and enrichment analysis" version="2019.06.27.1">\n+<tool id="cluter_profiler" name="GO terms classification and enrichment analysis" version="2019.09.26">\n <description>(Human, Mouse, Rat)[clusterProfiler]</description>\n <requirements>\n- <requirement type="package" version="3.4.1">R</requirement>\n- <requirement type="package" version="3.5.0">bioconductor-org.hs.eg.db</requirement>\n- <requirement type="package" version="3.5.0">bioconductor-org.mm.eg.db</requirement>\n- <requirement type="package" version="3.5.0">bioconductor-org.Rn.eg.db</requirement>\n- <requirement type="package" version="3.2.0">bioconductor-dose</requirement>\n- <requirement type="package" version="3.4.4">bioconductor-clusterprofiler</requirement>\n+ <requirement type="package">R</requirement>\n+ <requirement type="package" version="3.8.2">bioconductor-org.hs.eg.db</requirement>\n+ <requirement type="package" version="3.8.2">bioconductor-org.mm.eg.db</requirement>\n+ <requirement type="package" version="3.8.2">bioconductor-org.Rn.eg.db</requirement>\n+ <requirement type="package" version="3.10.2">bioconductor-dose</requirement>\n+ <requirement type="package" version="3.12.0">bioconductor-clusterprofiler</requirement>\n </requirements>\n <command detect_errors="exit_code"><![CDATA[\n Rscript "$__tool_directory__/GO-enrich.R"\n@@ -19,7 +19,7 @@\n --ncol="$input.ncol"\n --header="$input.header"\n #end if\n- \n+\n --id_type="$idti.idtypein"\n \n --species="$species"\n@@ -32,7 +32,7 @@\n #end if\n \n #if $ego.go_enrich == "true"\n- --plot="$ego.plot" \n+ --plot="$ego.plot"\n --go_enrich="true"\n --pval_cutoff="$ego.pval"\n --qval_cutoff="$ego.qval"\n@@ -51,7 +51,7 @@\n #else\n --go_enrich="false"\n #end if\n- \n+\n --onto_opt="$ontology" > $log\n ]]></command>\n <inputs>\n@@ -87,7 +87,7 @@\n </param>\n <when value="Uniprot"/>\n <when value="Entrez"/>\n- </conditional> \n+ </conditional>\n <param name="species" type="select" label="Species" >\n <option value="org.Hs.eg.db">Human (Homo sapiens) </option>\n <option value="org.Mm.eg.db">Mouse (Mus musculus) </option>\n@@ -226,11 +226,11 @@\n \n This tool is based on R package clusterProfiler and allows to perform GO terms classification and enrichment analyses on gene/protein sets (e.g. given a set of genes that are up-regulated under certain conditions, an enrichment analysis will find which GO terms are over-represented (or under-represented) using annotations for that gene/protein set).\n \n-Given a list of IDs, this tool: \n+Given a list of IDs, this tool:\n \n (i) performs gene classification based on GO distribution at a specific level,\n- \n-(ii) calculates GO categories enrichment (over- or under-representation) for the IDs of the input list, compared to a background. User has the possibility to use background corresponding to the whole organism or to a user-defined list. In this latter case, we recommand to use the "Build tissue-specific expression dataset" ProteoRE tool to create this list according to your need. \n+\n+(ii) calculates GO categories enrichment (over- or under-representation) for the IDs of the input list, compared to a background. User has the possibility to use background corresponding to the whole organism or to a user-defined list. In this latter case, we recommand to use the "Build tissue-specific expression dataset" ProteoRE tool to create this list according to your need.\n \n -----\n \n@@ -238,17 +238,17 @@\n \n Two modes are allowed: either by supplying a tabular file (.csv, .tsv, .txt, .tab) including your IDs (identifiers) or by copy/pasting your IDs (separated by a space).