Mercurial > repos > ethevenot > univariate
changeset 0:ab2ee3414e4e draft
planemo upload for repository https://github.com/workflow4metabolomics/univariate.git commit 98e8f4464b2f7321acb010e26e2a1c82fe37096e
| author | ethevenot |
|---|---|
| date | Tue, 24 Oct 2017 08:57:25 -0400 |
| parents | |
| children | c00848a8e302 |
| files | LICENSE.md README.md build.xml runit/example1/dataMatrix.tsv runit/example1/sampleMetadata.tsv runit/example1/variableMetadata.tsv runit/input/dataMatrix.tsv runit/input/sampleMetadata.tsv runit/input/variableMetadata.tsv runit/output/figure.pdf runit/output/information.txt runit/output/variableMetadata.tsv runit/univariate_runtests.R runit/univariate_tests.R static/images/univariate_workflowPositionImage.png test-data/dataMatrix.tsv test-data/output-variableMetadata.tsv test-data/sampleMetadata.tsv test-data/variableMetadata.tsv test/.gitignore test/test-univ univariate_config.xml univariate_script.R univariate_wrapper.R |
| diffstat | 24 files changed, 1881 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.md Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,87 @@ +Univariate parametric and non-parametric hypothesis testing with correction for multiple testing +================================================================================================ + +A Galaxy module from the [Workflow4metabolomics](http://workflow4metabolomics.org) infrastructure + +Status: [](https://travis-ci.org/workflow4metabolomics/univariate). + +### Description + +**Version:** 2.2.0 +**Date:** 2016-10-30 +**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 + + ```r + install.packages("batch", dep=TRUE) + ``` + + **PMCMR** from CRAN + + ```r + install.packages("PMCMR", dep=TRUE) + ``` + +### Tests + +The code in the wrapper can be tested by running the `runit/univariate_runtests.R` R file + +You will need to install **RUnit** package in order to make it run: +```r +install.packages('RUnit', dependencies = TRUE) +``` + +### Working example + +See the **W4M00001a_sacurine-subset-statistics**, **W4M00001b_sacurine-subset-complete**, **W4M00002_mtbls2**, **W4M00003_diaplasma** shared histories in the **Shared Data/Published Histories** menu (https://galaxy.workflow4metabolomics.org/history/list_published) + +### News + +###### CHANGES IN VERSION 2.2.0 + +MAJOR MODIFICATION + + * ANOVA and Kruskal-Wallis: The p-values of the post-hoc tests (i.e. from pairwise comparisons) are now further corrected for multiple testing over all variables (previously, only the p-value of the -first- omnibus test was corrected over all variables) + +MINOR MODIFICATION + + * All values in the 'dif', adjusted p-value, and 'sig' columns are now displayed (previously, the values were set to NA when the p-value of the omnibus test was not significant) + +NEW FEATURE + + * Graphic: a single pdf file containing the graphics of all significant tests is now produced as '_figure.pdf' output: boxplots (respectively scatterplots with the regression line in red and the R2 value) are displayed when the factor of interest is qualitative (respectively quantitative). The corrected p-value is indicated in the title of each plot + +###### CHANGES IN VERSION 2.1.4 + +NEW FEATURE + + * Level names are now separated by '.' instead of '-' previously in the column names of the output variableMetadata table (e.g., 'jour_ttest_J3.J10_fdr' instead of 'jour_ttest_J3-J10_fdr' previously) + +INTERNAL MODIFICATION + + * Minor internal changes + +###### CHANGES IN VERSION 2.1.2 + +INTERNAL MODIFICATION + + * Minor internal changes in .shed.yml for toolshed export + +###### CHANGES IN VERSION 2.1.1 + +INTERNAL MODIFICATION + + * Internal handling of 'NA' p-values (e.g. when intensities are identical in all samples).
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/build.xml Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,103 @@ +<project name="w4m.univariate" default="all"> + + <property name="tool.xml" value="univariate_config.xml"/> + <property name="conda.dir" value="${user.home}/w4m-conda"/> + + <!--~~~ + ~ ALL ~ + ~~~~~--> + + <target name="all"/> + + <!--~~~~ + ~ TEST ~ + ~~~~~--> + + <target name="test" depends="plain.test,planemo.lint,planemo.test"/> + + <!--~~~~~~~~~~ + ~ PLAIN TEST ~ + ~~~~~~~~~~~--> + + <target name="plain.test"> + <exec executable="test/test-univ" failonerror="true"/> + </target> + + <!--~~~~~~~~~~~~ + ~ PLANEMO LINT ~ + ~~~~~~~~~~~~~--> + + <target name="planemo.lint"> + <exec executable="planemo" failonerror="true"> + <arg value="lint"/> + <arg value="${tool.xml}"/> + </exec> + </target> + + <!--~~~~~~~~~~~~ + ~ PLANEMO TEST ~ + ~~~~~~~~~~~~~--> + + <target name="planemo.test" depends="planemo.conda.install"> + <exec executable="planemo" failonerror="true"> + <arg value="test"/> + <arg value="--conda_prefix"/> + <arg value="${conda.dir}"/> + <arg value="--galaxy_branch"/> + <arg value="release_16.07"/> + <arg value="--conda_dependency_resolution"/> + <arg value="${tool.xml}"/> + </exec> + </target> + + <!