Mercurial > repos > eschen42 > w4mkmeans
changeset 0:6ccbe18131a6 draft
planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit 299e5c7fdb0d6eb0773f3660009f6d63c2082a8d
| author | eschen42 |
|---|---|
| date | Tue, 08 Aug 2017 15:30:38 -0400 |
| parents | |
| children | 02cafb660b72 |
| files | LICENSE README test-data/input_dataMatrix.tsv test-data/input_sampleMetadata.tsv test-data/input_variableMetadata.tsv w4m_general_purpose_routines.R w4mkmeans.xml w4mkmeans_routines.R w4mkmeans_wrapper.R |
| diffstat | 9 files changed, 1336 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/LICENSE Tue Aug 08 15:30:38 2017 -0400 @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2017 Hegeman Lab + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README Tue Aug 08 15:30:38 2017 -0400 @@ -0,0 +1,2 @@ +# w4mkmeans_galaxy_wrapper +Planemo-based galaxy-tool-wrapper to wrap the stats::kmeans R package for the W4M dataMatrix
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input_dataMatrix.tsv Tue Aug 08 15:30:38 2017 -0400 @@ -0,0 +1,50 @@ + Y11_1_RA5_01_213 Y2_1_RB1_01_218 Y4_1_RB3_01_220 Y12_1_RB4_01_221 Y1_1_RC1_01_228 Y14_1_RC6_01_234 Y1_2_RD1_01_239 Y14_2_RD2_01_240 Y4_2_RD3_01_241 Y11_2_RD7_01_246 Y2_2_RE4_01_253 Y12_2_RE6_01_255 Y14_3_GA2_01_260 Y2_3_GA4_01_264 Y1_3_GA6_01_266 Y4_3_GA7_01_267 Y12_3_GB1_01_270 Y11_3_GC3_01_283 Y14_4_GC7_01_287 Y11_4_GD8_01_299 Y2_4_GE1_01_300 Y12_4_GE2_01_304 Y1_4_GE3_01_305 Y4_4_GE7_01_309 +M118T229 95180.7747000001 283910.279545455 172325.198333334 174004.4176 163525.775666667 194233.618999999 142895.435454546 201401.5926 170930.6395 156553.24775 306528.603090909 254326.335272728 205081.407083333 276873.723636364 172071.209999999 275012.5056 211128.511 168167.790333333 209328.198222222 207513.570083333 258813.932083334 229502.711454545 149345.9256 314364.292 +M144T249 326771.492625001 512639.358421052 353343.595999999 237878.822999999 339952.473666668 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input_sampleMetadata.tsv Tue Aug 08 15:30:38 2017 -0400 @@ -0,0 +1,25 @@ +sampleMetadata class polarity sampleType injectionOrder batch tissue hotelling_pval missing_pval decile_pval PCA_XSCOR.p1 PCA_XSCOR.p2 class_PLSDA_XSCOR.p1 class_PLSDA_XSCOR.p2 class_PLSDA_predictions +Y11_1_RA5_01_213 y1 positive sample 213 1 2 0.0955561581467602 1 0.0306775551319138 -2.26882060894901 1.94958116765736 -3.05527623038242 2.32594165405491 y1 +Y2_1_RB1_01_218 y2 positive sample 218 1 1 0.090775969547078 1 0.0334308308237932 -5.43790231069006 3.28509884002914 -5.09396184849057 3.13632363820691 y2 +Y4_1_RB3_01_220 y4 positive sample 220 1 1 0.0922380872343134 1 0.0343065627030201 -5.96519534532645 2.76065569212045 -5.61271725241037 2.60836215684335 y4 +Y12_1_RB4_01_221 y2 positive sample 221 1 2 0.0731025791938841 1 0.0335814402688999 -3.90074024447077 2.32567583717618 -4.06427508572245 2.47867307479093 y2 +Y1_1_RC1_01_228 y1 positive sample 228 1 1 0.948646526283138 1 0.0150153992414774 -5.79172889541087 3.18442356006801 -5.71894211121787 3.14075608795096 y1 +Y14_1_RC6_01_234 y4 positive sample 234 1 2 0.961424772615561 1 0.0889762542416943 -3.50543091786719 1.90332246047248 -3.60257250798236 1.95341548985651 y4 +Y1_2_RD1_01_239 y1 positive sample 239 1 1 0.391486624975171 1 0.419632697534464 -10.0290510611276 3.46916578350898 -9.30301404180818 3.22425994348737 y1 +Y14_2_RD2_01_240 y4 positive sample 240 1 2 0.334478686842038 1 0.471265236704114 -0.955577667004931 1.62643379077323 -1.17127923245195 1.73770179646644 y4 +Y4_2_RD3_01_241 y4 positive sample 241 1 1 0.243979127543208 1 0.115904447650611 -6.29053214398527 3.48768497975009 -5.86080882473825 3.19393908077519 y4 +Y11_2_RD7_01_246 y1 positive sample 246 1 2 0.639085015503201 1 0.496291025606805 -0.737703114796199 1.89206669195622 -1.33734677265265 2.19119862732508 y1 +Y2_2_RE4_01_253 y2 positive sample 253 1 1 0.681339414372971 1 0.713644697663014 -4.43122798643441 2.59136016132011 -4.21959323228049 2.4942150403311 y2 +Y12_2_RE6_01_255 y2 positive sample 255 1 2 0.581861317126264 1 0.446040669279691 -3.42333388673909 2.19844489197916 -3.40077262161465 2.21882800112511 y2 +Y14_3_GA2_01_260 y4 positive sample 260 1 2 0.792323381194401 1 0.812319191661791 -2.61403564986014 1.9025507158402 -2.90132077481451 2.05744453719897 y4 +Y2_3_GA4_01_264 y2 positive sample 264 1 1 0.278347988263537 1 0.668405316795454 -6.19672954480257 4.11371745717593 -5.72942704887795 3.94423530839635 y2 +Y1_3_GA6_01_266 y1 positive sample 266 1 1 0.303133108610158 1 0.521065147801524 -5.91283168480956 2.57721868167528 -5.8128281040434 2.56623732055011 y1 +Y4_3_GA7_01_267 y4 positive sample 267 1 1 0.204620420161485 1 0.53551459376182 -5.81862869528986 3.42191037440281 -5.37442797934098 3.22465790741629 y4 +Y12_3_GB1_01_270 y2 positive sample 270 1 2 0.7747649633382 1 0.698966513767803 -3.0854085700971 1.67899209632345 -3.14572560562774 1.75648836353689 y2 +Y11_3_GC3_01_283 y1 positive sample 283 1 2 0.918803505111851 1 0.396638581468035 -1.16946485386388 1.66851916844539 -1.7032576378218 1.94860768310552 y1 +Y14_4_GC7_01_287 y4 positive sample 287 1 2 0.577273975934045 1 0.14919566995266 -1.24666389579168 2.84891525888206 -1.58652468539139 2.90364189714377 y4 +Y11_4_GD8_01_299 y1 positive sample 299 1 2 0.31302025978985 1 0.426766355892969 -2.15936901108787 1.66989335813642 -2.66042240568943 1.95509478589954 y1 +Y2_4_GE1_01_300 y2 positive sample 300 1 1 0.0338929937565918 1 0.419149865807458 -5.76080121045973 3.47845733452933 -5.39212267305567 3.34452446158071 y2 +Y12_4_GE2_01_304 y2 positive sample 304 1 2 0.130905883509031 1 0.59698349195307 -4.15585900988913 3.22702525356271 -4.28382960930699 3.34874792551519 y2 +Y1_4_GE3_01_305 y1 positive sample 305 1 1 0.129479197101219 1 0.618449187175638 -2.9921645121991 3.14523577730793 -2.85639638001866 3.08197032598192 y1 +Y4_4_GE7_01_309 y4 positive sample 309 1 1 0.758837157578886 1 0.339564008612217 -5.95949084754462 3.20317151028856 -5.46675286145534 3.04585537553442 y4
