annotate multivariate_wrapper.R @ 0:fafba524dca6 draft

planemo upload for repository https://github.com/workflow4metabolomics/multivariate.git commit 6596dbd39d20ee1962d9ebdd87eec04821239760

#!/usr/bin/env Rscript

library(batch) ## parseCommandArgs

########
# MAIN #
########

argVc <- unlist(parseCommandArgs(evaluate=FALSE))


#### Start_of_tested_code  <- function() {}


##------------------------------
## Initializing
##------------------------------

## options
##--------

strAsFacL <- options()$stringsAsFactors
options(stringsAsFactors = FALSE)

## libraries
##----------

suppressMessages(library(ropls))

if(packageVersion("ropls") < "1.4.0")
    cat("\nWarning: new version of the 'ropls' package is available\n", sep="")

## constants
##----------

modNamC <- "Multivariate" ## module name

topEnvC <- environment()
flgC <- "\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="")


## arguments
##----------

xMN <- 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")
flgF("identical(rownames(xMN), rownames(samDF))", txtC = "Sample names (or number) in the data matrix (first row) and sample metadata (first column) are not identical; use the 'Check Format' module in the 'Quality Control' section")

varDF <- read.table(argVc["variableMetadata_in"],
                    check.names = FALSE,
                    header = TRUE,
                    row.names = 1,
                    sep = "\t")
flgF("identical(colnames(xMN), rownames(varDF))", txtC = "Variable names (or number) in the data matrix (first column) and sample metadata (first column) are not identical; use the 'Check Format' module in the 'Quality Control' section")

flgF("argVc['respC'] == 'none' || (argVc['respC'] %in% colnames(samDF))",
     txtC = paste0("Y Response argument (", argVc['respC'], ") must be either none or one of the column names (first row) of your sample metadata"))
if(argVc["respC"] != "none") {
    yMCN <- matrix(samDF[, argVc['respC']], ncol = 1, dimnames = list(rownames(xMN), argVc['respC']))
} else
    yMCN <- NULL

if(argVc["testL"] == "TRUE") {
    flgF("!is.null(yMCN)",
         txtC = "Predictions cannot be peformed with PCA models")
    flgF("'test.' %in% colnames(samDF)",
         txtC = "No 'test.' column found in the sample metadata")
    flgF("identical(sort(unique(samDF[, 'test.'])), c('no', 'yes'))",
         txtC = "'test.' column of sample metadata must contain both 'yes' (tested samples) and 'no' (samples to be used for model training) values, and nothing else")
    flgF("identical(sort(unique(samDF[, 'test.'])), c('no', 'yes'))",
         txtC = "'test.' column of sample metadata must contain both 'yes' (tested samples) and 'no' (samples to be used for model training) values, and nothing else")
    flgF("!any(is.na(yMCN[samDF[, 'test.'] == 'no', ]))",
         txtC = "samples for model training (i.e. 'no' value in the 'test.' column) should not contain NA in the response")
    tesVl <- samDF[, "test."] == "yes"
    xTesMN <- xMN[tesVl, , drop = FALSE]
    xMN <- xMN[!tesVl, , drop = FALSE]
    yMCN <- yMCN[!tesVl, , drop = FALSE]
} else
    tesVl <- NULL

if(!('parAsColC' %in% names(argVc)))
    argVc["parAsColC"] <- "none"
flgF("argVc['parAsColC'] == 'none' || argVc['parAsColC'] %in% colnames(samDF)", txtC = paste0("Sample color argument (", argVc['parAsColC'], ") must be either none or one of the column names (first row) of your sample metadata"))
if(argVc["parAsColC"] != "none") {
    parAsColFcVn <- samDF[, argVc['parAsColC']]
    if(is.character(parAsColFcVn))
        parAsColFcVn <- factor(parAsColFcVn)
} else
    parAsColFcVn <- NA