--~~~~~~~~~~~~~~~~~~~~~ + ~ PLANEMO CONDA INSTALL ~ + ~~~~~~~~~~~~~~~~~~~~~~--> + + <target name="planemo.conda.install" depends="planemo.conda.init"> + <exec executable="planemo" failonerror="true"> + <arg value="conda_install"/> + <arg value="--conda_prefix"/> + <arg value="${conda.dir}"/> + <arg value="${tool.xml}"/> + </exec> + </target> + + <!--~~~~~~~~~~~~~~~~~~ + ~ PLANEMO CONDA INIT ~ + ~~~~~~~~~~~~~~~~~~~--> + + <target name="planemo.conda.init"> + <exec executable="planemo" failonerror="true"> + <arg value="conda_init"/> + <arg value="--conda_prefix"/> + <arg value="${conda.dir}"/> + </exec> + </target> + + <!--~~~~~~~~~~~~~ + ~ PLANEMO SERVE ~ + ~~~~~~~~~~~~~~--> + + <target name="planemo.serve" depends="planemo.conda.install"> + <exec executable="planemo" failonerror="true"> + <arg value="serve"/> + <arg value="--conda_prefix"/> + <arg value="${conda.dir}"/> + <arg value="--galaxy_branch"/> + <arg value="release_16.01"/> + <arg value="--conda_dependency_resolution"/> + <arg value="${tool.xml}"/> + </exec> + </target> + + + <!--~~~~~ + ~ CLEAN ~ + ~~~~~~--> + + <target name="clean"> + <delete dir="${conda.dir}"/> + </target> + +</project>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runit/example1/dataMatrix.tsv Tue Oct 24 08:57:25 2017 -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
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runit/example1/sampleMetadata.tsv Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,7 @@ +sampleMetadata jour +Ech10 J10 +Ech11 J10 +Ech12 J10 +Ech13 J3 +Ech14 J3 +Ech15 J3
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runit/example1/variableMetadata.tsv Tue Oct 24 08:57:25 2017 -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
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runit/input/dataMatrix.tsv Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,7 @@ +dataMatrix s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 +v1 7.416640524 6.9916690517 7.3404441347 7.0128372669 7.1702617447 6.820858055 7.2648178466 6.9561684785 7.2966652122 7.2695129676 7.2695129676 6.7986507145 6.9749720403 6.7788745443 7.0791812822 7.3617278549 7.4281348102 6.967548023 7.2304489469 6.6608655728 6.9614211415 6.8959747875 7.1398791179 7.2201081142 7.1673173643 6.6857418281 7.1789769761 6.7788745443 6.7291648707 6.7466342768 6.8954226013 6.9454686344 +v2 7.4563660483 6.9014583759 7.3873898441 6.9599948859 7.2095150414 6.7007038037 7.3159703664 6.8876173566 7.3010300174 7.2966652122 7.3138672415 6.7275413384 6.9863238219 6.7435098431 7.1038037552 7.3838153839 7.4409090978 6.7737865181 7.2174839705 6.48995862 6.8494194752 6.8344207673 7.1238516736 7.212187631 7.2041200098 6.6910815806 7.2900346336 6.7427252098 6.7185017719 6.6180482014 6.8512584099 6.9329808726 +v3 6.8785218529 6.7839036507 6.9025468337 7.0086002143 6.9314579215 6.8129134235 6.8305887328 7.0374265378 6.8767950339 6.8457180799 6.7888751864 6.9929951426 7.0374265378 6.8215135939 7.0718820441 6.809559782 6.8122447637 6.7795965634 6.8129134235 6.9599948859 6.8014037786 6.7355989795 6.8075350957 6.9929951426 6.7686381752 6.9339932144 6.7611758884 6.9740509489 6.9768083831 6.7058637978 6.9790929462 6.7032914641 +v4 7.1303338007 7.1398791179 7.0899051467 7.110589744 6.9947569885 6.9978231244 7.0374265378 7.1003705796 6.9698816903 6.8048207468 6.8543061025 6.6655810848 6.705008045 6.7275413384 6.7442930614 6.9498777528 7.1583625223 7.2013971516 7.1673173643 6.9169800999 7.1003705796 7.0453230179 7.0606978781 7.1003705796 7.0492180614 6.7693773999 6.7774268949 6.9211661027 6.8561245047 6.9656720182 7.0086002143 6.9758911823 +v5 7.0530784819 6.99122612 7.0569048894 6.9698816903 6.8603380665 6.8375885014 6.9633155586 7.0170333811 7.0681858989 6.9633155586 6.95375974 6.7481881046 6.7686381752 6.8175654357 6.8394781102 7.0492180614 7.1038037552 7.0681858989 7.0755469979 6.7259117139 6.9339932144 6.8948697121 6.9464523141 7.0043214168 6.9009131223 6.6875290504 6.7634280684 6.7209858267 6.6454223676 6.7923917595 6.7902852344 6.8312297579 +v6 6.9876663096 7.0969100478 7.1072100036 7.0569048894 7.0000000434 6.9973864281 7.0644580267 7.0863598663 7.0043214168 6.9772662582 6.9434945654 6.9680157607 6.9556877984 7.1553360678 6.9454686344 6.9309490821 7.0086002143 7.0334237957 7.0086002143 6.9206450535 7.0043214168 6.9749720403 6.9489018098 6.9400182049 6.8750613213 6.9434945654 6.9057959343 6.9614211415 6.9518230838 6.9415114823 6.9304396457 6.9304396457
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runit/input/sampleMetadata.tsv Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,33 @@ +sampleMetadata quant qual +s1 56 A +s2 24 A +s3 35 A +s4 32 A +s5 54 A +s6 53 A +s7 30 A +s8 20 A +s9 41 B +s10 34 B +s11 44 B +s12 43 B +s13 52 B +s14 46 B +s15 51 B +s16 36 B +s17 31 C +s18 25 C +s19 40 C +s20 50 C +s21 22 C +s22 29 C +s23 49 C +s24 19 C +s25 55 D +s26 39 D +s27 45 D +s28 26 D +s29 23 D +s30 42 D +s31 33 D +s32 21 D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runit/input/variableMetadata.tsv Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,7 @@ +variableMetadata sample_mean +v1 12620948.56 +v2 12661823.42 +v3 7680621.526 +v4 9901182.687 +v5 8311866.237 +v6 9792909.819
