# HG changeset patch # User ethevenot # Date 1469634274 14400 # Node ID ef64d37520500808bf6f05d028164329e8256328 planemo upload for repository https://github.com/workflow4metabolomics/univariate.git commit ca0e312e1c986c45310f37effe031f60009fbcab diff -r 000000000000 -r ef64d3752050 LICENSE.md --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/LICENSE.md Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,517 @@ + CeCILL FREE SOFTWARE LICENSE AGREEMENT + +Version 2.1 dated 2013-06-21 + + + Notice + +This Agreement is a Free Software license agreement that is the result +of discussions between its authors in order to ensure compliance with +the two main principles guiding its drafting: + + * firstly, compliance with the principles governing the distribution + of Free Software: access to source code, broad rights granted to users, + * secondly, the election of a governing law, French law, with which it + is conformant, both as regards the law of torts and intellectual + property law, and the protection that it offers to both authors and + holders of the economic rights over software. + +The authors of the CeCILL (for Ce[a] C[nrs] I[nria] L[ogiciel] L[ibre]) +license are: + +Commissariat à l'énergie atomique et aux énergies alternatives - CEA, a +public scientific, technical and industrial research establishment, +having its principal place of business at 25 rue Leblanc, immeuble Le +Ponant D, 75015 Paris, France. + +Centre National de la Recherche Scientifique - CNRS, a public scientific +and technological establishment, having its principal place of business +at 3 rue Michel-Ange, 75794 Paris cedex 16, France. + +Institut National de Recherche en Informatique et en Automatique - +Inria, a public scientific and technological establishment, having its +principal place of business at Domaine de Voluceau, Rocquencourt, BP +105, 78153 Le Chesnay cedex, France. + + + Preamble + +The purpose of this Free Software license agreement is to grant users +the right to modify and redistribute the software governed by this +license within the framework of an open source distribution model. + +The exercising of this right is conditional upon certain obligations for +users so as to preserve this status for all subsequent redistributions. + +In consideration of access to the source code and the rights to copy, +modify and redistribute granted by the license, users are provided only +with a limited warranty and the software's author, the holder of the +economic rights, and the successive licensors only have limited liability. + +In this respect, the risks associated with loading, using, modifying +and/or developing or reproducing the software by the user are brought to +the user's attention, given its Free Software status, which may make it +complicated to use, with the result that its use is reserved for +developers and experienced professionals having in-depth computer +knowledge. 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These subsequent versions may +address new issues encountered by Free Software. + +12.3 Any Software distributed under a given version of the Agreement may +only be subsequently distributed under the same version of the Agreement +or a subsequent version, subject to the provisions of Article 5.3.4 +<#compatibility>. + + + Article 13 - GOVERNING LAW AND JURISDICTION + +13.1 The Agreement is governed by French law. The Parties agree to +endeavor to seek an amicable solution to any disagreements or disputes +that may arise during the performance of the Agreement. + +13.2 Failing an amicable solution within two (2) months as from their +occurrence, and unless emergency proceedings are necessary, the +disagreements or disputes shall be referred to the Paris Courts having +jurisdiction, by the more diligent Party. diff -r 000000000000 -r ef64d3752050 README.md --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.md Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,37 @@ +# Univariate parametric and non-parametric hypothesis testing with correction for multiple testing + +A Galaxy module from the [Workflow4metabolomics](http://workflow4metabolomics.org) project. + +Status: [![Build Status](https://travis-ci.org/workflow4metabolomics/univariate.svg?branch=master)](https://travis-ci.org/workflow4metabolomics/univariate). + +## Description + +**Version:** 2.1.1 +**Date:** 2015-09-30 +**Author:** Marie Tremblay-Franco (INRA, MetaToul, MetaboHUB, W4M Core Development Team) and Etienne A. Thevenot (CEA, LIST, MetaboHUB, W4M Core Development Team) +**Email:** [marie.tremblay-franco(at)toulouse.inra.fr](mailto:marie.tremblay-franco@toulouse.inra.fr); [etienne.thevenot(at)cea.fr](mailto:etienne.thevenot@cea.fr) +**Citation:** Thevenot E.A., Roux A., Xu Y., Ezan E. and Junot C. (2015). Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. *Journal of Proteome Research*, **14**:3322-3335. [doi:10.1021/acs.jproteome.5b00354](http://dx.doi.org/10.1021/acs.jproteome.5b00354) +**Reference history:** [W4M00001a_sacurine-subset-statistics](http://galaxy.workflow4metabolomics.org/history/list_published), [W4M00004_mtbls1](http://galaxy.workflow4metabolomics.org/history/list_published) +**Licence:** CeCILL +**Funding:** Agence Nationale de la Recherche ([MetaboHUB](http://www.metabohub.fr/index.php?lang=en&Itemid=473) national infrastructure for metabolomics and fluxomics, ANR-11-INBS-0010 grant) + +## Installation + + * Configuration file: **univariate_config.xml** + * Image file: + + **static/images/univariate_workflowPositionImage.png** + * Wrapper file: **univariate_wrapper.R** + * Script file: **univariate_script.R** + * R packages + + **batch** from CRAN: `install.packages("batch", dep=TRUE)`. + + **PMCMR** from Bioconductor: `install.packages("PMCMR", dep=TRUE)`. + +## Tests + +The code in the wrapper can be tested by running the **tests/univariate_tests.R** in R + +## News + +## CHANGES IN VERSION 2.1.1 + + * Internal handling of 'NA' p-values (e.g. when intensities are identical in all samples). diff -r 000000000000 -r ef64d3752050 build.