if(!('parMahalC' %in% names(argVc)) || argVc["parMahalC"] == "NA") {
    if(!is.null(yMCN) && ncol(yMCN) == 1 && mode(yMCN) == "character")
        argVc["parMahalC"] <- argVc["respC"]
    else
        argVc["parMahalC"] <- "none"
}
flgF("argVc['parMahalC'] == 'none' || (argVc['parMahalC'] %in% colnames(samDF))",
     txtC = paste0("Mahalanobis argument (", argVc['parMahalC'], ") must be either 'NA', 'none' or one of the column names (first row) of your sample metadata"))
if(argVc["parMahalC"] == "none") {
    parEllipsesL <- FALSE
} else {
    if(is.null(yMCN)) { ## PCA case
        flgF("mode(samDF[, argVc['parMahalC']]) == 'character'",
             txtC = paste0("Mahalanobis argument (", argVc['parMahalC'], ") must correspond to a column of characters in your sampleMetadata"))
        parAsColFcVn <- factor(samDF[, argVc["parMahalC"]])
        parEllipsesL <- TRUE
    } else { ## (O)PLS-DA case
        flgF("identical(as.character(argVc['respC']), as.character(argVc['parMahalC']))",
             txtC = paste0("The Mahalanobis argument (", argVc['parMahalC'], ") must be identical to the Y response argument (", argVc['respC'], ")"))
        parEllipsesL <- TRUE
    }
}

if(!('parLabVc' %in% names(argVc)))
    argVc["parLabVc"] <- "none"
flgF("argVc['parLabVc'] == 'none' || (argVc['parLabVc'] %in% colnames(samDF))",
     txtC = paste0("Sample labels argument (", argVc['parLabVc'], ") must be either none or one of the column names (first row) of your sample metadata"))
if('parLabVc' %in% names(argVc))
    if(argVc["parLabVc"] != "none") {
        flgF("mode(samDF[, argVc['parLabVc']]) == 'character'",
             txtC = paste0("The sample label argument (", argVc['parLabVc'], ") must correspond to a sample metadata column of characters (not numerics)"))
        parLabVc <- samDF[, argVc['parLabVc']]
    } else
        parLabVc <- NA

if('parPc1I' %in% names(argVc)) {
    parCompVi <-  as.numeric(c(argVc["parPc1I"], argVc["parPc2I"]))
} else
    parCompVi <- c(1, 2)


## checking
##---------


flgF("argVc['predI'] == 'NA' || argVc['orthoI'] == 'NA' || as.numeric(argVc['orthoI']) > 0 || parCompVi[2] <=  as.numeric(argVc['predI'])",
     txtC = paste0("The highest component to display (", parCompVi[2], ") must not exceed the number of predictive components of the model (", argVc['predI'], ")"))


if(argVc["orthoI"] == "NA" || argVc["orthoI"] != "0")
    if(argVc["predI"] == "NA" || argVc["predI"] != "0") {
        argVc["predI"] <- "1"
        cat("\nWarning: OPLS: number of predictive components ('predI' argument) set to 1\n", sep = "")
    }

if(argVc["predI"] != "NA")
    if(as.numeric(argVc["predI"]) > min(nrow(xMN), ncol(xMN))) {
        argVc["predI"] <- as.character(min(nrow(xMN), ncol(xMN)))
        cat("\nWarning: 'predI' set to the minimum of the dataMatrix dimensions: ", as.numeric(argVc["predI"]), "\n", sep = "")
    }

if("algoC" %in% names(argVc) && argVc["algoC"] == "svd" && length(which(is.na(c(xMN)))) > 0) {
    minN <- min(c(xMN[!is.na(xMN)])) / 2
    cat("\nWarning: Missing values set to ", round(minN, 1), " (half minimum value) for 'svd' algorithm to be used\n", sep = "")
}


##------------------------------
## Computation and plot
##------------------------------


sink()

optWrnN <- options()$warn
options(warn = -1)

ropLs <- opls(x = xMN,
              y = yMCN,
              predI = ifelse(argVc["predI"] == "NA", NA, as.numeric(argVc["predI"])),
              orthoI = ifelse(argVc["orthoI"] == "NA", NA, as.numeric(argVc["orthoI"])),
              algoC = ifelse('algoC' %in% names(argVc), argVc["algoC"], "default"),
              crossvalI = ifelse('crossvalI' %in% names(argVc), as.numeric(argVc["crossvalI"]), 7),
              log10L = ifelse('log10L' %in% names(argVc), as.logical(argVc["log10L"]), FALSE),
              permI = ifelse('permI' %in% names(argVc), as.numeric(argVc["permI"]), 20),
              scaleC = ifelse('scaleC' %in% names(argVc), argVc["scaleC"], "standard"),
              subset = NULL,
              printL = FALSE,
              plotL = FALSE,
              .sinkC = argVc['information'])

