Mercurial > repos > mbernt > proteomicsr_msigdb_workflow
diff msigdb_workflow.xml @ 0:0fbb062e0cf5 draft default tip
planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tree/master/tools/proteomicsr commit a73787be689a9af5641ff1b594c9a35d29093247-dirty
| author | mbernt |
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| date | Tue, 19 Dec 2023 15:51:04 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/msigdb_workflow.xml Tue Dec 19 15:51:04 2023 +0000 @@ -0,0 +1,322 @@ +<tool id="proteomicsr_msigdb_workflow" name="proteomicsr: enrichment using MSigDB gene sets" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="21.05"> + <macros> + <import>macros.xml</import> + </macros> + <expand macro="requirements"/> + <stdio> + <regex source="stdout" level="fatal" match="ERROR: Timeout" description="The ENSEMBL server timed out. A retry may help."/> + </stdio> + <command detect_errors="exit_code"><![CDATA[ + Rscript '$rscript' + && mv Rdata/Summary_ALL.csv . + ]]></command> + <configfiles> + <configfile name="rscript"><![CDATA[ +library(proteomicsr) + +#if $dat_calculated.ext == 'csv' + dat_calculated <- read.csv("$dat_calculated", row.names = 1) +#else + dat_calculated <- read.delim("$dat_calculated", header = TRUE, row.names = 1, sep = "\t") +#end if +@READ_SAMPLE_GENES_MAPPING@ + +null <- run_msigdb_workflow( + dat_calculated, + msigdb_category = "$msigdb_category", + #if $msigdb_subcategory + msigdb_subcategory = "$msigdb_subcategory", + #end if + ## knowledgebase = NULL, not needed + sampleGenes = NULL, + sampleMapping = NULL, + pvalue_decision = "$pvalue_decision", + significance_cutoff_candidates = $significance_cutoff_candidates, + get_ID_to_map = NULL, + ID_provided = "$ID_provided", + organism = "$organism", + padjust_method = "$padjust_method", + significance_cutoff_terms = $significance_cutoff_terms, + direction_calculation = "$direction_calculation", + topx = $topx, + topx_per_comparison = $topx_per_comparison, + #if $plot_term_candidates + plot_term_candidates = "$plot_term_candidates", + #end if + color_up = "${color_up}FF", + color_down = "${color_down}FF" +) + ]]></configfile> + </configfiles> + <inputs> + + <param argument="dat_calculated" type="data" format="csv,tabular" label="Sample table" help="Rows: unique identifiers (e.g. uniprot accessions), Columns: samples. Replicates should be indicated using _1, _2, .... Content should be numeric."/> + <param argument="msigdb_category" type="select" label="Gene set knowledgebase" help="Visit https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp to get more information on MSigDB categories and if the chosen category needs the definition of a subcategory. MEDICUS and LEGACY gene sets seem to be not supported yet."> + <option value="H">Hallmark gene sets</option> + <option value="C1">Positional gene sets</option> + <option value="C2">Curated gene sets</option> + <option value="C3">Regulatory target gene sets</option> + <option value="C4">Computational gene sets</option> + <option value="C5">Ontology gene sets</option> + <option value="C6">Oncogenic signature gene sets</option> + <option value="C7">Immunologic signature gene sets</option> + <option value="C8">Cell type signature gene sets</option> + </param> + <param argument="msigdb_subcategory" type="select" optional="true" label="Gene set knowledgebase subcategory" help="Visit https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp to get more information on MSigDB categories and if the chosen category needs the definition of a subcategory. MEDICUS and LEGACY gene sets seem to be not supported yet."> + <option value="CGP">C2 subcategory: chemical and genetic perturbations</option> + <option value="CP">C2 subcategory: canonical pathways</option> + <option value="CP:BIOCARTA">C2 subcategory: BioCarta canonical pathways</option> + <option value="CP:KEGG">C2 subcategory: KEGG canonical pathways (KEGG_MEDICUS and KEGG_LEGACY seem to be not supported yet)</option> + <option value="CP:PID">C2 subcategory: PID canonical pathways</option> + <option value="CP:REACTOME">C2 subcategory: Reactome canonical pathways</option> + <option value="MIR:MIRDB">C3 subcategory: gene sets containing high-confidence gene-level predictions of human miRNA targets as catalogued by miRDB v6.0 algorithm (MIR_LEGACY seems to be not supported yet)</option> + <option value="TFT:GTRD">C3 subcategory: genes that share GTRD predicted transcription factor binding sites in the region -1000,+100 bp around the TSS for the indicated transcription factor.