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runit/output/information.txt Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,49 @@ + +Start of the 'Univariate' Galaxy module call: Sat 03 Jun 2017 11:00:34 PM + +Performing 'kruskal' + +The following 3 variables (50%) were found significant at the 0.05 level: +v4 +v5 +v6 + +End of 'Univariate' Galaxy module call: 2017-06-03 23:00:34 + + + +============================================================================ +Additional information about the call: + +1) Parameters: + value +dataMatrix_in "./input/dataMatrix.tsv" +sampleMetadata_in "./input/sampleMetadata.tsv" +variableMetadata_in "./input/variableMetadata.tsv" +variableMetadata_out "./output/variableMetadata.tsv" +figure "./output/figure.pdf" +information "./output/information.txt" +facC "qual" +tesC "kruskal" +adjC "fdr" +thrN "0.05" + +2) Session Info: +R version 3.3.1 (2016-06-21) +Platform: x86_64-pc-linux-gnu (64-bit) +Running under: Ubuntu 16.04.1 LTS + +locale: + [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C + [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 + [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 + [7] LC_PAPER=en_US.UTF-8 LC_NAME=C + [9] LC_ADDRESS=C LC_TELEPHONE=C +[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C + +attached base packages: +[1] stats graphics grDevices utils datasets base + +other attached packages: +[1] PMCMR_4.1 batch_1.1-4 +============================================================================
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runit/output/variableMetadata.tsv Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,7 @@ +variableMetadata sample_mean qual_kruskal_fdr qual_kruskal_sig qual_kruskal_B.A_dif qual_kruskal_C.A_dif qual_kruskal_D.A_dif qual_kruskal_C.B_dif qual_kruskal_D.B_dif qual_kruskal_D.C_dif qual_kruskal_B.A_fdr qual_kruskal_C.A_fdr qual_kruskal_D.A_fdr qual_kruskal_C.B_fdr qual_kruskal_D.B_fdr qual_kruskal_D.C_fdr qual_kruskal_B.A_sig qual_kruskal_C.A_sig qual_kruskal_D.A_sig qual_kruskal_C.B_sig qual_kruskal_D.B_sig qual_kruskal_D.C_sig +v1 12620948.56 0.147579228303049 0 0.0827976191000008 -0.0378359353499995 -0.254400932999999 -0.12063355445 -0.3371985521 -0.21656499765 0.999945671392641 0.99999320349658 0.185342280966466 0.997174540126794 0.401201067449129 0.580170916884995 0 0 0 0 0 0 +v2 12661823.42 0.246364577097618 0 0.11547952005 -0.0981193892499999 -0.2877631538 -0.2135989093 -0.40324267385 -0.18964376455 0.999945671392641 0.99999320349658 0.291618067987092 0.997174540126794 0.401201067449129 0.890397531402986 0 0 0 0 0 0 +v3 7680621.526 0.293234285978368 0 -0.0292777863999998 -0.0806444136 -0.0392186484999995 -0.0513666272000002 -0.00994086209999967 0.0414257651000005 0.999945671392641 0.99999320349658 0.593281055056761 0.997174540126794 0.593281055056761 0.999155846185138 0 0 0 0 0 0 +v4 9901182.687 0.000819466212247517 1 -0.32058095905 0.00523271645000012 -0.1517188027 0.3258136755 0.16886215635 -0.15695151915 0.0133536911280914 0.99999320349658 0.185342280966466 0.00430289478770107 0.593281055056761 0.143731083414931 1 0 0 1 0 0 +v5 8311866.237 0.0115079177714078 1 -0.0839349800499996 -0.00516703969999988 -0.203697253750001 0.0787679403499997 -0.119762273700001 -0.198530214050001 0.999945671392641 0.99999320349658 0.0336157236420893 0.997174540126794 0.401201067449129 0.073221827596323 0 0 1 0 0 0 +v6 9792909.819 0.00429379409695042 1 -0.0988296785000005 -0.0710347295000009 -0.124705894050001 0.0277949489999996 -0.0258762155500003 -0.0536711645499999 0.535700151485478 0.99999320349658 0.00294595180681068 0.997174540126794 0.401201067449129 0.302648332248575 0 0 1 0 0 0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runit/univariate_runtests.R Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,103 @@ +#!/usr/bin/env Rscript + +## Package +##-------- + +library(RUnit) + +## Constants +##---------- + +testOutDirC <- "output" +argVc <- commandArgs(trailingOnly = FALSE) +scriptPathC <- sub("--file=", "", argVc[grep("--file=", argVc)]) + + +## Functions +##----------- + +## Reading tables (matrix or data frame) +readTableF <- function(fileC, typeC = c("matrix", "dataframe")[1]) { + + file.exists(fileC) || stop(paste0("No output file \"", fileC ,"\".")) + + switch(typeC, + matrix = return(t(as.matrix(read.table(file = fileC, + header = TRUE, + row.names = 1, + sep = "\t", + stringsAsFactors = FALSE)))), + dataframe = return(read.table(file = fileC, + header = TRUE, + row.names = 1, + sep = "\t", + stringsAsFactors = FALSE))) + +} + +## Call wrapper +wrapperCallF <- function(paramLs) { + + ## Set program path + wrapperPathC <- file.path(dirname(scriptPathC), "..", "univariate_wrapper.R") + + ## Set arguments + argLs <- NULL + for (parC in names(paramLs)) + argLs <- c(argLs, parC, paramLs[[parC]]) + + ## Call + wrapperCallC <- paste(c(wrapperPathC, argLs), collapse = " ") + + if(.Platform$OS.type == "windows") + wrapperCallC <- paste("Rscript", wrapperCallC) + + wrapperCodeN <- system(wrapperCallC) + + if (wrapperCodeN != 0) + stop("Error when running univariate_wrapper.R.") + + ## Get output + outLs <- list() + if ("dataMatrix_out" %in% names(paramLs)) + outLs[["datMN"]] <- readTableF(paramLs[["dataMatrix_out"]], "matrix") + if ("sampleMetadata_out" %in% names(paramLs)) + outLs[["samDF"]] <- readTableF(paramLs[["sampleMetadata_out"]], "dataframe") + if ("variableMetadata_out" %in% names(paramLs)) + outLs[["varDF"]] <- readTableF(paramLs[["variableMetadata_out"]], "dataframe") + if("information" %in% names(paramLs)) + outLs[["infVc"]] <- readLines(paramLs[["information"]]) + + return(outLs) +} + +## Setting default parameters +defaultArgF <- function(testInDirC) { + + defaultArgLs <- list() + if(file.exists(file.path(dirname(scriptPathC), testInDirC, "dataMatrix.tsv"))) + defaultArgLs[["dataMatrix_in"]] <- file.path(dirname(scriptPathC), testInDirC, "dataMatrix.tsv") + if(file.exists(file.path(dirname(scriptPathC), testInDirC, "sampleMetadata.tsv"))) + defaultArgLs[["sampleMetadata_in"]] <- file.path(dirname(scriptPathC), testInDirC, "sampleMetadata.tsv") + if(file.exists(file.path(dirname(scriptPathC), testInDirC, "variableMetadata.tsv"))) + defaultArgLs[["variableMetadata_in"]] <- file.path(dirname(scriptPathC), testInDirC, "variableMetadata.tsv") + + defaultArgLs[["variableMetadata_out"]] <- file.path(dirname(scriptPathC), testOutDirC, "variableMetadata.tsv") + defaultArgLs[["figure"]] <- file.path(dirname(scriptPathC), testOutDirC, "figure.pdf") + defaultArgLs[["information"]] <- file.path(dirname(scriptPathC), testOutDirC, "information.txt") + + defaultArgLs + +} + +## Main +##----- + +## Create output folder +file.exists(testOutDirC) || dir.create(testOutDirC) + +## Run tests +test.suite <- defineTestSuite('tests', dirname(scriptPathC), testFileRegexp = paste0('^.*_tests\\.R$'), testFuncRegexp = '^.*$') +isValidTestSuite(test.suite) +test.results <- runTestSuite(test.suite) +print(test.results)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runit/univariate_tests.R Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,63 @@ +test_input_anova <- function() { + + testDirC <- "input" + argLs <- list(facC = "qual", + tesC = "anova", + adjC = "fdr", + thrN = "0.05") + + argLs <- c(defaultArgF(testDirC), argLs) + outLs <- wrapperCallF(argLs) + + checkEqualsNumeric(outLs[["varDF"]]["v6", "qual_anova_fdr"], 1.924156e-03, tolerance = 1e-6) + + checkEqualsNumeric(outLs[["varDF"]]["v4", "qual_anova_D.C_fdr"], 0.01102016, tolerance = 1e-6) + +} + +test_input_kruskal <- function() { + + testDirC <- "input" + argLs <- list(facC = "qual", + tesC = "kruskal", + adjC = "fdr", + thrN = "0.05") + + argLs <- c(defaultArgF(testDirC), argLs) + outLs <- wrapperCallF(argLs) + + checkEqualsNumeric(outLs[["varDF"]]["v4", "qual_kruskal_fdr"], 0.0008194662, tolerance = 1e-7) + + checkEqualsNumeric(outLs[["varDF"]]["v6", "qual_kruskal_D.A_fdr"], 0.002945952, tolerance = 1e-7) + +} + +test_example1_wilcoxDif <- function() { + + testDirC <- "example1" + argLs <- list(facC = "jour", + tesC = "wilcoxon", + adjC = "fdr", + thrN = "0.05") + + argLs <- c(defaultArgF(testDirC), argLs) + outLs <- wrapperCallF(argLs) + + checkEqualsNumeric(outLs[["varDF"]]["MT3", "jour_wilcoxon_J3.J10_dif"], 0.216480042, tolerance = 1e-8) + +} + +test_example1_ttestFdr <- function() { + + testDirC <- "example1" + argLs <- list(facC = "jour", + tesC = "ttest", + adjC = "fdr", + thrN = "0.05") + + argLs <- c(defaultArgF(testDirC), argLs) + outLs <- wrapperCallF(argLs) + + checkEqualsNumeric(outLs[["varDF"]]["MT3", "jour_ttest_J3.J10_fdr"], 0.7605966, tolerance = 1e-6) + +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/dataMatrix.tsv Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,7 @@ +dataMatrix s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 +v1 7.416640524 6.9916690517 7.3404441347 7.0128372669 7.1702617447 6.820858055 7.2648178466 6.9561684785 7.2966652122 7.2695129676 7.2695129676 6.7986507145 6.9749720403 6.7788745443 7.0791812822 7.3617278549 7.4281348102 6.967548023 7.2304489469 6.6608655728 6.9614211415 6.8959747875 7.1398791179 7.2201081142 7.1673173643 6.6857418281 7.1789769761 6.7788745443 6.7291648707 6.7466342768 6.8954226013 6.9454686344 +v2 7.4563660483 6.9014583759 7.3873898441 6.9599948859 7.2095150414 6.7007038037 7.3159703664 6.8876173566 7.3010300174 7.2966652122 7.3138672415 6.7275413384 6.9863238219 6.7435098431 7.1038037552 7.3838153839 7.4409090978 6.7737865181 7.2174839705 6.48995862 6.8494194752 6.8344207673 7.1238516736 7.212187631 7.2041200098 6.6910815806 7.2900346336 6.7427252098 6.7185017719 6.6180482014 6.8512584099 6.9329808726 +v3 6.8785218529 6.7839036507 6.9025468337 7.0086002143 6.9314579215 6.8129134235 6.8305887328 7.0374265378 6.8767950339 6.8457180799 