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/w4m_general_purpose_routines.R Tue Aug 08 15:30:38 2017 -0400 @@ -0,0 +1,283 @@ +# prepare.data.matrix - Prepare x.datamatrix for multivariate statistical analaysis (MVA) +# - Motivation: +# - Selection: +# - You may want to exclude several samples from your analysis: +# - If so, set the argument 'exclude.samples' to a vector of sample names +# - You may want to exclude several features or features from your analysis: +# - If so, set the argument 'exclude.features' to a vector of feature names +# - Renaming samples: +# - You may want to rename several samples from your analysis: +# - If so, set the argument 'sample.rename.function' to a function accepting a vector +# of sample names and producing a vector of strings of equivalent length +# - MVA is confounded by missing values. +# - By default, this function imputes missing values as zero. +# - For a different imputation, set the 'data.imputation' argument to a function +# accepting a single matrix argument and returning a matrix of the same +# dimensions as the argument. +# - Transformation +# - It may be desirable to transform the intensity data to reduce the range. +# - By default, this function performs an eigth-root transformation: +# - Any root-tranformation has the advantage of never being negative. +# - Calculation of the eight-root is four times faster in my hands than log10. +# - However, it has the disadvantage that calculation of fold-differences +# is not additive as with log-transformation. +# - Rather, you must divide the values and raise to the eighth power. +# - For a different transformation, set the 'data.transformation' argument +# to a function accepting a single matrix argument. +# - The function should be written to return a matrix of the same dimensions +# as the argument. +# arguments: +# - x.matrix - matrix of intensities (or data.frame of sample metadata) +# - one row per sample +# - one column per feature or metadata attribute +# - exclude.samples - vector of labels of matrix rows (samples) to omit from analysis +# - exclude.features - vector of labels of matrix columnss (features) to omit from analysis +# - sample.rename.function - function to be used to rename rows if necessary, or NULL +# - e.g., sample.rename.function = function(x) { +# sub("(.*)_.*","\\1", row.names(x)) +# } +# - data.imputation - function applied to matrix to impute missing values +# - e.g., data.imputation = function(m) { +# m[is.na(m)] <- min(m, na.rm = TRUE) / 100 +# return (m) +# } +# - data.transformation - function applied to matrix cells +# - e.g., data.transformation = function(x) { return( log10(x) ) } +# or, data.transformation = log10 +# result value: +# transformed, imputed x.datamatrix with renamed rows and with neither excluded values nor features +# +################################ +## +## Notes regarding the effectiveness and performance of the data transformation method. +## +## The two transformations that I tried (log10 and 8th root) required different imputation methods. +## +## For the LCMS resin data set that I was working with, separation in MVA was nearly equivalent for: +## data.imputation <- function(x.matrix) { +## x.matrix[is.na(x.matrix)] <- 0 +## return (x.matrix) +## } +## data.transformation <- function(x) { +## sqrt( sqrt( sqrt(x) ) ) +## } +## and +## data.imputation <- function(x.matrix) { +## x.matrix[is.na(x.matrix)] <- min(x.matrix, na.rm = TRUE) / 100 +## return (x.matrix) +## } +## data.transformation <- function(x) { +## log10(x) +## } +## +## Note further that triple application of the square root: +## - may be four times faster than log10: +## - may be three times faster than log2: +## +## system.time( junk <- sqrt( sqrt( sqrt(1:100000000) ) ) ) +## user system elapsed +## 0.832 0.236 1.069 +## system.time( junk <- log10(1:100000000) ) +## user system elapsed +## 3.936 0.400 4.337 +## system.time( junk <- log2(1:100000000) ) +## user system elapsed +## 2.784 0.320 3.101 +## +################################ +# +prepare.data.matrix <- function( + x.matrix +, exclude.samples = NULL +, exclude.features = NULL +, sample.rename.function = NULL +, data.imputation = + function(m) { + # replace NA values with zero + m[is.na(m)] <- 0 + # replace negative values with zero, if applicable (It should never be applicable!) + if (min(m < 0)) { + m <- matrix(lapply(X = m, FUN = function(z) {max(z,0)}), nrow = nrow(m) ) + } + # return matrix as the result + return (m) + } +, data.transformation = function(x) { + sqrt( sqrt( sqrt(x) ) ) + } +, en = new.env() +) { + # MatVar - Compute variance of rows or columns of a matrix + # ref: http://stackoverflow.com/a/25100036 + # For row variance, dim == 1, for col variance, dim == 2 + MatVar <- function(x, dim = 1) { + if (dim == 1) { + dim.x.2 <- dim(x)[2] + if ( dim.x.2 == 0 ) + stop("MatVar: there are zero columns") + if ( dim.x.2 == 1 ) { + stop("MatVar: a single column is insufficient to calculate a variance") + # return ( rep.int(x = 0, times = nrow(x)) ) + } else { + return ( rowSums( (x - rowMeans(x))^2 ) / ( dim(x)[2] - 1 ) ) + } + } else if (dim == 2) { + dim.x.1 <- dim(x)[1] + if ( dim.x.1 == 0 ) { + stop("MatVar: there are zero rows") + } + if ( dim.x.1 == 1 ) { + stop("MatVar: a single row is insufficient to calculate a variance") + # return ( rep.int(x = 0, times = ncol(x)) ) + } else { + return ( rowSums( (t(x) - colMeans(x))^2 ) / ( dim(x)[1] - 1 ) ) + } + } else stop("Please enter valid dimension, for rows, dim = 1; for colums, dim = 2") + } + + nonzero.var <- function(x) { + if (nrow(x) == 0) { + print(str(x)) + stop("matrix has no rows") + } + if (ncol(x) == 0) { + print(str(x)) + stop("matrix has no columns") + } + if ( is.numeric(x) ) { + # exclude any rows with zero variance + row.vars <- MatVar(x, dim = 1) + nonzero.row.vars <- row.vars > 0 + nonzero.rows <- row.vars[nonzero.row.vars] + if ( length(rownames(x)) != length(rownames(nonzero.rows)) ) { + row.names <- attr(nonzero.rows,"names") + x <- x[ row.names, , drop = FALSE ] + } + + # exclude any columns with zero variance + column.vars <- MatVar(x, dim = 2) + nonzero.column.vars <- column.vars > 0 + nonzero.columns <- column.vars[nonzero.column.vars] + if ( length(colnames(x)) != length(colnames(nonzero.columns)) ) { + column.names <- attr(nonzero.columns,"names") + x <- x[ , column.names, drop = FALSE ] + } + } + return (x) + } + + if (is.null(x.matrix)) { + stop("FATAL ERROR - prepare.data.matrix was called with null x.matrix") + } + + en$xpre <- x <- x.matrix + + # exclude any samples as indicated + if ( !is.null(exclude.features) ) { + my.colnames <- colnames(x) + my.col.diff <- setdiff(my.colnames, exclude.features) + x <- x[ , my.col.diff , drop = FALSE ] + } + + # exclude any features as indicated + if ( !is.null(exclude.samples) ) { + my.rownames <- rownames(x) + my.row.diff <- setdiff(my.rownames, exclude.samples) + x <- x[ my.row.diff, , drop = FALSE ] + } + + # rename rows if desired + if ( !is.null(sample.rename.function) ) { + renamed <- sample.rename.function(x) + rownames(x) <- renamed + } + + # save redacted x.datamatrix to environment + en$redacted.data.matrix <- x + + # impute values missing from the x.datamatrix + if ( !is.null(data.imputation) ) { + x <- data.imputation(x) + } + + # perform transformation if desired + if ( !is.null(data.transformation) ) { + x <- data.transformation(x) + } else { + x <- x + } + + # purge rows and columns that have zero variance + if ( is.numeric(x) ) { + x <- nonzero.var(x) + } + + # save imputed, transformed x.datamatrix to environment + en$imputed.transformed.data.matrix <- x + + return(x) +} + + +##----------------------------------------------- +## helper functions for error detection/reporting +##----------------------------------------------- + +# log-printing to stderr +log_print <- function(x, ...) { + cat( + format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z") + , " " + , c(x, ...) + , "\n" + , sep="" + , file=stderr() + ) +} + +# tryCatchFunc produces a list +# On success of expr(), tryCatchFunc produces +# list(success TRUE, value = expr(), msg = "") +# On failure of expr(), tryCatchFunc produces +# list(success = FALSE, value = NA, msg = "the error message") +tryCatchFunc <- function(expr) { + # format error for logging + format_error <- function(e) { + paste(c("Error { message:", e$message, ", call:", e$call, "}"), collapse = " ") + } + my_expr <- expr + retval <- NULL + tryCatch( + expr = { + retval <- ( list( success = TRUE, value = my_expr(), msg = "" ) ) + } + , error = function(e) { + retval <<- list( success = FALSE, value = NA, msg = format_error(e) ) + } + ) + return (retval) +} + +# tryCatchProc produces a list +# On success of expr(), tryCatchProc produces +# list(success TRUE, msg = "") +# On failure of expr(), tryCatchProc produces +# list(success = FALSE, msg = "the error message") +tryCatchProc <- function(expr) { + # format error for logging + format_error <- function(e) { + paste(c("Error { message:", e$message, ", call:", e$call, "}"), collapse = " ") + } + retval <- NULL + tryCatch( + expr = { + expr() + retval <- ( list( success = TRUE, msg = "" ) ) + } + , error = function(e) { + retval <<- list( success = FALSE, msg = format_error(e) ) + } + ) + return (retval) +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/w4mkmeans.xml Tue Aug 08 15:30:38 2017 -0400 @@ -0,0 +1,319 @@ +<tool id="w4mkmeans" name="Kmeans_for_W4M" version="0.98.1"> + <description>Calculate K-means for dataMatrix features or samples</description> + + <requirements> + <requirement type="package" version="3.3.2">r-base</requirement> + <requirement type="package" version="1.1_4">r-batch</requirement> + </requirements> + + <stdio> + <exit_code range="1:" level="fatal" /> + </stdio> + + + <command detect_errors="aggressive"><![CDATA[ + Rscript $__tool_directory__/w4mkmeans_wrapper.R + tool_directory $__tool_directory__ + data_matrix_path '$dataMatrix_in' + variable_metadata_path '$variableMetadata_in' + sample_metadata_path '$sampleMetadata_in' + ksamples '$ksamples' + kfeatures '$kfeatures' + iter_max '$iter_max' + nstart '$nstart' + algorithm '$algorithm' + scores_out '$scores_out' + sampleMetadata_out '$sampleMetadata_out' + variableMetadata_out '$variableMetadata_out' + slots "\${GALAXY_SLOTS:-1}" + ; echo exit code $? + ]]></command> + + <inputs> + <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="variable x sample, decimal: '.', missing: NA, mode: numerical, separator: tab" /> + <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="sample x metadata columns, separator: tab" /> + <param name="variableMetadata_in" label="Variable metadata file" type="data" format="tabular" help="variable x metadata columns, separator: tab" /> + <param name="ksamples" label="K value(s) for samples" type="text" value = "0" help="[ksamples] Single K or comma-separated Ks for samples, or 0 for none." /> + <param name="kfeatures" label="K value(s) for features" type="text" value = "0" help="[kfeatures] Single K or comma-separated Ks for features (variables), or 0 for none." /> + <param name="iter_max" label="Max number of iterations" type="text" value = "10" help="[iter_max] The maximum number of iterations allowed; default 10." /> + <param name="nstart" label="Number of random sets" type="text" value = "1" help="[nstart] How many random sets should be chosen; default 1." /> + <param name="algorithm" label="Algorithm for clustering" type="select" value = "Hartigan-Wong" help="[algorithm] K-means clustering algorithm, default 'Hartigan-Wong'; alternatives 'Lloyd', 'MacQueen'; 'Forgy' is a synonym for 'Lloyd', see references for further info."