</option> + <option value="CGN">C4 subcategory: cancer gene neighborhoods</option> + <option value="CM">C4 subcategory: cancer modules</option> + <option value="GO:BP">C5 subcategory: gene sets derived from the GO Biological Process ontology</option> + <option value="GO:CC">C5 subcategory: gene sets derived from the GO Cellular Component ontology</option> + <option value="GO:MF">C5 subcategory: gene sets derived from the GO Molecular Function ontology</option> + <option value="HPO">C5 subcategory: Human Phenotype Ontology</option> + <option value="IMMUNESIGDB">C7 subcategory: gene sets representing chemical and genetic perturbations of the immune system generated by manual curation of published studies in human and mouse immunology</option> + <option value="VAX">C7 subcategory: gene sets curated by the Human Immunology Project Consortium (HIPC) describing human transcriptomic immune responses to vaccinations</option> + </param> + <!-- <param argument="knowledgebase" type="text" value="" label="Pattern to add to ouput, i.e. the database used for enrichment" help="Default is NULL, thus nothing is added to the output."/> --> + <expand macro="sample_genes_mapping"/> + <param argument="ID_provided" type="text" value="uniprotswissprot" label="Define provided identifier" help="Define the ID type used in your dataframe of average Log2(FCs) and (adjusted) p-values. The ID should relate to attributes available using attributes = biomaRt::listAttributes(biomaRt::useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = "hsapiens_gene_ensembl")) or the attributes specific for the defined organism (e.g. "mmusculus_gene_ensembl" or "rnorvegicus_gene_ensembl")."/> + <!-- set of supported species can be determined with msigdbr::msigdbr_species() + TODO commented species require input to get_ID_to_map --> + <param argument="organism" type="select" label="Organism used" help=""> + <!-- <option value="Anolis carolinensis"/> --> + <option value="Bos taurus"/> + <option value="Caenorhabditis elegans"/> + <option value="Canis lupus familiaris"/> + <option value="Danio rerio"/> + <option value="Drosophila melanogaster"/> + <!-- <option value="Equus caballus"/> --> + <!-- <option value="Felis catus"/> --> + <option value="Gallus gallus"/> + <option value="Homo sapiens" selected="true"/> + <!-- <option value="Macaca mulatta"/> --> + <!-- <option value="Monodelphis domestica"/> --> + <option value="Mus musculus"/> + <!-- <option value="Ornithorhynchus anatinus"/> --> + <!-- <option value="Pan troglodytes"/> --> + <option value="Rattus norvegicus"/> + <option value="Saccharomyces cerevisiae"/> + <!-- <option value="Schizosaccharomyces pombe 972h-"/> --> + <option value="Sus scrofa"/> + <!-- <option value="Xenopus tropicalis"/> --> + </param> + <!-- should be pvalue pvalueadj if used downstream of fc_workflow or intensity_workflow --> + <param argument="pvalue_decision" type="text" value="pvalueadj" label="Pattern to select columns containing p-values to use" help="Examples: When pvalue, all columns ending on _pvalue are used to filter for significantly altered candidates, whereas the pattern pvalueadj will use all columns ending with this pattern"/> + <param argument="significance_cutoff_candidates" type="float" value="0.05" min="0" max="1" label="Significance cutoff to filter for candidates used for enrichment" help="All candidates with (adjusted) p-value below this threshold will be subjected to enrichment analysis"/> + <param argument="significance_cutoff_terms" type="float" value="0.05" min="0" max="1" label="Significance cutoff to identify significantly enriched terms" help="All terms with (adjusted) p-value below this threshold will be considered significantly enriched"/> + <param argument="padjust_method" type="select" label="Method for p-value adjustment during enrichment analysis" help=""> + <option value="holm">Holm</option> + <option value="hochberg">Hochberg</option> + <option value="hommel">Hommel</option> + <option value="bonferroni">Bonferroni</option> + <option value="BY">Benjamini & Yekutieli (BY)</option> + <option value="fdr" selected="true">Benjamini & Hochberg (BH/fdr)</option> + <option value="none">None</option> + </param> + <param argument="direction_calculation" type="select" label="Decide how to calculate the direction of the term regulation" help="Decide whether to use median or mean values of the Log2(fold changes) of the candidates used for enrichment and assigned to the term."