6.7888751864 6.9929951426 7.0374265378 6.8215135939 7.0718820441 6.809559782 6.8122447637 6.7795965634 6.8129134235 6.9599948859 6.8014037786 6.7355989795 6.8075350957 6.9929951426 6.7686381752 6.9339932144 6.7611758884 6.9740509489 6.9768083831 6.7058637978 6.9790929462 6.7032914641 +v4 7.1303338007 7.1398791179 7.0899051467 7.110589744 6.9947569885 6.9978231244 7.0374265378 7.1003705796 6.9698816903 6.8048207468 6.8543061025 6.6655810848 6.705008045 6.7275413384 6.7442930614 6.9498777528 7.1583625223 7.2013971516 7.1673173643 6.9169800999 7.1003705796 7.0453230179 7.0606978781 7.1003705796 7.0492180614 6.7693773999 6.7774268949 6.9211661027 6.8561245047 6.9656720182 7.0086002143 6.9758911823 +v5 7.0530784819 6.99122612 7.0569048894 6.9698816903 6.8603380665 6.8375885014 6.9633155586 7.0170333811 7.0681858989 6.9633155586 6.95375974 6.7481881046 6.7686381752 6.8175654357 6.8394781102 7.0492180614 7.1038037552 7.0681858989 7.0755469979 6.7259117139 6.9339932144 6.8948697121 6.9464523141 7.0043214168 6.9009131223 6.6875290504 6.7634280684 6.7209858267 6.6454223676 6.7923917595 6.7902852344 6.8312297579 +v6 6.9876663096 7.0969100478 7.1072100036 7.0569048894 7.0000000434 6.9973864281 7.0644580267 7.0863598663 7.0043214168 6.9772662582 6.9434945654 6.9680157607 6.9556877984 7.1553360678 6.9454686344 6.9309490821 7.0086002143 7.0334237957 7.0086002143 6.9206450535 7.0043214168 6.9749720403 6.9489018098 6.9400182049 6.8750613213 6.9434945654 6.9057959343 6.9614211415 6.9518230838 6.9415114823 6.9304396457 6.9304396457
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output-variableMetadata.tsv Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,7 @@ +variableMetadata sample_mean qual_kruskal_fdr qual_kruskal_sig qual_kruskal_B.A_dif qual_kruskal_C.A_dif qual_kruskal_D.A_dif qual_kruskal_C.B_dif qual_kruskal_D.B_dif qual_kruskal_D.C_dif qual_kruskal_B.A_fdr qual_kruskal_C.A_fdr qual_kruskal_D.A_fdr qual_kruskal_C.B_fdr qual_kruskal_D.B_fdr qual_kruskal_D.C_fdr qual_kruskal_B.A_sig qual_kruskal_C.A_sig qual_kruskal_D.A_sig qual_kruskal_C.B_sig qual_kruskal_D.B_sig qual_kruskal_D.C_sig +v1 12620948.56 0.147579228303049 0 0.0827976191000008 -0.0378359353499995 -0.254400932999999 -0.12063355445 -0.3371985521 -0.21656499765 0.999945671392641 0.99999320349658 0.185342280966466 0.997174540126794 0.401201067449129 0.580170916884995 0 0 0 0 0 0 +v2 12661823.42 0.246364577097618 0 0.11547952005 -0.0981193892499999 -0.2877631538 -0.2135989093 -0.40324267385 -0.18964376455 0.999945671392641 0.99999320349658 0.291618067987092 0.997174540126794 0.401201067449129 0.890397531402986 0 0 0 0 0 0 +v3 7680621.526 0.293234285978368 0 -0.0292777863999998 -0.0806444136 -0.0392186484999995 -0.0513666272000002 -0.00994086209999967 0.0414257651000005 0.999945671392641 0.99999320349658 0.593281055056761 0.997174540126794 0.593281055056761 0.999155846185138 0 0 0 0 0 0 +v4 9901182.687 0.000819466212247517 1 -0.32058095905 0.00523271645000012 -0.1517188027 0.3258136755 0.16886215635 -0.15695151915 0.0133536911280914 0.99999320349658 0.185342280966466 0.00430289478770107 0.593281055056761 0.143731083414931 1 0 0 1 0 0 +v5 8311866.237 0.0115079177714078 1 -0.0839349800499996 -0.00516703969999988 -0.203697253750001 0.0787679403499997 -0.119762273700001 -0.198530214050001 0.999945671392641 0.99999320349658 0.0336157236420893 0.997174540126794 0.401201067449129 0.073221827596323 0 0 1 0 0 0 +v6 9792909.819 0.00429379409695042 1 -0.0988296785000005 -0.0710347295000009 -0.124705894050001 0.0277949489999996 -0.0258762155500003 -0.0536711645499999 0.535700151485478 0.99999320349658 0.00294595180681068 0.997174540126794 0.401201067449129 0.302648332248575 0 0 1 0 0 0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/sampleMetadata.tsv Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,33 @@ +sampleMetadata quant qual +s1 56 A +s2 24 A +s3 35 A +s4 32 A +s5 54 A +s6 53 A +s7 30 A +s8 20 A +s9 41 B +s10 34 B +s11 44 B +s12 43 B +s13 52 B +s14 46 B +s15 51 B +s16 36 B +s17 31 C +s18 25 C +s19 40 C +s20 50 C +s21 22 C +s22 29 C +s23 49 C +s24 19 C +s25 55 D +s26 39 D +s27 45 D +s28 26 D +s29 23 D +s30 42 D +s31 33 D +s32 21 D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/variableMetadata.tsv Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,7 @@ +variableMetadata sample_mean +v1 12620948.56 +v2 12661823.42 +v3 7680621.526 +v4 9901182.687 +v5 8311866.237 +v6 9792909.819
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test/.gitignore Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,1 @@ +*.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test/test-univ Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,16 @@ +#!/bin/bash + +# Constants {{{1 +################################################################ + +PROG_PATH=$(dirname $0) + +# MAIN {{{1 +################################################################ + +$PROG_PATH/../univariate_wrapper.R dataMatrix_in $PROG_PATH/../test-data/dataMatrix.tsv sampleMetadata_in $PROG_PATH/../test-data/sampleMetadata.tsv variableMetadata_in $PROG_PATH/../test-data/variableMetadata.tsv facC qual tesC kruskal adjC fdr thrN 0.05 variableMetadata_out $PROG_PATH/outputVariableMetadata.tsv + +if ! diff "$PROG_PATH/outputVariableMetadata.tsv" "$PROG_PATH/../test-data/output-variableMetadata.tsv" ; then + echo "Incorrect output variable metadata." >&2 + exit 1 +fi
