Mercurial > repos > eschen42 > w4mcorcov
changeset 0:23f9fad4edfc draft
planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit bd26542b811de06c1a877337a2840a9f899c2b94
| author | eschen42 |
|---|---|
| date | Mon, 16 Oct 2017 14:56:52 -0400 |
| parents | |
| children | 0c2ad44b6c9c |
| files | LICENSE README test-data/expected_contrast_corcov.tsv test-data/expected_contrast_corcov_all.tsv test-data/expected_contrast_corcov_global.tsv test-data/expected_contrast_salience.tsv test-data/input_dataMatrix.tsv test-data/input_sampleMetadata.tsv test-data/input_variableMetadata.tsv w4mcorcov.xml w4mcorcov_calc.R w4mcorcov_input.R w4mcorcov_lib.R w4mcorcov_output.R w4mcorcov_salience.R w4mcorcov_util.R w4mcorcov_wrapper.R |
| diffstat | 16 files changed, 5236 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/LICENSE Mon Oct 16 14:56:52 2017 -0400 @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2017 Hegeman Lab + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README Mon Oct 16 14:56:52 2017 -0400 @@ -0,0 +1,7 @@ +# w4mcorcov_galaxy_wrapper + +Planemo-based galaxy-tool-wrapper to wrap a SIMCA® OPLS-DA® S-PLOT® "work-alike" tool. +This tool plots the correlation of each feature with the OPLS-DA projection of multivariate data for two classes of data against the covariance of the same. + +The original plot which this tool emulates is described in: +> Wiklund, Susanne and Johansson, Erik and Sjöström, Lina and Mellerowicz, Ewa J. and Edlund, Ulf and Shockcor, John P. and Gottfries, Johan and Moritz, Thomas and Trygg, Johan (2008). Visualization of GC/TOF-MS-Based Metabolomics Data for Identification of Biochemically Interesting Compounds Using OPLS Class Models. In Analytical Chemistry, 80 (1), pp. 115–122. doi:10.1021/ac0713510
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/expected_contrast_corcov.tsv Mon Oct 16 14:56:52 2017 -0400 @@ -0,0 +1,643 @@ +featureID factorLevel1 factorLevel2 correlation covariance vip4p vip4o level1Level2Sig +M200.005T296 k1 other -0.205568289258149 -17.7677077386947 0.607800774858881 0.76895407082906 0 +M200.1286T209 k1 other -0.00170937109642714 -0.0773130525978799 0.00505407269126545 0.611367438686349 0 +M200.1286T214 k1 other -0.182819068559282 -6.2134875422604 0.540538484463285 0.421434534377586 0 +M200.8062T276 k1 other 0.23979010255191 15.4952138148085 0.708983913134179 0.485389578917022 0 +M201.7354T319 k1 other -0.481792701325989 -325.295925848215 1.42450948171074 0.433545223368707 1 +M201.8009T418 k1 other 0.106575042922809 35.0560223480028 0.315108881349675 0.426724552480981 0 +M201.8011T348 k1 other 0.143426230924376 49.1654403193539 0.424066253630452 0.228931998243512 0 +M201.8013T369 k1 other 0.254989702750156 63.6248135583442 0.753924350257912 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input_sampleMetadata.tsv Mon Oct 16 14:56:52 2017 -0400 @@ -0,0 +1,94 @@ +sampleMetadata k10 +R12S96 k3 +R12S97 k3 +R12S81 k3 +R12S117 k2 +R12S156 k2 +R12S43 k3 +R12S198 k2 +R12S34 k3 +R12S60 k3 +R12S47 k3 +R12S182 k2 +R12S138 k2 +R12S12 k3 +R12S39 k3 +R12S40 k3 +R12S219 k2 +R12S180 k1 +R12S100 k3 +R12S75 k3 +R12S114 k2 +R12S79 k3 +R12S115 k2 +R12S145 k2 +R12S48 k3 +R12S108 k3 +R12S110 k2 +R12S109 k3 +R12S7 k3 +R12S10 k3 +R12S14 k1 +R12S36 k3 +R12S56 k3 +R12S209 k2 +R12S210 k2 +R12S211 k1 +R12S212 k1 +R12S227 k4 +R12S228 k4 +R12S139 k4 +R12S121 k2 +R12S160 k2 +R12S161 k2 +R12S162 k4 +R12S224 k2 +R12S32 k4 +R12S92 k4 +R12S72 k3 +R12S83 k3 +R12S86 k1 +R12S87 k1 +R12S133 k2 +R12S135 k2 +R12S46 k3 +R12S2 k3 +R12S74 k3 +R12S17 k3 +R12S167 k2 +R12S1 k1 +R12S31 k4 +R12S125 k3 +R12S33 k3 +R12S78 k3 +R12S84 k1 +R12S154 k2 +R12S6 k3 +R12S204 k2 +R12S205 k1 +R12S189 k2 +R12S190 k2 +R12S3 k3 +R12S5 k3 +R12S73 k3 +R12S158 k2 +R12S221 k2 +R12S104 k3 +R12S24 k3 +R12S85 k3 +R12S53 k3 +R12S215 k2 +R12S217 k2 +R12S218 k2 +R12S94 k4 +R12S107 k1 +R12S16 k3 +R12S55 k3 +R12S222 k2 +R12S332 k1 +R12S29 k3 +R12S184 k3 +R12S76 k3 +R12S183 k2 +R12S28 k1 +R12S38 k3
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input_variableMetadata.tsv Mon Oct 16 14:56:52 2017 -0400 @@ -0,0 +1,125 @@ +variableMetadata k10_kruskal_sig k10_kruskal_k2.k1_sig k10_kruskal_k3.k1_sig k10_kruskal_k4.k1_sig k10_kruskal_k3.k2_sig k10_kruskal_k4.k2_sig k10_kruskal_k4.k3_sig +M200.005T296 1 0 0 0 1 0 0 +M200.1286T209 1 0 0 0 0 0 0 +M200.1286T214 1 0 0 0 1 0 0 +M200.8062T276 1 0 0 0 0 0 0 +M201.1135T595 0 0 0 0 0 0 0 +M201.7354T319 1 0 0 1 0 0 1 +M201.8009T418 1 0 0 0 0 0 0 +M201.8011T348 1 0 0 0 0 0 0 +M201.8011T521 0 0 0 0 0 0 0 +M201.8013T369 1 0 0 0 0 0 0 +M201.8014T535 1 0 0 0 0 0 0 +M201.8015T228 0 0 0 0 0 0 0 +M201.8016T454 0 0 0 0 0 0 0 +M201.8017T217 1 0 0 0 0 0 0 +M201.8017T476 1 0 0 0 0 0 0 +M201.8018T251 0 0 0 0 0 0 0 +M201.801T459 0 0 0 0 0 0 0 +M201.8021T276 0 0 0 0 0 0 0 +M201.8022T437 1 0 0 0 0 0 0 +M202.95T726 1 0 0 0 0 0 0 +M203.0345T357 1 0 0 0 0 0 0 +M203.7984T363 1 0 0 0 0 0 0 +M203.7987T252 0 0 0 0 0 0 0 +M203.7988T212 0 0 0 0 0 0 0 +M203.7989T238 1 0 0 0 0 0 0 +M205.8387T251 1 0 0 0 1 0 0 +M205.8387T276 1 0 0 0 1 0 0 +M205.8394T201 1 0 0 0 1 0 0 +M205.8394T207 1 0 0 0 1 0 0 +M205.8394T217 1 0 0 0 1 0 0 +M205.8394T231 1 0 0 0 1 0 0 +M205.8394T245 1 0 0 0 1 0 0 +M205.8398T239 1 0 0 0 1 0 0 +M205.8398T264 1 0 0 0 1 0 0 +M205.839T273 1 0 0 0 1 0 0 +M205.8401T322 1 0 0 0 1 0 0 +M206.8403T273 1 0 0 0 1 0 0 +M207.0654T373 1 0 0 1 1 0 1 +M207.0661T267 1 0 0 0 1 1 0 +M207.066T243 1 0 0 1 0 1 1 +M207.1386T512 1 0 0 0 1 0 1 +M207.8358T251 1 0 0 0 1 0 0 +M207.8364T207 1 0 0 0 1 0 0 +M207.8364T217 1 0 0 0 1 0 0 +M207.8364T245 1 0 0 0 1 0 0 +M207.8365T363 1 0 0 0 1 0 0 +M207.8366T347 1 0 0 0 1 0 0 +M207.8368T238 1 0 0 0 1 0 0 +M207.9308T206 1 1 0 0 1 0 0 +M207.9308T302 1 0 0 0 1 0 0 +M207.9308T311 1 1 0 0 1 0 0 +M208.0692T243 1 0 0 0 0 1 0 +M210.9378T464 1 1 0 0 1 1 0 +M210.9385T503 1 1 0 0 1 1 0 +M210.9388T632 1 1 0 0 1 0 0 +M210.9391T578 1 1 0 0 1 0 0 +M211.0388T319 1 0 0 1 0 1 1 +M211.0395T228 1 0 0 1 0 0 1 +M211.039T418 1 0 0 1 0 0 0 +M211.0607T263 1 0 0 0 1 0 1 +M211.0755T367 1 0 0 1 1 0 1 +M212.0743T273 1 1 0 0 1 0 0 +M212.1367T256 1 1 0 0 1 0 0 +M217.0506T407 1 0 0 0 0 0 1 +M217.8836T288 1 0 0 0 1 0 1 +M219.1389T250 1 1 0 0 1 0 0 +M221.0817T230 1 0 0 0 0 0 1 +M221.0819T313 1 0 0 1 0 1 1 +M221.1545T359 1 0 0 1 0 1 1 +M221.1907T677 1 1 0 1 0 0 0 +M222.9577T319 1 1 0 0 1 0 0 +M222.9579T348 1 1 0 0 1 0 0 +M222.9585T217 1 1 0 0 1 0 0 +M222.9585T226 1 1 0 0 1 0 0 +M222.9585T465 1 1 0 0 1 0 0 +M222.9586T206 1 1 0 0 1 0 0 +M222.9586T239 1 1 0 0 1 0 0 +M222.9586T326 1 1 0 0 1 0 0 +M222.9586T818 1 1 0 0 1 0 0 +M222.9586T827 1 1 0 0 1 0 0 +M222.9587T335 1 1 0 0 1 0 0 +M222.958T245 1 1 0 0 1 0 0 +M222.958T307 1 1 0 0 1 0 0 +M223.0971T254 1 1 0 0 1 0 0 +M223.0972T392 1 0 0 1 0 0 1 +M223.1701T415 1 0 0 0 1 0 1 +M223.2066T590 1 0 0 1 0 0 0 +M224.0475T263 1 0 0 0 1 0 1 +M224.9538T465 1 1 0 0 1 0 0 +M225.0555T347 1 0 0 0 0 1 0 +M225.1492T219 1 0 0 0 0 0 1 +M226.063T307 1 1 0 0 1 0 1 +M227.0337T239 1 0 0 1 0 0 1 +M227.0337T297 1 0 0 1 0 0 1 +M230.9242T307 1 1 0 0 1 1 0 +M231.1021T437 1 0 0 1 0 1 0 +M231.9313T509 1 1 0 0 1 1 0 +M233.0821T333 1 0 0 0 0 1 0 +M234.0851T333 1 0 0 0 0 1 0 +M235.0975T362 1 0 0 0 0 0 1 +M235.8222T811 1 1 0 0 1 1 0 +M236.1009T363 1 0 0 0 1 0 1 +M238.8458T327 1 1 0 0 1 1 0 +M239.0703T450 1 0 0 0 0 1 0 +M239.0705T263 1 1 0 0 1 0 1 +M239.0713T322 1 0 0 1 0 0 1 +M239.092T226 1 0 0 0 0 0 1 +M239.2014T293 1 0 0 1 0 0 0 +M239.201T299 1 0 0 0 1 1 0 +M241.0506T270 1 0 0 1 0 1 0 +M241.0507T327 1 0 0 1 0 0 0 +M241.0508T258 1 0 0 1 1 0 1 +M241.0881T263 1 0 0 1 1 0 1 +M242.0543T326 1 0 0 1 1 0 1 +M243.0659T229 1 0 0 1 0 0 1 +M243.1968T424 1 0 0 1 0 0 1 +M243.1969T605 1 0 0 1 0 0 0 +M246.9633T231 1 1 0 0 1 1 0 +M247.1344T557 1 0 0 0 0 1 0 +M247.1344T570 1 0 0 0 0 1 0 +M249.1135T417 1 0 0 0 0 1 0 +M249.1136T427 1 0 0 0 0 1 1 +M249.1864T510 1 0 0 1 0 1 0 +M349.2383T700 1 0 1 0 1 0 0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/w4mcorcov.xml Mon Oct 16 14:56:52 2017 -0400 @@ -0,0 +1,581 @@ +<tool id="w4mcorcov" name="OPLS-DA_Contrasts" version="0.98.2"> + + <description>OPLS-DA Contrasts of Univariate Results</description> + + <requirements> + <requirement type="package">r-batch</requirement> + <requirement type="package">bioconductor-ropls</requirement> + <!-- <requirement type="package">r-foreach</requirement> --> + </requirements> + + <stdio> + <exit_code range="1:" level="fatal" /> + </stdio> + + <command><![CDATA[ + cd $__tool_directory__; Rscript w4mcorcov_wrapper.R + dataMatrix_in "$dataMatrix_in" + sampleMetadata_in "$sampleMetadata_in" + variableMetadata_in "$variableMetadata_in" + tesC "$tesC" + facC "$facC" + pairSigFeatOnly "$pairSigFeatOnly" + levCSV '$levCSV' + matchingC '$matchingC' + labelFeatures '$labelFeatures' + contrast_detail '$contrast_detail' + contrast_corcov '$contrast_corcov' + contrast_salience '$contrast_salience' + ]]></command> + + <inputs> + <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="Features x samples (tabular data - decimal: '.'; missing: NA; mode: numerical; separator: tab character)" /> + <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="Samples x metadata (tabular data - decimal: '.'; missing: NA; mode: character or numerical; separator: tab character)" /> + <param name="variableMetadata_in" label="Variable metadata file (from Univariate)" type="data" format="tabular" help="Features x metadata (tabular data - decimal: '.'; missing: NA; mode: character or numerical; separator: tab character)" /> + <param name="facC" label="Factor of interest" type="text" help="The name of the column of sampleMetadata corresponding to the qualitative variable used to define the contrasts. This also must be a portion of the column names in the variableMetadata file except when the 'Univariate Significance-test' is set to 'none'."