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Changeset 0:23f9fad4edfc (2017-10-16)

Next changeset 1:0c2ad44b6c9c (2017-10-22)

Commit message:
planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit bd26542b811de06c1a877337a2840a9f899c2b94

added:
LICENSE
README
test-data/expected_contrast_corcov.tsv
test-data/expected_contrast_corcov_all.tsv
test-data/expected_contrast_corcov_global.tsv
test-data/expected_contrast_salience.tsv
test-data/input_dataMatrix.tsv
test-data/input_sampleMetadata.tsv
test-data/input_variableMetadata.tsv
w4mcorcov.xml
w4mcorcov_calc.R
w4mcorcov_input.R
w4mcorcov_lib.R
w4mcorcov_output.R
w4mcorcov_salience.R
w4mcorcov_util.R
w4mcorcov_wrapper.R

diff -r 000000000000 -r 23f9fad4edfc LICENSE
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/LICENSE Mon Oct 16 14:56:52 2017 -0400

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2017 Hegeman Lab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

diff -r 000000000000 -r 23f9fad4edfc README
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/README Mon Oct 16 14:56:52 2017 -0400

@@ -0,0 +1,7 @@
+# w4mcorcov_galaxy_wrapper
+
+Planemo-based galaxy-tool-wrapper to wrap a SIMCA® OPLS-DA® S-PLOT® "work-alike" tool.
+This tool plots the correlation of each feature with the OPLS-DA projection of multivariate data for two classes of data against the covariance of the same.
+
+The original plot which this tool emulates is described in:
+> Wiklund, Susanne and Johansson, Erik and Sjöström, Lina and Mellerowicz, Ewa J. and Edlund, Ulf and Shockcor, John P. and Gottfries, Johan and Moritz, Thomas and Trygg, Johan (2008). Visualization of GC/TOF-MS-Based Metabolomics Data for Identification of Biochemically Interesting Compounds Using OPLS Class Models. In Analytical Chemistry, 80 (1), pp. 115–122. doi:10.1021/ac0713510

diff -r 000000000000 -r 23f9fad4edfc test-data/expected_contrast_corcov.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/expected_contrast_corcov.tsv Mon Oct 16 14:56:52 2017 -0400

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diff -r 000000000000 -r 23f9fad4edfc test-data/expected_contrast_corcov_all.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/expected_contrast_corcov_all.tsv Mon Oct 16 14:56:52 2017 -0400

b'@@ -0,0 +1,1151 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diff -r 000000000000 -r 23f9fad4edfc test-data/expected_contrast_corcov_global.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/expected_contrast_corcov_global.tsv Mon Oct 16 14:56:52 2017 -0400

b'@@ -0,0 +1,1241 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diff -r 000000000000 -r 23f9fad4edfc test-data/expected_contrast_salience.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/expected_contrast_salience.tsv Mon Oct 16 14:56:52 2017 -0400

@@ -0,0 +1,125 @@
+featureID salientLevel salientRCV salience
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diff -r 000000000000 -r 23f9fad4edfc test-data/input_dataMatrix.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input_dataMatrix.tsv Mon Oct 16 14:56:52 2017 -0400

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diff -r 000000000000 -r 23f9fad4edfc test-data/input_sampleMetadata.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input_sampleMetadata.tsv Mon Oct 16 14:56:52 2017 -0400

@@ -0,0 +1,94 @@
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diff -r 000000000000 -r 23f9fad4edfc test-data/input_variableMetadata.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input_variableMetadata.tsv Mon Oct 16 14:56:52 2017 -0400

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+M349.2383T700 1 0 1 0 1 0 0

diff -r 000000000000 -r 23f9fad4edfc w4mcorcov.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/w4mcorcov.xml Mon Oct 16 14:56:52 2017 -0400