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/univariate_config.xml Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,297 @@ +<tool id="Univariate" name="Univariate" version="2.2.3"> + <description>Univariate statistics</description> + + <requirements> + <requirement type="package">r-batch</requirement> + <requirement type="package">r-PMCMR</requirement> + </requirements> + + <stdio> + <exit_code range="1:" level="fatal" /> + </stdio> + + <command><![CDATA[ + Rscript $__tool_directory__/univariate_wrapper.R + + dataMatrix_in "$dataMatrix_in" + sampleMetadata_in "$sampleMetadata_in" + variableMetadata_in "$variableMetadata_in" + + facC "$facC" + tesC "$tesC" + adjC "$adjC" + thrN "$thrN" + + variableMetadata_out "$variableMetadata_out" + figure "$figure" + information "$information" + ]]></command> + <inputs> + <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="variable x sample, decimal: '.', missing: NA, mode: numerical, sep: tabular" /> + <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="sample x metadata, decimal: '.', missing: NA, mode: character and numerical, sep: tabular" /> + <param name="variableMetadata_in" label="Variable metadata file" type="data" format="tabular" help="variable x metadata, decimal: '.', missing: NA, mode: character and numerical, sep: tabular" /> + <param name="facC" label="Factor of interest" type="text" help="Name of the column of the sample metadata table corresponding to the qualitative or quantitative variable"/> + <param name="tesC" label="Test" type="select" help=""> + <option value="ttest">ttest (qualitative, 2 levels)</option> + <option value="wilcoxon">Wilcoxon test (qualitative, 2 levels)</option> + <option value="anova">Analysis of variance (qualitative, more than 2 levels)</option> + <option value="kruskal">Kruskal-Wallis rank test (qualitative, more than 2 levels)</option> + <option value="pearson">Pearson correlation test (quantitative)</option> + <option value="spearman">Spearman correlation rank test (quantitative)</option> + </param> + <param name="adjC" label="Method for multiple testing correction" type="select" help=""> + <option value="fdr">fdr</option> + <option value="BH">BH</option> + <option value="bonferroni">bonferroni</option> + <option value="BY">BY</option> + <option value="hochberg">hochberg</option> + <option value="holm">holm</option> + <option value="hommel">hommel</option> + <option value="none">none</option> + </param> + <param name="thrN" type="float" value="0.05" label="(Corrected) p-value significance threshold" help="Must be between 0 and 1"/> + </inputs> + + <outputs> + <data name="variableMetadata_out" label="${tool.name}_${variableMetadata_in.name}" format="tabular" ></data> + <data name="figure" label="${tool.name}_figure.pdf" format="pdf"/> + <data name="information" label="${tool.name}_information.txt" format="txt"/> + </outputs> + + <tests> + <test> + <param name="dataMatrix_in" value="dataMatrix.tsv"/> + <param name="sampleMetadata_in" value="sampleMetadata.tsv"/> + <param name="variableMetadata_in" value="variableMetadata.tsv"/> + <param name="facC" value="qual"/> + <param name="tesC" value="kruskal"/> + <param name="adjC" value="fdr"/> + <param name="thrN" value="0.05"/> + <output name="variableMetadata_out" file="output-variableMetadata.tsv"/> + </test> + </tests> + + <help> + +.. 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 then 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); note that since version 2.2.0, the p-values of the post-hoc pairwise comparisons (ANOVA or Kruskal) are further corrected for multiple testing over all variables (as the p-value of the omnibus test in column 2) + | + +figure.pdf + | File containing the graphics of all significant tests: boxplots (respectively scatterplots with the regression line in red and the R2 value) are displayed when the factor of interest is qualitative (respectively quantitative). The variable name and the corrected p-value is indicated in the title of each plot + | + +information.txt + | File with all messages and warnings generated during the computation + | + +--------------------------------------------------- + +--------------- +Working example +--------------- + +.. class:: infomark + +See the **W4M00001a_sacurine-subset-statistics**, **W4M00001b_sacurine-subset-complete**, **W4M00002_mtbls2**, **W4M00003_diaplasma** shared histories in the **Shared Data/Published Histories** menu (https://galaxy.workflow4metabolomics.org/history/list_published) + +--------------------------------------------------- + +---- +NEWS +---- + +CHANGES IN VERSION 2.2.0 +======================== + +MAJOR MODIFICATION + +ANOVA and Kruskal-Wallis: The p-values of the post-hoc tests (i.e. from pairwise comparisons) are now further corrected for multiple testing over all variables (previously, only the p-value of the omnibus test was corrected over all variables) + +MINOR MODIFICATION + +All values in the 'dif', adjusted p-value, and 'sig' columns are now displayed (previously, the values were set to NA when the p-value of the omnibus test was not significant) + +NEW FEATURE + +Graphic: a single pdf file containing the graphics of all significant tests is now produced as '_figure.pdf' output: boxplots (respectively scatterplots with the regression line in red and the R2 value) are displayed when the factor of interest is qualitative (respectively quantitative). The corrected p-value is indicated in the title of each plot + + +CHANGES IN VERSION 2.1.4 +======================== + +NEW FEATURE + +Level names are now separated by '.' instead of '-' previously in the column names of the output variableMetadata table (e.g., 'jour_ttest_J3.J10_fdr' instead of 'jour_ttest_J3-J10_fdr' previously) + +INTERNAL MODIFICATIONS + +Minor internal changes for toolshed export + +CHANGES IN VERSION 2.1.2 +======================== + +INTERNAL MODIFICATIONS + +Minor internal changes for toolshed export + +CHANGES IN VERSION 2.1.1 +======================== + +INTERNAL MODIFICATIONS + +Internal handling of 'NA' p-values (e.g. when intensities are identical in all samples) + +CHANGES IN VERSION 2.0.1 +======================== + +NEW FEATURE + +(corrected) p-value threshold can be set to any value between 0 and 1 + + </help> + + <citations> + <citation type="bibtex">@Manual{, + title = {R: A Language and Environment for Statistical Computing}, + author = {{R Core Team}}, + organization = {R Foundation for Statistical Computing}, + address = {Vienna, Austria}, + year = {2016}, + url = {https://www.R-project.org/}, + }</citation> + <citation type="bibtex">@Article{Thevenot2015, + Title = {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}, + Author = {Thevenot, Etienne A. and Roux, Aurelie and Xu, Ying and Ezan, Eric and Junot, Christophe}, + Journal = {Journal of Proteome Research}, + Year = {2015}, + Note = {PMID: 26088811}, + Number = {8}, + Pages = {3322-3335}, + Volume = {14}, + + Doi = {10.1021/acs.jproteome.5b00354}, + Url = {http://pubs.acs.org/doi/full/10.1021/acs.jproteome.5b00354} + }</citation> + <citation type="doi">10.1093/bioinformatics/btu813</citation> + </citations> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/univariate_script.R Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,332 @@ +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, + pdfC) { + + + ## Option + + strAsFacL <- options()$stringsAsFactors + options(stingsAsFactors = FALSE) + options(warn = -1) + + ## Getting the response (either a factor or a numeric) + + 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) + + adjVn <- p.adjust(apply(datMN, + 2, + tesF), + method = adjC) + + sigVn <- as.numeric(adjVn < 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)] <- adjVn + + varDF[, paste0(varPfxC, "sig")] <- sigVn + + ## graphic + + pdf(pdfC, onefile = TRUE) + + varVi <- which(sigVn > 0) + + if(tesC %in% c("ttest", "wilcoxon")) { + + facVc <- as.character(facFcVn) + names(facVc) <- rownames(samDF) + + for(varI in varVi) { + + varC <- rownames(varDF)[varI] + + boxF(facFcVn, + datMN[, varI], + paste0(varC, " (", adjC, " = ", signif(adjVn[varI], 2), ")"), + facVc) + + } + + } else { ## pearson or spearman + + for(varI in varVi) { + + varC <- rownames(varDF)[varI] + + mod <- lm(datMN[, varI] ~ facFcVn) + + plot(facFcVn, datMN[, varI], + xlab = facC, + ylab = "", + pch = 18, + main = paste0(varC, " (", adjC, " = ", signif(adjVn[varI], 2), ", R2 = ", signif(summary(mod)$r.squared, 2), ")")) + + abline(mod, col = "red") + + } + + } + + dev.off() + + + } else if(tesC == "anova") { + + + ## getting the names of the pairwise comparisons 'class1Vclass2' + prwVc <- rownames(TukeyHSD(aov(datMN[, 1] ~ facFcVn))[["facFcVn"]]) + + prwVc <- gsub("-", ".", prwVc, fixed = TRUE) ## 2016-08-05: '-' character in dataframe column names seems not to be converted to "." by write.table on ubuntu R-3.3.1 + + ## omnibus and post-hoc tests + + 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")])) + + })) + + difVi <- 1:length(prwVc) + 1 + + ## difference of the means for each pairwise comparison + + difMN <- aovMN[, difVi] + colnames(difMN) <- paste0(varPfxC, prwVc, "_dif") + + ## correction for multiple testing + + aovMN <- aovMN[, -difVi, drop = FALSE] + aovMN <- apply(aovMN, 2, function(pvaVn) p.adjust(pvaVn, method = adjC)) + + ## significance coding (0 = not significant, 1 = significant) + + adjVn <- aovMN[, 1] + sigVn <- as.numeric(adjVn < thrN) + + aovMN <- aovMN[, -1, drop = FALSE] + colnames(aovMN) <- paste0(varPfxC, prwVc, "_", adjC) + + aovSigMN <- aovMN < thrN + mode(aovSigMN) <- "numeric" + colnames(aovSigMN) <- paste0(varPfxC, prwVc, "_sig") + + ## final aggregated table + + resMN <- cbind(adjVn, sigVn, difMN, aovMN, aovSigMN) + colnames(resMN)[1:2] <- paste0(varPfxC, c(adjC, "sig")) + + varDF <- cbind.data.frame(varDF, as.data.frame(resMN)) + + ## graphic + + pdf(pdfC, onefile = TRUE) + + for(varI in 1:nrow(varDF)) { + + if(sum(aovSigMN[varI, ]) > 0) { + + varC <- rownames(varDF)[varI] + + boxplot(datMN[, varI] ~ facFcVn, + main = paste0(varC, " (", adjC, " = ", signif(adjVn[varI], 2), ")")) + + for(prwI in 1:length(prwVc)) { + + if(aovSigMN[varI, paste0(varPfxC, prwVc[prwI], "_sig")] == 1) { + + claVc <- unlist(strsplit(prwVc[prwI], ".", fixed = TRUE)) + aovClaVl <- facFcVn %in% claVc + aovFc <- facFcVn[aovClaVl, drop = TRUE] + aovVc <- as.character(aovFc) + names(aovVc) <- rownames(samDF)[aovClaVl] + boxF(aovFc, + datMN[aovClaVl, varI], + paste0(varC, " (", adjC, " = ", signif(aovMN[varI, paste0(varPfxC, prwVc[prwI], "_", adjC)], 2), ")"), + aovVc) + + } + + } + + } + + } + + dev.off() + + + } else if(tesC == "kruskal") { + + + ## getting the names of the pairwise comparisons 'class1.class2' + + 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]) + pfxNemVc <- paste0(varPfxC, nemNamVc) + + ## omnibus and post-hoc tests + + 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)) + + })) + + ## correction for multiple testing + + nemMN <- apply(nemMN, 2, + function(pvaVn) p.adjust(pvaVn, method = adjC)) + adjVn <- nemMN[, 1] + sigVn <- as.numeric(adjVn < thrN) + nemMN <- nemMN[, c(FALSE, nemVl)] + colnames(nemMN) <- paste0(pfxNemVc, "_", adjC) + + ## significance coding (0 = not significant, 1 = significant) + + nemSigMN <- nemMN < thrN + mode(nemSigMN) <- "numeric" + colnames(nemSigMN) <- paste0(pfxNemVc, "_sig") + + ## difference of the medians for each pairwise comparison + + 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)) + + ## final aggregated table + + resMN <- cbind(adjVn, sigVn, difMN, nemMN, nemSigMN) + colnames(resMN)[1:2] <- paste0(varPfxC, c(adjC, "sig")) + + varDF <- cbind.data.frame(varDF, as.data.frame(resMN)) + + ## graphic + + pdf(pdfC, onefile = TRUE) + + for(varI in 1:nrow(varDF)) { + + if(sum(nemSigMN[varI, ]) > 0) { + + varC <- rownames(varDF)[varI] + + boxplot(datMN[, varI] ~ facFcVn, + main = paste0(varC, " (", adjC, " = ", signif(adjVn[varI], 2), ")")) + + for(nemI in 1:length(nemNamVc)) { + + if(nemSigMN[varI, paste0(varPfxC, nemNamVc[nemI], "_sig")] == 1) { + + nemClaVc <- nemClaMC[nemI, ] + nemClaVl <- facFcVn %in% nemClaVc + nemFc <- facFcVn[nemClaVl, drop = TRUE] + nemVc <- as.character(nemFc) + names(nemVc) <- rownames(samDF)[nemClaVl] + boxF(nemFc, + datMN[nemClaVl, varI], + paste0(varC, " (", adjC, " = ", signif(nemMN[varI, paste0(varPfxC, nemNamVc[nemI], "_", adjC)], 2), ")"), + nemVc) + + } + + } + + } + + } + + dev.off() + + } + + 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) + +} + + +boxF <- function(xFc, + yVn, + maiC, + xVc) { + + boxLs <- boxplot(yVn ~ xFc, + main = maiC) + + outVn <- boxLs[["out"]] + + if(length(outVn)) { + + for(outI in 1:length(outVn)) { + levI <- which(levels(xFc) == xVc[names(outVn)[outI]]) + text(levI, + outVn[outI], + labels = names(outVn)[outI], + pos = ifelse(levI == 2, 2, 4)) + } + + } + +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/univariate_wrapper.R Tue Oct 24 08:57:25 2017 -0400 @@ -0,0 +1,190 @@ +#!/usr/bin/env Rscript + +library(batch) ## parseCommandArgs + +# Constants +argv <- commandArgs(trailingOnly = FALSE) +script.path <- sub("--file=","",argv[grep("--file=",argv)]) +prog.name <- basename(script.path) + +# Print help +if (length(grep('-h', argv)) >0) { + cat("Usage:", prog.name, + "dataMatrix_in myDataMatrix.tsv", + "sampleMetadata_in mySampleData.tsv", + "variableMetadata_in myVariableMetadata.tsv", + "facC qual", + "tesC kruskal", + "adjC fdr", + "thrN 0.05", + "variableMetadata_out myVariableMetadata_out.tsv", + "figure figure.pdf", + "information information.txt", + "\n") + quit(status = 0) +} + +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)) + +##------------------------------ +## 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"]), + pdfC = argVc["figure"]) + + +##------------------------------ +## 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 = "") + +cat("\n\n\n============================================================================") +cat("\nAdditional information about the call:\n") +cat("\n1) Parameters:\n") +print(cbind(value = argVc)) + +cat("\n2) Session Info:\n") +sessioninfo <- sessionInfo() +cat(sessioninfo$R.version$version.string,"\n") +cat("Main packages:\n") +for (pkg in names(sessioninfo$otherPkgs)) { cat(paste(pkg,packageVersion(pkg)),"\t") }; cat("\n") +cat("Other loaded packages:\n") +for (pkg in names(sessioninfo$loadedOnly)) { cat(paste(pkg,packageVersion(pkg)),"\t") }; cat("\n") + +cat("============================================================================\n") + +sink() + +options(stringsAsFactors = strAsFacL) + +rm(list = ls())