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/build.xml Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,95 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff -r 000000000000 -r ef64d3752050 docker/Dockerfile --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/docker/Dockerfile Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,28 @@ +FROM ubuntu:14.04 + +MAINTAINER Etienne Thevenot (etienne.thevenot@cea.fr) + +# Setup package repos +RUN echo "deb http://mirrors.ebi.ac.uk/CRAN/bin/linux/ubuntu trusty/" >> /etc/apt/sources.list +RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9 + +# Update and upgrade system +RUN apt-get update +RUN apt-get -y upgrade + +# Install R and other needed packages +RUN apt-get -y install r-base +RUN R -e "install.packages('batch', lib='/usr/lib/R/library', dependencies = TRUE, repos='http://mirrors.ebi.ac.uk/CRAN')" + +# Clone tool +RUN apt-get -y install git +RUN git clone -b docker https://github.com/workflow4metabolomics/univariate /files/univariate + +# Put univariate folder into PATH +ENV PATH=$PATH:/files/univariate + +# Clean up +RUN apt-get clean && apt-get autoremove -y && rm -rf /var/lib/{apt,dpkg,cache,log}/ /tmp/* /var/tmp/* + +# Define Entry point script +ENTRYPOINT ["/files/univariate/univariate_wrapper.R"] diff -r 000000000000 -r ef64d3752050 static/images/univariate_workflowPositionImage.png Binary file static/images/univariate_workflowPositionImage.png has changed diff -r 000000000000 -r ef64d3752050 test-data/dataMatrix.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/dataMatrix.tsv Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,4 @@ +profile HU_017 HU_021 HU_027 HU_032 HU_041 HU_048 HU_049 HU_050 HU_052 HU_059 HU_060 HU_066 HU_072 HU_077 HU_090 HU_109 HU_110 HU_125 HU_126 HU_131 HU_134 HU_149 HU_150 HU_173 HU_179 HU_180 HU_182 HU_202 HU_204 HU_209 +HMDB01032 2569204.92420381 6222035.77434915 17070707.9912636 1258838.24348419 13039543.0754619 1909391.77026598 3495.09386434063 2293521.90928998 128503.275117713 81872.5276382213 8103557.56578035 149574887.036181 1544036.41049333 7103429.53933206 14138796.50382 4970265.57952158 263054.73056162 1671332.30008058 88433.1944958815 23602331.2894815 18648126.5206986 1554657.98756878 34152.3646391152 209372.71275317 33187733.370626 202438.591636003 13581070.0886437 354170.810678102 9120781.48986975 43419175.4051586 +HMDB03072 3628416.30251025 65626.9834353751 112170.118946651 3261804.34422417 42228.2787747563 343254.201250707 1958217.69317664 11983270.0435677 5932111.41638028 5511385.83359531 9154521.47755199 2632133.21209418 9500411.14556502 6551644.51726592 7204319.80891836 1273412.04795188 3260583.81592376 8932005.5351622 8340827.52597275 9256460.69197759 11217839.169041 5919262.81433556 11790077.0657915 9567977.80797097 73717.5811684739 9991787.29074293 4208098.14739633 623970.649925847 10904221.2642849 2171793.93621067 +HMDB00792 429568.609438384 3887629.50527037 1330692.11658995 1367446.73023821 844197.447472453 2948090.71886592 1614157.90566884 3740009.19379795 3292251.66531919 2310688.79492013 4404239.59008605 3043289.12780863 825736.467181043 2523241.91730649 6030501.02648005 474901.604069803 2885792.42617652 2955990.64049134 1917716.3427982 1767962.67737699 5926203.40397675 1639065.69474684 346810.763557826 1054776.22313737 2390258.27543894 1831346.37315857 1026696.36904362 7079792.50047866 4368341.01359769 3495986.87280275 diff -r 000000000000 -r ef64d3752050 test-data/sampleMetadata.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/sampleMetadata.tsv Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,31 @@ +sample age ageGroup +HU_017 41 experienced +HU_021 34 junior +HU_027 37 experienced +HU_032 38 experienced +HU_041 28 junior +HU_048 39 experienced +HU_049 50 senior +HU_050 30 junior +HU_052 51 senior +HU_059 81 senior +HU_060 55 senior +HU_066 25 junior +HU_072 47 experienced +HU_077 27 junior +HU_090 46 experienced +HU_109 32 junior +HU_110 50 senior +HU_125 58 senior +HU_126 45 experienced +HU_131 42 experienced +HU_134 48 experienced +HU_149 35 experienced +HU_150 49 experienced +HU_173 55 senior +HU_179 33 junior +HU_180 53 senior +HU_182 43 experienced +HU_202 42 experienced +HU_204 31 junior +HU_209 17.5 junior diff -r 000000000000 -r ef64d3752050 test-data/variableMetadata-output.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/variableMetadata-output.tsv Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,4 @@ +variableMetadata name ageGroup_kruskal_fdr ageGroup_kruskal_sig ageGroup_kruskal_junior-experienced_dif ageGroup_kruskal_senior-experienced_dif ageGroup_kruskal_senior-junior_dif ageGroup_kruskal_junior-experienced_pva ageGroup_kruskal_senior-experienced_pva ageGroup_kruskal_senior-junior_pva ageGroup_kruskal_junior-experienced_sig ageGroup_kruskal_senior-experienced_sig ageGroup_kruskal_senior-junior_sig +HMDB01032 Dehydroepiandrosterone sulfate 0.0117826825222329 1 7211389.71960377 -1703486.11807139 -8914875.83767516 0.204550960009346 0.123124593762726 0.00251932966039092 0 0 1 +HMDB03072 Quinic acid 0.461634758626427 0 -3747468.87812489 1512795.66143568 5260264.53956057 NA NA NA NA NA NA +HMDB00792 Sebacic acid 0.469555338459932 0 1404223.43306179 959174.915801485 -445048.517260305 NA NA NA NA NA NA diff -r 000000000000 -r ef64d3752050 test-data/variableMetadata.