[

b'@@ -0,0 +1,581 @@\n+<tool id="w4mcorcov" name="OPLS-DA_Contrasts" version="0.98.2">\n+\n+ <description>OPLS-DA Contrasts of Univariate Results</description>\n+ \n+ <requirements>\n+ <requirement type="package">r-batch</requirement>\n+ <requirement type="package">bioconductor-ropls</requirement>\n+ \n+ </requirements>\n+ \n+ <stdio>\n+ <exit_code range="1:" level="fatal" />\n+ </stdio>\n+\n+ <command><![CDATA[\n+ cd $__tool_directory__; Rscript w4mcorcov_wrapper.R\n+ dataMatrix_in "$dataMatrix_in"\n+ sampleMetadata_in "$sampleMetadata_in"\n+ variableMetadata_in "$variableMetadata_in"\n+ tesC "$tesC"\n+ facC "$facC"\n+ pairSigFeatOnly "$pairSigFeatOnly"\n+ levCSV \'$levCSV\'\n+ matchingC \'$matchingC\'\n+ labelFeatures \'$labelFeatures\'\n+ contrast_detail \'$contrast_detail\'\n+ contrast_corcov \'$contrast_corcov\'\n+ contrast_salience \'$contrast_salience\'\n+ ]]></command>\n+\n+ <inputs>\n+ <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="Features x samples (tabular data - decimal: \'.\'; missing: NA; mode: numerical; separator: tab character)" />\n+ <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="Samples x metadata (tabular data - decimal: \'.\'; missing: NA; mode: character or numerical; separator: tab character)" />\n+ <param name="variableMetadata_in" label="Variable metadata file (from Univariate)" type="data" format="tabular" help="Features x metadata (tabular data - decimal: \'.\'; missing: NA; mode: character or numerical; separator: tab character)" />\n+ <param name="facC" label="Factor of interest" type="text" help="The name of the column of sampleMetadata corresponding to the qualitative variable used to define the contrasts. This also must be a portion of the column names in the variableMetadata file except when the \'Univariate Significance-test\' is set to \'none\'."/>\n+ <param name="tesC" label="Univariate Significance-Test" type="select" help="Either \'none\' or the name of the statistical test that was run by the \'Univariate\' tool to produce the variableMetadata file; that name must also be a portion of the column names in that file">\n+ <option value="none">none - Display all features from variableMetadata (rather than choosing a subset based on significance in univariate testing)</option>\n+ <option value="ttest">ttest - Student\'s t-test (parametric test, qualitative factor with exactly 2 levels)</option>\n+ <option value="anova">anova - Analysis of variance (parametric test, qualitative factor with more than 2 levels)</option>\n+ <option value="wilcoxon">wilcoxon - Wilcoxon rank test (nonparametric test, qualitative factor with exactly 2 levels)</option> \n+ <option value="kruskal">kruskal - Kruskal-Wallis rank test (nonparametric test, qualitative factor with more than 2 levels)</option>\n+ </param>\n+ <param\n+ name="pairSigFeatOnly"\n+ type="boolean"\n+ checked="true"\n+ truevalue="TRUE"\n+ falsevalue="FALSE"\n+ label="Retain only pairwise-significant features"\n+ help="Ignored when \'none\' is chosen. Otherwise, when \'Yes\', analyze only features that differ significantly for the pair of levels being contrasted; when \'No\', include any feature that varies significantly across all levels."/>\n+ <param name="levCSV" label="Levels of interest" type="text" value = "" help="Comma-separated level-names (or comma-less regular expressions to match level-names) to consider in analysis; must match at least two levels; may include wild cards or regular expressions">\n+ <sanitizer>\n+ <valid initial="string.letters">\n+ <add preset="string.digits"/>\n+ <add value="$" /> \n+ <add value="(" /> \n+ <add value=")" /> \n+ <add value="*" /> \n+ <citation type="doi">10.1002/cem.2627</citation>\n+ \n+ <citation type="doi">10.1093/bioinformatics/btu813</citation>\n+ \n+ <citation type="doi">10.1016/j.biocel.2017.07.002</citation>\n+ \n+ <citation type="doi">10.1186/1748-7188-6-27</citation>\n+ \n+ <citation type="doi">10.1016/j.chemolab.2008.08.004</citation>\n+ \n+ <citation type="doi">10.1038/srep22274</citation>\n+ \n+ <citation type="doi">10.1021/acs.jproteome.5b00354</citation>\n+ \n+ <citation type="bibtex"><![CDATA[\n+@incollection{Thevenot_ropls_2017,\n+ author = {Th{\\\'{e}}venot, Etienne A.},\n+ title = {ropls: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data},\n+ publisher = {bioconductor.org},\n+ year = {2017},\n+ doi = {10.18129/B9.bioc.ropls},\n+ booktitle = {Bioconductor: Open source software for bioinformatics},\n+ address = {Roswell Park Cancer Institute},\n+}\n+ ]]></citation>\n+ \n+ <citation type="doi">10.1021/ac0713510</citation>\n+ </citations>\n+ \n+</tool>\n'

diff -r 000000000000 -r 23f9fad4edfc w4mcorcov_calc.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/w4mcorcov_calc.R Mon Oct 16 14:56:52 2017 -0400

