| Next changeset 1:bd693a71a50b (2020-04-02) |
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Commit message:
"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/pyprophet commit a83d231286a8df67483df46e76b4b3a2ef90b251" |
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added:
macros.xml pyprophet_export.xml test-data/merged.osw test-data/open_swath_output1.osw test-data/open_swath_output2.osw test-data/output.tabular test-data/patient_specific_OSW_optimized_decoys.pqp test-data/peptide1.osw test-data/peptide1.pdf test-data/peptide2.osw test-data/peptide2.pdf test-data/protein1.osw test-data/protein1.pdf test-data/protein2.osw test-data/protein2.pdf test-data/score.osw test-data/score_plots.pdf test-data/score_report.pdf test-data/study_design.tabular test-data/subsample.tabular test-data/test_data.osw |
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| diff -r 000000000000 -r 2bc6bbf651b9 macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Tue Feb 25 18:23:48 2020 -0500 |
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| @@ -0,0 +1,23 @@ +<macros> + <token name="@VERSION@">2.1.4</token> + + <xml name="requirements"> + <requirements> + <requirement type="package" version="2.1.4">pyprophet</requirement> + <yield/> + </requirements> + </xml> + + <xml name="citations"> + <citations> + <citation type="doi">10.1038/nmeth.4398</citation> + <citation type="doi">10.1038/nbt.3908</citation> + <citation type="doi">10.1093/bioinformatics/btu686</citation> + <citation type="doi">10.1038/nmeth.1584</citation> + <yield/> + </citations> + </xml> + + <token name="@link@">http://openswath.org/en/latest/docs/pyprophet.html</token> + +</macros> |
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| diff -r 000000000000 -r 2bc6bbf651b9 pyprophet_export.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/pyprophet_export.xml Tue Feb 25 18:23:48 2020 -0500 |
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| b'@@ -0,0 +1,364 @@\n+<tool id="pyprophet_export" name="PyProphet export" version="@VERSION@.0">\n+ <description>\n+ Export tabular files, optional swath2stats export\n+ </description>\n+ <macros>\n+ <import>macros.xml</import>\n+ </macros>\n+ <expand macro="requirements">\n+ <requirement type="package" version="1.16.0">bioconductor-swath2stats</requirement>\n+ <requirement type="package" version="0.8.4">r-dplyr</requirement>\n+ <requirement type="package" version="1.12.8">r-data.table</requirement>\n+ <requirement type="package" version="2.3">r-gridextra</requirement>\n+ </expand>\n+ <command detect_errors="aggressive">\n+ <![CDATA[\n+ ln -s \'$input\' ./input.osw &&\n+ pyprophet export\n+ --in=./input.osw\n+ --format=$conditional_output.format\n+\n+ #if $conditional_output.format==\'legacy_split\':\n+ $conditional_output.transition_quant\n+ --max_transition_pep=$conditional_output.max_transition_pep\n+ --ipf=$conditional_output.ipf\n+ --ipf_max_peptidoform_pep=$conditional_output.ipf_max_peptidoform_pep\n+ --max_rs_peakgroup_qvalue=$conditional_output.max_rs_peakgroup_qvalue\n+ --max_global_peptide_qvalue=$conditional_output.max_global_peptide_qvalue\n+ --max_global_protein_qvalue=$conditional_output.max_global_protein_qvalue\n+\n+ #elif $conditional_output.format==\'legacy_merged\':\n+ $conditional_output.transition_quant\n+ --max_transition_pep=$conditional_output.max_transition_pep\n+ --ipf=$conditional_output.ipf\n+ --ipf_max_peptidoform_pep=$conditional_output.ipf_max_peptidoform_pep\n+ --max_rs_peakgroup_qvalue=$conditional_output.max_rs_peakgroup_qvalue\n+ --max_global_peptide_qvalue=$conditional_output.max_global_peptide_qvalue\n+ --max_global_protein_qvalue=$conditional_output.max_global_protein_qvalue\n+\n+ #elif $conditional_output.format==\'matrix\':\n+ --ipf=$conditional_output.ipf\n+ --ipf_max_peptidoform_pep=$conditional_output.ipf_max_peptidoform_pep\n+ --max_rs_peakgroup_qvalue=$conditional_output.max_rs_peakgroup_qvalue\n+ --max_global_peptide_qvalue=$conditional_output.max_global_peptide_qvalue\n+ --max_global_protein_qvalue=$conditional_output.max_global_protein_qvalue\n+ #end if\n+ $peptide_error\n+ $protein_error\n+ --out=./output.tsv\n+\n+ #if $conditional_swath2stats.swath2stats==\'yes_swath2stats\':\n+ && cat \'${swath2stats}\'\n+ && Rscript \'${swath2stats}\'\n+ #end if\n+\n+ #if $conditional_output.format==\'score_plots\':\n+ && mv *score_plots.pdf \'$score_plots\'\n+ #else:\n+ && mv output.tsv \'$export_file\'\n+ #end if\n+\n+\n+ ]]>\n+ </command>\n+ <configfiles>\n+ <configfile name="swath2stats"><![CDATA[\n+\n+#if $conditional_swath2stats.swath2stats==\'yes_swath2stats\':\n+\n+library("SWATH2stats")\n+library("data.table")\n+library("dplyr")\n+library(gridExtra)\n+\n+########################### Input ##############################################\n+\n+## read in pyprophet export file\n+data_me <- data.frame(fread(\'output.tsv\', sep=\'\\t\', header=TRUE))\n+\n+## read in study design template\n+study_design <- data.frame(fread(\'$conditional_swath2stats.study_design\', sep=\'\\t\', header=TRUE))\n+\n+## merge both files on filename column\n+data.annotated <- sample_annotation(data_me, study_design, column.file = "filename")\n+\n+\n+########################### QC plots and tabular files #########################\n+\n+## remove decoys when generating plots\n+data.annotated.nodecoy <- subset(data.annotated, decoy==FALSE)\n+\n+pdf("summary.pdf", fonts = "Times", pointsize = 12)\n+plot(0,type=\'n\',axes=FALSE,ann=FALSE)\n+title(main="Summarized plots and tables from pyprophet export file")\n+\n+## Look at Numbers of peptides and proteins per run\n+grid.table(count_analytes(data.annotated.nodeco'..b'.tabular" >\n+ <filter>conditional_swath2stats[\'swath2stats\'] == \'yes_swath2stats\'</filter>\n+ </data>\n+ <data name="msstats_input" format="tabular" label="${tool.name} on ${on_string}: msstats_input.tabular" from_work_dir="msstats_input.tabular" >\n+ <filter>conditional_swath2stats[\'swath2stats\'] == \'yes_swath2stats\'</filter>\n+ </data>\n+ </outputs>\n+ <tests>\n+ <test expect_num_outputs="1">\n+ <param name="input" value="protein2.osw" ftype="osw" />\n+ <param name="format" value="legacy_merged" />\n+ <param name="max_global_peptide_qvalue" value="0.2" />\n+ <conditional name="conditional_swath2stats">\n+ <param name="swath2stats" value="no_swath2stats"/>\n+ </conditional>\n+ <output name="export_file" file="output.tabular" />\n+ </test>\n+ <test expect_num_outputs="1">\n+ <param name="input" value="protein2.osw" ftype="osw" />\n+ <param name="format" value="score_plots" />\n+ <conditional name="conditional_swath2stats">\n+ <param name="swath2stats" value="no_swath2stats"/>\n+ </conditional>\n+ <output name="score_plots" file="score_plots.pdf" />\n+ </test>\n+ <test expect_failure="true">\n+ <param name="input" value="protein2.osw" ftype="osw" />\n+ <param name="format" value="legacy_merged" />\n+ <conditional name="conditional_swath2stats">\n+ <param name="study_design" value="study_design.tabular" ftype="tabular" />\n+ <conditional name="conditional_fdr_replica">\n+ <param name="calc_fdr_replica" value="calc_fdr_replica_no"/>\n+ </conditional>\n+ <conditional name="conditional_max_pep">\n+ <param name="filter_max_pep" value="filter_max_pep_no" />\n+ </conditional>\n+ <conditional name="conditional_min_pep">\n+ <param name="filter_min_pep" value="filter_min_pep_no" />\n+ </conditional>\n+ </conditional>\n+ <assert_stderr>\n+ <has_text text="replacement has 1 row, data has 0" />\n+ </assert_stderr>\n+ </test>\n+ </tests>\n+ <help>\n+<![CDATA[\n+**What it does**\n+\n+PyProphet: Semi-supervised learning and scoring of OpenSWATH results.\n+\n+Export tabular (tsv) tables. \n+\n+Optional SWATH2stats output. SWATH2stats is intended to transform SWATH data from the OpenSWATH software into a format readable by other statistics packages while performing filtering, annotation and FDR estimation.\n+\n+**Study desing file for SWATH2stats**\n+\n+ - Tabular file with columns that are named: Filename, Condition, BioReplicate, Run. \n+ - The Filename should be part or the same as the original filenames used in OpenSWATH workflow\n+ - The Condition should be a \n+ - The BioReplicate is corresponds to the biological replicate\n+ - The Run is the number of the run in which the sample was measured\n+\n+ ::\n+\n+ Filename Condition BioReplicate Run\n+ healthy1.mzml healthy 1 1\n+ healthy2.mzml healthy 2 2\n+ diseased1.mzml diseased 3 3\n+ ...\n+ ...\n+\n+\n+PyProphet is a Python re-implementation of the mProphet algorithm (Reiter 2010 Nature Methods) optimized for SWATH-MS data acquired by data-independent acquisition (DIA). The algorithm was originally published in (Telemann 2014 Bioinformatics) and has since been extended to support new data types and analysis modes (Rosenberger 2017, Nature biotechnology and Nature methods).\n+\n+For more information, visit @link@\n+\n+]]>\n+ </help>\n+ <expand macro="citations">\n+ <citation type="doi">10.1371/journal.pone.0153160</citation>\n+ </expand>\n+</tool>\n' |
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| @@ -0,0 +1,1 @@ +transition_group_id decoy run_id filename RT assay_rt delta_rt iRT assay_iRT delta_iRT Sequence FullPeptideName Charge mz Intensity aggr_prec_Peak_Area aggr_prec_Peak_Apex leftWidth rightWidth peak_group_rank d_score m_score id aggr_Peak_Area aggr_Peak_Apex aggr_Fragment_Annotation ProteinName m_score_peptide_experiment_wide m_score_peptide_global m_score_protein_experiment_wide m_score_protein_global |
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| @@ -0,0 +1,3 @@ +Filename Condition BioReplicate Run +./TN22.mzML late 1 1 +./TN23.mzML early 2 2 |
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