Mercurial > repos > mbernt > proteomicsr_intensity_workflow
diff 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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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/intensity_workflow.xml Tue Dec 19 15:50:36 2023 +0000 @@ -0,0 +1,104 @@ +<tool id="proteomicsr_intensity_workflow" name="proteomicsr: intensity workflow" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="21.05"> + <macros> + <import>macros.xml</import> + </macros> + <expand macro="requirements"/> + <command detect_errors="exit_code"><![CDATA[ + Rscript '$rscript' + && mv Rdata/dat_calculated.csv . + ]]></command> + <configfiles> + <configfile name="rscript"><![CDATA[ +library(proteomicsr) + +@READ_INPUTS@ + +#set controlSamples = 'c("' + '", "'.join(str($control_samples).split(",")) + '")' + +null <- run_intensity_workflow( + @COMMON_WF_PARAMETERS@, + control_samples = $controlSamples, + 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") + run_vsn = $run_vsn, +#if $impute.run_imputation == "TRUE" + run_imputation = $impute.run_imputation, + imp_fun = $impute.imp_fun, + imp_q = $impute.imp_q, + impute_completely_missing_only = $impute_completely_missing_only, +#end if + +) + ]]></configfile> + </configfiles> + <inputs> + <param argument="control_samples" type="text" label="Control samples" help="Comma separated list of sample names, as used in the input table"/> + <expand macro="common_wf_paramerters"> + <param argument="run_vsn" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Apply variance stabilization using the DEP package" help=""/> + <conditional name="impute"> + <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"> + <option value="TRUE">TRUE</option> + <option value="FALSE" selected="true">FALSE</option> + </param> + <when value="TRUE"> + <param argument="impute_completely_missing_only" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" label="Decide which missing data should be imputed." help=""/> + <param argument="imp_fun" type="select" label="Method for imputation" help=""> + <option value="MLE">Maximum likelihood-based imputation method using the EM algorithm</option> + <option value="bpca">Bayesian missing value imputation</option> + <option value="knn">Nearest neighbour averaging</option> + <option value="QRILC"> Imputation of left-censored missing data using random draws from a truncated distribution with parameters estimated using quantile regression</option> + <option value="MinDet">Imputation of left-censored missing data using a deterministic minimal value approach</option> + <option value="MinProb" selected="true"> Imputation of left-censored missing data by random draws from a Gaussian distribution centred to a minimal value</option> + <option value="min">Replaces the missing values by the smallest non-missing value in the data</option> + <option value="zero">Replaces the missing values by 0</option> + <option value="nbavg">Average neighbour imputation for fractions collected along a fractionation/separation gradient</option> + <option value="none">No imputation</option> + </param> + <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."/> + </when> + <when value="FALSE"/> + </conditional> + </expand> + <param name="out_select" type="select" multiple="true" optional="true" label="Optional outputs"> + <option value="tables">Detailed tables</option> + <option value="plots">Plots</option> + </param> + </inputs> + <outputs> + <data name="dat_calculated" format="csv" from_work_dir="dat_calculated.csv"/> + <collection name="output" type="list" label="${tool.name} on ${on_string}: additional tables"> + <discover_datasets pattern="__name_and_ext__" directory="Rdata"/> + <filter>out_select and "tables" in out_select</filter> + </collection> + <collection name="plots" type="list" label="${tool.name} on ${on_string}: plots"> + <discover_datasets pattern="__name_and_ext__" directory="Plots"/> + <filter>out_select and "plots" in out_select</filter> + </collection> + </outputs> + <tests> + <test expect_num_outputs="1"> + <param name="sampleTable" value="sampleTable.csv" ftype="csv"/> + <param name="control_samples" value="control_04h_plusLPS_vs_control_04h_noLPS"/> + <output name="dat_calculated"> + <assert_contents> + <has_n_lines n="4269"/> + <has_n_columns sep="," n="31"/> + </assert_contents> + </output> + </test> + </tests> + <help><![CDATA[ + +Intensity workflow + +1. Evaluating data quality +2. Identification (and removal) of outliers (param: remove_outliers) +3. Log2 transformation +4. Optional: median normalization (param: median_normalize) +5. Filtering for reliably identified candidates (param: number_replicates_reliable, reliable_all_comparisons) +6. Optional: variance stabilization (param: run_vsn) +7. Optional: imputation, which includes variance stabilization as data preparation step (param: run_imputation, imp_fun, imp_q, impute_completely_missing_only) +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) +9. Visualization of the results (param: pvalue_decision, significance_cutoff, color_up, color_none, color_down) + ]]></help> + <expand macro="citations"/> +</tool>