> + <option value="Forgy">Forgy</option> + <option value="Hartigan-Wong" selected="True">Hartigan-Wong</option> + <option value="Lloyd">Lloyd</option> + <option value="MacQueen">MacQueen</option> + </param> + </inputs> + + <outputs> + <data name="sampleMetadata_out" label="${tool.name}_${sampleMetadata_in.name}" format="tabular" ></data> + <data name="variableMetadata_out" label="${tool.name}_${variableMetadata_in.name}" format="tabular" ></data> + <data name="scores_out" label="${tool.name}_${dataMatrix_in.name}.kmeans" format="tabular" ></data> + </outputs> + + <tests> + <test> + <param name="dataMatrix_in" value="input_dataMatrix.tsv"/> + <param name="sampleMetadata_in" value="input_sampleMetadata.tsv"/> + <param name="variableMetadata_in" value="input_variableMetadata.tsv"/> + <param name="ksamples" value="3,4"/> + <param name="kfeatures" value="5,6,7"/> + <param name="iter_max" value="10"/> + <param name="nstart" value="1"/> + <param name="algorithm" value="Hartigan-Wong"/> + <output name="scores_out"> + <assert_contents> + <has_text text="proportion" /> + <has_text text="0.87482" /> + <has_text text="0.89248" /> + <has_text text="0.95355" /> + <has_text text="0.95673" /> + <has_text text="0.95963" /> + </assert_contents> + </output> + </test> + </tests> + + <help> + <![CDATA[ + +**Author** - Arthur Eschenlauer (University of Minnesota, esch0041@umn.edu) + +--------------------------------------------------------------------------- + + +**Source** - The source code for the w4mkmeans tool is available (from the Hegeman lab github repository) at https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper + +**R code used** - The R code invoked by this wrapper is the R 'stats::kmeans' package + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------- + + +**Tool updates** + +See the **NEWS** section at the bottom of this page + +--------------------------------------------------- + +=========================== +K-means for W4M data matrix +=========================== + +----------- +Description +----------- + +Calculate K-means for sample-clusters (or feature-clusters, or both) using W4M dataMatrix (i.e., XCMS-preprocessed data files) as input. + +*Please note that XCMS refers to features as 'variables'. This documentation does not use either term consistently.* + + +----------------- +Workflow Position +----------------- + + - Tool category: Statistical Analysis + - Upstream tool category: Preprocessing + - Downstream tool categories: Statistical Analysis + + +---------- +Motivation +---------- + +This tool clusters samples, features (variables), or both from the W4M dataMatrix and writes the results to new columns in sampleMetadata, variableMetadata, or both, respectively. + + - If several, comma-separated K's are supplied, then one column is added for each K. + - This clustering is **not** hierarchical; each member of a cluster is not a member of any other cluster. + - For feature-clustering, each feature is assigned to a cluster such that the feature's response for all samples is closer to the mean of all features for that cluster than to the mean for any other cluster. + - For sample-clustering, each sample is assigned to a cluster such that the sample's response for all features is closer to the mean of all samples for that cluster than to the mean for any other cluster. + + +----------- +Input files +----------- + ++--------------------------------------------+------------+ +| File | Format | ++============================================+============+ +| Data matrix | tabular | ++--------------------------------------------+------------+ +| Sample metadata | tabular | ++--------------------------------------------+------------+ +| Variable (i.e., feature) metadata | tabular | ++--------------------------------------------+------------+ + + +---------- +Parameters +---------- + +**Data matrix** - input-file dataset + + - XCMS variable x sample 'dataMatrix' (tabular separated values) file of the numeric data matrix, with . as decimal, and NA for missing values; the table must not contain metadata apart from row and column names; the row and column names must be identical to the rownames of the sample and feature metadata, respectively (see below) + +**Sample metadata** - input-file dataset + + - XCMS sample x metadata 'sampleMetadata' (tabular separated values) file of the numeric and/or character sample metadata, with . as decimal and NA for missing values + +**Feature metadata** - input-file dataset + + - XCMS variable x metadata 'variableMetadata' (tabular separated values) file of the numeric and/or character feature metadata, with . as decimal and NA for missing values + +**kfeatures** - K or K's for features (default = 0) + + - integer or comma-separated integers ; zero (the default) or less will result in no calculation. + +**ksamples** - K or K-range for samples (default = 0) + + - integer or comma-separated integers ; zero (the default) or less will result in no calculation. + +**iter_max** - maximum_iterations (default = 10) + + - maximum number of iterations per calculation (see https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html). + +**nstart** - how many random sets should be chosen (default = 1) + + - maximum number of iterations per calculation (see https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html). + +------------ +Output files +------------ + +**XCMS sampleMetadata** - (tabular separated values) file identical to the Sample metadata file given as an input argument, excepting one column added for each K + + - **k#** - cluster number for clustering samples with K = # + +**XCMS variableMetadata** - (tabular separated values) file identical to the Feature metadata file given as an input argument, excepting one column added for each K + + - **k#** - cluster number for clustering features with K = # + +**scores** - (tabular separated values) file with one line for each K. + + - **clusterOn** - what was clustered - either 'sample' or 'feature' + - **k** - the chosen K for clustering + - **totalSS** - total (*between-treatements* plus total of *within-treatements*) sum of squares + - **betweenSS** - *between-treatements* sum of squares + - **proportion** - betweenSS / totalSS + +--------------- +Working example +--------------- + +**Input files** + ++-------------------+-------------------------------------------------------------------------------------------------------------------+ +| Input File | Download from URL | ++===================+===================================================================================================================+ +| Data matrix | https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/input_dataMatrix.tsv | ++-------------------+-------------------------------------------------------------------------------------------------------------------+ +| Sample metadata | https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/input_sampleMetadata.tsv | ++-------------------+-------------------------------------------------------------------------------------------------------------------+ +| Feature metadata | https://raw.githubusercontent.com/HegemanLab/w4mkmeans_galaxy_wrapper/master/test-data/input_variableMetadata.tsv | ++-------------------+-------------------------------------------------------------------------------------------------------------------+ + +**Other input parameters** + ++-----------------+---------------+ +| Input Parameter | Value | ++=================+===============+ +| ksamples | 3,4 | ++-----------------+---------------+ +| kfeatures | 5,6,7 | ++-----------------+---------------+ +| iter_max | 10 | ++-----------------+---------------+ +| nstart | 1 | ++-----------------+---------------+ +| algorithm | Hartigan-Wong | ++-----------------+---------------+ + +---- +NEWS +---- + +August 2017, Version 0.98.1 - First release + +--------- +Citations +--------- + + ]]> + </help> + <citations> + <citation type="bibtex"><![CDATA[ +@incollection{RCoreTeam2017, + title = {stats::kmeans - K-Means Clustering}, + booktitle = {R: A Language and Environment for Statistical Computing}, + author = {{R Core Team}}, + publisher = {R Foundation for Statistical Computing}, + address = {Vienna, Austria}, + year = {2017}, + url = {https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html}, +} + ]]></citation> + <!-- Forgy algorithm --> + <citation type="bibtex"><![CDATA[ +@article{forgy65, + added-at = {2006-03-23T12:22:43.000+0100}, + author = {Forgy, E.}, + biburl = {https://www.bibsonomy.org/bibtex/21e31409932ce91df646c4731350e1207/hotho}, + interhash = {c86383cba8cfe00d5e6ef200016aca3f}, + intrahash = {1e31409932ce91df646c4731350e1207}, + journal = {Biometrics}, + keywords = {clustering kmeans}, + number = 3, + pages = {768-769}, + timestamp = {2006-03-23T12:22:43.000+0100}, + title = {Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of Classification}, + volume = 21, + year = 1965 +} + ]]></citation> + <!-- W4M 3.0 - Guitton et al. 2017--> + <citation type="doi">10.1016/j.biocel.2017.07.002</citation> + <!-- W4M 2.5 - Giacomini et al. 2014 --> + <citation type="doi">10.1093/bioinformatics/btu813</citation> + <!-- Hartigan and Wong algorithm --> + <citation type="bibtex"><![CDATA[ +@article{Hartigan79, + added-at = {2007-02-27T16:22:09.000+0100}, + author = {Hartigan, J. and Wong, M.}, + biburl = {https://www.bibsonomy.org/bibtex/23d8bfc440c5725783876929c022f67ce/pierpaolo.pk81}, + description = {WSD}, + interhash = {10d6d33920d9af578a4d0a556dc1477d}, + intrahash = {3d8bfc440c5725783876929c022f67ce}, + journal = {Applied Statistics}, + keywords = {imported}, + pages = {100-108}, + timestamp = {2007-02-27T16:22:11.000+0100}, + title = {Algorithm AS136: A k-means clustering algorithm}, + volume = 28, + year = 1979 +} + ]]></citation> + <!-- Lloyd algorithm --> + <citation type="doi">10.1109/TIT.1982.1056489</citation> + <!-- MacQueen algorithm --> + <citation type="bibtex"><![CDATA[ +@inproceedings{MacQueen1967, + added-at = {2011-01-11T13:35:01.000+0100}, + author = {MacQueen, J. B.}, + biburl = {https://www.bibsonomy.org/bibtex/25dcdb8cd9fba78e0e791af619d61d66d/enitsirhc}, + booktitle = {Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability}, + editor = {Cam, L. M. Le and Neyman, J.}, + interhash = {8d7d4dfe7d3a06b8c9c3c2bb7aa91e28}, + intrahash = {5dcdb8cd9fba78e0e791af619d61d66d}, + keywords = {kmeans clustering}, + pages = {281-297}, + publisher = {University of California Press}, + timestamp = {2011-01-11T13:35:01.000+0100}, + title = {Some Methods for Classification and Analysis of MultiVariate Observations}, + volume = 1, + year = 1967 +} + ]]></citation> + </citations> + <!-- + vim:et:sw=2:ts=2: +--> </tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/w4mkmeans_routines.R Tue Aug 08 15:30:38 2017 -0400 @@ -0,0 +1,216 @@ +##------------------------------------------------------------------------------------------------------ +## these are the batch-independent and file-structure-independent routines to support the w4mkmeans tool +##------------------------------------------------------------------------------------------------------ + +library(parallel) + +w4kmeans_usage <- function() { + return ( + c( + "w4mkmeans: bad input.", + "# contract:", + " required - caller will provide an environment comprising:", + " log_print - a logging function with the signature function(x, ...) expecting strings as x and ...", + " variableMetadata - the corresponding W4M data.frame having feature metadata", + " sampleMetdata - the corresponding W4M data.frame having sample metadata", + " dataMatrix - the corresponding W4M matrix", + " slots - the number of parallel slots for calculating kmeans", + " optional - environment may comprise:", + " kfeatures - an array of integers, the k's to apply for clustering by feature (default, empty array)", + " ksamples - an array of integers, the k's to apply for clustering by sample (default, empty array)", + " iter.max - the maximum number of iterations when calculating a cluster (default = 10)", + " nstart - how many random sets of centers should be chosen (default = 1)", + " algorithm - string from c('Hartigan-Wong', 'Lloyd', 'Forgy', 'MacQueen') (default = Hartigan-Wong)", + " ", + " this routine will return a list comprising:", + " variableMetadata - the input variableMetadata data.frame with updates, if any", + " sampleMetadata - the input sampleMetadata data.frame with updates, if any", + " scores - an array of strings, each representing a line of a tsv having the following header:", + " clusterOn TAB k TAB totalSS TAB betweenSS TAB proportion" + ) + ) +} + +w4mkmeans <- function(env) { + # abort if 'env' is null or is not an environment + if ( is.null(env) || ! is.environment(env) ) { + lapply(w4kmeans_usage(),print) + } + # supply default arguments + if ( ! exists("iter.max" , env) ) env$iter.max <- 10 + if ( ! exists("nstart" , env) ) env$nstart <- 1 + if ( ! exists("algorithm", env) ) env$algorithm <- 'Hartigan-Wong' + if ( ! exists("ksamples" , env) ) env$ksamples <- c() + if ( ! exists("kfeatures", env) ) env$kfeatures <- c() + # check mandatory arguments + expected <- c( + "log_print" + , "variableMetadata" + , "sampleMetadata" + , "dataMatrix" + , "slots" + ) + missing_from_env <- setdiff(expected, (ls(env))) + if ( length(missing_from_env) > 0 ) { + print(paste(c('expected environment members not found: ', as.character(missing_from_env)), collapse = ", ")) + lapply(w4kmeans_usage(),print) + stop("w4mkmeans: contract has been broken") + } + # extract parameters from 'env' + failure_action <- env$log_print + scores <- c( "clusterOn\tk\ttotalSS\tbetweenSS\tproportion" ) + sampleMetadata <- env$sampleMetadata + featureMetadata <- env$variableMetadata + ksamples <- as.numeric(env$ksamples) + kfeatures <- as.numeric(env$kfeatures) + slots <- env$slots + + myLapply <- parLapply + # uncomment the next line to mimic parLapply, but without parallelization (for testing/experimentation) + # myLapply <- function(cl, ...) lapply(...) + cl <- NULL + if ( identical(myLapply, parLapply) ) { + failure_action(sprintf("w4mkmeans: using parallel evaluation with %d slots", slots)) + failure_action(names(cl)) + cl <- makePSOCKcluster(names = slots) + # from ?makePSOCKcluster: "It is good practice to shut down the workers by calling stopCluster." + clusterExport( + cl = cl + , varlist = c( + "tryCatchFunc" + , "calc_kmeans_one_dimension_one_k" + , "prepare.data.matrix" + ) + ) + final <- function(cl) { + # from ?makePSOCKcluster: "It is good practice to shut down the workers by calling stopCluster." + if ( !is.null(cl) ) { + failure_action("w4mkmeans: stopping cluster used for parallel evaluation") + stopCluster(cl) + } + } + } else { + failure_action("w4mkmeans: using sequential evaluation (1 slot)") + final <- function(cl) { } + } + + tryCatch( + expr = { + # These myLapply calls produce lists of lists of results: + # - The outer list has no keys and its members are accessed by index + # - The inner list has keys "clusters" and "scores" + + # for each $i in ksamples, append column 'k$i' to data frame sampleMetadata + ksamples_length <- length(ksamples) + if ( ksamples_length > 0 ) { + smpl_result_list <- myLapply( + cl = cl + , ksamples + , calc_kmeans_one_dimension_one_k + , env = env + , dimension = "samples" + ) + for ( i in 1:ksamples_length ) { + result <- smpl_result_list[[i]] + if (result$success) { + sampleMetadata[sprintf("k%d",ksamples[i])] <- result$value$clusters + scores <- c(scores, result$value$scores) + } + } + } + + # for each $i in kfeatures, append column 'k$i' to data frame featureMetadata + kfeatures_length <- length(kfeatures) + if ( kfeatures_length > 0 ) { + feat_result_list <- myLapply( + cl = cl + , kfeatures + , calc_kmeans_one_dimension_one_k + , env = env + , dimension = "features" + ) + for ( i in 1:kfeatures_length ) { + result <- feat_result_list[[i]] + if (result$success) { + featureMetadata[sprintf("k%d",kfeatures[i])] <- result$value$clusters + scores <- c(scores, result$value$scores) + } + } + } + + return ( + list( + variableMetadata = featureMetadata + , sampleMetadata = sampleMetadata + , scores = scores + ) + ) + } + , finally = final(cl) + ) +} + +# calculate k-means for features or samples +# - recall that the dataMatrix has features in rows and samples in columns +# return value: +# list(clusters = km$cluster, scores = scores) +# arguments: +# env: +# environment having dataMatrix +# dimension: +# - "samples": produce clusters column to add to the sampleMetadata table +# - this is the default case +# - "variables": produce clusters column to add to the variableMetadata table +# k: +# integer, the number of clusters to make +calc_kmeans_one_dimension_one_k <- function(k, env, dimension = "samples") { + # abort if environment is not as expected + if ( is.null(env) || ! is.environment(env) ) { + stop("calc_kmeans_one_dimension_one_k - argument 'env' is not an environment") + } + if ( ! exists("log_print", env) || ! is.function(env$log_print) ) { + stop("calc_kmeans_one_dimension_one_k - argument 'env' - environment does not include log_print or it is not a function") + } + # abort if k is not as expected + if ( ! is.numeric(k) ) { + stop(sprintf("calc_kmeans_one_dimension_one_k - expected numeric argument 'k' but type is %s", typeof(k))) + } + k <- as.integer(k) + # abort if dimension is not as expected + if ( ! is.character(dimension) + || ! Reduce( f =`|`, x = sapply(X = c("features","samples"), FUN = `==`, dimension), init = FALSE) ) { + stop("calc_kmeans_one_dimension_one_k - argument 'dimension' is neither 'features' nor 'samples'") + } + dm <- env$dataMatrix + iter.max <- env$iter.max + nstart <- env$nstart + algorithm <- env$algorithm + dim_features <- dimension == "features" + # tryCatchFunc produces a list + # On success of expr(), tryCatchFunc produces + # list(success TRUE, value = expr(), msg = "") + # On failure of expr(), tryCatchFunc produces + # list(success = FALSE, value = NA, msg = "the error message") + result_list <- tryCatchFunc( expr = function() { + # kmeans clusters the rows; features are the columns of args_env$dataMatrix; samples, the rows + # - to calculate sample-clusters, no transposition is needed because samples are rows + # - to calculate feature-clusters, transposition is needed so that features will be the rows + if ( ! dim_features ) dm <- t(dm) + dm <- prepare.data.matrix( x.matrix = dm, data.transformation = function(x) { x } ) + # need to set.seed to get reproducible results from kmeans + set.seed(4567) + # do the k-means clustering + km <- kmeans( x = dm, centers = k, iter.max, nstart = nstart, algorithm = algorithm ) + scores <- + sprintf("%s\t%d\t%0.5e\t%0.5e\t%0.5f" + , dimension + , k + , km$totss + , km$betweenss + , km$betweenss/km$totss + ) + list(clusters = km$cluster, scores = scores) + }) + return ( result_list ) +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/w4mkmeans_wrapper.R Tue Aug 08 15:30:38 2017 -0400 @@ -0,0 +1,370 @@ +#!