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/variableMetadata.tsv Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,4 @@ +variable name +HMDB01032 Dehydroepiandrosterone sulfate +HMDB03072 Quinic acid +HMDB00792 Sebacic acid diff -r 000000000000 -r ef64d3752050 tests/example1/dataMatrix.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tests/example1/dataMatrix.tsv Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,4 @@ +dataMatrix Ech10 Ech11 Ech12 Ech13 Ech14 Ech15 +MT1 3.439956551 3.399847335 3.335401704 3.4201777 3.24585851 3.401256321 +MT2 5.008458405 4.461291924 4.068043169 4.42768414 4.406640829 4.500370048 +MT3 3.99527636 4.051758488 4.332552332 4.348474118 4.253679544 4.26823853 diff -r 000000000000 -r ef64d3752050 tests/example1/sampleMetadata.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tests/example1/sampleMetadata.tsv Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,7 @@ +sampleMetadata jour +Ech10 J10 +Ech11 J10 +Ech12 J10 +Ech13 J3 +Ech14 J3 +Ech15 J3 diff -r 000000000000 -r ef64d3752050 tests/example1/variableMetadata.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tests/example1/variableMetadata.tsv Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,4 @@ +variableMetadata PCA_XLOAD-h1 PCA_XLOAD-h2 +MT1 -0.048723936 0.05648187 +MT2 -0.067609139 0.084300327 +MT3 0.080335733 -0.0215397 diff -r 000000000000 -r ef64d3752050 tests/input/dataMatrix.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tests/input/dataMatrix.tsv Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,4 @@ +profile HU_017 HU_021 HU_027 HU_032 HU_041 HU_048 HU_049 HU_050 HU_052 HU_059 HU_060 HU_066 HU_072 HU_077 HU_090 HU_109 HU_110 HU_125 HU_126 HU_131 HU_134 HU_149 HU_150 HU_173 HU_179 HU_180 HU_182 HU_202 HU_204 HU_209 +HMDB01032 2569204.92420381 6222035.77434915 17070707.9912636 1258838.24348419 13039543.0754619 1909391.77026598 3495.09386434063 2293521.90928998 128503.275117713 81872.5276382213 8103557.56578035 149574887.036181 1544036.41049333 7103429.53933206 14138796.50382 4970265.57952158 263054.73056162 1671332.30008058 88433.1944958815 23602331.2894815 18648126.5206986 1554657.98756878 34152.3646391152 209372.71275317 33187733.370626 202438.591636003 13581070.0886437 354170.810678102 9120781.48986975 43419175.4051586 +HMDB03072 3628416.30251025 65626.9834353751 112170.118946651 3261804.34422417 42228.2787747563 343254.201250707 1958217.69317664 11983270.0435677 5932111.41638028 5511385.83359531 9154521.47755199 2632133.21209418 9500411.14556502 6551644.51726592 7204319.80891836 1273412.04795188 3260583.81592376 8932005.5351622 8340827.52597275 9256460.69197759 11217839.169041 5919262.81433556 11790077.0657915 9567977.80797097 73717.5811684739 9991787.29074293 4208098.14739633 623970.649925847 10904221.2642849 2171793.93621067 +HMDB00792 429568.609438384 3887629.50527037 1330692.11658995 1367446.73023821 844197.447472453 2948090.71886592 1614157.90566884 3740009.19379795 3292251.66531919 2310688.79492013 4404239.59008605 3043289.12780863 825736.467181043 2523241.91730649 6030501.02648005 474901.604069803 2885792.42617652 2955990.64049134 1917716.3427982 1767962.67737699 5926203.40397675 1639065.69474684 346810.763557826 1054776.22313737 2390258.27543894 1831346.37315857 1026696.36904362 7079792.50047866 4368341.01359769 3495986.87280275 diff -r 000000000000 -r ef64d3752050 tests/input/sampleMetadata.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tests/input/sampleMetadata.tsv Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,31 @@ +sample age ageGroup +HU_017 41 experienced +HU_021 34 junior +HU_027 37 experienced +HU_032 38 experienced +HU_041 28 junior +HU_048 39 experienced +HU_049 50 senior +HU_050 30 junior +HU_052 51 senior +HU_059 81 senior +HU_060 55 senior +HU_066 25 junior +HU_072 47 experienced +HU_077 27 junior +HU_090 46 experienced +HU_109 32 junior +HU_110 50 senior +HU_125 58 senior +HU_126 45 experienced +HU_131 42 experienced +HU_134 48 experienced +HU_149 35 experienced +HU_150 49 experienced +HU_173 55 senior +HU_179 33 junior +HU_180 53 senior +HU_182 43 experienced +HU_202 42 experienced +HU_204 31 junior +HU_209 17.5 junior diff -r 000000000000 -r ef64d3752050 tests/input/variableMetadata.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tests/input/variableMetadata.tsv Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,4 @@ +variable name +HMDB01032 Dehydroepiandrosterone sulfate +HMDB03072 Quinic acid +HMDB00792 Sebacic acid diff -r 000000000000 -r ef64d3752050 tests/output/information.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tests/output/information.txt Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,8 @@ + +Start of the 'Univariate' Galaxy module call: sam. 21 mai 2016 20:25:53 + +Performing 'ttest' + +No significant variable found at the selected 0.05 level + +End of 'Univariate' Galaxy module call: 2016-05-21 20:25:53 diff -r 000000000000 -r ef64d3752050 tests/output/variableMetadata.