modC <- ropLs@typeC
sumDF <- getSummaryDF(ropLs)
desMC <- ropLs@descriptionMC
scoreMN <- getScoreMN(ropLs)
loadingMN <- getLoadingMN(ropLs)

vipVn <- coeMN <- orthoScoreMN <- orthoLoadingMN <- orthoVipVn <- NULL

if(grepl("PLS", modC)) {

    vipVn <- getVipVn(ropLs)
    coeMN <- coef(ropLs)

    if(grepl("OPLS", modC)) {
        orthoScoreMN <- getScoreMN(ropLs, orthoL = TRUE)
        orthoLoadingMN <- getLoadingMN(ropLs, orthoL = TRUE)
        orthoVipVn <- getVipVn(ropLs, orthoL = TRUE)
    }

}

ploC <- ifelse('typeC' %in% names(argVc), argVc["typeC"], "summary")

if(sumDF[, "pre"] + sumDF[, "ort"] < 2) {
    if(!(ploC %in% c("permutation", "overview"))) {
        ploC <- "summary"
        plotWarnL <- TRUE
    }
} else
    plotWarnL <- FALSE

plot(ropLs,
     typeVc = ploC,
     parAsColFcVn = parAsColFcVn,
     parCexN = ifelse('parCexN' %in% names(argVc), as.numeric(argVc["parCexN"]), 0.8),
     parCompVi = parCompVi,
     parEllipsesL = parEllipsesL,
     parLabVc = parLabVc,
     file.pdfC = argVc['figure'],
     .sinkC = argVc['information'])

options(warn = optWrnN)


##------------------------------
## Print
##------------------------------


sink(argVc["information"], append = TRUE)

if(plotWarnL)
    cat("\nWarning: For single component models, only 'overview' (and 'permutation' in case of single response (O)PLS(-DA)) plot(s) are available\n", sep = "")


cat("\n", modC, "\n", sep = "")

cat("\n", desMC["samples", ],
    " samples x ",
    desMC["X_variables", ],
    " variables",
    ifelse(modC != "PCA",
           " and 1 response",
           ""),
    "\n", sep = "")

cat("\n", ropLs@suppLs[["scaleC"]], " scaling of dataMatrix",
            ifelse(modC == "PCA",
                   "",
                   paste0(" and ",
                          ifelse(mode(ropLs@suppLs[["yMCN"]]) == "character" && ropLs@suppLs[["scaleC"]] != "standard",
                                 "standard scaling of ",
                                 ""),
                          "response\n")), sep = "")

if(substr(desMC["missing_values", ], 1, 1) != "0")
    cat("\n", desMC["missing_values", ], " NAs\n", sep = "")

if(substr(desMC["near_zero_excluded_X_variables", ], 1, 1) != "0")
    cat("\n", desMC["near_zero_excluded_X_variables", ],
        " excluded variables during model building (because of near zero variance)\n", sep = "")

cat("\n")

optDigN <- options()[["digits"]]
options(digits = 3)
print(ropLs@modelDF)
options(digits = optDigN)


##------------------------------
## Ending
##------------------------------


## Saving
##-------


rspModC <- gsub("-", "", modC)
if(rspModC != "PCA")
    rspModC <- paste0(make.names(argVc['respC']), "_", rspModC)

if(sumDF[, "pre"] + sumDF[, "ort"] < 2) {

    tCompMN <- scoreMN
    pCompMN <- loadingMN

} else {

    if(sumDF[, "ort"] > 0) {
        if(parCompVi[2] > sumDF[, "ort"] + 1)
            stop("Selected orthogonal component for plotting (ordinate) exceeds the total number of orthogonal components of the model", call. = FALSE)
        tCompMN <- cbind(scoreMN[, 1], orthoScoreMN[, parCompVi[2] - 1])
        pCompMN <- cbind(loadingMN[, 1], orthoLoadingMN[, parCompVi[2] - 1])
        colnames(pCompMN) <- colnames(tCompMN) <- c("h1", paste("o", parCompVi[2] - 1, sep = ""))
    } else {
        if(max(parCompVi) > sumDF[, "pre"])
            stop("Selected component for plotting as ordinate exceeds the total number of predictive components of the model", call. = FALSE)
        tCompMN <- scoreMN[, parCompVi, drop = FALSE]
        pCompMN <- loadingMN[, parCompVi, drop = FALSE]
    }

