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"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/pyprophet commit 8b9f6963836c6ccb227343ce952e7b9a015d0483"
author galaxyp
date Fri, 05 Jun 2020 12:38:25 -0400
parents 102d940d365c
children
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3:ece83e6b5328 4:3cf580bf28e2
1 <tool id="pyprophet_export" name="PyProphet export" version="@VERSION@.0"> 1 <tool id="pyprophet_export" name="PyProphet export" version="@VERSION@.1">
2 <description> 2 <description>
3 Export tabular files, optional swath2stats export 3 Export tabular files, optional swath2stats export
4 </description> 4 </description>
5 <macros> 5 <macros>
6 <import>macros.xml</import> 6 <import>macros.xml</import>
86 ########################### QC plots and tabular files ######################### 86 ########################### QC plots and tabular files #########################
87 87
88 ## remove decoys when generating plots 88 ## remove decoys when generating plots
89 data.annotated.nodecoy <- subset(data.annotated, decoy==FALSE) 89 data.annotated.nodecoy <- subset(data.annotated, decoy==FALSE)
90 90
91 pdf("summary.pdf", fonts = "Times", pointsize = 12) 91 pdf("summary.pdf", fonts = "Times", pointsize = 8)
92 plot(0,type='n',axes=FALSE,ann=FALSE) 92 plot(0,type='n',axes=FALSE,ann=FALSE)
93 title(main="Summarized plots and tables from pyprophet export file") 93 title(main="Summarized plots and tables from pyprophet export file")
94 94
95 ## Look at Numbers of peptides and proteins per run 95 ## Look at Numbers of peptides and proteins per run
96 grid.table(count_analytes(data.annotated.nodecoy), rows= NULL) 96 ## for many runs table needs to be split over several pages
97 number_samples = nrow(count_analytes(data.annotated.nodecoy))
98
99 ### for more than 20 annotation groups print only 20 samples per page:
100 if (number_samples <= 20){
101 grid.table(count_analytes(data.annotated.nodecoy), rows= NULL)
102 }else{
103 grid.table(count_analytes(data.annotated.nodecoy)[1:20,], rows= NULL)
104 mincount = 21
105 maxcount = 40
106 for (count15 in 1:(ceiling(number_samples/20)-1)){
107 plot(0,type='n',axes=FALSE,ann=FALSE)
108 if (maxcount <= number_samples){
109 grid.table(count_analytes(data.annotated.nodecoy)[mincount:maxcount,], rows= NULL)
110 mincount = mincount+20
111 maxcount = maxcount+20
112 }else{### stop last page with last sample otherwise NA in table
113 grid.table(count_analytes(data.annotated.nodecoy)[mincount:number_samples,], rows= NULL)}
114 }
115 }
116
97 117
98 ## Correlation of the intensities 118 ## Correlation of the intensities
99 correlation_int <- plot_correlation_between_samples(data.annotated.nodecoy, column.values = 'Intensity') 119 correlation_int <- plot_correlation_between_samples(data.annotated.nodecoy, column.values = 'Intensity')
100 120
101 ## Plot the correlation of the delta_rt, which is the deviation of the retention time from the expected retention time 121 ## Plot the correlation of the delta_rt, which is the deviation of the retention time from the expected retention time
171 </configfiles> 191 </configfiles>
172 <inputs> 192 <inputs>
173 <param name="input" type="data" format="osw" label="Input file" help="This file needs to be in OSW format (--in)" /> 193 <param name="input" type="data" format="osw" label="Input file" help="This file needs to be in OSW format (--in)" />
174 <conditional name="conditional_output"> 194 <conditional name="conditional_output">
175 <param argument="format" type="select" label="Export format, either matrix, legacy_split, legacy_merged (mProphet/PyProphet) or score_plots format" > 195 <param argument="format" type="select" label="Export format, either matrix, legacy_split, legacy_merged (mProphet/PyProphet) or score_plots format" >
176 <option value="legacy_split" selected="True">legaxy_split</option> 196 <option value="legacy_split" selected="True">legacy_split</option>
177 <option value="legacy_merged">legacy_merged</option> 197 <option value="legacy_merged">legacy_merged</option>
178 <option value="matrix">matrix</option> 198 <option value="matrix">matrix</option>
179 <option value="score_plots">score_plots</option> 199 <option value="score_plots">score_plots</option>
180 </param> 200 </param>
181 <when value="legacy_split"> 201 <when value="legacy_split">
328 <![CDATA[ 348 <![CDATA[
329 **What it does** 349 **What it does**
330 350
331 PyProphet: Semi-supervised learning and scoring of OpenSWATH results. 351 PyProphet: Semi-supervised learning and scoring of OpenSWATH results.
332 352
333 Export tabular (tsv) tables. 353 Export tabular (tsv) tables. By default, both peptide- and transition-level quantification is reported, which is necessary for requantification or SWATH2stats. If peptide and protein inference in the global context was conducted, the results will be filtered to 1% FDR by default.
334 354
335 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. 355 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.
336 356
337 **Study desing file for SWATH2stats** 357 **Study desing file for SWATH2stats**
338 358
339 - Tabular file with columns that are named: Filename, Condition, BioReplicate, Run. 359 - Tabular file with columns that are named: Filename, Condition, BioReplicate, Run.
340 - The Filename should be part or the same as the original filenames used in OpenSWATH workflow 360 - The Filename should be part or the same as the original filenames used in OpenSWATH workflow
341 - The Condition should be a 361 - The Condition will be used for statistical analysis. In case multiple conditions are of interest for statistical analysis (e.g. diagnosis and age), this tool has to be run multiple times as SWATH2stats can only handle one condition at a time
342 - The BioReplicate is corresponds to the biological replicate 362 - The BioReplicate is corresponds to the biological replicate
343 - The Run is the number of the run in which the sample was measured 363 - The Run is the number of the MS run in which the sample was measured
364
365 - **Example for one replicate per patient**
344 366
345 :: 367 ::
346 368
347 Filename Condition BioReplicate Run 369 Filename Condition BioReplicate Run
348 healthy1.mzml healthy 1 1 370 healthy1.mzml healthy 1 1
349 healthy2.mzml healthy 2 2 371 healthy2.mzml healthy 2 2
350 diseased1.mzml diseased 3 3 372 diseased1.mzml diseased 3 3
373 diseased2.mzml diseased 4 4
351 ... 374 ...
352 ... 375 ...
353 376
377
378 - **Example for two replicates per patient**
379
380 ::
381
382 Filename Condition BioReplicate Run
383 healthy1.mzml healthy 1 1
384 healthy2.mzml healthy 1 2
385 diseased1.mzml diseased 2 3
386 diseased2.mzml diseased 2 4
387 ...
388 ...
354 389
355 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). 390 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).
356 391
357 For more information, visit @link@ 392 For more information, visit @link@
358 393