[

b'@@ -0,0 +1,545 @@\n+# center with \'colMeans()\' - ref: http://gastonsanchez.com/visually-enforced/how-to/2014/01/15/Center-data-in-R/\n+center_colmeans <- function(x) {\n+ xcenter = colMeans(x)\n+ x - rep(xcenter, rep.int(nrow(x), ncol(x)))\n+}\n+\n+#### OPLS-DA\n+algoC <- "nipals"\n+\n+do_detail_plot <- function(x_dataMatrix, x_predictor, x_is_match, x_algorithm, x_prefix, x_show_labels, x_progress = print, x_env) {\n+ off <- function(x) if (x_show_labels) x else 0\n+ salience_lookup <- x_env$salience_lookup\n+ salient_rcv_lookup <- x_env$salient_rcv_lookup\n+ # x_progress("head(salience_df): ", head(salience_df))\n+ # x_progress("head(salience): ", head(salience))\n+ if (x_is_match && ncol(x_dataMatrix) > 0 && length(unique(x_predictor))> 1) {\n+ my_oplsda <- opls(\n+ x = x_dataMatrix\n+ , y = x_predictor\n+ , algoC = x_algorithm\n+ , predI = 1\n+ , orthoI = if (ncol(x_dataMatrix) > 1) 1 else 0\n+ , printL = FALSE\n+ , plotL = FALSE\n+ )\n+ my_oplsda_suppLs_y_levels <- levels(as.factor(my_oplsda@suppLs$y))\n+ fctr_lvl_1 <- my_oplsda_suppLs_y_levels[1]\n+ fctr_lvl_2 <- my_oplsda_suppLs_y_levels[2]\n+ my_cor_vs_cov <- cor_vs_cov(\n+ matrix_x = x_dataMatrix\n+ , ropls_x = my_oplsda\n+ )\n+ with(\n+ my_cor_vs_cov\n+ , {\n+ min_x <- min(covariance)\n+ max_x <- max(covariance)\n+ lim_x <- max(sapply(X=c(min_x, max_x), FUN=abs))\n+ covariance <- covariance / lim_x\n+ lim_x <- 1.2\n+ main_label <- sprintf("%s for levels %s versus %s", x_prefix, fctr_lvl_1, fctr_lvl_2)\n+ # print("main_label")\n+ # print(main_label)\n+ main_cex <- min(1.0, 46.0/nchar(main_label))\n+ # "It is generally accepted that a variable should be selected if vj>1, [27\xe2\x80\x9329],\n+ # but a proper threshold between 0.83 and 1.21 can yield more relevant variables according to [28]."\n+ # (Mehmood 2012 doi:10.1186/1748-7188-6-27)\n+ vipco <- pmax(0, pmin(1,(vip4p-0.83)/(1.21-0.83)))\n+ alpha <- 0.1 + 0.4 * vipco\n+ red <- as.numeric(correlation < 0) * vipco\n+ blue <- as.numeric(correlation > 0) * vipco\n+ minus_cor <- -correlation\n+ minus_cov <- -covariance\n+ # cex <- salience_lookup(feature = names(minus_cor))\n+ # cex <- 0.25 + (1.25 * cex / max(cex))\n+ cex <- 0.75\n+ plot(\n+ y = minus_cor\n+ , x = minus_cov\n+ , type="p"\n+ , xlim=c(-lim_x, lim_x + off(0.1))\n+ , ylim=c(-1.0 - off(0.1), 1.0)\n+ , xlab = sprintf("relative covariance(feature,t1)")\n+ , ylab = sprintf("correlation(feature,t1)")\n+ , main = main_label\n+ , cex.main = main_cex\n+ , cex = cex\n+ , pch = 16\n+ , col = rgb(blue = blue, red = red, green = 0, alpha = alpha)\n+ )\n+ low_x <- -0.7 * lim_x\n+ high_x <- 0.7 * lim_x\n+ text(x = low_x, y = -0.15, labels = fctr_lvl_1)\n+ text(x = high_x, y = 0.15, labels = fctr_lvl_2)\n+ if (x_show_labels) {\n+ text(\n+ y = minus_cor - 0.013\n+ , x = minus_cov + 0.020\n+ , cex = 0.3\n+ , labels = tsv1$featureID\n+ , col = rgb(blue = blue, red = red, green = 0, alpha = 0.2 + 0.8 * alpha)\n+ , srt = -30 # slant 30 degrees downward\n+ , adj = 0 # left-justified\n+ )\n+ }\n+ }\n+ )\n+ typeVc <- c("correlation", # 1\n+ "outlier", # 2\n+ "overview", # 3\n+ "permutation", # 4\n+ "predict-train", # 5\n+ "predict-test", # 6\n+ "summary", # 7 = c(2,3,4,9)\n+ "x-loading", # 8\n+ "x-score", # 9\n+ "x-variance", # 10\n+ "xy-score", # 11\n+ "xy-weight" # 12\n+ ) # [c(3,8,9)] # [c(4,3,8,9)]\n+ if ( length(my'..b'ra_tools/blob/master/generic_PCA.R\n+cor_vs_cov <- function(matrix_x, ropls_x) {\n+ x_class <- class(ropls_x)\n+ if ( !( as.character(x_class) == "opls" ) ) { # || !