Mercurial > repos > mbernt > proteomicsr_intensity_workflow

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planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tree/master/tools/proteomicsr commit a73787be689a9af5641ff1b594c9a35d29093247-dirty
author mbernt
date Tue, 19 Dec 2023 15:50:36 +0000
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31212f7e7611 planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tree/master/tools/proteomicsr commit a73787be689a9af5641ff1b594c9a35d29093247-dirty
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1 <tool id="proteomicsr_intensity_workflow" name="proteomicsr: intensity workflow" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="21.05">
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2 <macros>
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3 <import>macros.xml</import>
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4 </macros>
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5 <expand macro="requirements"/>
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6 <command detect_errors="exit_code"><![CDATA[
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7 Rscript '$rscript'
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8 && mv Rdata/dat_calculated.csv .
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9 ]]></command>
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10 <configfiles>
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11 <configfile name="rscript"><![CDATA[
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12 library(proteomicsr)
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14 @READ_INPUTS@
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16 #set controlSamples = 'c("' + '", "'.join(str($control_samples).split(",")) + '")'
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18 null <- run_intensity_workflow(
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19 @COMMON_WF_PARAMETERS@,
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20 control_samples = $controlSamples,
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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")
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22 run_vsn = $run_vsn,
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23 #if $impute.run_imputation == "TRUE"
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24 run_imputation = $impute.run_imputation,
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25 imp_fun = $impute.imp_fun,
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26 imp_q = $impute.imp_q,
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27 impute_completely_missing_only = $impute_completely_missing_only,
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28 #end if
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29
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30 )
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31 ]]></configfile>
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32 </configfiles>
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33 <inputs>
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34 <param argument="control_samples" type="text" label="Control samples" help="Comma separated list of sample names, as used in the input table"/>
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35 <expand macro="common_wf_paramerters">
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36 <param argument="run_vsn" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Apply variance stabilization using the DEP package" help=""/>
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37 <conditional name="impute">
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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">
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39 <option value="TRUE">TRUE</option>
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40 <option value="FALSE" selected="true">FALSE</option>
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41 </param>
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42 <when value="TRUE">
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43 <param argument="impute_completely_missing_only" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Decide which missing data should be imputed." help=""/>
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44 <param argument="imp_fun" type="select" label="Method for imputation" help="">
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45 <option value="MLE">Maximum likelihood-based imputation method using the EM algorithm</option>
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46 <option value="bpca">Bayesian missing value imputation</option>
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47 <option value="knn">Nearest neighbour averaging</option>
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48 <option value="QRILC"> Imputation of left-censored missing data using random draws from a truncated distribution with parameters estimated using quantile regression</option>
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49 <option value="MinDet">Imputation of left-censored missing data using a deterministic minimal value approach</option>
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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>
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51 <option value="min">Replaces the missing values by the smallest non-missing value in the data</option>
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52 <option value="zero">Replaces the missing values by 0</option>
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53 <option value="nbavg">Average neighbour imputation for fractions collected along a fractionation/separation gradient</option>
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54 <option value="none">No imputation</option>
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55 </param>
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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."/>
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57 </when>
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58 <when value="FALSE"/>
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59 </conditional>
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60 </expand>
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61 <param name="out_select" type="select" multiple="true" optional="true" label="Optional outputs">
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62 <option value="tables">Detailed tables</option>
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63 <option value="plots">Plots</option>
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64 </param>
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65 </inputs>
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66 <outputs>
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67 <data name="dat_calculated" format="csv" from_work_dir="dat_calculated.csv"/>
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68 <collection name="output" type="list" label="${tool.name} on ${on_string}: additional tables">
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69 <discover_datasets pattern="__name_and_ext__" directory="Rdata"/>
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70 <filter>out_select and "tables" in out_select</filter>
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71 </collection>
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72 <collection name="plots" type="list" label="${tool.name} on ${on_string}: plots">
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73 <discover_datasets pattern="__name_and_ext__" directory="Plots"/>
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74 <filter>out_select and "plots" in out_select</filter>
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75 </collection>
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76 </outputs>
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77 <tests>
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78 <test expect_num_outputs="1">
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79 <param name="sampleTable" value="sampleTable.csv" ftype="csv"/>
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80 <param name="control_samples" value="control_04h_plusLPS_vs_control_04h_noLPS"/>
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81 <output name="dat_calculated">
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82 <assert_contents>
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83 <has_n_lines n="4269"/>
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84 <has_n_columns sep="," n="31"/>
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85 </assert_contents>
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86 </output>
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87 </test>
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88 </tests>
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89 <help><![CDATA[
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90
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91 Intensity workflow
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92
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93 1. Evaluating data quality
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94 2. Identification (and removal) of outliers (param: remove_outliers)
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95 3. Log2 transformation
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96 4. Optional: median normalization (param: median_normalize)
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97 5. Filtering for reliably identified candidates (param: number_replicates_reliable, reliable_all_comparisons)
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98 6. Optional: variance stabilization (param: run_vsn)
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99 7. Optional: imputation, which includes variance stabilization as data preparation step (param: run_imputation, imp_fun, imp_q, impute_completely_missing_only)
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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)
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101 9. Visualization of the results (param: pvalue_decision, significance_cutoff, color_up, color_none, color_down)
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102 ]]></help>
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103 <expand macro="citations"/>
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104 </tool>