Mercurial > repos > mbernt > proteomicsr_intensity_workflow
comparison intensity_workflow.xml @ 0:31212f7e7611 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 15:50:36 +0000 |
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-1:000000000000 | 0:31212f7e7611 |
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1 <tool id="proteomicsr_intensity_workflow" name="proteomicsr: intensity workflow" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="21.05"> | |
2 <macros> | |
3 <import>macros.xml</import> | |
4 </macros> | |
5 <expand macro="requirements"/> | |
6 <command detect_errors="exit_code"><![CDATA[ | |
7 Rscript '$rscript' | |
8 && mv Rdata/dat_calculated.csv . | |
9 ]]></command> | |
10 <configfiles> | |
11 <configfile name="rscript"><![CDATA[ | |
12 library(proteomicsr) | |
13 | |
14 @READ_INPUTS@ | |
15 | |
16 #set controlSamples = 'c("' + '", "'.join(str($control_samples).split(",")) + '")' | |
17 | |
18 null <- run_intensity_workflow( | |
19 @COMMON_WF_PARAMETERS@, | |
20 control_samples = $controlSamples, | |
21 comparisons_relevant = NULL, ## if NULL, script continues with all possible comparisons. Otherwise, a vector should be provided, e.g. c("treatment1_vs_ctrl", "treatment2_vs_ctrl", "treatment2_vs_treatment1") | |
22 run_vsn = $run_vsn, | |
23 #if $impute.run_imputation == "TRUE" | |
24 run_imputation = $impute.run_imputation, | |
25 imp_fun = $impute.imp_fun, | |
26 imp_q = $impute.imp_q, | |
27 impute_completely_missing_only = $impute_completely_missing_only, | |
28 #end if | |
29 | |
30 ) | |
31 ]]></configfile> | |
32 </configfiles> | |
33 <inputs> | |
34 <param argument="control_samples" type="text" label="Control samples" help="Comma separated list of sample names, as used in the input table"/> | |
35 <expand macro="common_wf_paramerters"> | |
36 <param argument="run_vsn" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Apply variance stabilization using the DEP package" help=""/> | |
37 <conditional name="impute"> | |
38 <param argument="run_imputation" type="select" label="Apply imputation using the DEP package" help="If TRUE, variance stabilization is performed anyway and also the parameters imp_fun, imp_q, and impute_completely_missing_only should be double-checked and adjusted if necessary"> | |
39 <option value="TRUE">TRUE</option> | |
40 <option value="FALSE" selected="true">FALSE</option> | |
41 </param> | |
42 <when value="TRUE"> | |
43 <param argument="impute_completely_missing_only" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Decide which missing data should be imputed." help=""/> | |
44 <param argument="imp_fun" type="select" label="Method for imputation" help=""> | |
45 <option value="MLE">Maximum likelihood-based imputation method using the EM algorithm</option> | |
46 <option value="bpca">Bayesian missing value imputation</option> | |
47 <option value="knn">Nearest neighbour averaging</option> | |
48 <option value="QRILC"> Imputation of left-censored missing data using random draws from a truncated distribution with parameters estimated using quantile regression</option> | |
49 <option value="MinDet">Imputation of left-censored missing data using a deterministic minimal value approach</option> | |
50 <option value="MinProb" selected="true"> Imputation of left-censored missing data by random draws from a Gaussian distribution centred to a minimal value</option> | |
51 <option value="min">Replaces the missing values by the smallest non-missing value in the data</option> | |
52 <option value="zero">Replaces the missing values by 0</option> | |
53 <option value="nbavg">Average neighbour imputation for fractions collected along a fractionation/separation gradient</option> | |
54 <option value="none">No imputation</option> | |
55 </param> | |
56 <param argument="imp_q" type="float" value="0.01" min="0" max="1" label="q-th quantile for left-censored imputation" help="The minimal value observed is estimated as being the q-th quantile of the observed values in that sample."/> | |
57 </when> | |
58 <when value="FALSE"/> | |
59 </conditional> | |
60 </expand> | |
61 <param name="out_select" type="select" multiple="true" optional="true" label="Optional outputs"> | |
62 <option value="tables">Detailed tables</option> | |
63 <option value="plots">Plots</option> | |
64 </param> | |
65 </inputs> | |
66 <outputs> | |
67 <data name="dat_calculated" format="csv" from_work_dir="dat_calculated.csv"/> | |
68 <collection name="output" type="list" label="${tool.name} on ${on_string}: additional tables"> | |
69 <discover_datasets pattern="__name_and_ext__" directory="Rdata"/> | |
70 <filter>out_select and "tables" in out_select</filter> | |
71 </collection> | |
72 <collection name="plots" type="list" label="${tool.name} on ${on_string}: plots"> | |
73 <discover_datasets pattern="__name_and_ext__" directory="Plots"/> | |
74 <filter>out_select and "plots" in out_select</filter> | |
75 </collection> | |
76 </outputs> | |
77 <tests> | |
78 <test expect_num_outputs="1"> | |
79 <param name="sampleTable" value="sampleTable.csv" ftype="csv"/> | |
80 <param name="control_samples" value="control_04h_plusLPS_vs_control_04h_noLPS"/> | |
81 <output name="dat_calculated"> | |
82 <assert_contents> | |
83 <has_n_lines n="4269"/> | |
84 <has_n_columns sep="," n="31"/> | |
85 </assert_contents> | |
86 </output> | |
87 </test> | |
88 </tests> | |
89 <help><![CDATA[ | |
90 | |
91 Intensity workflow | |
92 | |
93 1. Evaluating data quality | |
94 2. Identification (and removal) of outliers (param: remove_outliers) | |
95 3. Log2 transformation | |
96 4. Optional: median normalization (param: median_normalize) | |
97 5. Filtering for reliably identified candidates (param: number_replicates_reliable, reliable_all_comparisons) | |
98 6. Optional: variance stabilization (param: run_vsn) | |
99 7. Optional: imputation, which includes variance stabilization as data preparation step (param: run_imputation, imp_fun, imp_q, impute_completely_missing_only) | |
100 8. Principal component analysis of processed dataCalculation of average log2 fold changes and (adjusted) p-values (param: control_samples, comparisons_relevant, alternative, var.equal, paired, pvalue_adjustment) | |
101 9. Visualization of the results (param: pvalue_decision, significance_cutoff, color_up, color_none, color_down) | |
102 ]]></help> | |
103 <expand macro="citations"/> | |
104 </tool> |