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tests/output/variableMetadata.tsv Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,4 @@ +variableMetadata PCA_XLOAD-h1 PCA_XLOAD-h2 jour_ttest_J3-J10_dif jour_ttest_J3-J10_fdr jour_ttest_J3-J10_sig +MT1 -0.048723936 0.05648187 -0.0359710196666665 0.827558403950534 0 +MT2 -0.067609139 0.084300327 -0.0676994936666668 0.827558403950534 0 +MT3 0.080335733 -0.0215397 0.163601670666666 0.760596565270778 0 diff -r 000000000000 -r ef64d3752050 tests/univariate_tests.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tests/univariate_tests.R Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,194 @@ +library(RUnit) + +wrapperF <- function(argVc) { + + + source("../univariate_script.R") + + +#### Start_of_testing_code <- function() {} + + + ##------------------------------ + ## Initializing + ##------------------------------ + + ## options + ##-------- + + strAsFacL <- options()$stringsAsFactors + options(stringsAsFactors = FALSE) + + ## packages + ##--------- + + library(PMCMR) + + ## constants + ##---------- + + modNamC <- "Univariate" ## module name + + topEnvC <- environment() + flagC <- "\n" + + ## functions + ##---------- + + flgF <- function(tesC, + envC = topEnvC, + txtC = NA) { ## management of warning and error messages + + tesL <- eval(parse(text = tesC), envir = envC) + + if(!tesL) { + + sink(NULL) + stpTxtC <- ifelse(is.na(txtC), + paste0(tesC, " is FALSE"), + txtC) + + stop(stpTxtC, + call. = FALSE) + + } + + } ## flgF + + ## log file + ##--------- + + sink(argVc["information"]) + + cat("\nStart of the '", modNamC, "' Galaxy module call: ", + format(Sys.time(), "%a %d %b %Y %X"), "\n", sep="") + + ## loading + ##-------- + + datMN <- t(as.matrix(read.table(argVc["dataMatrix_in"], + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t"))) + + samDF <- read.table(argVc["sampleMetadata_in"], + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t") + + varDF <- read.table(argVc["variableMetadata_in"], + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t") + + tesC <- argVc["tesC"] + + ## checking + ##--------- + + flgF("identical(rownames(datMN), rownames(samDF))", txtC = "Column names of the dataMatrix are not identical to the row names of the sampleMetadata; check your data with the 'Check Format' module in the 'Quality Control' section") + flgF("identical(colnames(datMN), rownames(varDF))", txtC = "Row names of the dataMatrix are not identical to the row names of the variableMetadata; check your data with the 'Check Format' module in the 'Quality Control' section") + + flgF("argVc['facC'] %in% colnames(samDF)", txtC = paste0("Required factor of interest '", argVc['facC'], "' could not be found in the column names of the sampleMetadata")) + flgF("mode(samDF[, argVc['facC']]) %in% c('character', 'numeric')", txtC = paste0("The '", argVc['facC'], "' column of the sampleMetadata should contain either number only, or character only")) + + flgF("!(tesC %in% c('ttest', 'wilcoxon')) || (mode(samDF[, argVc['facC']]) == 'character' && length(unique(samDF[, argVc['facC']])) == 2)", txtC = paste0("For 'ttest' and 'wilcoxon', the chosen factor column ('", argVc['facC'], "') of the sampleMetadata should contain characters with only two different classes")) + flgF("!(tesC %in% c('anova', 'kruskal')) || (mode(samDF[, argVc['facC']]) == 'character' && length(unique(samDF[, argVc['facC']])) > 2)", txtC = paste0("For 'anova' and 'kruskal', the chosen factor column ('", argVc['facC'], "') of the sampleMetadata should contain characters with at least three different classes")) + flgF("!(tesC %in% c('pearson', 'spearman')) || mode(samDF[, argVc['facC']]) == 'numeric'", txtC = paste0("For 'pearson' and 'spearman', the chosen factor column ('", argVc['facC'], "') of the sampleMetadata should contain numbers only")) + + flgF("argVc['adjC'] %in% c('holm', 'hochberg', 'hommel', 'bonferroni', 'BH', 'BY', 'fdr', 'none')") + + flgF("0 <= as.numeric(argVc['thrN']) && as.numeric(argVc['thrN']) <= 1", + txtC = "(corrected) p-value threshold must be between 0 and 1") + + + ##------------------------------ + ## Computation + ##------------------------------ + + + varDF <- univariateF(datMN = datMN, + samDF = samDF, + varDF = varDF, + facC = argVc["facC"], + tesC = tesC, + adjC = argVc["adjC"], + thrN = as.numeric(argVc["thrN"])) + + + ##------------------------------ + ## Ending + ##------------------------------ + + + ## saving + ##-------- + + varDF <- cbind.data.frame(variableMetadata = rownames(varDF), + varDF) + write.table(varDF, + file = argVc["variableMetadata_out"], + quote = FALSE, + row.names = FALSE, + sep = "\t") + + ## closing + ##-------- + + cat("\nEnd of '", modNamC, "' Galaxy module call: ", + as.character(Sys.time()), "\n", sep = "") + + sink() + + options(stringsAsFactors = strAsFacL) + + + +#### End_of_testing_code <- function() {} + + + return(list(varDF = varDF)) + + rm(list = ls()) + +} + +exaDirOutC <- "output" +if(!file.exists(exaDirOutC)) + stop("Please create an 'output' subfolder into the (current) 'tests' folder") + +tesArgLs <- list(input_kruskal = c(facC = "ageGroup", + tesC = "kruskal", + adjC = "fdr", + thrN = "0.05", + .chkC = "checkEqualsNumeric(outLs[['varDF']]['HMDB01032', 'ageGroup_kruskal_senior-experienced_pva'], 0.1231246, tolerance = 1e-6)"), + example1_wilcoxDif = c(facC = "jour", + tesC = "wilcoxon", + adjC = "fdr", + thrN = "0.05", + .chkC = "checkEqualsNumeric(outLs[['varDF']]['MT3', 'jour_wilcoxon_J3-J10_dif'], 0.216480042, tolerance = 1e-8)"), + example1_ttestFdr = c(facC = "jour", + tesC = "ttest", + adjC = "fdr", + thrN = "0.05", + .chkC = "checkEqualsNumeric(outLs[['varDF']]['MT3', 'jour_ttest_J3-J10_fdr'], 0.7605966, tolerance = 1e-6)")) + +for(tesC in names(tesArgLs)) + tesArgLs[[tesC]] <- c(tesArgLs[[tesC]], + dataMatrix_in = file.path(unlist(strsplit(tesC, "_"))[1], "dataMatrix.tsv"), + sampleMetadata_in = file.path(unlist(strsplit(tesC, "_"))[1], "sampleMetadata.tsv"), + variableMetadata_in = file.path(unlist(strsplit(tesC, "_"))[1], "variableMetadata.tsv"), + variableMetadata_out = file.path(exaDirOutC, "variableMetadata.tsv"), + information = file.path(exaDirOutC, "information.txt")) + +for(tesC in names(tesArgLs)) { + print(tesC) + outLs <- wrapperF(tesArgLs[[tesC]]) + if(".chkC" %in% names(tesArgLs[[tesC]])) + stopifnot(eval(parse(text = tesArgLs[[tesC]][[".chkC"]]))) +} + +message("Checks successfully completed") diff -r 000000000000 -r ef64d3752050 univariate_config.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/univariate_config.xml Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,313 @@ + + Univariate statistics + + + R + r-batch + r-pmcmr + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +.. class:: infomark + +| **Tool update: See the 'NEWS' section at the bottom of the page** + +--------------------------------------------------- + +.. class:: infomark + +**Authors** + +| **Marie Tremblay-Franco (marie.tremblay-franco@toulouse.inra.fr)** and **Etienne Thevenot (etienne.thevenot@cea.fr)** wrote this wrapper of R univariate statistical tests. +| MetaboHUB: The French National Infrastructure for Metabolomics and Fluxomics (http://www.metabohub.fr/en) + +--------------------------------------------------- + +.. class:: infomark + +**Please cite** + +R Core Team (2013). R: A language and Environment for Statistical Computing. http://www.r-project.org + +--------------------------------------------------- + +.. class:: infomark + +**References** + +| Benjamini Y. and Hochberg Y. (1995). Controlling the false discovery rate: a practical and powerful approach for multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57:289-300. +| Dalgaard P. (2002). Introductory statistics with R. Springer. +| Kvam P. and Vidakovic B. (2007). Nonparametric statistics with applications to science and engineering. Wiley. +| Van Belle G., Fisher L., Heagerty P. and Lumley T. (2004). Biostatistics - a methodology for the health sciences. Wiley. +| Pohlert T. (2015). PMCMR: Calculate pairwise multiple comparisons of mean rank sums. R package on CRAN. + +--------------------------------------------------- + +===================== +Univariate statistics +===================== + +----------- +Description +----------- + +| The module performs two sample tests (t-test and Wilcoxon rank test), analysis of variance and Kruskal-Wallis rank test, and correlation tests (by using either the pearson or the spearman correlation) + +----------------- +Workflow position +----------------- + +.. image:: univariate_workflowPositionImage.png + :width: 584 + +----------- +Input files +----------- + ++------------------------------+------------+ +| File | Format | ++==============================+============+ +| 1) Data matrix | tabular | ++------------------------------+------------+ +| 2) Sample metadata | tabular | ++------------------------------+------------+ +| 3) Variable metadata | tabular | ++------------------------------+------------+ + +---------- +Parameters +---------- + +Data matrix file + | variable x sample **dataMatrix** tabular separated 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) + | + +Sample metadata file + | sample x metadata **sampleMetadata** tabular separated file of the numeric and/or character sample metadata, with . as decimal and NA for missing values + | + +Variable metadata file + | variable x metadata **variableMetadata** tabular separated file of the numeric and/or character variable metadata, with . as decimal and NA for missing values + | + +Factor + | Column of the sample metadata table to be used as qualitative factor (t-test, Wilcoxon test, Analysis of variance, Kruskal-Wallis test) or quantitative variable (correlation) + | + +Test + | Depending on the factor of interest (qualitative with 2 or more levels, or quantitative), and on the normality of the sample values (determining whether a parametric or nonparametric test is required), you can choose one of the 6 tests available: + + ++---------------------------+------------------+----------------------+----------------------+ +| Factor to be tested | Number of levels | Parametric test | Nonparametric test | ++===========================+==================+======================+======================+ +| Qualitative | 2 | t-test | Wilcoxon test | ++ +------------------+----------------------+----------------------+ +| | > 2 | Analysis of variance | Kruskal-Wallis | ++---------------------------+------------------+----------------------+----------------------+ +| Quantitative | | Pearson correlation | Spearman correlation | ++---------------------------+------------------+----------------------+----------------------+ + +Method for multiple testing correction + | The 7 methods implemented in the 'p.adjust' R function are available and documented as follows: + | "The adjustment methods include the Bonferroni correction ("bonferroni") in which the p-values are multiplied by the number of comparisons. Less conservative corrections are also included by Holm (1979) ("holm"), Hochberg (1988) ("hochberg"), Hommel (1988) ("hommel"), Benjamini