}

## x-scores and prediction

colnames(tCompMN) <- paste0(rspModC, "_XSCOR-", colnames(tCompMN))
tCompDF <- as.data.frame(tCompMN)[rownames(samDF), , drop = FALSE]

if(modC != "PCA") {

    if(!is.null(tesVl)) {
        tCompFulMN <- matrix(NA,
                             nrow = nrow(samDF),
                             ncol = ncol(tCompMN),
                             dimnames = list(rownames(samDF), colnames(tCompMN)))
        mode(tCompFulMN) <- "numeric"
        tCompFulMN[rownames(tCompMN), ] <- tCompMN
        tCompMN <- tCompFulMN

        fitMCN <- fitted(ropLs)
        fitFulMCN <- matrix(NA,
                            nrow = nrow(samDF),
                            ncol = 1,
                            dimnames = list(rownames(samDF), NULL))
        mode(fitFulMCN) <- mode(fitMCN)
        fitFulMCN[rownames(fitMCN), ] <- fitMCN
        yPreMCN <- predict(ropLs, newdata = as.data.frame(xTesMN))
        fitFulMCN[rownames(yPreMCN), ] <- yPreMCN
        fitMCN <- fitFulMCN

    } else
        fitMCN <- fitted(ropLs)

    colnames(fitMCN) <- paste0(rspModC,
                               "_predictions")
    fitDF <- as.data.frame(fitMCN)[rownames(samDF), , drop = FALSE]

    tCompDF <- cbind.data.frame(tCompDF, fitDF)
}

samDF <- cbind.data.frame(samDF, tCompDF)

## x-loadings and VIP

colnames(pCompMN) <- paste0(rspModC, "_XLOAD-", colnames(pCompMN))
if(!is.null(vipVn)) {
    pCompMN <- cbind(pCompMN, vipVn)
    colnames(pCompMN)[ncol(pCompMN)] <- paste0(rspModC,
                                               "_VIP",
                                               ifelse(!is.null(orthoVipVn),
                                                      "_pred",
                                                      ""))
    if(!is.null(orthoVipVn)) {
        pCompMN <- cbind(pCompMN, orthoVipVn)
        colnames(pCompMN)[ncol(pCompMN)] <- paste0(rspModC,
                                                   "_VIP_ortho")
    }
}
if(!is.null(coeMN)) {
    pCompMN <- cbind(pCompMN, coeMN)
    colnames(pCompMN)[ncol(pCompMN)] <- paste0(rspModC, "_COEFF")
}
pCompDF <- as.data.frame(pCompMN)[rownames(varDF), , drop = FALSE]
varDF <- cbind.data.frame(varDF, pCompDF)

## sampleMetadata

samDF <- cbind.data.frame(sampleMetadata = rownames(samDF),
                          samDF)
write.table(samDF,
            file = argVc["sampleMetadata_out"],
            quote = FALSE,
            row.names = FALSE,
            sep = "\t")

## variableMetadata

varDF <- cbind.data.frame(variableMetadata = rownames(varDF),
                          varDF)
write.table(varDF,
            file = argVc["variableMetadata_out"],
            quote = FALSE,
            row.names = FALSE,
            sep = "\t")

# Output ropLs
if ( ! is.null(argVc['ropls_out']))
	save(ropLs, file = argVc['ropls_out'])

## Closing
##--------

cat("\nEnd of '", modNamC, "' Galaxy module call: ",
    as.character(Sys.time()), "\n", sep = "")

sink()

options(stringsAsFactors = strAsFacL)


#### End_of_tested_code <- function() {}


rm(list = ls())