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comparison build/tools/ORApathwayEnrichment/ORApathwayEnrichment.xml @ 10:6a112eaf8f38 draft

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planemo upload for repository https://forgemia.inra.fr/metexplore/met4j-galaxy commit 71071300dd662ad01bd064abcf6866a192eeea95
author metexplore
date Mon, 03 Feb 2025 15:59:46 +0000
parents 0976a6257300
children 40c15b7467f1
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9:0976a6257300 10:6a112eaf8f38
1 <?xml version="1.0" encoding="UTF-8" standalone="no"?> 1 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
2 <tool id="met4j_ORApathwayEnrichment" name="ORApathwayEnrichment" version="develop"> 2 <tool id="met4j_ORApathwayEnrichment" name="ORApathwayEnrichment" version="2.0.0">
3 <description>Perform Over Representation Analysis for Pathway Enrichment, using one-tailed exact Fisher Test.</description> 3 <description>Perform Over Representation Analysis for Pathway Enrichment, using one-tailed exact Fisher Test.</description>
4 <xrefs> 4 <xrefs>
5 <xref type="bio.tools">met4j</xref> 5 <xref type="bio.tools">met4j</xref>
6 </xrefs> 6 </xrefs>
7 <requirements> 7 <requirements>
8 <container type="singularity">oras://registry.forgemia.inra.fr/metexplore/met4j/met4j-singularity:develop</container> 8 <container type="singularity">oras://registry.forgemia.inra.fr/metexplore/met4j/met4j-singularity:2.0.0</container>
9 </requirements> 9 </requirements>
10 <command detect_errors="exit_code"><![CDATA[sh /usr/bin/met4j.sh mapping.ORApathwayEnrichment#if str($th) != 'nan': 10 <command detect_errors="exit_code"><![CDATA[sh /usr/bin/met4j.sh mapping.ORApathwayEnrichment#if str($th) != 'nan':
11 -th "$th" 11 -th "$th"
12 #end if 12 #end if
13 -i "$sbml" 13 -i "$sbml"
30 <outputs> 30 <outputs>
31 <data format="tsv" name="outputFile"/> 31 <data format="tsv" name="outputFile"/>
32 </outputs> 32 </outputs>
33 <tests> 33 <tests>
34 <test> 34 <test>
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46 <param name="sbml" value="XF_network.sbml"/> 35 <param name="sbml" value="XF_network.sbml"/>
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58 <param name="input" value="XF_network_C_NOI.txt"/> 36 <param name="input" value="XF_network_C_NOI.txt"/>
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70 <output name="outputFile"> 37 <output name="outputFile">
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82 <assert_contents> 38 <assert_contents>
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94 <has_n_columns n="3"/> 39 <has_n_columns n="3"/>
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106 <has_n_lines n="3"/> 40 <has_n_lines n="3"/>
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118 </assert_contents> 41 </assert_contents>
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130 </output> 42 </output>
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142 </test> 43 </test>
143 <test> 44 <test>
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155 <param name="sbml" value="XF_network.sbml"/> 45 <param name="sbml" value="XF_network.sbml"/>
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167 <param name="input" value="XF_network_C_NOI.txt"/> 46 <param name="input" value="XF_network_C_NOI.txt"/>
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179 <param name="corr" value="HolmBonferroni"/> 47 <param name="corr" value="HolmBonferroni"/>
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191 <param name="th" value="0.005"/> 48 <param name="th" value="0.005"/>
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203 <output name="outputFile"> 49 <output name="outputFile">
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215 <assert_contents> 50 <assert_contents>
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227 <has_n_columns n="3"/> 51 <has_n_columns n="3"/>
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239 <has_n_lines n="2"/> 52 <has_n_lines n="2"/>
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251 </assert_contents> 53 </assert_contents>
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263 </output> 54 </output>
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275 </test> 55 </test>
276 </tests> 56 </tests>
277 <help><![CDATA[Perform Over Representation Analysis for Pathway Enrichment, using one-tailed exact Fisher Test. 57 <help><![CDATA[Perform Over Representation Analysis for Pathway Enrichment, using one-tailed exact Fisher Test.
278 The fisher exact test computes the probability p to randomly get the given set of values. 58 The fisher exact test computes the probability p to randomly get the given set of values.
279 This version computes the probability to get at least the given overlap between the given set and the given modality : 59 This version computes the probability to get at least the given overlap between the given set and the given modality :
280 Sum the hypergeometric probability with increasing target/query intersection cardinality. 60 Sum the hypergeometric probability with increasing target/query intersection cardinality.
281
282 The hypergeometric probability is computed from the following contingency table entries. 61 The hypergeometric probability is computed from the following contingency table entries.
283 (values in cells correspond to the marginal totals of each intersection groups) 62 (values in cells correspond to the marginal totals of each intersection groups)
284 Query !Query 63 Query !Query
285 Target a b 64 Target a b
286 !Target c d 65 !Target c d
287
288 The probability of obtaining the set of value is computed as following: 66 The probability of obtaining the set of value is computed as following:
289 p = ((a+b)!(c+d)!(a+c)!(b+d)!)/(a!b!c!d!(a+b+c+d)!) 67 p = ((a+b)!(c+d)!(a+c)!(b+d)!)/(a!b!c!d!(a+b+c+d)!)
290
291 The obtained p-value is then adjusted for multiple testing using one of the following methods: 68 The obtained p-value is then adjusted for multiple testing using one of the following methods:
292 - Bonferroni: adjusted p-value = p*n 69 - Bonferroni: adjusted p-value = p*n
293 - Benjamini-Hochberg: adjusted p-value = p*n/k 70 - Benjamini-Hochberg: adjusted p-value = p*n/k
294 - Holm-Bonferroni: adjusted p-value = p*(n+1-k) 71 - Holm-Bonferroni: adjusted p-value = p*(n+1-k)
295 n : number of tests; k : pvalue rank]]></help> 72 n : number of tests; k : pvalue rank]]></help>