/> + <param name="tesC" label="Univariate Significance-Test" type="select" help="Either 'none' or the name of the statistical test that was run by the 'Univariate' tool to produce the variableMetadata file; that name must also be a portion of the column names in that file"> + <option value="none">none - Display all features from variableMetadata (rather than choosing a subset based on significance in univariate testing)</option> + <option value="ttest">ttest - Student's t-test (parametric test, qualitative factor with exactly 2 levels)</option> + <option value="anova">anova - Analysis of variance (parametric test, qualitative factor with more than 2 levels)</option> + <option value="wilcoxon">wilcoxon - Wilcoxon rank test (nonparametric test, qualitative factor with exactly 2 levels)</option> + <option value="kruskal">kruskal - Kruskal-Wallis rank test (nonparametric test, qualitative factor with more than 2 levels)</option> + </param> + <param + name="pairSigFeatOnly" + type="boolean" + checked="true" + truevalue="TRUE" + falsevalue="FALSE" + label="Retain only pairwise-significant features" + help="Ignored when 'none' is chosen. Otherwise, when 'Yes', analyze only features that differ significantly for the pair of levels being contrasted; when 'No', include any feature that varies significantly across all levels."/> + <param name="levCSV" label="Levels of interest" type="text" value = "" help="Comma-separated level-names (or comma-less regular expressions to match level-names) to consider in analysis; must match at least two levels; may include wild cards or regular expressions"> + <sanitizer> + <valid initial="string.letters"> + <add preset="string.digits"/> + <add value="$" /> <!-- $ dollar, dollar-sign --> + <add value="(" /> <!-- ( left-paren --> + <add value=")" /> <!-- ) right-paren --> + <add value="*" /> <!-- * splat, asterisk --> + <add value="+" /> <!-- + plus --> + <add value="," /> <!-- , comma --> + <add value="-" /> <!-- - dash, minus-sign --> + <add value="." /> <!-- . dot, period --> + <add value=":" /> <!-- : colon --> + <add value=";" /> <!-- ; semi, semicolon --> + <add value="?" /> <!-- ? what, question mark --> + <add value="[" /> <!-- [ l-squib, left-squre-bracket --> + <add value="\" /> <!-- \ whack, backslash --> + <add value="]" /> <!-- ] r-squib, right-squre-bracket --> + <add value="^" /> <!-- ^ hat, caret --> + <add value="{" /> <!-- { l-cube, left-curly-bracket --> + <add value="|" /> <!-- | pipe --> + <add value="}" /> <!-- } r-cube, right-curly-bracket --> + <!-- IMPORTANT - Note that single and double quotes are not part of this list; they have the potential to make the 'command' section insecure or broken. --> + </valid> + </sanitizer> + </param> + <param name="matchingC" label="Level-name matching" type="select" help="How to specify levels generically"> + <option value="none" selected="true">do no generic matching (default)</option> + <option value="wildcard">use wild-cards for matching level-names</option> + <option value="regex">use regular expressions for matching level-names</option> + </param> + <param name="labelFeatures" type="boolean" checked="true" truevalue="TRUE" falsevalue="FALSE" label="Label features on detail plot" help="When true, add feature labels to points on the covariance-versus-correlation plot."/> + </inputs> + + <outputs> + <!-- + pdf1: summaries of each contrasts, clearly labeled by level=pair name + * first PCA score-plot + * then PLS score-plot + * then PLS S-PLOT; color in red features with VIP > 1; color in grey any non-pairwise-significant features, if these are included + --> + <data name="contrast_detail" label="${tool.name}_${variableMetadata_in.name}_detail" format="pdf" /> + <!-- + tsv1: cor and cov table with columns: + * feature-ID + * factor-level 1 + * factor-level 2, lexically greater than level 1 + * Wiklund_2008 correlation + * Wiklund_2008 covariance + * Galindo_Prieto_2014 VIP for predictive components, VIP[4,p] + * Galindo_Prieto_2014 VIP for orthogonal components, VIP[4,o] + * (When filtering on significance of univariate tests) Significance of test of null hypothesis that there is no difference between the two classes, i.e, the pair-wise test. + --> + <data name="contrast_corcov" label="${tool.name}_${variableMetadata_in.name}_corcov" format="tabular" /> + <!-- + tsv2: salience table with columns (experimental feature): + * feature-ID + * Salient level, i.e., for the feature, the class-level having the greatest median intensity + * Salient robust coefficient of variation, i.e., for the feature, the mean absolute deviation of the intensity for the salient level divided by the median intensity for the salient level + * Salience, i.e., for the feature, the median of the class-level having the greatest intensity divided by the mean of the medians for all class-levels. + --> + <data name="contrast_salience" label="${tool.name}_${variableMetadata_in.name}_salience" format="tabular" /> + </outputs> + + <tests> + <test> + <param name="dataMatrix_in" value="input_dataMatrix.tsv"/> + <param name="sampleMetadata_in" value="input_sampleMetadata.tsv"/> + <param name="variableMetadata_in" value="input_variableMetadata.tsv"/> + <param name="tesC" value="kruskal"/> + <param name="facC" value="k10"/> + <param name="pairSigFeatOnly" value="FALSE"/> + <param name="labelFeatures" value="TRUE"/> + <param name="levCSV" value="k[12],k[3-4]"/> + <param name="matchingC" value="regex"/> + <output name="contrast_corcov"> + <assert_contents> + <!-- column-labels line --> + <has_text text="featureID" /> + <has_text text="factorLevel1" /> + <has_text text="factorLevel2" /> + <has_text text="correlation" /> + <has_text text="covariance" /> + <has_text text="vip4p" /> + <has_text text="vip4o" /> + <has_text text="level1Level2Sig" /> + <!-- first matched line --> + <has_text text="M349.2383T700" /> + <has_text text="-0.05007" /> + <has_text text="-5.8455" /> + <has_text text="0.0961269" /> + <has_text text="0.1848301" /> + <!-- second matched line --> + <has_text text="M207.9308T206" /> + <has_text text="-0.2967565" /> + <has_text text="-19.56942" /> + <has_text text="1.6023" /> + <has_text text="1.35368" /> + <!-- third matched line --> + <has_text text="M211.0607T263" /> + <has_text text="0.47052" /> + <has_text text="15.910087" /> + <has_text text="0.89838" /> + <has_text text="0.125372" /> + </assert_contents> + </output> + <output name="contrast_salience"> + <assert_contents> + <!-- column-labels line --> + <has_text text="featureID" /> + <has_text text="salientLevel" /> + <has_text text="salientRCV" /> + <has_text text="salience" /> + <!-- first matched line --> + <has_text text="M349.2383T700" /> + <has_text text="0.659554" /> + <has_text text="8.81866595" /> + <!-- second matched line --> + <has_text text="M207.9308T206" /> + <has_text text="0.0578578" /> + <has_text text="2.27527985" /> + <!-- third matched line --> + <has_text text="M211.0607T263" /> + <has_text text="9999" /> + <has_text text="12.87766096" /> + </assert_contents> + </output> + </test> + <test> + <param name="dataMatrix_in" value="input_dataMatrix.tsv"/> + <param name="sampleMetadata_in" value="input_sampleMetadata.tsv"/> + <param name="variableMetadata_in" value="input_variableMetadata.tsv"/> + <param name="tesC" value="kruskal"/> + <param name="facC" value="k10"/> + <param name="pairSigFeatOnly" value="TRUE"/> + <param name="labelFeatures" value="TRUE"/> + <param name="levCSV" value="k[12],k[3-4]"/> + <param name="matchingC" value="regex"/> + <output name="contrast_corcov"> + <assert_contents> + <!-- column-labels line --> + <has_text text="featureID" /> + <has_text text="factorLevel1" /> + <has_text text="factorLevel2" /> + <has_text text="correlation" /> + <has_text text="covariance" /> + <has_text text="vip4p" /> + <has_text text="vip4o" /> + <has_text text="level1Level2Sig" /> + <!-- first matched line --> + <has_text text="M349.2383T700" /> + <has_text text="-0.99601577" /> + <has_text text="-947.55795176" /> + <!-- second matched line --> + <has_text text="M207.9308T206" /> + <has_text text="0.688549" /> + <has_text text="58.22352" /> + <has_text text="1.394687" /> + <has_text text="0.06049885" /> + <!-- third matched line --> + <has_text text="M211.0607T263" /> + <has_text text="-0.572018" /> + <has_text text="-14.57769" /> + <has_text text="0.7780899" /> + <has_text text="0.3678166776" /> + </assert_contents> + </output> + <output name="contrast_salience"> + <assert_contents> + <!-- column-labels line --> + <has_text text="featureID" /> + <has_text text="salientLevel" /> + <has_text text="salientRCV" /> + <has_text text="salience" /> + <!-- first matched line --> + <has_text text="M349.2383T700" /> + <has_text text="0.659554" /> + <has_text text="8.81866595" /> + <!-- second matched line --> + <has_text text="M207.9308T206" /> + <has_text text="0.0578578" /> + <has_text text="2.27527985" /> + <!-- third matched line --> + <has_text text="M211.0607T263" /> + <has_text text="9999" /> + <has_text text="12.87766096" /> + </assert_contents> + </output> + </test> + <test> + <param name="dataMatrix_in" value="input_dataMatrix.tsv"/> + <param name="sampleMetadata_in" value="input_sampleMetadata.tsv"/> + <param name="variableMetadata_in" value="input_variableMetadata.tsv"/> + <param name="tesC" value="none"/> + <param name="facC" value="k10"/> + <param name="pairSigFeatOnly" value="TRUE"/> + <param name="labelFeatures" value="FALSE"/> + <param name="levCSV" value="k[12],k[3-4]"/> + <param name="matchingC" value="regex"/> + <output name="contrast_corcov"> + <assert_contents> + <!-- column-labels line --> + <has_text text="featureID" /> + <has_text text="factorLevel1" /> + <has_text text="factorLevel2" /> + <has_text text="correlation" /> + <has_text text="covariance" /> + <has_text text="vip4p" /> + <has_text text="vip4o" /> + <!