and Hochberg (1995) ("BH" or its alias "fdr"), and Benjamini and Yekutieli (2001) ("BY"), respectively. A pass-through option ("none") is also included. The set of methods are contained in the p.adjust.methods vector for the benefit of methods that need to have the method as an option and pass it on to p.adjust. The first four methods are designed to give strong control of the family-wise error rate. There seems no reason to use the unmodified Bonferroni correction because it is dominated by Holm's method, which is also valid under arbitrary assumptions. Hochberg's and Hommel's methods are valid when the hypothesis tests are independent or when they are non-negatively associated (Sarkar, 1998; Sarkar and Chang, 1997). Hommel's method is more powerful than Hochberg's, but the difference is usually small and the Hochberg p-values are faster to compute. The "BH" (aka "fdr") and "BY" method of Benjamini, Hochberg, and Yekutieli control the false discovery rate, the expected proportion of false discoveries amongst the rejected hypotheses. The false discovery rate is a less stringent condition than the family-wise error rate, so these methods are more powerful than the others." + | + +(Corrected) p-value significance threshold + | + | + + +------------ +Output files +------------ + +variableMetadata_out.tabular + | **variableMetadata** file identical to the file given as argument, except that (at least) three columns have been added: + | 1) [factor]_[test]_[class'a']-[class'b']_dif or [factor]_[test]_cor: difference of the means (ttest) or the medians (wilcoxon) between the two classes, or 'pearson' or 'spearman' correlations + | 2) [factor]_[test]_[class'a']-[class'b']_[method] or [factor]_[test]_[method]: adjusted p-values + | 3) [factor]_[test]_[class'a']-[class'b']_sig or [factor]_[test]_sig: significance (coded as '1' if below the threshold and '0' otherwise) + | In the case of 'anova' and 'kruskal', the columns 2) and 3) appear first to give the results from the ANOVA or Kruskal Wallis test, and, when these tests are significant, the results of the pairwise comparisons are reported in additional columns (otherwise NA in these columns): in the case of ANOVA, the Tukey HSD post-hoc analysis is used (for each comparison, the difference between means, p value, and significance are provided); in the case of Kruskal Wallis, the Nemenyi is performed (PMCMR package) (for each pairwise comparison, the difference between medians, p value and significance are provided) + | + +information.txt + | File with all messages and warnings generated during the computation + | + +--------------------------------------------------- + +--------------- +Working example +--------------- + +Input files +=========== + +| **To generate the "dataMatrix", "sampleMetadata" and "variableMetadata" files:** +| **1) copy/paste the values below in three distinct .txt files** +| **2) use the "Get Data" / "Upload File" in the "Tools" (left) panel from the Galaxy / ABiMS page by choosing:** +| **a) File Format: 'tabular'** +| **b) Convert spaces to tabs: 'Yes'** +| + +**dataMatrix file**:: + + dataMatrix HU_017 HU_021 HU_027 HU_032 HU_041 HU_048 HU_049 HU_050 HU_052 HU_059 HU_060 HU_066 HU_072 HU_077 HU_090 HU_109 HU_110 HU_125 HU_126 HU_131 HU_134 HU_149 HU_150 HU_173 HU_179 HU_180 HU_182 HU_202 HU_204 HU_209 + HMDB01032 2569204.92420381 6222035.77434915 17070707.9912636 1258838.24348419 13039543.0754619 1909391.77026598 3495.09386434063 2293521.90928998 128503.275117713 81872.5276382213 8103557.56578035 149574887.036181 1544036.41049333 7103429.53933206 14138796.50382 4970265.57952158 263054.73056162 1671332.30008058 88433.1944958815 23602331.2894815 18648126.5206986 1554657.98756878 34152.3646391152 209372.71275317 33187733.370626 202438.591636003 13581070.0886437 354170.810678102 9120781.48986975 43419175.4051586 + HMDB03072 3628416.30251025 65626.9834353751 112170.118946651 3261804.34422417 42228.2787747563 343254.201250707 1958217.69317664 11983270.0435677 5932111.41638028 5511385.83359531 9154521.47755199 2632133.21209418 9500411.14556502 6551644.51726592 7204319.80891836 1273412.04795188 3260583.81592376 8932005.5351622 8340827.52597275 9256460.69197759 11217839.169041 5919262.81433556 11790077.0657915 9567977.80797097 73717.5811684739 9991787.29074293 4208098.14739633 623970.649925847 10904221.2642849 2171793.93621067 + HMDB00792 429568.609438384 3887629.50527037 1330692.11658995 1367446.73023821 844197.447472453 2948090.71886592 1614157.90566884 3740009.19379795 3292251.66531919 2310688.79492013 4404239.59008605 3043289.12780863 825736.467181043 2523241.91730649 6030501.02648005 474901.604069803 2885792.42617652 2955990.64049134 1917716.3427982 1767962.67737699 5926203.40397675 1639065.69474684 346810.763557826 1054776.22313737 2390258.27543894 1831346.37315857 1026696.36904362 7079792.50047866 4368341.01359769 3495986.87280275 + + +**sampleMetadata file**:: + + sampleMetadata age ageGrp + HU_017 41 experienced + HU_021 34 junior + HU_027 37 experienced + HU_032 38 experienced + HU_041 28 junior + HU_048 39 experienced + HU_049 50 senior + HU_050 30 junior + HU_052 51 senior + HU_059 81 senior + HU_060 55 senior + HU_066 25 junior + HU_072 47 experienced + HU_077 27 junior + HU_090 46 experienced + HU_109 32 junior + HU_110 50 senior + HU_125 58 senior + HU_126 45 experienced + HU_131 42 experienced + HU_134 48 experienced + HU_149 35 experienced + HU_150 49 experienced + HU_173 55 senior + HU_179 33 