Mercurial > repos > vijay > pancancer_apply_weights

comparison pancancer_apply_weights.xml @ 0:3543d3e66ecb draft default tip

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"planemo upload for repository http://github.com/nvk747/papaa/galaxy/ commit 954b283ef7f82f59f55476a4b3a230d655187ac1"
author vijay
date Wed, 16 Dec 2020 23:30:39 +0000
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-1:000000000000 0:3543d3e66ecb
1 <tool id="pancancer_apply_weights" name="PAPAA: PanCancer apply weights" version="@VERSION@">
2 <description>apply weights</description>
3 <macros>
4 <import>macros.xml</import>
5 </macros>
6 <expand macro="requirements"/>
7 <expand macro="stdio"/>
8 <version_command><![CDATA['papaa_apply_weights.py' --version]]></version_command>
9 <command><![CDATA[
10 mkdir 'classifier' &&
11 ln -s '${pancan_classifier_summary}' 'classifier/classifier_summary.txt' &&
12 ln -s '${pancan_classifier_coefficients}' 'classifier/classifier_coefficients.tsv' &&
13 papaa_apply_weights.py
14 --classifier_summary 'classifier'
15 @INPUTS_BASIC@
16 @INPUTS_COPY_NUMBER_CLASS_FILE_CONDITIONAL@
17 > '${log}'
18 ]]></command>
19 <inputs>
20 <expand macro="inputs_basic"/>
21 <expand macro="inputs_copy_number_class_file_conditional"/>
22 <param argument="--classifier_summary" label="pancancer classifier summary" name="pancan_classifier_summary" optional="false" type="data" format="txt" help="classifer_summary.txt"/>
23 <param label="pancancer classifier coefficients" name="pancan_classifier_coefficients" optional="false" type="data" format="tabular" help="classifier_coefficients.tsv"/>
24 </inputs>
25 <outputs>
26 <data format="txt" name="log" label="${tool.name} on ${on_string} (Log)"/>
27 <data format="tabular" name="classifier_decisions" label="${tool.name} on ${on_string} (classifier_decisions.tsv)" from_work_dir="classifier/classifier_decisions.tsv"/>
28 </outputs>
29 <tests>
30 <test>
31 <param name="x_matrix" value="pancan_rnaseq_freeze_t1p.tsv.gz" ftype="tabular"/>
32 <param name="filename_mut" value="pancan_mutation_freeze_t1p.tsv.gz" ftype="tabular"/>
33 <param name="filename_mut_burden" value="mutation_burden_freeze.tsv" ftype="tabular"/>
34 <param name="filename_sample" value="sample_freeze.tsv" ftype="tabular"/>
35 <param name="copy_number" value="yes"/>
36 <param name="filename_copy_loss" value="copy_number_loss_status_t10p.tsv.gz" ftype="tabular"/>
37 <param name="filename_copy_gain" value="copy_number_gain_status_t10p.tsv.gz" ftype="tabular"/>
38 <param name="filename_cancer_gene_classification" value="cosmic_cancer_classification.tsv" ftype="tabular"/>
39 <param name="pancan_classifier_summary" value="classifier_summary.txt" ftype="txt"/>
40 <param name="pancan_classifier_coefficients" value="classifier_coefficients.tsv" ftype="tabular"/>
41 <output name="log" file="apply_weights_Log.txt"/>
42 <output name="classifier_decisions">
43 <assert_contents>
44 <has_line line="SAMPLE_BARCODE&#009;log10_mut&#009;total_status&#009;weight&#009;AKT1&#009;AKT1_gain&#009;ERBB2&#009;ERBB2_gain&#009;KRAS&#009;KRAS_gain&#009;PIK3CA&#009;PIK3CA_gain&#009;PATIENT_BARCODE&#009;DISEASE&#009;SUBTYPE&#009;hypermutated&#009;include" />
45 <has_n_columns n="17" />
46 <has_n_lines n="90" />
47 </assert_contents>
48 </output>
49 </test>
50 </tests>
51 <help><![CDATA[
52
53 **Pancancer_Aberrant_Pathway_Activity_Analysis scripts/papaa_apply_weights.py:**
54
55 **Inputs:**
56 --classifier_summary String of the location of classifier_summary.txt file
57 --copy_number Supplement Y matrix with copy number events
58 --x_matrix Filename of features to use in model
59 --filename_mut_burden Filename of sample/gene mutations to use in model
60 --filename_mut_burden Filename of sample mutation burden to use in model
61 --filename_sample Filename of patient/samples to use in model
62 --filename_copy_loss Filename of copy number loss
63 --filename_copy_gain Filename of copy number gain
64 --filename_cancer_gene_classification Filename of cancer gene classification table
65
66 **Outputs:**
67 Apply a logit transform on expression values (y = 1/(1+e^(-wX))) to output mutational probabilities. Generates "classifier_decisions.tsv" file which has scores/probabilities and other covariate information. The scores/probabilities will be used for gene ranking and variant specific classification. ]]>
68 </help>
69 <expand macro="citations" />
70 </tool>