( attr(class(x_class),"package") == "ropls" ) ) \n+ stop( "cor_vs_cov: Expected ropls_x to be of class ropls::opls but instead it was of class ", as.character(x_class) )\n+ }\n+ result <- list()\n+ # suppLs$algoC - Character: algorithm used - "svd" for singular value decomposition; "nipals" for NIPALS\n+ if ( ropls_x@suppLs$algoC == "nipals") {\n+ # Equations (1) and (2) from *Supplement to* Wiklund 2008, doi:10.1021/ac0713510\n+ mag <- function(one_dimensional) sqrt(sum(one_dimensional * one_dimensional))\n+ mag_xi <- sapply(X = 1:ncol(matrix_x), FUN = function(x) mag(matrix_x[,x]))\n+ score_matrix <- ropls_x@scoreMN\n+ score_matrix_transposed <- t(score_matrix)\n+ score_matrix_magnitude <- mag(score_matrix)\n+ result$covariance <- score_matrix_transposed %*% matrix_x / ( score_matrix_magnitude * score_matrix_magnitude )\n+ result$correlation <- score_matrix_transposed %*% matrix_x / ( score_matrix_magnitude * mag_xi )\n+ } else {\n+ # WARNING - untested code - I don\'t have test data to exercise this branch\n+ # Equations (1) and (2) from Wiklund 2008, doi:10.1021/ac0713510\n+ # scoreMN - Numerical matrix of x scores (T; dimensions: nrow(x) x predI) X = TP\' + E; Y = TC\' + F\n+ score_matrix <- ropls_x@scoreMN\n+ score_matrix_transposed <- t(score_matrix)\n+ cov_divisor <- nrow(matrix_x) - 1\n+ result$covariance <- sapply(\n+ X = 1:ncol(matrix_x)\n+ , FUN = function(x) score_matrix_transposed %*% matrix_x[,x] / cov_divisor\n+ )\n+ score_sd <- sapply(\n+ X = 1:ncol(score_matrix)\n+ , FUN = function(x) sd(score_matrix[,x])\n+ )\n+ # xSdVn - Numerical vector: variable standard deviations of the \'x\' matrix\n+ xSdVn <- ropls_x@xSdVn\n+ result$correlation <- sapply(\n+ X = 1:ncol(matrix_x)\n+ , FUN = function(x) {\n+ ( score_matrix_transposed / score_sd ) %*% ( matrix_x[,x] / (xSdVn[x] * cov_divisor) )\n+ }\n+ )\n+ }\n+ result$correlation <- result$correlation[1,,drop = TRUE]\n+ result$covariance <- result$covariance[1,,drop = TRUE]\n+\n+ # Variant 4 of Variable Influence on Projection for OPLS from Galindo_Prieto_2014\n+ # Length = number of features; labels = feature identifiers. (The same is true for $correlation and $covariance.)\n+ result$vip4p <- as.numeric(ropls_x@vipVn)\n+ result$vip4o <- as.numeric(ropls_x@orthoVipVn)\n+ # get the level names\n+ level_names <- sort(levels(as.factor(ropls_x@suppLs$y)))\n+ feature_count <- length(ropls_x@vipVn)\n+ result$level1 <- rep.int(x = level_names[1], times = feature_count)\n+ result$level2 <- rep.int(x = level_names[2], times = feature_count)\n+ # print(sprintf("sd(covariance) = %f; sd(correlation) = %f", sd(result$covariance), sd(result$correlation)))\n+ superresult <- list()\n+ if (length(result$vip4o) == 0) result$vip4o <- NA\n+ superresult$tsv1 <- data.frame(\n+ featureID = names(ropls_x@vipVn)\n+ , factorLevel1 = result$level1\n+ , factorLevel2 = result$level2\n+ , correlation = result$correlation\n+ , covariance = result$covariance\n+ , vip4p = result$vip4p\n+ , vip4o = result$vip4o\n+ , row.names = NULL\n+ )\n+ rownames(superresult$tsv1) <- superresult$tsv1$featureID\n+ superresult$covariance <- result$covariance\n+ superresult$correlation <- result$correlation\n+ superresult$vip4p <- result$vip4p\n+ superresult$vip4o <- result$vip4o\n+ superresult$details <- result\n+ # #print(superresult$tsv1)\n+ result$superresult <- superresult\n+ # Include thise in case future consumers of this routine want to use it in currently unanticipated ways\n+ result$oplsda <- ropls_x \n+ result$predictor <- ropls_x@suppLs$y # in case future consumers of this routine want to use it in currently unanticipated ways\n+ return (superresult)\n+}\n+\n+\n'