-- first matched line --> + <has_text text="M349.2383T700" /> + <has_text text="-0.64331257" /> + <has_text text="-161.82220" /> + <has_text text="1.862455" /> + <has_text text="0.2105143" /> + <!-- second matched line --> + <has_text text="M207.9308T206" /> + <has_text text="-0.313507" /> + <has_text text="-20.0476" /> + <has_text text="1.6956987" /> + <has_text text="1.19247" /> + <!-- third matched line --> + <has_text text="M211.0607T263" /> + <has_text text="-0.38986114" /> + <has_text text="-23.747718" /> + <has_text text="1.064296856" /> + <has_text text="1.16507455" /> + </assert_contents> + </output> + <output name="contrast_salience"> + <assert_contents> + <!-- column-labels line --> + <has_text text="featureID" /> + <has_text text="salientLevel" /> + <has_text text="salientRCV" /> + <has_text text="salience" /> + <!-- first matched line --> + <has_text text="M349.2383T700" /> + <has_text text="0.659554" /> + <has_text text="8.81866595" /> + <!-- second matched line --> + <has_text text="M207.9308T206" /> + <has_text text="0.0578578" /> + <has_text text="2.27527985" /> + <!-- third matched line --> + <has_text text="M211.0607T263" /> + <has_text text="9999" /> + <has_text text="12.87766096" /> + </assert_contents> + </output> + </test> + </tests> + <!-- + .. |reg| unicode:: U+000AE .. REGISTERED SIGN - see http://docutils.sourceforge.net/docutils/parsers/rst/include/isonum.txt or /usr/share/docutils/parsers/rst/include/isonum.txt + --> + <help><![CDATA[ + +**Run PLS-DA Contrasts of Univariate Results** +---------------------------------------------- + +**Author** - Arthur Eschenlauer (University of Minnesota, esch0041@umn.edu) + + +Motivation +---------- + +OPLS-DA\ :superscript:`®` and the SIMCA\ :superscript:`®` S-PLOT\ :superscript:`®` (Wiklund *et al.*, 2008) may be employed (e.g. Sun *et al.*, 2016) to identify features that are potential biomarkers, i.e. features that are potentially useful to discriminate to which class a sample should be assigned. Workflow4Metabolomics (W4M, Giacomoni *et al.*, 2014, Guitton *et al.*, 2017) provides a suite of tools for preprocessing and statistical analysis of LC-MS, GC-MS, and NMR metabolomics data; however, it does not (as of release 3.0) include a tool for making the equivalent of an S-PLOT. + +The S-PLOT is computed from mean-centered, pareto-scaled data. This plot presents the correlation of the first score vector from an OPLS-DA model with the sample-variables used to produce that model versus the covariance of the scores with the sample-variables. For OPLS-DA, the first score vector represents the variation among the sample-variables that is explained by the contrasting factor. + +The principal aims of this tool are: + +- To compute multiple contrasts with OPLS-DA and the covariance vs. correlation plot. +- To visualize multiple contrasts compactly yet informatively. +- To write the results to data files for use in further multivariate analysis or visualization. + + +Description +----------- + +The purpose of the 'PLS-DA Contrasts' tool is to identify and visualize GC-MS or LC-MS features that are possible biomarkers. + +The W4M 'Univariate' tool (Thévenot *et al.*, 2015) adds the results of family-wise corrected pairwise significance-tests as columns of the **variableMetadata** dataset. +For instance, suppose that you ran Kruskal-Wallis testing for a column named 'cluster' in sampleMetadata that has values 'k1' and 'k2' and at least one other value. + +- A column of variableMetadata would be labeled 'cluster_kruskal_sig' and would have values '1' and '0', where '1' means that, when the samples are grouped by 'cluster', there is strong evidence against the hypothesis that there is no difference among the intensities for the feature across all sample-groups. +- A column of variableMetadata would be labeled 'cluster_kruskal_k1.k2_sig' and would have values '1' and '0', where '1' means that there is significant evidence against the hypothesis that samples from sampleMetadata whose 'cluster' column contains 'k1' or 'k2' have the same intensity for that feature. + +The 'PLS-DA Contrasts' tool produces graphics and data for OPLS-DA contrasts of feature-intensities between significantly different pairs of factor-levels. For each factor-level, the tool performs a contrast with all other factor-levels combined and then separately with each other factor-level. + +This tool can be used in a purely exploratory manner by supplying the variableMetadata file without the columns added by the W4M 'Univariate' tool. However, the preferred workflow (Thévenot *et al.*, 2015) is to use univariate testing to exclude features that are not significantly different and use OPLS-DA to visualize the differences identified in univariate testing; an appropriate exception would be to visualize contrasts of a specific list of metabolites. + +It must be stressed that there may be no definitive computational approach to select features that are guaranteed to be reliable biomarkers. Possible methods are examining extreme values on S-PLOTs, examining "variable importance in projection VIP for OPLS-DA" (Galindo-Prieto *et al.* 2014), or examining a feature's "selectivity ratio" (Rajalahti *et al.*, 2009). In this spirit, this tool reports the S-PLOT covariance and correlation (Wiklund *op. cit.*) and VIP metrics, and it introduces an informal "salience" metric to flag features that may merit attention without dimensional reduction; future versions may add selectivity ratio. Regardless of how any potential biomarker is identified, it should be subjected to further validation analysis before it is recommended for general application. + + +W4M Workflow Position +--------------------- + +- Upstream tool: **Univariate** (category: Statistical Analysis) or (not generally recommended) any **Preprocessing** tool that produces or updates a 'variableMetadata' file. +- Downstream tool categories: **Statistical Analysis** + +Input files +----------- + ++----------------------+-----------+ +| File | Format | ++======================+===========+ +| Data matrix | tabular | ++----------------------+-----------+ +| Sample metadata | tabular | ++----------------------+-----------+ +| Variable metadata | tabular | ++----------------------+-----------+ + +Output files +------------ + ++------------------------------+-----------+ +| File | Format | ++==============================+===========+ +| Contrast detail | pdf | ++------------------------------+-----------+ +| Contrast cor and cov | tabular | ++------------------------------+-----------+ +| Variable metadata supplement | tabular | ++------------------------------+-----------+ + +Parameters +---------- + +[IN] Data matrix file + | variable x sample **dataMatrix** (tabular separated values) file of the numeric data matrix, with . as decimal, and NA for missing values; the table must not contain metadata apart from row and column names; the row and column names must be identical to the rownames of the sample and variable metadata, respectively (see below) + | + +[IN] Sample metadata file + | sample x metadata **sampleMetadata** (tabular separated values) file of the numeric and/or character sample metadata, with . as decimal and NA for missing values + | + +[IN] Variable metadata file + | variable x metadata **variableMetadata** (tabular separated values) file of the numeric and/or character variable metadata, with . as decimal and NA for missing values + | + +[IN] Test + | Name of the **statistical test** - a component of column names in variable metadata table + | May be one of 'none', 'ttest', 'gwilcoxon', 'anova', 'kruskal', 'pearson', 'spearman' + | + +[IN] Factor of interest + | Name of the **column of sampleMetadata** corresponding to the qualitative or quantitative variable + | + +[IN] Retain only pairwise-significant features + | When true, for each contrast of two levels, include only those features which pass the significance threshold for that contrast. Choosing true results in an OPLS-DA model that better reflects and visualizes the difference detected by univariate analysis, with somewhat increased reliability of prediction (as assessed by cross-validation). + | When false, include all features that pass the significance threshold when testing for difference across all factor-levels. This choice produces a plot that displays more features but is not necessarily more informative. + | + +[IN] Levels of interest + | comma-separated **level-names** (or comma-less regular expressions to match level-names) to consider in analysis; must match at least two levels; may include wild cards or regular expressions + | + +[IN] Level-name matching + | How to **specify levels generically** - wild cards, regular expressions, or none (no generic matching) + | + +[OUT] Contrast-detail output PDF + | Several plots for each two-projection OPLS-DA analysis: + +- (top-left) **correlation-versus-covariance plot** of OPLS-DA results (a work-alike for the S-PLOT, computed using formula in Supplement to Wiklund, *op. cit.*); point-color becomes saturated as the "variable importance in projection to the predictive components" (VIP\ :subscript:`4,p` from Galindo-Prieto *et al.* 2014) ranges from 0.83 and 1.21 (Mehmood *et al.* 2012) +- (bottom-left) **model-overview plot** for the two projections; grey bars are the correlation coefficient for the fitted data; black bars indicate performance in cross-validation tests (Thévenot, 2017) +- (top-right) OPLS-DA **scores-plot** for the two projections (Thévenot *et al.*, 2015) +- (bottom-right) OPLS-DA **loadings-plot** for the two projections (*ibid.*) + +[OUT] Contrast Correlation-Covarinace data TABULAR + | A tab-separated values file having the following columns: + +- **featureID** - feature-identifier +- **factorLevel1** - factor-level 1 +- **factorLevel2** - factor-level 2 (or "other" when contrasting factor-level 1 with all other levels) +- **correlation** - correlation of the features projection explaining the difference between the features, < 0 when intensity for level 1 is greater (from formula in Supplement to Wiklund, *op. cit.