Mercurial > repos > marie-tremblay-metatoul > spectral_normalization
view NmrNormalization_wrapper.R @ 0:a5e6499f1b4d draft default tip
planemo upload for repository https://github.com/workflow4metabolomics/spectral_normalization commit 559844c168f0450b9657346ba890d9c028d7537a
author | marie-tremblay-metatoul |
---|---|
date | Tue, 22 Nov 2016 06:42:44 -0500 |
parents | |
children |
line wrap: on
line source
#!/usr/local/public/bin/Rscript --vanilla --slave --no-site-file ## 070115_NmrBucketing2galaxy_v1.R ## Marie Tremblay-Franco ## MetaboHUB: The French Infrastructure for Metabolomics and Fluxomics ## www.metabohub.fr/en ## marie.tremblay-franco@toulouse.inra.fr runExampleL <- FALSE ##------------------------------ ## Options ##------------------------------ strAsFacL <- options()$stringsAsFactors options(stringsAsFactors = FALSE) ##------------------------------ ## Libraries laoding ##------------------------------ # For parseCommandArgs function library(batch) # R script call source_local <- function(fname) { argv <- commandArgs(trailingOnly = FALSE) base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) source(paste(base_dir, fname, sep="/")) } #Import the different functions source_local("NmrNormalization_script.R") ##------------------------------ ## Errors ????????????????????? ##------------------------------ ##------------------------------ ## Constants ##------------------------------ topEnvC <- environment() flagC <- "\n" ##------------------------------ ## Script ##------------------------------ if(!runExampleL) argLs <- parseCommandArgs(evaluate=FALSE) ## Parameters Loading ##------------------- # Inputs data <- read.table(argLs[["dataMatrix"]],check.names=FALSE,header=TRUE,sep="\t") rownames(data) <- data[,1] data <- data[,-1] scaling <- argLs[["scalingMethod"]] graphique <- argLs[["graphType"]] if (scaling=='PQN') { metadataSample <- read.table(argLs[["sampleMetadata"]],check.names=FALSE,header=TRUE,sep="\t") factor<- argLs[["factor"]] ControlGroup <- argLs[["controlGroup"]] } if (scaling=='QuantitativeVariable') { metadataSample <- read.table(argLs[["sampleMetadata"]],check.names=FALSE,header=TRUE,sep="\t") factor <- argLs[["factor"]] } # Outputs nomGraphe <- argLs[["graphOut"]] dataMatrixOut <- argLs[["dataMatrixOut"]] sampleMetadataOut <- argLs[["sampleMetadataOut"]] variableMetadataOut <- argLs[["variableMetadataOut"]] log <- argLs[["logOut"]] ## Checking arguments ##------------------- error.stock <- "\n" if(length(error.stock) > 1) stop(error.stock) ## Computation ##------------ NormalizationResults <- NmrNormalization(dataMatrix=data,scalingMethod=scaling,sampleMetadata=metadataSample, bioFactor=factor,ControlGroup=ControlGroup, graph=graphique,nomFichier=nomGraphe,savLog.txtC=log) data_normalized <- NormalizationResults[[1]] data_sample <- NormalizationResults[[2]] data_variable <- NormalizationResults[[3]] ## Saving ##------- # Data data_normalized <- cbind(rownames(data_normalized),data_normalized) colnames(data_normalized) <- c("Bucket",colnames(data_normalized)[-1]) write.table(data_normalized,file=argLs$dataMatrixOut,quote=FALSE,row.names=FALSE,sep="\t") # Sample data_sample <- cbind(rownames(data_sample),data_sample) colnames(data_sample) <- c("Sample",colnames(data_sample)[-1]) write.table(data_sample,file=argLs$sampleMetadataOut,quote=FALSE,row.names=FALSE,sep="\t") # Variable data_variable <- cbind(rownames(data_variable),data_variable) colnames(data_variable) <- c("Bucket",colnames(data_variable)[-1]) write.table(data_variable,file=argLs$variableMetadataOut,quote=FALSE,row.names=FALSE,sep="\t") ## Ending ##--------------------- cat("\nEnd of 'Normalization' Galaxy module call: ", as.character(Sys.time()), sep = "") ## sink(NULL) options(stringsAsFactors = strAsFacL) rm(list = ls())