> + <option value="median">Hallmark gene sets</option> + <option value="mean">Positional gene sets</option> + </param> + <param argument="topx" type="integer" min="1" value="10" label="Number of top enriched pathways to return and visualize" help="In addition to exporting and visualizing all enriched terms and the significantly enriched terms, the top enriched terms will be exported and visualized based on the value defined here."/> + <param argument="topx_per_comparison" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Extract the top enriched terms condition-wise" help="Decide whether to extract the top enriched terms condition-wise or based on their summed enrichment over all conditions"/> + <param argument="plot_term_candidates" type="select" optional="true" label="Decide whether to visualize candidates assigned to enriched terms" help=""> + <option value="significant">Candidates of significantly enriched terms</option> + <option value="all">Candidates of all enriched terms</option> + </param> + <param argument="color_up" type="color" value="#DC0000" label="Color for up-regulated candidates"/> + <param argument="color_down" type="color" value="#3C5488" label="Color for down-regulated candidates"/> + <param name="out_select" type="select" multiple="true" optional="true" label="Optional outputs"> + <option value="tables" selected="true">Detailed tables</option> + <option value="plots" selected="true">Plots</option> + </param> + </inputs> + <outputs> + <data name="summary" format="csv" from_work_dir="Summary_ALL.csv"/> + <collection name="output" type="list" label="${tool.name} on ${on_string}: additional tables"> + <discover_datasets pattern="__name_and_ext__" directory="Rdata"/> + <filter>out_select and "tables" in out_select</filter> + </collection> + <collection name="plots" type="list" label="${tool.name} on ${on_string}: plots"> + <discover_datasets pattern="__name_and_ext__" directory="Plots"/> + <filter>out_select and "plots" in out_select</filter> + </collection> + + <collection name="sig_output" type="list" label="${tool.name} on ${on_string}: additional tables for significantly enriched terms"> + <discover_datasets pattern="__name_and_ext__" directory="CandidatesSignificantTerms/Rdata"/> + <filter>"significant" in plot_term_candidates</filter> + <filter>out_select and "tables" in out_select</filter> + </collection> + <collection name="sig_plots" type="list" label="${tool.name} on ${on_string}: plots for significantly enriched terms"> + <discover_datasets pattern="__name_and_ext__" directory="CandidatesSignificantTerms/Plots"/> + <filter>"significant" in plot_term_candidates</filter> + <filter>out_select and "plots" in out_select</filter> + </collection> + + <collection name="all_output" type="list" label="${tool.name} on ${on_string}: additional tables for all enriched terms"> + <discover_datasets pattern="__name_and_ext__" directory="CandidatesAllTerms/Rdata"/> + <filter>"all" in plot_term_candidates</filter> + <filter>out_select and "tables" in out_select</filter> + </collection> + <collection name="all_plots" type="list" label="${tool.name} on ${on_string}: plots for all enriched terms"> + <discover_datasets pattern="__name_and_ext__" directory="CandidatesAllTerms/Plots"/> + <filter>"all" in plot_term_candidates</filter> + <filter>out_select and "plots" in out_select</filter> + </collection> + </outputs> + <tests> + <test expect_num_outputs="5"> + <param name="dat_calculated" value="dat_calculated.csv" ftype="csv"/> + <param name="plot_term_candidates" value="significant"/> + <output name="summary"> + <assert_contents> + <has_n_lines n="89"/> + <has_n_columns sep="," n="7"/> + </assert_contents> + </output> + <output_collection name="output" count="5" type="list"> + <element name="Enrichment_results_log.p.adjust_pvalueadj_0.05" ftype="csv"> + <assert_contents> + <has_n_lines n="45"/> + <has_n_columns sep="," n="3"/> + </assert_contents> + </element> + <element name="Enrichment_results_long_pvalueadj_0.05_median_FC" ftype="csv"> + <assert_contents> + <has_n_lines n="70"/> + <has_n_columns sep="," n="12"/> + </assert_contents> + </element> + <element name="Enrichment_results_median_FC_pvalueadj_0.05" ftype="csv"> + <assert_contents> + <has_n_lines