diff -r 000000000000 -r 23f9fad4edfc w4mcorcov_input.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/w4mcorcov_input.R Mon Oct 16 14:56:52 2017 -0400

[

@@ -0,0 +1,201 @@
+# read_data_frame - read a w4m data frame, with error handling
+#   e.g., data_matrix_input_env <- read_data_frame(dataMatrix_in, "data matrix input")
+read_data_frame <- function(file_path, kind_string, failure_action = failure_action) {
+  my.env <- new.env()
+  my.env$success <- FALSE
+  my.env$msg <- sprintf("no message reading %s", kind_string)
+  tryCatch(
+    expr = {
+      my.env$data    <- utils::read.delim( fill = FALSE, file = file_path )
+      my.env$success <- TRUE
+    }
+  , error = function(e) {
+     my.env$ msg <- sprintf("%s read failed", kind_string)
+    }
+  )
+  if (!my.env$success) {
+    failure_action(my.env$msg)
+    return ( FALSE )
+  }
+  return (my.env)
+}
+
+# read one of three XCMS data elements: dataMatrix, sampleMetadata, variableMetadata
+# returns respectively: matrix, data.frame, data.frame, or FALSE if there is a failure
+read_xcms_data_element <- function(xcms_data_in, xcms_data_type, failure_action = stop) {
+  # note that 'stop' effectively means 'throw'; if 'warning' and 'message' are caught, they mean 'throw' as well
+  my_failure_action <- function(...) { failure_action("read_xcms_data_element: ", ...) }
+  # xcms_data_type must be in c("sampleMetadata", "variableMetadata", "dataMatrix")
+  if ( ! is.character(xcms_data_type) ) {
+    my_failure_action(sprintf("bad parameter xcms_data_type '%s'", deparse(xcms_data_type)))
+    return ( FALSE )
+  }
+  if ( 1 != length(xcms_data_type)
+       || ! ( xcms_data_type %in% c("sampleMetadata", "variableMetadata", "dataMatrix") )
+  ) {
+    my_failure_action( sprintf("bad parameter xcms_data_type '%s'", xcms_data_type) )
+    return ( FALSE )
+  }
+  if ( is.character(xcms_data_in) ){
+    # case: xcms_data_in is a path to a file
+    xcms_data_input_env <- read_data_frame( xcms_data_in, sprintf("%s input", xcms_data_type) )
+    if (!xcms_data_input_env$success) {
+      my_failure_action(xcms_data_input_env$msg)
+      return ( FALSE )
+    }
+    return ( xcms_data_input_env$data )
+    # commenting out pasted code that is not tested here
+    # } else if ( is.data.frame(xcms_data_in) || is.matrix(xcms_data_in) ) {
+    #   # case: xcms_data_in is a data.frame or matrix
+    #   return(xcms_data_in)
+    # } else if ( is.list(xcms_data_in) || is.environment(xcms_data_in) ) {
+    #   # NOTE WELL: is.list succeeds for data.frame, so the is.data.frame test must appear before the is.list test
+    #   # case: xcms_data_in is a list
+    #   if ( ! exists(xcms_data_type, where = xcms_data_in) ) {
+    #     my_failure_action(sprintf("%s xcms_data_in is missing member '%s'"), ifelse(is.environment(xcms_data_in),"environment","list"), xcms_data_type)
+    #     return ( FALSE )
+    #   }
+    #   prospect <- getElement(name = xcms_data_type, object = xcms_data_in)
+    #   if ( ! is.data.frame(prospect) && ! is.matrix(prospect) ) {
+    #     utils::str("list - str(prospect)")
+    #     utils::str(prospect)
+    #     if ( is.list(xcms_data_in) ) {
+    #       my_failure_action(sprintf("the first member of xcms_data_in['%s'] is neither a data.frame nor a matrix but is a %s", xcms_data_type, typeof(prospect)))
+    #     } else {
+    #       my_failure_action(sprintf("the first member of xcms_data_in$%s is neither a data.frame nor a matrix but is a %s", xcms_data_type, typeof(prospect)))
+    #     }
+    #     return ( prospect )
+    #   }
+    #   # stop("stopping here for a snapshot")
+    #   return ( prospect )
+  } else {
+    # case: xcms_data_in is invalid
+    my_failure_action( sprintf("xcms_data_in has unexpected type %s", typeof(xcms_data_in)) )
+    return ( FALSE )
+  }
+}
+
+read_inputs <- function(input_env, failure_action = print) {
+  if ( ! is.environment(input_env) ) {
+    failure_action("read_inputs: fatal error - 'input_env' is not an environment")
+    return ( FALSE )
+  }
+
+  if (!is.null(sampleMetadata_in <- input_env$sampleMetadata_in)) {
+    # ---
+    # read in the sample metadata
+    read_data_result <- tryCatchFunc(
+      expr = {
+        read_xcms_data_element(xcms_data_in = sampleMetadata_in, xcms_data_type = "sampleMetadata")
+      }
+    )
+    if ( read_data_result$success ) {
+      smpl_metadata <- read_data_result$value
+    } else {
+      failure_action(read_data_result$msg)
+      return ( FALSE )
+    }
+
+    # extract rownames
+    rownames(smpl_metadata) <- smpl_metadata[,1]
+
+    input_env$smpl_metadata <- smpl_metadata
+    # ...
+  } else {
+    failure_action("read_inputs: fatal error - 'sampleMetadata_in' is missing from 'input_env'")
+    return ( FALSE )
+  }
+
+  if (!is.null(variableMetadata_in <- input_env$variableMetadata_in)) {
+    # ---
+    # read in the variable metadata
+    read_data_result <- tryCatchFunc(
+      expr = {
+        read_xcms_data_element(xcms_data_in = variableMetadata_in, xcms_data_type = "variableMetadata")
+      }
+    )
+    if ( read_data_result$success ) {
+      vrbl_metadata <- read_data_result$value
+    } else {
+      failure_action(read_data_result$msg)
+      return (FALSE)
+    }
+
+
+    # extract rownames (using make.names to handle degenerate feature names)
+    err.env <- new.env()
+    err.env$success <- FALSE
+    err.env$msg <- "no message setting vrbl_metadata rownames"
+    tryCatch(
+      expr = {
+        rownames(vrbl_metadata) <- make.names( vrbl_metadata[,1], unique = TRUE )
+        vrbl_metadata[,1] <- rownames(vrbl_metadata)
+        err.env$success     <- TRUE
+      }
+    , error = function(e) {
+       err.env$ msg <- sprintf("failed to set rownames for vrbl_metadata read because '%s'", e$message)
+      }
+    )
+    if (!err.env$success) {
+      failure_action(err.env$msg)
+      return ( FALSE )
+    }
+
+    input_env$vrbl_metadata <- vrbl_metadata
+    # ...
+  } else {
+    failure_action("read_inputs: fatal error - 'variableMetadata_in' is missing from 'input_env'")
+    return ( FALSE )
+  }
+
+  if (!is.null(dataMatrix_in <- input_env$dataMatrix_in)) {
+    # ---
+    # read in the data matrix
+    read_data_result <- tryCatchFunc(
+      expr = {
+        read_xcms_data_element(xcms_data_in = dataMatrix_in, xcms_data_type = "dataMatrix")
+      }
+    )
+    if ( read_data_result$success ) {
+      data_matrix <- read_data_result$value
+    } else {
+      failure_action(read_data_result$msg)
+      return (FALSE)
+    }
+
+    if ( ! is.matrix(data_matrix) ) {
+      # extract rownames (using make.names to handle degenerate feature names)
+      err.env <- new.env()
+      err.env$success <- FALSE
+      err.env$msg <- "no message setting data_matrix rownames"
+      tryCatch(
+        expr = {
+          rownames(data_matrix) <- make.names( data_matrix[,1], unique = TRUE )
+          err.env$success     <- TRUE
+        }
+      , error = function(e) {
+         err.env$msg <- sprintf("failed to set rownames for data_matrix read because '%s'", e$message)
+        }
+      )
+      if (!err.env$success) {
+        failure_action(err.env$msg)
+        return ( FALSE )
+      }
+
+      # remove rownames column
+      data_matrix <- data_matrix[,2:ncol(data_matrix)]
+
+      # convert data_matrix to matrix from data.frame
+      data_matrix <- as.matrix(data_matrix)
+    }
+
+    input_env$data_matrix <- data_matrix
+    # ...
+  } else {
+    failure_action("read_inputs: fatal error - 'dataMatrix_in' is missing from 'input_env'")
+    return ( FALSE )
+  }
+
+  return ( TRUE )
+}
+