\n \n-"Select type/source of IDs":'..b'us musculus and Rattus norvegicus\n \n "Perform GO categories representation analysis?": classify genes based on their projection at a specific level of the GO corpus (see parameter below), and provides functions (set to "Yes")\n \n@@ -260,24 +260,24 @@\n \n "Q-value cut off": to prevent high false discovery rate (FDR) in multiple testing, Q-values (adjusted P-values) are estimated for FDR control. (default is < 0.05)\n \n-"Define your own background IDs?": by default the whole genome/proteome is used as a reference background to compute the enrichment. As this reference set should normally only include genes/proteins that were monitored during your analysis, this option allows to provide your own background; this could be for instance, the total number of genes/proteins expressed in the tissue/sample under study. \n+"Define your own background IDs?": by default the whole genome/proteome is used as a reference background to compute the enrichment. As this reference set should normally only include genes/proteins that were monitored during your analysis, this option allows to provide your own background; this could be for instance, the total number of genes/proteins expressed in the tissue/sample under study.\n \n-If you want to use your own background, click on the "Yes" button. Your gene/protein set must be a list of Entrez gene ID or Uniprot accession number (otherwise, use the ID-Converter tool of ProteoRE). Select the file containing your list of ID (as background), then specify the column number which contains IDs and the type of IDs (gene Entrez or Uniprot Accession number) as requested. \n+If you want to use your own background, click on the "Yes" button. Your gene/protein set must be a list of Entrez gene ID or Uniprot accession number (otherwise, use the ID-Converter tool of ProteoRE). Select the file containing your list of ID (as background), then specify the column number which contains IDs and the type of IDs (gene Entrez or Uniprot Accession number) as requested.\n \n Of note: for Human species, you can build your own background by using the "Build tissue-specific expression dataset" tool of ProteoRE.\n \n------ \n+-----\n \n **Output**\n \n-Diagram output: graphical output in the form of bar-plot or dot-plot (png, jpeg or pdf format), one figure for each GO category. \n-Text tables: with the following information GO category description (e.g.BP.Description), GO term identifier (e.g. BP.GOID) and GO term frequency (e.g. BP.Frequency)d graphics representing the repartition and/or enrichment of GO categories. One table and one graphic will be produced for each GO catagory. \n+Diagram output: graphical output in the form of bar-plot or dot-plot (png, jpeg or pdf format), one figure for each GO category.\n+Text tables: with the following information GO category description (e.g.BP.Description), GO term identifier (e.g. BP.GOID) and GO term frequency (e.g. BP.Frequency)d graphics representing the repartition and/or enrichment of GO categories. One table and one graphic will be produced for each GO catagory.\n \n -----\n \n **Authors**\n \n-G Yu, LG Wang, Y Han, QY He. clusterProfiler: an R package for comparing biological themes among gene clusters. \n+G Yu, LG Wang, Y Han, QY He. clusterProfiler: an R package for comparing biological themes among gene clusters.\n OMICS: A Journal of Integrative Biology 2012, 16(5):284-287. doi:[10.1089/omi.2011.0118](http://dx.doi.org/10.1089/omi.2011.0118)\n \n User manual / Documentation of the clusterProfiler R package (functions and parameters):\n@@ -287,13 +287,13 @@\n \n .. class:: infomark\n \n-Bioconductor Packages used: \n+Bioconductor Packages used:\n \n- - bioconductor-org.hs.eg.db v3.5.0\n- - bioconductor-org.mm.eg.db v3.5.0\n- - bioconductor-org.rn.eg.db v3.5.0\n- - dose v3.2.0\n- - clusterprofiler v 3.4.4\n+ - bioconductor-org.hs.eg.db v3.8.2\n+ - bioconductor-org.mm.eg.db v3.8.2\n+ - bioconductor-org.rn.eg.db v3.8.2\n+ - dose v3.10.2\n+ - clusterprofiler v 3.12.0\n \n .. class:: infomark\n \n' |