junior + HU_180 53 senior + HU_182 43 experienced + HU_202 42 experienced + HU_204 31 junior + HU_209 17.5 junior + + +**variableMetadata file**:: + + variableMetadata name + HMDB01032 Dehydroepiandrosterone sulfate + HMDB03072 Quinic acid + HMDB00792 Sebacic acid + + +Parameters +========== + +**Factor of interest:** "ageGroup" + +**Test:** "Kruskal-Wallis rank test (qualitative, > 2 levels)" + +**Method for multiple testing correction:** "fdr" + +**(Corrected) p-value significance threshold:** 0.05 + + +Output files +============ + ++------------------------------+------------+ +| File | Format | ++==============================+============+ +| 1) dataMatrix | tabular | ++------------------------------+------------+ +| 2) sampleMetadata | tabular | ++------------------------------+------------+ +| 3) variableMetadata | tabular | ++------------------------------+------------+ +| 4) information | text | ++------------------------------+------------+ + + +--------------------------------------------------- + +---- +NEWS +---- + +CHANGES IN VERSION 2.1.1 +======================== + +Internal handling of 'NA' p-values (e.g. when intensities are identical in all samples) + +CHANGES IN VERSION 2.0.1 +======================== + +(corrected) p-value threshold can be set to any value between 0 and 1 + + + + + + + diff -r 000000000000 -r ef64d3752050 univariate_script.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/univariate_script.R Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,148 @@ +univariateF <- function(datMN, + samDF, + varDF, + facC, + tesC = c("ttest", "wilcoxon", "anova", "kruskal", "pearson", "spearman")[1], + adjC = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none")[7], + thrN = 0.05) { + + + ## Option + ##--------------- + + strAsFacL <- options()$stringsAsFactors + options(stingsAsFactors = FALSE) + options(warn = -1) + + if(mode(samDF[, facC]) == "character") { + facFcVn <- factor(samDF[, facC]) + facLevVc <- levels(facFcVn) + } else + facFcVn <- samDF[, facC] + + cat("\nPerforming '", tesC, "'\n", sep="") + + varPfxC <- paste0(make.names(facC), "_", tesC, "_") + + if(tesC %in% c("ttest", "wilcoxon", "pearson", "spearman")) { + + switch(tesC, + ttest = { + staF <- function(y) diff(tapply(y, facFcVn, function(x) mean(x, na.rm = TRUE))) + tesF <- function(y) t.test(y ~ facFcVn)[["p.value"]] + }, + wilcoxon = { + staF <- function(y) diff(tapply(y, facFcVn, function(x) median(x, na.rm = TRUE))) + tesF <- function(y) wilcox.test(y ~ facFcVn)[["p.value"]] + }, + pearson = { + staF <- function(y) cor(facFcVn, y, method = "pearson", use = "pairwise.complete.obs") + tesF <- function(y) cor.test(facFcVn, y, method = "pearson", use = "pairwise.complete.obs")[["p.value"]] + }, + spearman = { + staF <- function(y) cor(facFcVn, y, method = "spearman", use = "pairwise.complete.obs") + tesF <- function(y) cor.test(facFcVn, y, method = "spearman", use = "pairwise.complete.obs")[["p.value"]] + }) + + staVn <- apply(datMN, 2, staF) + + fdrVn <- p.adjust(apply(datMN, + 2, + tesF), + method = adjC) + + sigVn <- as.numeric(fdrVn < thrN) + + if(tesC %in% c("ttest", "wilcoxon")) + varPfxC <- paste0(varPfxC, paste(rev(facLevVc), collapse = "-"), "_") + + varDF[, paste0(varPfxC, ifelse(tesC %in% c("ttest", "wilcoxon"), "dif", "cor"))] <- staVn + + varDF[, paste0(varPfxC, adjC)] <- fdrVn + + varDF[, paste0(varPfxC, "sig")] <- sigVn + + } else if(tesC == "anova") { + + ## getting the names of the pairwise comparisons 'class1Vclass2' + prwVc <- rownames(TukeyHSD(aov(datMN[, 1] ~ facFcVn))[["facFcVn"]]) + + aovMN <- t(apply(datMN, 2, function(varVn) { + + aovMod <- aov(varVn ~ facFcVn) + pvaN <- summary(aovMod)[[1]][1, "Pr(>F)"] + hsdMN <- TukeyHSD(aovMod)[["facFcVn"]] + c(pvaN, c(hsdMN[, c("diff", "p adj")]), as.numeric(hsdMN[, "p adj"] < thrN)) + + })) + + aovMN[, 1] <- p.adjust(aovMN[, 1], method = adjC) + sigVn <- as.numeric(aovMN[, 1] < thrN) + aovMN <- cbind(aovMN[, 1], sigVn, aovMN[, 2:ncol(aovMN)]) + ## aovMN[which(aovMN[, 2] < 1), (3 + length(prwVc)):ncol(aovMN)] <- NA + colnames(aovMN) <- paste0(varPfxC, + c(adjC, + "sig", + paste0(prwVc, "_dif"), + paste0(prwVc, "_pva"), + paste0(prwVc, "_sig"))) + aovMN[which(aovMN[, paste0(varPfxC, "sig")] < 1), paste0(varPfxC, c(paste0(prwVc, "_pva"), paste0(prwVc, "_sig")))] <- NA + + varDF <- cbind.data.frame(varDF, as.data.frame(aovMN)) + + } else if(tesC == "kruskal") { + + ## getting the names of the pairwise comparisons 'class1Vclass2' + nemMN <- posthoc.kruskal.nemenyi.test(datMN[, 1], facFcVn, "Tukey")[["p.value"]] + nemVl <- c(lower.tri(nemMN, diag = TRUE)) + nemClaMC <- cbind(rownames(nemMN)[c(row(nemMN))][nemVl], + colnames(nemMN)[c(col(nemMN))][nemVl]) + nemNamVc <- paste0(nemClaMC[, 1], "-", nemClaMC[, 2]) + nemNamVc <- paste0(varPfxC, nemNamVc) + + nemMN <- t(apply(datMN, 2, function(varVn) { + + pvaN <- kruskal.test(varVn ~ facFcVn)[["p.value"]] + varNemMN <- posthoc.kruskal.nemenyi.test(varVn, facFcVn, "Tukey")[["p.value"]] + c(pvaN, c(varNemMN)) + + })) + pvaVn <- nemMN[, 1] + fdrVn <- p.adjust(pvaVn, method = adjC) + sigVn <- as.numeric(fdrVn < thrN) + nemMN <- nemMN[, c(FALSE, nemVl)] + colnames(nemMN) <- paste0(nemNamVc, "_pva") + nemSigMN <- nemMN < thrN + mode(nemSigMN) <- "numeric" + colnames(nemSigMN) <- paste0(nemNamVc, "_sig") + nemMN[sigVn < 1, ] <- NA + nemSigMN[sigVn < 1, ] <- NA + + difMN <- sapply(1:nrow(nemClaMC), function(prwI) { + prwVc <- nemClaMC[prwI, ] + prwVi <- which(facFcVn %in% prwVc) + prwFacFc <- factor(as.character(facFcVn)[prwVi], levels = prwVc) + apply(datMN[prwVi, ], 2, function(varVn) -diff(as.numeric(tapply(varVn, prwFacFc, function(x) median(x, na.rm = TRUE))))) + }) + colnames(difMN) <- gsub("_sig", "_dif", colnames(nemSigMN)) + + nemMN <- cbind(fdrVn, sigVn, difMN, nemMN, nemSigMN) + colnames(nemMN)[1:2] <- paste0(varPfxC, c(adjC, "sig")) + + varDF <- cbind.data.frame(varDF, as.data.frame(nemMN)) + + } + + names(sigVn) <- rownames(varDF) + sigSumN <- sum(sigVn, na.rm = TRUE) + if(sigSumN) { + cat("\nThe following ", sigSumN, " variable", ifelse(sigSumN > 1, "s", ""), " (", round(sigSumN / length(sigVn) * 100), "%) ", ifelse(sigSumN > 1, "were", "was"), " found significant at the ", thrN, " level:\n", sep = "") + cat(paste(rownames(varDF)[sigVn > 0], collapse = "\n"), "\n", sep = "") + } else + cat("\nNo significant variable found at the selected ", thrN, " level\n", sep = "") + + options(stingsAsFactors = strAsFacL) + + return(varDF) + +} diff -r 000000000000 -r ef64d3752050 univariate_wrapper.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/univariate_wrapper.R Wed Jul 27 11:44:34 2016 -0400 @@ -0,0 +1,159 @@ +#!/usr/bin/env Rscript + +library(batch) ## parseCommandArgs + +source_local <- function(fname){ + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep="/")) +} + +source_local("univariate_script.R") + +argVc <- unlist(parseCommandArgs(evaluate=FALSE)) + + +#### Start_of_tested_code <- function() {} + + +##------------------------------ +## Initializing +##------------------------------ + +## options +##-------- + +strAsFacL <- options()$stringsAsFactors +options(stringsAsFactors = FALSE) + +## packages +##--------- + +library(PMCMR) + +## constants +##---------- + +modNamC <- "Univariate" ## module name + +topEnvC <- environment() +flagC <- "\n" + +## functions +##---------- + +flgF <- function(tesC, + envC = topEnvC, + txtC = NA) { ## management of warning and error messages + + tesL <- eval(parse(text = tesC), envir = envC) + + if(!tesL) { + + sink(NULL) + stpTxtC <- ifelse(is.na(txtC), + paste0(tesC, " is FALSE"), + txtC) + + stop(stpTxtC, + call. = FALSE) + + } + +} ## flgF + +## log file +##--------- + +sink(argVc["information"]) + +cat("\nStart of the '", modNamC, "' Galaxy module call: ", + format(Sys.time(), "%a %d %b %Y %X"), "\n", sep="") + +## loading +##-------- + +datMN <- t(as.matrix(read.table(argVc["dataMatrix_in"], + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t"))) + +samDF <- read.table(argVc["sampleMetadata_in"], + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t") + +varDF <- read.table(argVc["variableMetadata_in"], + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t") + +tesC <- argVc["tesC"] + +## checking +##--------- + +flgF("identical(rownames(datMN), rownames(samDF))", txtC = "Column names of the dataMatrix are not identical to the row names of the sampleMetadata; check your data with the 'Check Format' module in the 'Quality Control' section") +flgF("identical(colnames(datMN), rownames(varDF))", txtC = "Row names of the dataMatrix are not identical to the row names of the variableMetadata; check your data with the 'Check Format' module in the 'Quality Control' section") + +flgF("argVc['facC'] %in% colnames(samDF)", txtC = paste0("Required factor of interest '", argVc['facC'], "' could not be found in the column names of the sampleMetadata")) +flgF("mode(samDF[, argVc['facC']]) %in% c('character', 'numeric')", txtC = paste0("The '", argVc['facC'], "' column of the sampleMetadata should contain either number only, or character only")) + +flgF("!(tesC %in% c('ttest', 'wilcoxon')) || (mode(samDF[, argVc['facC']]) == 'character' && length(unique(samDF[, argVc['facC']])) == 2)", txtC = paste0("For 'ttest' and 'wilcoxon', the chosen factor column ('", argVc['facC'], "') of the sampleMetadata should contain characters with only two different classes")) +flgF("!(tesC %in% c('anova', 'kruskal')) || (mode(samDF[, argVc['facC']]) == 'character' && length(unique(samDF[, argVc['facC']])) > 2)", txtC = paste0("For 'anova' and 'kruskal', the chosen factor column ('", argVc['facC'], "') of the sampleMetadata should contain characters with at least three different classes")) +flgF("!(tesC %in% c('pearson', 'spearman')) || mode(samDF[, argVc['facC']]) == 'numeric'", txtC = paste0("For 'pearson' and 'spearman', the chosen factor column ('", argVc['facC'], "') of the sampleMetadata should contain numbers only")) + +flgF("argVc['adjC'] %in% c('holm', 'hochberg', 'hommel', 'bonferroni', 'BH', 'BY', 'fdr', 'none')") + +flgF("0 <= as.numeric(argVc['thrN']) && as.numeric(argVc['thrN']) <= 1", + txtC = "(corrected) p-value threshold must be between 0 and 1") + + +##------------------------------ +## Computation +##------------------------------ + + +varDF <- univariateF(datMN = datMN, + samDF = samDF, + varDF = varDF, + facC = argVc["facC"], + tesC = tesC, + adjC = argVc["adjC"], + thrN = as.numeric(argVc["thrN"])) + + +##------------------------------ +## Ending +##------------------------------ + + +## saving +##-------- + +varDF <- cbind.data.frame(variableMetadata = rownames(varDF), + varDF) +write.table(varDF, + file = argVc["variableMetadata_out"], + quote = FALSE, + row.names = FALSE, + sep = "\t") + +## closing +##-------- + +cat("\nEnd of '", modNamC, "' Galaxy module call: ", + as.character(Sys.time()), "\n", sep = "") + +sink() + +options(stringsAsFactors = strAsFacL) + + +#### End_of_tested_code <- function() {} + + +rm(list = ls())