n="45"/> + <has_n_columns sep="," n="3"/> + </assert_contents> + </element> + <element name="MSigDB_gene_set_ID_mapping" ftype="csv"> + <assert_contents> + <has_n_lines n="8210"/> + <has_n_columns sep="," n="2"/> + </assert_contents> + </element> + <element name="Summary_Top10_combined" ftype="csv"> + <assert_contents> + <has_n_lines n="29"/> + <has_n_columns sep="," n="7"/> + </assert_contents> + </element> + </output_collection> + <output_collection name="plots" count="9" type="list"/> + <output_collection name="sig_output" count="8" type="list"> + <element name="Candidates_HALLMARK_COMPLEMENT" ftype="csv"> + <assert_contents> + <has_n_lines n="13"/> + <has_n_columns sep="," n="8"/> + </assert_contents> + </element> + <element name="Candidates_HALLMARK_INFLAMMATORY_RESPONSE" ftype="csv"> + <assert_contents> + <has_n_lines n="10"/> + <has_n_columns sep="," n="8"/> + </assert_contents> + </element> + <element name="Candidates_HALLMARK_TNFA_SIGNALING_VIA_NFKB" ftype="csv"> + <assert_contents> + <has_n_lines n="16"/> + <has_n_columns sep="," n="8"/> + </assert_contents> + </element> + <element name="Candidates_HALLMARK_UV_RESPONSE_UP" ftype="csv"> + <assert_contents> + <has_n_lines n="8"/> + <has_n_columns sep="," n="8"/> + </assert_contents> + </element> + <element name="Candidates_ggplot_HALLMARK_COMPLEMENT" ftype="csv"> + <assert_contents> + <has_n_lines n="25"/> + <has_n_columns sep="," n="8"/> + </assert_contents> + </element> + <element name="Candidates_ggplot_HALLMARK_INFLAMMATORY_RESPONSE" ftype="csv"> + <assert_contents> + <has_n_lines n="19"/> + <has_n_columns sep="," n="8"/> + </assert_contents> + </element> + <element name="Candidates_ggplot_HALLMARK_TNFA_SIGNALING_VIA_NFKB" ftype="csv"> + <assert_contents> + <has_n_lines n="31"/> + <has_n_columns sep="," n="8"/> + </assert_contents> + </element> + <element name="Candidates_ggplot_HALLMARK_UV_RESPONSE_UP" ftype="csv"> + <assert_contents> + <has_n_lines n="15"/> + <has_n_columns sep="," n="8"/> + </assert_contents> + </element> + </output_collection> + <output_collection name="sig_plots" count="8" type="list"/> + </test> + <!-- same + sample genes --> + <test expect_num_outputs="5"> + <param name="dat_calculated" value="dat_calculated.csv" ftype="csv"/> + <param name="sampleGenes" value="sampleGenes.csv" ftype="csv"/> + <param name="plot_term_candidates" value="significant"/> + <output name="summary"> + <assert_contents> + <has_n_lines n="89"/> + <has_n_columns sep="," n="7"/> + </assert_contents> + </output> + <output_collection name="output" count="5" type="list"/> + <output_collection name="plots" count="9" type="list"/> + <output_collection name="sig_output" count="8" type="list"/> + <output_collection name="sig_plots" count="8" type="list"/> + </test> + <!-- same + sample genes + sample mapping --> + <test expect_num_outputs="5"> + <param name="dat_calculated" value="dat_calculated.csv" ftype="csv"/> + <param name="sampleGenes" value="sampleGenes.csv" ftype="csv"/> + <param name="sampleMapping" value="sampleMapping.csv" ftype="csv"/> + <param name="plot_term_candidates" value="significant"/> + <output name="summary"> + <assert_contents> + <has_n_lines n="89"/> + <has_n_columns sep="," n="7"/> + </assert_contents> + </output> + <output_collection name="output" count="5" type="list"/> + <output_collection name="plots" count="9" type="list"/> + <output_collection name="sig_output" count="8" type="list"/> + <output_collection name="sig_plots" count="8" type="list"/> + </test> + <!-- same as 1st test but plot all candidates + only output tables --> + <test expect_num_outputs="3"> + <param name="dat_calculated" value="dat_calculated.csv" ftype="csv"/> + <param name="plot_term_candidates" value="all"/> + <param name="out_select" value="tables"/> + <output name="summary"> + <assert_contents> + <has_n_lines n="89"/> + <has_n_columns sep="," n="7"/> + </assert_contents> + </output> + <output_collection name="output" count="5" type="list"/> + <output_collection name="all_output" count="88" type="list"/> + </test> + </tests> + <help><![CDATA[ +Enrichment analysis workflow using MSigDB gene sets + +Providing a table with average Log2(FCs) and (adjusted) p-values, enrichment analysis is conducted against the gene sets provided by the MSigDB + ]]></help> + <expand macro="citations"/> +</tool> \ No newline at end of file