diff -r 000000000000 -r 23f9fad4edfc w4mcorcov_lib.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/w4mcorcov_lib.R Mon Oct 16 14:56:52 2017 -0400

@@ -0,0 +1,3 @@
+suppressMessages(library(batch))
+# suppressMessages(library(foreach))
+suppressMessages(library(ropls))

diff -r 000000000000 -r 23f9fad4edfc w4mcorcov_salience.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/w4mcorcov_salience.R Mon Oct 16 14:56:52 2017 -0400

[

@@ -0,0 +1,117 @@
+w4msalience <- function(
+  data_matrix   # a matrix of intensities; features as rows, and samples as columns
+, sample_class  # a vector of sample class-levels; length(sample_class) == ncol(data_matrix)
+, failure_action = stop
+) {
+  library(stats)
+  # begin sanity checks
+  if ( !is.vector(sample_class) || !( is.character(sample_class) || is.factor(sample_class) ) ) {
+    failure_action("w4msalience:  Expected sample_class to be a vector of characters of factor-levels")
+    return (NULL)
+  }
+  if ( !is.matrix(data_matrix) && !is.data.frame(data_matrix) ) {
+    failure_action("w4msalience:  Expected data_matrix to be a matrix (or data.frame) of numeric")
+    return (NULL)
+  }
+  # transpose data_matrix so that columns are the features
+  t_data_matrix <- t(data_matrix)
+  if ( !is.matrix(t_data_matrix) || !is.numeric(t_data_matrix) ) {
+    failure_action("w4msalience:  Expected data_matrix to be a matrix (or data.frame) of numeric")
+    return (NULL)
+  }
+  n_features <- ncol(t_data_matrix)
+  n_features_plus_1 <- 1 + n_features
+  features   <- colnames(t_data_matrix)
+  n_samples  <- nrow(t_data_matrix)
+  if ( length(sample_class) != n_samples ) {
+    strF(data_matrix)
+    strF(sample_class)
+    failure_action(sprintf("w4msalience:  The data_matrix has %d samples but sample_class has %d", n_samples, length(sample_class)))
+    return (NULL)
+  }
+  # end sanity checks
+
+  # "For each feature, 'select sample_class, median(intensity) from feature group by sample_class'."
+  # The first column(s) of the result of aggregate has the classifier value(s) specified in the 'by' list.
+  medianOfFeatureBySampleClassLevel <- aggregate(
+      x = as.data.frame(t_data_matrix)
+    , by = list(sample_class)
+    , FUN = "median"
+    )
+
+  # "For each feature, 'select sample_class, max(intensity) from feature group by sample_class'."
+  maxOfFeatureBySampleClassLevel <- aggregate(
+      x = as.data.frame(t_data_matrix)
+    , by = list(sample_class)
+    , FUN = "max"
+    )
+
+  # "For each feature, 'select sample_class, rcv(intensity) from feature group by sample_class'."
+  #   cv is less robust; deviation from normality degrades performance
+  #     cv(x) == sd(x) / mean(x)
+  #   rcv is a "robust" coefficient of variation, expressed as a proportion
+  #     rcv(x) == mad(x) / median(x)
+  madOfFeatureBySampleClassLevel <- aggregate(
+      x = as.data.frame(t_data_matrix)
+    , by = list(sample_class)
+    , FUN = "mad"
+  )
+  rcvOfFeatureBySampleClassLevel <- as.matrix(
+    madOfFeatureBySampleClassLevel[,2:n_features_plus_1] / medianOfFeatureBySampleClassLevel[,2:n_features_plus_1]
+  )
+  rcvOfFeatureBySampleClassLevel[is.nan(rcvOfFeatureBySampleClassLevel)] <- max(9999,max(rcvOfFeatureBySampleClassLevel, na.rm = TRUE))
+
+  # "For each feature, 'select max(median_feature_intensity) from feature'."
+  maxApplyMedianOfFeatureBySampleClassLevel <- sapply(
+      X = 1:n_features
+    , FUN = function(i) {
+        match(
+          max(maxOfFeatureBySampleClassLevel[, i + 1])
+        , maxOfFeatureBySampleClassLevel[, i + 1]
+        )
+      }
+    )
+
+  # "For each feature, 'select mean(median_feature_intensity) from feature'."
+  meanApplyMedianOfFeatureBySampleClassLevel <- sapply(
+      X = 1:n_features
+    , FUN = function(i) mean(medianOfFeatureBySampleClassLevel[, i + 1])
+    )
+
+  # Compute the 'salience' for each feature, i.e., how salient the intensity of a feature
+  #   is for one particular class-level relative to the intensity across all class-levels.
+  salience_df <- data.frame(
+    # the feature name
+    feature = features
+    # the name (or factor-level) of the class-level with the highest median intensity for the feature
+  , max_level = medianOfFeatureBySampleClassLevel[maxApplyMedianOfFeatureBySampleClassLevel,1]
+    # the median intensity for the feature and the level max_level
+  , max_median = sapply(
+        X = 1:n_features
+      , FUN = function(i) {
+          maxOfFeatureBySampleClassLevel[maxApplyMedianOfFeatureBySampleClassLevel[i], 1 + i]
+        }
+    )
+    # the coefficient of variation (expressed as a proportion) for the intensity for the feature and the level max_level
+  , max_rcv = sapply(
+        X = 1:n_features
+      , FUN = function(i) {
+          rcvOfFeatureBySampleClassLevel[maxApplyMedianOfFeatureBySampleClassLevel[i], i]
+        }
+    )
+    # the mean of the medians of intensity for all class-levels for the feature
+  , mean_median = meanApplyMedianOfFeatureBySampleClassLevel
+    # don't coerce strings to factors (this is a parameter for the data.frame constructor, not a column of the data.frame)
+  , stringsAsFactors = FALSE
+  )
+  # raw salience is the ratio of the most-prominent level to the mean of all levels for the feature
+  salience_df$salience <- sapply(
+      X = 1:nrow(salience_df)
+    , FUN = function(i) with(salience_df[i,], if (mean_median > 0) { max_median / mean_median } else { 0 } )
+    )
+  # "robust coefficient of variation, i.e., mad(feature-intensity for class-level max_level) / median(feature-intensity for class-level max_level)
+  salience_df$salient_rcv <- salience_df$max_rcv
+
+  return (salience_df)
+}
+