*) +- **covariance** - covariance of the features projection explaining the difference between the features, < 0 when intensity for level 1 is greater (from formula in *ibid.*) +- **vip4p** - "variable importance in projection" to the predictive components (VIP\ :subscript:`4,p` from Galindo-Prieto *op. cit.*) +- **vip4o** - "variable importance in projection" to the orthogonal components (VIP\ :subscript:`4,o` from Galindo-Prieto *op. cit.*) +- **level1Level2Sig** - (Only present when a test other than "none" is chosen) '1' when feature varies significantly across all classes (i.e., not pair-wise); '0' otherwise + +[OUT] Feature "Salience" data TABULAR + | Metrics for the "salient level" for each feature, i.e., the level at which the feature is more prominent than any other level. This is *not* at all related to the SIMCA OPLS-DA S-PLOT; rather, it is intended as a potential (and unproven) way to identify features that may suggest potential biomarkers without dimensional reduction of data. This is a tab-separated values file having the following columns: + +- **featureID** - feature identifier +- **salientLevel** - salient level, i.e., for the feature, the class-level having the greatest median intensity +- **salientRCV** - salient robust coefficient of variation, i.e., for the feature, the mean absolute deviation of the intensity for the salient level divided by the median intensity for the salient level +- **salience** - salience, i.e., for the feature, the median of the class-level having the greatest intensity divided by the mean of the medians for all class-levels + +Wild card patterns to match level-names +--------------------------------------- + +"wild card" patterns may be used to select level-names. + +- use '``?``' to match a single character +- use '``*``' to match zero or more characters +- the entire pattern must match the level name + +For example + +- '``??.le*``' matches '``my.level``' but not '``my.own.level``' +- '``*.level``' matches '``my.level``' and '``my.own.level``' +- '``*.level``' matches neither '``my.level``' nor '``my.own.level``' + +Regular expression patterns to match level-names +------------------------------------------------ + +"regular expression" patterns may be used to select level-names. + +POSIX 1003.2 standard regular expressions allow precise pattern-matching and are exhaustively defined at: +http://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html + +However, only a few basic building blocks of regular expressions need to be mastered for most cases: + +- '``^``' matches the beginning of a level-name +- '``$``' matches the end of a level-name +- '``.``' outside of square brackets matches a single character +- '``*``' matches character specified immediately before zero or more times +- square brackets specify a set of characters to be matched. + +Within square brackets + +- '``^``' as the first character specifies that the list of characters are those that should **not** be matched. +- '``-``' is used to specify ranges of characters + +Caveat: The tool wrapper uses the comma ('``,``') to split a list of sample-level names, so **commas may not be used within regular expressions for this tool** + +First Example: Consider a field of level-names consisting of '``marq3,marq6,marq9,marq12,front3,front6,front9,front12``' + +- The regular expression '``^front[0-9][0-9]*$``' will match the same sample-levels as '``front3,front6,front9,front12``' +- The regular expression '``^[a-z][a-z]3$``' will match the same sample-levels as '``front3,marq3``' +- The regular expression '``^[a-z][a-z]12$``' will match the same sample-levels as '``front12,marq12``' +- The regular expression '``^[a-z][a-z][0-9]$``' will match the same sample-levels as '``front3,front6,front9,marq3,marq6,marq9``' + +Second Example: Consider these regular expression patterns as possible matches to a sample-level name '``AB0123``': + +- '``^[A-Z][A-Z][0-9][0-9]*$``' - MATCHES '``**^AB0123$**``' +- '``^[A-Z][A-Z]*[0-9][0-9]*$``' - MATCHES '``**^AB0123$**``' +- '``^[A-Z][0-9]*``' - MATCHES '``**^A** B0123$``' - first character is a letter, '``*``' can specify zero characters, and end of line did not need to be matched. +- '``^[A-Z][A-Z][0-9]``' - MATCHES '``**^AB0** 123$``' - first two characters are letters aind the third is a digit. +- '``^[A-Z][A-Z]*[0-9][0-9]$``' - NO MATCH - the name does not end with the pattern '``[A-Z][0-9][0-9]$``', i.e., it ends with four digits, not two. +- '``^[A-Z][0-9]*$``' - NO MATCH - the pattern specifies that second character and all those that follow, if present, must be digits. + +Working example +--------------- + +**Input files** + ++-------------------+-------------------------------------------------------------------------------------------------------------------+ +| Input File | Download from URL | ++===================+===================================================================================================================+ +| Data matrix | https://raw.githubusercontent.com/HegemanLab/w4mcorcov_galaxy_wrapper/master/test-data/input_dataMatrix.tsv | ++-------------------+-------------------------------------------------------------------------------------------------------------------+ +| Sample metadata | https://raw.githubusercontent.com/HegemanLab/w4mcorcov_galaxy_wrapper/master/test-data/input_sampleMetadata.tsv | ++-------------------+-------------------------------------------------------------------------------------------------------------------+ +| Variable metadata | https://raw.githubusercontent.com/HegemanLab/w4mcorcov_galaxy_wrapper/master/test-data/input_variableMetadata.tsv | ++-------------------+-------------------------------------------------------------------------------------------------------------------+ + ++-------------------------------------------+--------------------------------------------------+ +| Input Parameter | Input value | ++===========================================+==================================================+ +| Factor of interest | k10 | ++-------------------------------------------+--------------------------------------------------+ +| Univariate Significance-Test | kruskal | ++-------------------------------------------+--------------------------------------------------+ +| Retain only pairwise-significant features | Yes | ++-------------------------------------------+--------------------------------------------------+ +| Levels of interest | k[12],k[3-4] | ++-------------------------------------------+--------------------------------------------------+ +| Level-name matching | use regular expressions for matching level-names | ++-------------------------------------------+--------------------------------------------------+ +| Label features on detail plot | Yes | ++-------------------------------------------+--------------------------------------------------+ + ++-------------------+---------------------------------------------------------------------------------------------------------------------+ +| Expected Output | Download from URL | ++===================+=====================================================================================================================+ +| Data matrix | https://raw.githubusercontent.com/HegemanLab/w4mcorcov_galaxy_wrapper/master/test-data/expected_contrast_corcov.tsv | ++-------------------+---------------------------------------------------------------------------------------------------------------------+ + + +Trademarks +---------- + +OPLS-DA\ :superscript:`®`, SIMCA\ :superscript:`®`, and S-PLOT\ :superscript:`®` are registered trademarks of the Umetrics company. http://umetrics.com/about-us/trademarks + + +Release notes +------------- + +v0.98.2 - first release + + + ]]></help> + <citations> + <!-- Galindo_Prieto_2014 Variable influence on projection (VIP) for OPLS --> + <citation type="doi">10.1002/cem.2627</citation> + <!-- Giacomoni_2014 W4M 2.5 --> + <citation type="doi">10.1093/bioinformatics/btu813</citation> + <!-- Guitton_2017 W4M 3.0 --> + <citation type="doi">10.1016/j.biocel.2017.07.002</citation> + <!-- Mehmood_2012 PLS-based variable-selection --> + <citation type="doi">10.1186/1748-7188-6-27</citation> + <!-- Rajalahti_2009 Biomarker discovery using selectivity ratio --> + <citation type="doi">10.1016/j.chemolab.2008.08.004</citation> + <!-- Sun_2016 Urinary Biomarkers for adolescent idiopathic scoliosis --> + <citation type="doi">10.1038/srep22274</citation> + <!-- Th_venot_2015 Urinary metabolome statistics --> + <citation type="doi">10.1021/acs.jproteome.5b00354</citation> + <!-- ropls package --> + <citation type="bibtex"><![CDATA[ +@incollection{Thevenot_ropls_2017, + author = {Th{\'{e}}venot, Etienne A.}, + title = {ropls: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data}, + publisher = {bioconductor.org}, + year = {2017}, + doi = {10.18129/B9.bioc.ropls}, + booktitle = {Bioconductor: Open source software for bioinformatics}, + address = {Roswell Park Cancer Institute}, +} + ]]></citation> + <!-- Wiklund_2008 OPLS PLS-DA and S-PLOT --> + <citation type="doi">10.1021/ac0713510</citation> + </citations> + <!