/usr/bin/env Rscript + +# references: +# what this does: +# - [stats::kmeans](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html) +# - [stats::p.adjust](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/p.adjust.html) +# how this does what it does: +# - [parallel::clusterApply](https://stat.ethz.ch/R-manual/R-devel/library/parallel/html/clusterApply.html) + +# invocation: +# Rscript $__tool_directory__/w4mkmeans_wrapper.R \ +# tool_directory $__tool_directory__ +# data_matrix_path '$dataMatrix_in' \ +# variable_metadata_path '$variableMetadata_in' \ +# sample_metadata_path '$sampleMetadata_in' \ +# kfeatures '$kfeatures' \ +# ksamples '$ksamples' \ +# iter_max '$iter_max' \ +# nstart '$nstart' \ +# algorithm '$algorithm' \ +# scores '$scores' \ +# sampleMetadata_out '$sampleMetadata_out' \ +# variableMetadata_out '$variableMetadata_out' \ +# slots "\${GALAXY_SLOTS:-1}" \ +# +# <inputs> +# <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="variable x sample, decimal: '.', missing: NA, mode: numerical, separator: tab" /> +# <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="sample x metadata columns, separator: tab" /> +# <param name="variableMetadata_in" label="Variable metadata file" type="data" format="tabular" help="variable x metadata columns, separator: tab" /> +# <param name="kfeatures" label="K value(s) for features" type="text" value="0" help="Single or min,max value(s) for K for features (variables), or 0 for none." /> +# <param name="ksamples" label="K value(s) for samples" type="text" value="0" help="Single or min,max value(s) for K for samples, or 0 for none." /> +# <param name="iter_max" label="Max number of iterations" type="text" value="10" help="The maximum number of iterations allowed; default 10." /> +# <param name="nstart" label="Number of random sets" type="text" value="1" help="How many random sets should be chosen; default 1." /> +# <param name="algorithm" label="Algorithm for clustering" type="select" value = "Hartigan-Wong" help="K-means clustering algorithm, default 'Hartigan-Wong'; alternatives 'Lloyd', 'MacQueen'; 'Forgy' is a synonym for 'Lloyd', see stats::kmeans reference for further info and references."> +# <option value="Hartigan-Wong" selected="TRUE">Hartigan-Wong</option> +# <option value="Lloyd">Lloyd</option> +# <option value="MacQueen">MacQueen</option> +# <option value="Forgy">Forgy</option> +# </param> +# </inputs> +# <outputs> +# <data name="sampleMetadata_out" label="${tool.name}_${sampleMetadata_in.name}" format="tabular" ></data> +# <data name="variableMetadata_out" label="${tool.name}_${variableMetadata_in.name}" format="tabular" ></data> +# </outputs> + +##------------------------ +## libraries for this file +##------------------------ + +library(batch) ## for 'parseCommandArgs' + +##------------------- +## Pre-initialization +##------------------- + +argVc <- unlist(parseCommandArgs(evaluate=FALSE)) +if ( Reduce( `|`, grepl("tool_directory",names(argVc)) ) ) { + tool_directory <- as.character(argVc["tool_directory"]) +} else { + tool_directory <- "." +} +r_path <- function(f) paste( tool_directory, f, sep = "/" ) + +##---------------------------------------------------------- +## Computation - source general and module-specific routines +##---------------------------------------------------------- + +log_print <- function(x, ...) { + cat( + format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z") + , " " + , c(x, ...) + , "\n" + , sep="" + , file=stderr() + ) +} + +# log_print(sprintf("tool_directory is %s", tool_directory)) + +w4m_general_purpose_routines_path <- r_path("w4m_general_purpose_routines.R") +# log_print(sprintf("w4m_general_purpose_routines_path is %s", w4m_general_purpose_routines_path)) +if ( ! file.exists(w4m_general_purpose_routines_path) ) { + log_print("cannot find file w4m_general_purpose_routines.R") + q(save = "no", status = 1, runLast = TRUE) +} +# log_print("sourcing ",w4m_general_purpose_routines_path) +source(w4m_general_purpose_routines_path) +if ( ! exists("prepare.data.matrix") ) { + log_print("'prepare.data.matrix' was not read from file w4m_general_purpose_routines.R") + q(save = "no", status = 1, runLast = TRUE) +} + +w4mkmeans_routines_path <- r_path("w4mkmeans_routines.R") +# log_print(sprintf("w4mkmeans_routines_path is %s", w4mkmeans_routines_path)) +if ( ! file.exists(w4mkmeans_routines_path) ) { + log_print("cannot find file w4mkmeans_routines.R") + q(save = "no", status = 1, runLast = TRUE) +} +# log_print("sourcing ",w4mkmeans_routines_path) +source(w4mkmeans_routines_path) +if ( ! exists("w4mkmeans") ) { + log_print("'w4mkmeans' was not read from file w4mkmeans_routines.R") + q(save = "no", status = 1, runLast = TRUE) +} + +##----------------------------------------- +## Computation - W4m data-suppport routines +##----------------------------------------- + +# read_data_frame - read a w4m data frame from a tsv, 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 = log_print) { + 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$msg) + } + return (my.env) +} + +# write_result - write a w4m data frame to a tsv +write_result <- function(result, file_path, kind_string, failure_action = log_print) { + my.env <- new.env() + my.env$success <- FALSE + my.env$msg <- sprintf("no message writing %s", kind_string) + tryCatch( + expr = { + write.table( + x = result + , sep = "\t" + , file = file_path + , quote = FALSE + , row.names = FALSE + ) + my.env$success <- TRUE + } + , error = function(e) { + my.env$msg <<- sprintf("%s write failed", kind_string) + } + ) + if (!my.env$success) { + failure_action(my.env$msg) + return (my.env) + } + return (my.env) +} + +# read the three input files +read_input_data <- function(env, failure_action = log_print) { + kind_string <- "none" + tryCatch( + expr = { + # read in the sample metadata + kind_string <- "sample metadata input" + smpl_metadata_input_env <- + read_data_frame( + file_path = env$sample_metadata_path + , kind_string = kind_string + , failure_action = failure_action + ) + if (!smpl_metadata_input_env$success) { + failure_action(smpl_metadata_input_env$msg) + return ( FALSE ) + } + env$sampleMetadata <- smpl_metadata_input_env$data + + # read in the variable metadata + kind_string <- "variable metadata input" + vrbl_metadata_input_env <- + read_data_frame( + file_path = env$variable_metadata_path + , kind_string = kind_string + , failure_action = failure_action + ) + if (!vrbl_metadata_input_env$success) { + failure_action(vrbl_metadata_input_env$msg) + return ( FALSE ) + } + env$variableMetadata <- vrbl_metadata_input_env$data + + # read in the data matrix + kind_string <- "data matrix input" + data_matrix_input_env <- + read_data_frame( + file_path = env$data_matrix_path + , kind_string = kind_string + , failure_action = failure_action + ) + if (!data_matrix_input_env$success) { + failure_action(data_matrix_input_env$msg) + return ( FALSE ) + } + # data frame for dataMatrix has rownames in first column + data_matrix_df <- data_matrix_input_env$data + rownames(data_matrix_df) <- data_matrix_df[,1] + data_matrix <- data_matrix_df[,2:ncol(data_matrix_df)] + env$dataMatrix <- as.matrix(data_matrix) + + } + , error = function(e) { + failure_action( sprintf("read_input_data failed for '%s' - %s", kind_string, format_error(e)) ) + return ( FALSE ) + } + ) + return ( TRUE ) +} + + +read_input_failure_action <- function(x, ...) { + log_print("Failure reading input for '", modNamC, "' Galaxy module call") + log_print(x, ...) +} + +##-------------------------- +## Computation - Entry Point +##-------------------------- + +##---------- +## Constants +##---------- + +modNamC <- "w4mkmeans" ## module name + +## options +##-------- + +# Set the handler for R error-handling +options( show.error.messages = F + , error = function () { + log_print( "Fatal error in '", modNamC, "': ", geterrmessage() ) + q( "no", 1, F ) + } + , warn = -1 + ) + +# strings as factors? - not by default! +# save old value +strAsFacL <- options()$stringsAsFactors +options(stringsAsFactors = FALSE) + + +## log file +##--------- + +log_print("Start of the '", modNamC, "' Galaxy module call") + +## arguments +##---------- + +args_env <- new.env() + +# files + +log_print("PARAMETERS (raw):") +invisible( + lapply( + X = 1:length(argVc) + , FUN = function(i) { + log_print(sprintf(" - %s: %s", names(argVc)[i], argVc[i])) + } + ) +) + +# write.table(as.matrix(argVc), col.names=F, quote=F, sep='\t') + +## output files +sampleMetadata_out <- as.character(argVc["sampleMetadata_out"]) +variableMetadata_out <- as.character(argVc["variableMetadata_out"]) +scores_out <- as.character(argVc["scores_out"]) +## input files +args_env$data_matrix_path <- as.character(argVc["data_matrix_path"]) +args_env$variable_metadata_path <- as.character(argVc["variable_metadata_path"]) +args_env$sample_metadata_path <- as.character(argVc["sample_metadata_path"]) + +# other parameters + +# multi-string args - split csv: "1,2,3" -> c("1","2","3") +args_env$kfeatures <- strsplit(x = as.character(argVc['kfeatures']), split = ",", fixed = TRUE)[[1]] +args_env$ksamples <- strsplit(x = as.character(argVc['ksamples' ]), split = ",", fixed = TRUE)[[1]] +# numeric args +args_env$iter_max <- as.numeric( argVc['iter_max' ]) +args_env$nstart <- as.numeric( argVc['nstart' ]) +args_env$slots <- as.numeric( argVc['slots' ]) +# string args +args_env$algorithm <- as.character( argVc['algorithm']) +args_env$log_print <- log_print + +log_print("PARAMETERS (parsed):") +for (member in ls(args_env)) { + value <- get(member, args_env) + value <- ifelse(length(value) == 1, value, sprintf("c(%s)", paste(value, collapse=", "))) + + log_print(sprintf(" - %s: %s", member, ifelse( !is.function(value) , value, "function" ))) +} +log_print("") + +##--------------------------------------------------------- +## Computation - attempt to read input data +##--------------------------------------------------------- +if ( ! read_input_data(args_env, failure_action = read_input_failure_action) ) { + result <- -1 +} else { + log_print("Input data was read successfully.") + result <- w4mkmeans(env = args_env) + log_print("returned from call to w4mkmeans.") +} + +if ( length(result) == 0 ) { + log_print("no results were produced") + # exit with status code non-zero to indicate error + q(save = "no", status = 1, runLast = FALSE) +} else if ( ! setequal(names(result),c("variableMetadata","sampleMetadata","scores")) ) { + log_print(sprintf("unexpected result keys %s", names(result))) + # exit with status code non-zero to indicate error + q(save = "no", status = 1, runLast = FALSE) +} else if ( ! write_result(result = result$variableMetadata, file_path = variableMetadata_out, kind_string = "clustered variableMetadata")$success ) { + log_print("failed to write output file for clustered variableMetadata") + # exit with status code non-zero to indicate error + q(save = "no", status = 1, runLast = FALSE) +} else if ( ! write_result(result = result$sampleMetadata, file_path = sampleMetadata_out, kind_string = "clustered sampleMetadata")$success ) { + log_print("failed to write output file for clustered sampleMetadata") + # exit with status code non-zero to indicate error + q(save = "no", status = 1, runLast = FALSE) +} else { + tryCatch( + expr = { + fileConn<-file(scores_out) + writeLines(result$scores, fileConn) + close(fileConn) + } + , error = function(e) { + log_print(sprintf("failed to write output file for cluster scores - %s", format_error(e))) + # exit with status code non-zero to indicate error + q(save = "no", status = 1, runLast = FALSE) + } + ) +} + +##-------- +## Closing +##-------- + + +if (!file.exists(sampleMetadata_out)) { + log_print(sprintf("ERROR %s::w4m_kmeans_wrapper - file '%s' was not created", modNamC, sampleMetadata_out)) +} + +if (!file.exists(variableMetadata_out)) { + log_print(sprintf("ERROR %s::w4m_kmeans_wrapper - file '%s' was not created", modNamC, variableMetadata_out)) +} + +if (!file.exists(scores_out)) { + log_print(sprintf("ERROR %s::w4m_kmeans_wrapper - file '%s' was not created", modNamC, scores_out)) +} + +log_print("Normal termination of '", modNamC, "' Galaxy module call") + +# exit with status code zero +q(save = "no", status = 0, runLast = FALSE)