diff -r 000000000000 -r 23f9fad4edfc w4mcorcov_util.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/w4mcorcov_util.R Mon Oct 16 14:56:52 2017 -0400

@@ -0,0 +1,88 @@
+# tryCatchFunc wraps an expression that produces a value if it does not stop:
+#   tryCatchFunc produces a list
+#   On success of expr(), tryCatchFunc produces
+#     list(success TRUE, value = expr(), msg = "")
+#   On failure of expr(), tryCatchFunc produces
+#     list(success = FALSE, value = NA, msg = "the error message")
+tryCatchFunc <- function(expr) {
+  # format error for logging
+  format_error <- function(e) {
+    paste(c("Error { message:", e$message, ", call:", e$call, "}"), collapse = " ")
+  }
+  retval <- NULL
+  tryCatch(
+    expr = {
+      retval <- ( list( success = TRUE, value = eval(expr = expr), msg = "" ) )
+    }
+    , error = function(e) {
+      retval <<- list( success = FALSE, value = NA, msg = format_error(e) )
+    }
+  )
+  return (retval)
+}
+
+# turn off all plotting devices
+dev.off.all <- function() {
+  while (!is.null(dev.list())) { dev.off() }
+}
+
+# capture plot and write to PDF; then close any devices opened in the process
+plot2pdf <- function(
+  file.name
+, plot.function
+, width = 12
+, height = 12
+) {
+  # capture plot and write to PDF
+  cur.dev <- dev.list()
+  filename <- file.name
+  pdf(file = filename, width = width, height = height)
+  plot.function()
+  # close any devices opened in the process
+  dev.off()
+  if (is.null(cur.dev)) {
+      dev.off.all()
+  } else {
+      while ( length(dev.list()) > length(cur.dev) ) { dev.off() }
+  }
+}
+
+# print and capture plot and write to PDF; then close any devices opened in the process
+#   This is needed for ggplot which does not print the plot when invoked within a function.
+print2pdf <- function(
+  file.name
+, plot.function
+, width = 12
+, height = 12
+) {
+  plot2pdf(
+    file.name = file.name
+  , width = width
+  , height = height
+  , plot.function = function() {
+      print(plot.function())
+    }
+  )
+}
+
+iso8601.znow <- function()
+{
+  strftime(as.POSIXlt(Sys.time(), "UTC"), "%Y-%m-%dT%H:%M:%SZ")
+}
+
+# pdf.name <- function(name)
+# {
+#   paste0(name, "_", iso8601.filename.fragment(), ".pdf")
+# }
+#
+# tsv.name <- function(name)
+# {
+#   paste0(name, "_", iso8601.filename.fragment(), ".tsv")
+# }
+#
+# # pseudo-inverse - computational inverse non-square matrix a
+# p.i <- function(a) {
+#   solve(t(a) %*% a) %*% t(a)
+# }
+
+

diff -r 000000000000 -r 23f9fad4edfc w4mcorcov_wrapper.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/w4mcorcov_wrapper.R Mon Oct 16 14:56:52 2017 -0400