-- + vim:noet:sw=4:ts=4 +--> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/w4mcorcov_calc.R Mon Oct 16 14:56:52 2017 -0400 @@ -0,0 +1,545 @@ +# center with 'colMeans()' - ref: http://gastonsanchez.com/visually-enforced/how-to/2014/01/15/Center-data-in-R/ +center_colmeans <- function(x) { + xcenter = colMeans(x) + x - rep(xcenter, rep.int(nrow(x), ncol(x))) +} + +#### OPLS-DA +algoC <- "nipals" + +do_detail_plot <- function(x_dataMatrix, x_predictor, x_is_match, x_algorithm, x_prefix, x_show_labels, x_progress = print, x_env) { + off <- function(x) if (x_show_labels) x else 0 + salience_lookup <- x_env$salience_lookup + salient_rcv_lookup <- x_env$salient_rcv_lookup + # x_progress("head(salience_df): ", head(salience_df)) + # x_progress("head(salience): ", head(salience)) + if (x_is_match && ncol(x_dataMatrix) > 0 && length(unique(x_predictor))> 1) { + my_oplsda <- opls( + x = x_dataMatrix + , y = x_predictor + , algoC = x_algorithm + , predI = 1 + , orthoI = if (ncol(x_dataMatrix) > 1) 1 else 0 + , printL = FALSE + , plotL = FALSE + ) + my_oplsda_suppLs_y_levels <- levels(as.factor(my_oplsda@suppLs$y)) + fctr_lvl_1 <- my_oplsda_suppLs_y_levels[1] + fctr_lvl_2 <- my_oplsda_suppLs_y_levels[2] + my_cor_vs_cov <- cor_vs_cov( + matrix_x = x_dataMatrix + , ropls_x = my_oplsda + ) + with( + my_cor_vs_cov + , { + min_x <- min(covariance) + max_x <- max(covariance) + lim_x <- max(sapply(X=c(min_x, max_x), FUN=abs)) + covariance <- covariance / lim_x + lim_x <- 1.2 + main_label <- sprintf("%s for levels %s versus %s", x_prefix, fctr_lvl_1, fctr_lvl_2) + # print("main_label") + # print(main_label) + main_cex <- min(1.0, 46.0/nchar(main_label)) + # "It is generally accepted that a variable should be selected if vj>1, [27–29], + # but a proper threshold between 0.83 and 1.21 can yield more relevant variables according to [28]." + # (Mehmood 2012 doi:10.1186/1748-7188-6-27) + vipco <- pmax(0, pmin(1,(vip4p-0.83)/(1.21-0.83))) + alpha <- 0.1 + 0.4 * vipco + red <- as.numeric(correlation < 0) * vipco + blue <- as.numeric(correlation > 0) * vipco + minus_cor <- -correlation + minus_cov <- -covariance + # cex <- salience_lookup(feature = names(minus_cor)) + # cex <- 0.25 + (1.25 * cex / max(cex)) + cex <- 0.75 + plot( + y = minus_cor + , x = minus_cov + , type="p" + , xlim=c(-lim_x, lim_x + off(0.1)) + , ylim=c(-1.0 - off(0.1), 1.0) + , xlab = sprintf("relative covariance(feature,t1)") + , ylab = sprintf("correlation(feature,t1)") + , main = main_label + , cex.main = main_cex + , cex = cex + , pch = 16 + , col = rgb(blue = blue, red = red, green = 0, alpha = alpha) + ) + low_x <- -0.7 * lim_x + high_x <- 0.7 * lim_x + text(x = low_x, y = -0.15, labels = fctr_lvl_1) + text(x = high_x, y = 0.15, labels = fctr_lvl_2) + if (x_show_labels) { + text( + y = minus_cor - 0.013 + , x = minus_cov + 0.020 + , cex = 0.3 + , labels = tsv1$featureID + , col = rgb(blue = blue, red = red, green = 0, alpha = 0.2 + 0.8 * alpha) + , srt = -30 # slant 30 degrees downward + , adj = 0 # left-justified + ) + } + } + ) + typeVc <- c("correlation", # 1 + "outlier", # 2 + "overview", # 3 + "permutation", # 4 + "predict-train", # 5 + "predict-test", # 6 + "summary", # 7 = c(2,3,4,9) + "x-loading", # 8 + "x-score", # 9 + "x-variance", # 10 + "xy-score", # 11 + "xy-weight" # 12 + ) # [c(3,8,9)] # [c(4,3,8,9)] + if ( length(my_oplsda@orthoVipVn) > 0 ) { + my_typevc <- typeVc[c(9,3,8)] + } else { + my_typevc <- c("(dummy)","overview","(dummy)") + } + for (my_type in my_typevc) { + if (my_type %in% typeVc) { + # print(sprintf("plotting type %s", my_type)) + plot( + x = my_oplsda + , typeVc = my_type + , parCexN = 0.4 + , parDevNewL = FALSE + , parLayL = TRUE + , parEllipsesL = TRUE + ) + } else { + # print("plotting dummy graph") + plot(x=1, y=1, xaxt="n", yaxt="n", xlab="", ylab="", type="n") + text(x=1, y=1, labels="no orthogonal projection is possible") + } + } + return (my_cor_vs_cov) + } else { + # x_progress(sprintf("x_is_match = %s, ncol(x_dataMatrix) = %d, length(unique(x_predictor)) = %d",x_is_match, ncol(x_dataMatrix), length(unique(x_predictor)))) + return (NULL) + } +} + +# S-PLOT and OPLS reference: Wiklund_2008 doi:10.1021/ac0713510 +corcov_calc <- function(calc_env, failure_action = stop, progress_action = function(x){}, corcov_tsv_action = function(t){}, salience_tsv_action = function(t){}) { + if ( ! is.environment(calc_env) ) { + failure_action("corcov_calc: fatal error - 'calc_env' is not an environment") + return ( FALSE ) + } + if ( ! is.function(corcov_tsv_action) ) { + failure_action("corcov_calc: fatal error - 'corcov_tsv_action' is not a function") + return ( FALSE ) + } + if ( ! is.function(salience_tsv_action) ) { + failure_action("salience_calc: fatal error - 'salience_tsv_action' is not a function") + return ( FALSE ) + } + + # extract parameters from the environment + vrbl_metadata <- calc_env$vrbl_metadata + vrbl_metadata_names <- vrbl_metadata[,1] + smpl_metadata <- calc_env$smpl_metadata + data_matrix <- calc_env$data_matrix + pairSigFeatOnly <- calc_env$pairSigFeatOnly + facC <- calc_env$facC + tesC <- calc_env$tesC + # extract the levels from the environment + originalLevCSV <- levCSV <- calc_env$levCSV + # matchingC is one of { "none", "wildcard", "regex" } + matchingC <- calc_env$matchingC + labelFeatures <- calc_env$labelFeatures + + # arg/env checking + if (!(facC %in% names(smpl_metadata))) { + failure_action(sprintf("bad parameter! Factor name '%s' not found in sampleMetadata", facC)) + return ( FALSE ) + } + + # calculate salience_df as data.frame(feature, max_level, max_median, max_rcv, mean_median, salience, salient_rcv) + salience_df <- calc_env$salience_df <- w4msalience( + data_matrix = data_matrix + , sample_class = smpl_metadata[,facC] + , failure_action = failure_action + ) + salience_tsv_action({ + my_df <- data.frame( + featureID = salience_df$feature + , salientLevel = salience_df$max_level + , salientRCV = salience_df$salient_rcv + , salience = salience_df$salience + ) + my_df[order(-my_df$salience),] + }) + salience <- salience_df$salience + names(salience) <- salience_df$feature + salience_lookup <- calc_env$salience_lookup <- function(feature) unname(salience[feature]) + salient_rcv <- salience_df$salient_rcv + names(salient_rcv) <- salience_df$feature + salient_rcv_lookup <- calc_env$salient_rcv_lookup <- function(feature) unname(salient_rcv[feature]) + salient_level <- salience_df$max_level + names(salient_level) <- salience_df$feature + salient_level_lookup <- calc_env$salient_level_lookup <- function(feature) unname(salient_level[feature]) + + # transform wildcards to regexen + if (matchingC == "wildcard") { + # strsplit(x = "hello,wild,world", split = ",") + levCSV <- gsub("[.]", "[.]", levCSV) + levCSV <- strsplit(x = levCSV, split = ",") + levCSV <- sapply(levCSV, utils::glob2rx, trim.tail = FALSE) + levCSV <- paste(levCSV, collapse = ",") + } + # function to determine whether level is a member of levCSV + isLevelSelected <- function(lvl) { + matchFun <- if (matchingC != "none") grepl else `==` + return( + Reduce( + f = "||" + , x = sapply( + X = strsplit( + x = levCSV + , split = "," + , fixed = TRUE + )[[1]] + , FUN = matchFun + , lvl + ) + ) + ) + } + + # transpose matrix because ropls matrix is the transpose of XCMS matrix + # Wiklund_2008 centers and pareto-scales data before OPLS-DA S-plot + # center + cdm <- center_colmeans(t(data_matrix)) + # pareto-scale + my_scale <- sqrt(apply(cdm, 2, sd, na.rm=TRUE)) + scdm <- sweep(cdm, 2, my_scale, "/") + + # pattern to match column names like k10_kruskal_k4.k3_sig + col_pattern <- sprintf('^%s_%s_(.*)[.](.*)_sig$', facC, tesC) + # column name like k10_kruskal_sig + intersample_sig_col <- sprintf('%s_%s_sig', facC, tesC) + # get the facC column from sampleMetadata, dropping to one dimension + smpl_metadata_facC <- smpl_metadata[,facC] + + # allocate a slot in the environment for the contrast_list, each element of which will be a data.frame with this structure: + # - feature ID + # - value1 + # - value2 + # - Wiklund_2008 correlation + # - Wiklund_2008 covariance + # - Wiklund_2008 VIP + calc_env$contrast_list <- list() + + + did_plot <- FALSE + if (tesC != "none") { + # for each column name, extract the parts of the name matched by 'col_pattern', if any + the_colnames <- colnames(vrbl_metadata) + if ( ! Reduce( f = "||", x = grepl(tesC, the_colnames) ) ) { + failure_action(sprintf("bad parameter! variableMetadata must contain results of W4M Univariate test '%s'.", tesC)) + return ( FALSE ) + } + col_matches <- lapply( + X = the_colnames, + FUN = function(x) { + regmatches( x, regexec(col_pattern, x) )[[1]] + } + ) + ## first contrast each selected level with all other levels combined into one "super-level" ## + # process columns matching the pattern + level_union <- c() + for (i in 1:length(col_matches)) { + col_match <- col_matches[[i]] + if (length(col_match) > 0) { + # it's an actual match; extract the pieces, e.g., k10_kruskal_k4.k3_sig + vrbl_metadata_col <- col_match[1] # ^^^^^^^^^^^^^^^^^^^^^ # Column name + fctr_lvl_1 <- col_match[2] # ^^ # Factor-level 1 + fctr_lvl_2 <- col_match[3] # ^^ # Factor-level 2 + # only process this column if both factors are members of lvlCSV + is_match <- isLevelSelected(fctr_lvl_1) && isLevelSelected(fctr_lvl_2) + if (is_match) { + level_union <- c(level_union, col_match[2], col_match[3]) + } + } + } + level_union <- unique(sort(level_union)) + overall_significant <- 1 == vrbl_metadata[,intersample_sig_col] + if ( length(level_union) > 1 ) { + chosen_samples <- smpl_metadata_facC %in% level_union + chosen_facC <- as.character(smpl_metadata_facC[chosen_samples]) + col_selector <- vrbl_metadata_names[ overall_significant ] + my_matrix <- scdm[ chosen_samples, col_selector, drop = FALSE ] + fctr_lvl_2 <- "other" + for ( fctr_lvl_1 in level_union[1:length(level_union)] ) { + progress_action(sprintf("calculating/plotting contrast of %s vs. %s", fctr_lvl_1, fctr_lvl_2)) + predictor <- sapply( X = chosen_facC, FUN = function(fac) if ( fac == fctr_lvl_1 ) fctr_lvl_1 else fctr_lvl_2 ) + my_cor_cov <- do_detail_plot( + x_dataMatrix = my_matrix + , x_predictor = predictor + , x_is_match = is_match + , x_algorithm = algoC + , x_prefix = if (pairSigFeatOnly) "Significantly contrasting features" else "Significant features" + , x_show_labels = labelFeatures + , x_progress = progress_action + , x_env = calc_env + ) + if ( is.null(my_cor_cov) ) { + progress_action("NOTHING TO PLOT.") + } else { + tsv <- my_cor_cov$tsv1 + # tsv$salientLevel <- salient_level_lookup(tsv$featureID) + # tsv$salientRCV <- salient_rcv_lookup(tsv$featureID) + # tsv$salience <- salience_lookup(tsv$featureID) + tsv["level1Level2Sig"] <- vrbl_metadata[ match(tsv$featureID, vrbl_metadata_names), vrbl_metadata_col ] + corcov_tsv_action(tsv) + did_plot <- TRUE + } + } + } + + ## next, contrast each selected level with each of the other levels individually ## + # process columns matching the pattern + for (i in 1:length(col_matches)) { + # for each potential match of the pattern + col_match <- col_matches[[i]] + if (length(col_match) > 0) { + # it's an actual match; extract the pieces, e.g., k10_kruskal_k4.k3_sig + vrbl_metadata_col <- col_match[1] # ^^^^^^^^^^^^^^^^^^^^^ # Column name + fctr_lvl_1 <- col_match[2] # ^^ # Factor-level 1 + fctr_lvl_2 <- col_match[3] # ^^ # Factor-level 2 + # only process this column if both factors are members of lvlCSV + is_match <- isLevelSelected(fctr_lvl_1) && isLevelSelected(fctr_lvl_2) + progress_action(sprintf("calculating/plotting contrast of %s vs. %s", fctr_lvl_1, fctr_lvl_2)) + # TODO delete next line displaying currently-processed column + # cat(sprintf("%s %s %s %s\n", vrbl_metadata_col, fctr_lvl_1, fctr_lvl_2, is_match)) + # choose only samples with one of the two factors for this column + chosen_samples <- smpl_metadata_facC %in% c(fctr_lvl_1, fctr_lvl_2) + predictor <- smpl_metadata_facC[chosen_samples] + # extract only the significantly-varying features and the chosen samples + fully_significant <- 1 == vrbl_metadata[,vrbl_metadata_col] * vrbl_metadata[,intersample_sig_col] + col_selector <- vrbl_metadata_names[ if ( pairSigFeatOnly ) fully_significant else overall_significant ] + my_matrix <- scdm[ chosen_samples, col_selector, drop = FALSE ] + my_cor_cov <- do_detail_plot( + x_dataMatrix = my_matrix + , x_predictor = predictor + , x_is_match = is_match + , x_algorithm = algoC + , x_prefix = if (pairSigFeatOnly) "Significantly contrasting features" else "Significant features" + , x_show_labels = labelFeatures + , x_progress = progress_action + , x_env = calc_env + ) + if ( is.null(my_cor_cov) ) { + progress_action("NOTHING TO PLOT.") + } else { + tsv <- my_cor_cov$tsv1 + # tsv$salientLevel <- salient_level_lookup(tsv$featureID) + # tsv$salientRCV <- salient_rcv_lookup(tsv$featureID) + # tsv$salience <- salience_lookup(tsv$featureID) + tsv["level1Level2Sig"] <- vrbl_metadata[ match(tsv$featureID, vrbl_metadata_names), vrbl_metadata_col ] + corcov_tsv_action(tsv) + did_plot <- TRUE + } + } + } + } else { # tesC == "none" + level_union <- unique(sort(smpl_metadata_facC)) + if ( length(level_union) > 1 ) { + ## pass 1 - contrast each selected level with all other levels combined into one "super-level" ## + completed <- c() + lapply( + X = level_union + , FUN = function(x) { + fctr_lvl_1 <- x[1] + fctr_lvl_2 <- { + if ( fctr_lvl_1 %in% completed ) + return("DUMMY") + # strF(completed) + completed <<- c(completed, fctr_lvl_1) + setdiff(level_union, fctr_lvl_1) + } + chosen_samples <- smpl_metadata_facC %in% c(fctr_lvl_1, fctr_lvl_2) + fctr_lvl_2 <- "other" + # print( sprintf("sum(chosen_samples) %d, factor_level_2 %s", sum(chosen_samples), fctr_lvl_2) ) + progress_action(sprintf("calculating/plotting contrast of %s vs. %s", fctr_lvl_1, fctr_lvl_2)) + if (length(unique(chosen_samples)) < 1) { + progress_action("NOTHING TO PLOT...") + } else { + chosen_facC <- as.character(smpl_metadata_facC[chosen_samples]) + predictor <- sapply( X = chosen_facC, FUN = function(fac) if ( fac == fctr_lvl_1 ) fctr_lvl_1 else fctr_lvl_2 ) + my_matrix <- scdm[ chosen_samples, , drop = FALSE ] + # only process this column if both factors are members of lvlCSV + is_match <- isLevelSelected(fctr_lvl_1) + my_cor_cov <- do_detail_plot( + x_dataMatrix = my_matrix + , x_predictor = predictor + , x_is_match = is_match + , x_algorithm = algoC + , x_prefix = "Features" + , x_show_labels = labelFeatures + , x_progress = progress_action + , x_env = calc_env + ) + if ( is.null(my_cor_cov) ) { + progress_action("NOTHING TO PLOT") + } else { + tsv <- my_cor_cov$tsv1 + # tsv$salientLevel <- salient_level_lookup(tsv$featureID) + # tsv$salientRCV <- salient_rcv_lookup(tsv$featureID) + # tsv$salience <- salience_lookup(tsv$featureID) + corcov_tsv_action(tsv) + did_plot <<- TRUE + } + } + #print("baz") + "dummy" # need to return a value; otherwise combn fails with an error + } + ) + ## pass 2 - contrast each selected level with each of the other levels individually ## + completed <- c() + utils::combn( + x = level_union + , m = 2 + , FUN = function(x) { + fctr_lvl_1 <- x[1] + fctr_lvl_2 <- x[2] + chosen_samples <- smpl_metadata_facC %in% c(fctr_lvl_1, fctr_lvl_2) + # print( sprintf("sum(chosen_samples) %d, factor_level_2 %s", sum(chosen_samples), fctr_lvl_2) ) + progress_action(sprintf("calculating/plotting contrast of %s vs. %s", fctr_lvl_1, fctr_lvl_2)) + if (length(unique(chosen_samples)) < 1) { + progress_action("NOTHING TO PLOT...") + } else { + chosen_facC <- as.character(smpl_metadata_facC[chosen_samples]) + predictor <- chosen_facC + my_matrix <- scdm[ chosen_samples, , drop = FALSE ] + # only process this column if both factors are members of lvlCSV + is_match <- isLevelSelected(fctr_lvl_1) && isLevelSelected(fctr_lvl_2) + my_cor_cov <- do_detail_plot( + x_dataMatrix = my_matrix + , x_predictor = predictor + , x_is_match = is_match + , x_algorithm = algoC + , x_prefix = "Features" + , x_show_labels = labelFeatures + , x_progress = progress_action + , x_env = calc_env + ) + if ( is.null(my_cor_cov) ) { + progress_action("NOTHING TO PLOT") + } else { + tsv <- my_cor_cov$tsv1 + # tsv$salientLevel <- salient_level_lookup(tsv$featureID) + # tsv$salientRCV <- salient_rcv_lookup(tsv$featureID) + # tsv$salience <- salience_lookup(tsv$featureID) + corcov_tsv_action(tsv) + did_plot <<- TRUE + } + } + #print("baz") + "dummy" # need to return a value; otherwise combn fails with an error + } + ) + } else { + progress_action("NOTHING TO PLOT....") + } + } + if (!did_plot) { + failure_action(sprintf("bad parameter! sampleMetadata must have at least two levels of factor '%s' matching '%s'", facC, originalLevCSV)) + return ( FALSE ) + } + return ( TRUE ) +} + +# Calculate data for correlation-versus-covariance plot +# Adapted from: +# Wiklund_2008 doi:10.1021/ac0713510 +# Galindo_Prieto_2014 doi:10.1002/cem.2627 +# https://github.com/HegemanLab/extra_tools/blob/master/generic_PCA.R +cor_vs_cov <- function(matrix_x, ropls_x) { + x_class <- class(ropls_x) + if ( !( as.character(x_class) == "opls" ) ) { # || !( attr(class(x_class),"package") == "ropls" ) ) + stop( "cor_vs_cov: Expected ropls_x to be of class ropls::opls but instead it was of class ", as.character(x_class) ) + } + result <- list() + # suppLs$algoC - Character: algorithm used - "svd" for singular value decomposition; "nipals" for NIPALS + if ( ropls_x@suppLs$algoC == "nipals") { + # Equations (1) and (2) from *Supplement to* Wiklund 2008, doi:10.1021/ac0713510 + mag <- function(one_dimensional) sqrt(sum(one_dimensional * one_dimensional)) + mag_xi <- sapply(X = 1:ncol(matrix_x), FUN = function(x) mag(matrix_x[,x])) + score_matrix <- ropls_x@scoreMN + score_matrix_transposed <- t(score_matrix) + score_matrix_magnitude <- mag(score_matrix) + result$covariance <- score_matrix_transposed %*% matrix_x / ( score_matrix_magnitude * score_matrix_magnitude ) + result$correlation <- score_matrix_transposed %*% matrix_x / ( score_matrix_magnitude * mag_xi ) + } else { + # WARNING - untested code - I don't have test data to exercise this branch + # Equations (1) and (2) from Wiklund 2008, doi:10.1021/ac0713510 + # scoreMN - Numerical matrix of x scores (T; dimensions: nrow(x) x predI) X = TP' + E; Y = TC' + F + score_matrix <- ropls_x@scoreMN + score_matrix_transposed <- t(score_matrix) + cov_divisor <- nrow(matrix_x) - 1 + result$covariance <- sapply( + X = 1:ncol(matrix_x) + , FUN = function(x) score_matrix_transposed %*% matrix_x[,x] / cov_divisor + ) + score_sd <- sapply( + X = 1:ncol(score_matrix) + , FUN = function(x) sd(score_matrix[,x]) + ) + # xSdVn - Numerical vector: variable standard deviations of the 'x' matrix + xSdVn <- ropls_x@xSdVn + result$correlation <- sapply( + X = 1:ncol(matrix_x) + , FUN = function(x) { + ( score_matrix_transposed / score_sd ) %*% ( matrix_x[,x] / (xSdVn[x] * cov_divisor) ) + } + ) + } + result$correlation <- result$correlation[1,,drop = TRUE] + result$covariance <- result$covariance[1,,drop = TRUE] + + # Variant 4 of Variable Influence on Projection for OPLS from Galindo_Prieto_2014 + # Length = number of features; labels = feature identifiers. (The same is true for $correlation and $covariance.) + result$vip4p <- as.numeric(ropls_x@vipVn) + result$vip4o <- as.numeric(ropls_x@orthoVipVn) + # get the level names + level_names <- sort(levels(as.factor(ropls_x@suppLs$y))) + feature_count <- length(ropls_x@vipVn) + result$level1 <- rep.int(x = level_names[1], times = feature_count) + result$level2 <- rep.int(x = level_names[2], times = feature_count) + # print(sprintf("sd(covariance) = %f; sd(correlation) = %f", sd(result$covariance), sd(result$correlation))) + superresult <- list() + if (length(result$vip4o) == 0) result$vip4o <- NA + superresult$tsv1 <- data.frame( + featureID = names(ropls_x@vipVn) + , factorLevel1 = result$level1 + , factorLevel2 = result$level2 + , correlation = result$correlation + , covariance = result$covariance + , vip4p = result$vip4p + , vip4o = result$vip4o + , row.names = NULL + ) + rownames(superresult$tsv1) <- superresult$tsv1$featureID + superresult$covariance <- result$covariance + superresult$correlation <- result$correlation + superresult$vip4p <- result$vip4p + superresult$vip4o <- result$vip4o + superresult$details <- result + # #print(superresult$tsv1) + result$superresult <- superresult + # Include thise in case future consumers of this routine want to use it in currently unanticipated ways + result$oplsda <- ropls_x + result$predictor <- ropls_x@suppLs$y # in case future consumers of this routine want to use it in currently unanticipated ways + return (superresult) +} + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/w4mcorcov_input.R Mon Oct 16 14:56:52 2017 -0400 @@ -0,0 +1,201 @@ +# read_data_frame - read a w4m data frame, with error handling +# e.g., data_matrix_input_env <- read_data_frame(dataMatrix_in, "data matrix input") +read_data_frame <- function(file_path, kind_string, failure_action = failure_action) { + my.env <- new.env() + my.env$success <- FALSE + my.env$msg <- sprintf("no message reading %s", kind_string) + tryCatch( + expr = { + my.env$data <- utils::read.delim( fill = FALSE, file = file_path ) + my.env$success <- TRUE + } + , error = function(e) { + my.env$ msg <- sprintf("%s read failed", kind_string) + } + ) + if (!my.env$success) { + failure_action(my.env$msg) + return ( FALSE ) + } + return (my.env) +} + +# read one of three XCMS data elements: dataMatrix, sampleMetadata, variableMetadata +# returns respectively: matrix, data.frame, data.frame, or FALSE if there is a failure +read_xcms_data_element <- function(xcms_data_in, xcms_data_type, failure_action = stop) { + # note that 'stop' effectively means 'throw'; if 'warning' and 'message' are caught, they mean 'throw' as well + my_failure_action <- function(...) { failure_action("read_xcms_data_element: ", ...) } + # xcms_data_type must be in c("sampleMetadata", "variableMetadata", "dataMatrix") + if ( ! is.character(xcms_data_type) ) { + my_failure_action(sprintf("bad parameter xcms_data_type '%s'", deparse(xcms_data_type))) + return ( FALSE ) + } + if ( 1 != length(xcms_data_type) + || ! ( xcms_data_type %in% c("sampleMetadata", "variableMetadata", "dataMatrix") ) + ) { + my_failure_action( sprintf("bad parameter xcms_data_type '%s'", xcms_data_type) ) + return ( FALSE ) + } + if ( is.character(xcms_data_in) ){ + # case: xcms_data_in is a path to a file + xcms_data_input_env <- read_data_frame( xcms_data_in, sprintf("%s input", xcms_data_type) ) + if (!xcms_data_input_env$success) { + my_failure_action(xcms_data_input_env$msg) + return ( FALSE ) + } + return ( xcms_data_input_env$data ) + # commenting out pasted code that is not tested here + # } else if ( is.data.frame(xcms_data_in) || is.matrix(xcms_data_in) ) { + # # case: xcms_data_in is a data.frame or matrix + # return(xcms_data_in) + # } else if ( is.list(xcms_data_in) || is.environment(xcms_data_in) ) { + # # NOTE WELL: is.list succeeds for data.frame, so the is.data.frame test must appear before the is.list test + # # case: xcms_data_in is a list + # if ( ! exists(xcms_data_type, where = xcms_data_in) ) { + # my_failure_action(sprintf("%s xcms_data_in is missing member '%s'"), ifelse(is.environment(xcms_data_in),"environment","list"), xcms_data_type) + # return ( FALSE ) + # } + # prospect <- getElement(name = xcms_data_type, object = xcms_data_in) + # if ( ! is.data.frame(prospect) && ! is.matrix(prospect) ) { + # utils::str("list - str(prospect)") + # utils::str(prospect) + # if ( is.list(xcms_data_in) ) { + # my_failure_action(sprintf("the first member of xcms_data_in['%s'] is neither a data.frame nor a matrix but is a %s", xcms_data_type, typeof(prospect))) + # } else { + # my_failure_action(sprintf("the first member of xcms_data_in$%s is neither a data.frame nor a matrix but is a %s", xcms_data_type, typeof(prospect))) + # } + # return ( prospect ) + # } + # # stop("stopping here for a snapshot") + # return ( prospect ) + } else { + # case: xcms_data_in is invalid + my_failure_action( sprintf("xcms_data_in has unexpected type %s", typeof(xcms_data_in)) ) + return ( FALSE ) + } +} + +read_inputs <- function(input_env, failure_action = print) { + if ( ! is.environment(input_env) ) { + failure_action("read_inputs: fatal error - 'input_env' is not an environment") + return ( FALSE ) + } + + if (!is.null(sampleMetadata_in <- input_env$sampleMetadata_in)) { + # --- + # read in the sample metadata + read_data_result <- tryCatchFunc( + expr = { + read_xcms_data_element(xcms_data_in = sampleMetadata_in, xcms_data_type = "sampleMetadata") + } + ) + if ( read_data_result$success ) { + smpl_metadata <- read_data_result$value + } else { + failure_action(read_data_result$msg) + return ( FALSE ) + } + + # extract rownames + rownames(smpl_metadata) <- smpl_metadata[,1] + + input_env$smpl_metadata <- smpl_metadata + # ... + } else { + failure_action("read_inputs: fatal error - 'sampleMetadata_in' is missing from 'input_env'") + return ( FALSE ) + } + + if (!is.null(variableMetadata_in <- input_env$variableMetadata_in)) { + # --- + # read in the variable metadata + read_data_result <- tryCatchFunc( + expr = { + read_xcms_data_element(xcms_data_in = variableMetadata_in, xcms_data_type = "variableMetadata") + } + ) + if ( read_data_result$success ) { + vrbl_metadata <- read_data_result$value + } else { + failure_action(read_data_result$msg) + return (FALSE) + } + + + # extract rownames (using make.names to handle degenerate feature names) + err.env <- new.env() + err.env$success <- FALSE + err.env$msg <- "no message setting vrbl_metadata rownames" + tryCatch( + expr = { + rownames(vrbl_metadata) <- make.names( vrbl_metadata[,1], unique = TRUE ) + vrbl_metadata[,1] <- rownames(vrbl_metadata) + err.env$success <- TRUE + } + , error = function(e) { + err.env$ msg <- sprintf("failed to set rownames for vrbl_metadata read because '%s'", e$message) + } + ) + if (!err.env$success) { + failure_action(err.env$msg) + return ( FALSE ) + } + + input_env$vrbl_metadata <- vrbl_metadata + # ... + } else { + failure_action("read_inputs: fatal error - 'variableMetadata_in' is missing from 'input_env'") + return ( FALSE ) + } + + if (!is.null(dataMatrix_in <- input_env$dataMatrix_in)) { + # --- + # read in the data matrix + read_data_result <- tryCatchFunc( + expr = { + read_xcms_data_element(xcms_data_in = dataMatrix_in, xcms_data_type = "dataMatrix") + } + ) + if ( read_data_result$success ) { + data_matrix <- read_data_result$value + } else { + failure_action(read_data_result$msg) + return (FALSE) + } + + if ( ! is.matrix(data_matrix) ) { + # extract rownames (using make.names to handle degenerate feature names) + err.env <- new.env() + err.env$success <- FALSE + err.env$msg <- "no message setting data_matrix rownames" + tryCatch( + expr = { + rownames(data_matrix) <- make.names( data_matrix[,1], unique = TRUE ) + err.env$success <- TRUE + } + , error = function(e) { + err.env$msg <- sprintf("failed to set rownames for data_matrix read because '%s'", e$message) + } + ) + if (!err.env$success) { + failure_action(err.env$msg) + return ( FALSE ) + } + + # remove rownames column + data_matrix <- data_matrix[,2:ncol(data_matrix)] + + # convert data_matrix to matrix from data.frame + data_matrix <- as.matrix(data_matrix) + } + + input_env$data_matrix <- data_matrix + # ... + } else { + failure_action("read_inputs: fatal error - 'dataMatrix_in' is missing from 'input_env'") + return ( FALSE ) + } + + return ( TRUE ) +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/w4mcorcov_lib.R Mon Oct 16 14:56:52 2017 -0400 @@ -0,0 +1,3 @@ +suppressMessages(library(batch)) +# suppressMessages(library(foreach)) +suppressMessages(library(ropls))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/w4mcorcov_salience.R Mon Oct 16 14:56:52 2017 -0400 @@ -0,0 +1,117 @@ +w4msalience <- function( + data_matrix # a matrix of intensities; features as rows, and samples as columns +, sample_class # a vector of sample class-levels; length(sample_class) == ncol(data_matrix) +, failure_action = stop +) { + library(stats) + # begin sanity checks + if ( !is.vector(sample_class) || !( is.character(sample_class) || is.factor(sample_class) ) ) { + failure_action("w4msalience: Expected sample_class to be a vector of characters of factor-levels") + return (NULL) + } + if ( !is.matrix(data_matrix) && !is.data.frame(data_matrix) ) { + failure_action("w4msalience: Expected data_matrix to be a matrix (or data.frame) of numeric") + return (NULL) + } + # transpose data_matrix so that columns are the features + t_data_matrix <- t(data_matrix) + if ( !is.matrix(t_data_matrix) || !is.numeric(t_data_matrix) ) { + failure_action("w4msalience: Expected data_matrix to be a matrix (or data.frame) of numeric") + return (NULL) + } + n_features <- ncol(t_data_matrix) + n_features_plus_1 <- 1 + n_features + features <- colnames(t_data_matrix) + n_samples <- nrow(t_data_matrix) + if ( length(sample_class) != n_samples ) { + strF(data_matrix) + strF(sample_class) + failure_action(sprintf("w4msalience: The data_matrix has %d samples but sample_class has %d", n_samples, length(sample_class))) + return (NULL) + } + # end sanity checks + + # "For each feature, 'select sample_class, median(intensity) from feature group by sample_class'." + # The first column(s) of the result of aggregate has the classifier value(s) specified in the 'by' list. + medianOfFeatureBySampleClassLevel <- aggregate( + x = as.data.frame(t_data_matrix) + , by = list(sample_class) + , FUN = "median" + ) + + # "For each feature, 'select sample_class, max(intensity) from feature group by sample_class'." + maxOfFeatureBySampleClassLevel <- aggregate( + x = as.data.frame(t_data_matrix) + , by = list(sample_class) + , FUN = "max" + ) + + # "For each feature, 'select sample_class, rcv(intensity) from feature group by sample_class'." + # cv is less robust; deviation from normality degrades performance + # cv(x) == sd(x) / mean(x) + # rcv is a "robust" coefficient of variation, expressed as a proportion + # rcv(x) == mad(x) / median(x) + madOfFeatureBySampleClassLevel <- aggregate( + x = as.data.frame(t_data_matrix) + , by = list(sample_class) + , FUN = "mad" + ) + rcvOfFeatureBySampleClassLevel <- as.matrix( + madOfFeatureBySampleClassLevel[,2:n_features_plus_1] / medianOfFeatureBySampleClassLevel[,2:n_features_plus_1] + ) + rcvOfFeatureBySampleClassLevel[is.nan(rcvOfFeatureBySampleClassLevel)] <- max(9999,max(rcvOfFeatureBySampleClassLevel, na.rm = TRUE)) + + # "For each feature, 'select max(median_feature_intensity) from feature'." + maxApplyMedianOfFeatureBySampleClassLevel <- sapply( + X = 1:n_features + , FUN = function(i) { + match( + max(maxOfFeatureBySampleClassLevel[, i + 1]) + , maxOfFeatureBySampleClassLevel[, i + 1] + ) + } + ) + + # "For each feature, 'select mean(median_feature_intensity) from feature'." + meanApplyMedianOfFeatureBySampleClassLevel <- sapply( + X = 1:n_features + , FUN = function(i) mean(medianOfFeatureBySampleClassLevel[, i + 1]) + ) + + # Compute the 'salience' for each feature, i.e., how salient the intensity of a feature + # is for one particular class-level relative to the intensity across all class-levels. + salience_df <- data.frame( + # the feature name + feature = features + # the name (or factor-level) of the class-level with the highest median intensity for the feature + , max_level = medianOfFeatureBySampleClassLevel[maxApplyMedianOfFeatureBySampleClassLevel,1] + # the median intensity for the feature and the level max_level + , max_median = sapply( + X = 1:n_features + , FUN = function(i) { + maxOfFeatureBySampleClassLevel[maxApplyMedianOfFeatureBySampleClassLevel[i], 1 + i] + } + ) + # the coefficient of variation (expressed as a proportion) for the intensity for the feature and the level max_level + , max_rcv = sapply( + X = 1:n_features + , FUN = function(i) { + rcvOfFeatureBySampleClassLevel[maxApplyMedianOfFeatureBySampleClassLevel[i], i] + } + ) + # the mean of the medians of intensity for all class-levels for the feature + , mean_median = meanApplyMedianOfFeatureBySampleClassLevel + # don't coerce strings to factors (this is a parameter for the data.frame constructor, not a column of the data.frame) + , stringsAsFactors = FALSE + ) + # raw salience is the ratio of the most-prominent level to the mean of all levels for the feature + salience_df$salience <- sapply( + X = 1:nrow(salience_df) + , FUN = function(i) with(salience_df[i,], if (mean_median > 0) { max_median / mean_median } else { 0 } ) + ) + # "robust coefficient of variation, i.e., mad(feature-intensity for class-level max_level) / median(feature-intensity for class-level max_level) + salience_df$salient_rcv <- salience_df$max_rcv + + return (salience_df) +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/w4mcorcov_util.R Mon Oct 16 14:56:52 2017 -0400 @@ -0,0 +1,88 @@ +# tryCatchFunc wraps an expression that produces a value if it does not stop: +# tryCatchFunc produces a list +# On success of expr(), tryCatchFunc produces +# list(success TRUE, value = expr(), msg = "") +# On failure of expr(), tryCatchFunc produces +# list(success = FALSE, value = NA, msg = "the error message") +tryCatchFunc <- function(expr) { + # format error for logging + format_error <- function(e) { + paste(c("Error { message:", e$message, ", call:", e$call, "}"), collapse = " ") + } + retval <- NULL + tryCatch( + expr = { + retval <- ( list( success = TRUE, value = eval(expr = expr), msg = "" ) ) + } + , error = function(e) { + retval <<- list( success = FALSE, value = NA, msg = format_error(e) ) + } + ) + return (retval) +} + +# turn off all plotting devices +dev.off.all <- function() { + while (!is.null(dev.list())) { dev.off() } +} + +# capture plot and write to PDF; then close any devices opened in the process +plot2pdf <- function( + file.name +, plot.function +, width = 12 +, height = 12 +) { + # capture plot and write to PDF + cur.dev <- dev.list() + filename <- file.name + pdf(file = filename, width = width, height = height) + plot.function() + # close any devices opened in the process + dev.off() + if (is.null(cur.dev)) { + dev.off.all() + } else { + while ( length(dev.list()) > length(cur.dev) ) { dev.off() } + } +} + +# print and capture plot and write to PDF; then close any devices opened in the process +# This is needed for ggplot which does not print the plot when invoked within a function. +print2pdf <- function( + file.name +, plot.function +, width = 12 +, height = 12 +) { + plot2pdf( + file.name = file.name + , width = width + , height = height + , plot.function = function() { + print(plot.function()) + } + ) +} + +iso8601.znow <- function() +{ + strftime(as.POSIXlt(Sys.time(), "UTC"), "%Y-%m-%dT%H:%M:%SZ") +} + +# pdf.name <- function(name) +# { +# paste0(name, "_", iso8601.filename.fragment(), ".pdf") +# } +# +# tsv.name <- function(name) +# { +# paste0(name, "_", iso8601.filename.fragment(), ".tsv") +# } +# +# # pseudo-inverse - computational inverse non-square matrix a +# p.i <- function(a) { +# solve(t(a) %*% a) %*% t(a) +# } + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/w4mcorcov_wrapper.R Mon Oct 16 14:56:52 2017 -0400 @@ -0,0 +1,169 @@ +#!/usr/bin/env Rscript + +# This script assumes that it is being executed in a current working directory containing the following files: +# - w4mcorcov_lib.R +# - w4mcorcov_input.R +# - w4mcorcov_calc.R + +## constants +##---------- + +modNamC <- "w4mcorcov" ## module name + +topEnvC <- environment() +nl <- "\n" + +## options +##-------- + +strAsFacL <- options()$stringsAsFactors +options(stringsAsFactors = FALSE) + +## subroutines +##---------- + +source("w4mcorcov_lib.R") +source("w4mcorcov_util.R") +source("w4mcorcov_input.R") +source("w4mcorcov_salience.R") +source("w4mcorcov_calc.R") +source("w4mcorcov_output.R") + +## log file +##--------- + +my_log <- function(x, ...) { cat(paste(iso8601.znow(), " ", x, ..., nl, sep=""))} +my_fatal <- function(x, ...) { + my_log("ERROR: ", x, ...) + quit(save = "no", status = 11, runLast = TRUE) +} + +my_log("Start of the '", modNamC, "' Galaxy module call: ") + + +######## +# MAIN # +######## + +argVc <- unlist(parseCommandArgs(evaluate=FALSE)) +my_env <- new.env() + +##------------------------------ +## Initializing +##------------------------------ + +## arguments +##---------- + +# files + +my_env$dataMatrix_in <- as.character(argVc["dataMatrix_in"]) +my_env$sampleMetadata_in <- as.character(argVc["sampleMetadata_in"]) +my_env$variableMetadata_in <- as.character(argVc["variableMetadata_in"]) +my_env$contrast_detail <- as.character(argVc["contrast_detail"]) +my_env$contrast_corcov <- as.character(argVc["contrast_corcov"]) +my_env$contrast_salience <- as.character(argVc["contrast_salience"]) +# print(sprintf("contrast_salience: %s", my_env$contrast_salience)) + +# other parameters + +my_env$tesC <- as.character(argVc["tesC"]) +my_env$facC <- as.character(argVc["facC"]) +my_env$pairSigFeatOnly <- as.logical(argVc["pairSigFeatOnly"]) +my_env$levCSV <- as.character(argVc["levCSV"]) +my_env$matchingC <- as.character(argVc["matchingC"]) +my_env$labelFeatures <- as.logical(argVc["labelFeatures"]) + +tsv_action_factory <- function(file, colnames, append) { + return ( + function(tsv) { + write.table( + x = tsv + , file = file + , sep = "\t" + , quote = FALSE + , row.names = FALSE + , col.names = colnames + , append = append + ) + } + ) +} + +corcov_tsv_colnames <- TRUE +corcov_tsv_append <- FALSE +corcov_tsv_action <- function(tsv) { + tsv_action_factory( + file = my_env$contrast_corcov + , colnames = corcov_tsv_colnames + , append = corcov_tsv_append + )(tsv) + corcov_tsv_colnames <<- FALSE + corcov_tsv_append <<- TRUE +} + +salience_tsv_colnames <- TRUE +salience_tsv_append <- FALSE +salience_tsv_action <- function(tsv) { + tsv_action_factory( + file = my_env$contrast_salience + , colnames = salience_tsv_colnames + , append = salience_tsv_append + )(tsv) + salience_tsv_colnames <<- FALSE + salience_tsv_append <<- TRUE +} + +my_log( "-------------------------- Reading input data --------------------------") + +# read_inputs is defined in w4mcorcov_input.R +my_result <- read_inputs(input_env = my_env, failure_action = my_log) + +if ( is.logical(my_result) && my_result) { + my_log( "-------------------------- Beginning data processing --------------------------") + + # receiver for result of the call to corcov_calc + my_result <- NULL + + # compute and plot the correlation_vs_covariance details plot + # The parameter settings here are generally taken from bioconductor ropls::plot.opls source. + marVn <- c(4.6, 4.1, 2.6, 1.6) + old_par <- par( + font = 2 # bold font face + , font.axis = 2 # bold font face for axis + , font.lab = 2 # bold font face for x and y labels + , lwd = 2 # line-width - interpretation is device spcific + , mar = marVn # margins + , pch = 18 # black diamond plot-character, see help for graphics::points + # , mfrow = c(2,2) # two rows by two columns + , pty = "s" # force plots to be square + ) + plot2pdf( + file.name = my_env$contrast_detail + , width = 8 + , height = 8 + , plot.function = function() { + # plot layout four plots per page + layout(matrix(1:4, byrow = TRUE, nrow = 2)) + my_result <<- corcov_calc( + calc_env = my_env + , failure_action = my_fatal + , progress_action = my_log + , corcov_tsv_action = corcov_tsv_action + , salience_tsv_action = salience_tsv_action + ) + } + ) + par(old_par) + + my_log( "-------------------------- Finished data processing --------------------------") +} + +my_log( "End of the '", modNamC, "' Galaxy module call") + +if (is.logical(my_result) && my_result) { + quit(save = "no", status = 0, runLast = TRUE) +} else { + my_log("failure :(") + quit(save = "no", status = 10, runLast = TRUE) +}