[

@@ -0,0 +1,169 @@
+#!/usr/bin/env Rscript
+
+# This script assumes that it is being executed in a current working directory containing the following files:
+#   - w4mcorcov_lib.R
+#   - w4mcorcov_input.R
+#   - w4mcorcov_calc.R
+
+## constants
+##----------
+
+modNamC <- "w4mcorcov" ## module name
+
+topEnvC <- environment()
+nl <- "\n"
+
+## options
+##--------
+
+strAsFacL <- options()$stringsAsFactors
+options(stringsAsFactors = FALSE)
+
+## subroutines
+##----------
+
+source("w4mcorcov_lib.R")
+source("w4mcorcov_util.R")
+source("w4mcorcov_input.R")
+source("w4mcorcov_salience.R")
+source("w4mcorcov_calc.R")
+source("w4mcorcov_output.R")
+
+## log file
+##---------
+
+my_log <- function(x, ...) { cat(paste(iso8601.znow(), " ", x, ..., nl, sep=""))}
+my_fatal <- function(x, ...) {
+  my_log("ERROR: ", x, ...)
+  quit(save = "no", status = 11, runLast = TRUE)
+}
+
+my_log("Start of the '", modNamC, "' Galaxy module call: ")
+
+
+########
+# MAIN #
+########
+
+argVc <- unlist(parseCommandArgs(evaluate=FALSE))
+my_env <- new.env()
+
+##------------------------------
+## Initializing
+##------------------------------
+
+## arguments
+##----------
+
+# files
+
+my_env$dataMatrix_in        <- as.character(argVc["dataMatrix_in"])
+my_env$sampleMetadata_in    <- as.character(argVc["sampleMetadata_in"])
+my_env$variableMetadata_in  <- as.character(argVc["variableMetadata_in"])
+my_env$contrast_detail      <- as.character(argVc["contrast_detail"])
+my_env$contrast_corcov      <- as.character(argVc["contrast_corcov"])
+my_env$contrast_salience    <- as.character(argVc["contrast_salience"])
+# print(sprintf("contrast_salience: %s", my_env$contrast_salience))
+
+# other parameters
+
+my_env$tesC            <- as.character(argVc["tesC"])
+my_env$facC            <- as.character(argVc["facC"])
+my_env$pairSigFeatOnly <- as.logical(argVc["pairSigFeatOnly"])
+my_env$levCSV          <- as.character(argVc["levCSV"])
+my_env$matchingC       <- as.character(argVc["matchingC"])
+my_env$labelFeatures   <- as.logical(argVc["labelFeatures"])
+
+tsv_action_factory <- function(file, colnames, append) {
+  return (
+    function(tsv) {
+      write.table(
+        x = tsv
+      , file = file
+      , sep = "\t"
+      , quote = FALSE
+      , row.names = FALSE
+      , col.names = colnames
+      , append = append
+      )
+    }
+  )
+}
+
+corcov_tsv_colnames <- TRUE
+corcov_tsv_append   <- FALSE
+corcov_tsv_action <- function(tsv) {
+  tsv_action_factory(
+    file     = my_env$contrast_corcov
+  , colnames = corcov_tsv_colnames
+  , append   = corcov_tsv_append
+  )(tsv)
+  corcov_tsv_colnames <<- FALSE
+  corcov_tsv_append   <<- TRUE
+}
+
+salience_tsv_colnames <- TRUE
+salience_tsv_append   <- FALSE
+salience_tsv_action <- function(tsv) {
+  tsv_action_factory(
+    file     = my_env$contrast_salience
+  , colnames = salience_tsv_colnames
+  , append   = salience_tsv_append
+  )(tsv)
+  salience_tsv_colnames <<- FALSE
+  salience_tsv_append   <<- TRUE
+}
+
+my_log( "--------------------------  Reading input data  --------------------------")
+
+# read_inputs is defined in w4mcorcov_input.R
+my_result <- read_inputs(input_env = my_env, failure_action = my_log)
+
+if ( is.logical(my_result) && my_result) {
+  my_log( "--------------------------  Beginning data processing  --------------------------")
+
+  # receiver for result of the call to corcov_calc
+  my_result <- NULL
+
+  # compute and plot the correlation_vs_covariance details plot
+  #   The parameter settings here are generally taken from bioconductor ropls::plot.opls source.
+  marVn <- c(4.6, 4.1, 2.6, 1.6)
+  old_par <- par(
+    font      = 2         # bold font face
+  , font.axis = 2         # bold font face for axis
+  , font.lab  = 2         # bold font face for x and y labels
+  , lwd       = 2         # line-width - interpretation is device spcific
+  , mar       = marVn     # margins
+  , pch       = 18        # black diamond plot-character, see help for graphics::points
+  # , mfrow     = c(2,2)    # two rows by two columns
+  , pty       = "s"       # force plots to be square
+  )
+  plot2pdf(
+    file.name = my_env$contrast_detail
+  , width  = 8
+  , height = 8
+  , plot.function = function() {
+      # plot layout four plots per page
+      layout(matrix(1:4, byrow = TRUE, nrow = 2))
+      my_result <<- corcov_calc(
+          calc_env            = my_env
+        , failure_action      = my_fatal
+        , progress_action     = my_log
+        , corcov_tsv_action   = corcov_tsv_action
+        , salience_tsv_action = salience_tsv_action
+        )
+    }
+  )
+  par(old_par)
+
+  my_log( "--------------------------  Finished data processing  --------------------------")
+}
+
+my_log( "End of the '", modNamC, "' Galaxy module call")
+
+if (is.logical(my_result) && my_result) {
+  quit(save = "no", status = 0, runLast = TRUE)
+} else {
+  my_log("failure :(")
+  quit(save = "no", status = 10, runLast = TRUE)
+}