--- a/galaxy/asca_wrapper.R	Mon Jul 30 07:47:12 2018 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,225 +0,0 @@
-#!/usr/bin/env Rscript
-
-###################################################################################################
-#
-# MetStaT ASCA.calculate function
-#
-#
-# R-Package: MetStaT
-#
-# Version: 1.0
-#
-# Author (asca.calculate): Tim Dorscheidt
-# Author (wrapper & .r adaptation for workflow4metabolomics.org): M. Tremblay-Franco & Y. Guitton #
-# 
-# Expected parameters from the commandline
-# input files:
-#             dataMatrix
-#             sampleMetadata
-#             variableMetadata
-# params:
-#             Factors (Factor1 & Factor2)
-#             scaling
-#             Number of permutations
-#             Significance threshold
-# output files:
-#             sampleMetadata
-#             variableMetadata
-#			  Graphical outputs
-#			  Information text
-###################################################################################################
-pkgs=c("MetStaT","batch","pcaMethods")
-for(pkg in pkgs) {
-  suppressPackageStartupMessages( stopifnot( library(pkg, quietly=TRUE, logical.return=TRUE, character.only=TRUE)))
-  cat(pkg,"\t",as.character(packageVersion(pkg)),"\n",sep="")
-}
-
-
-listArguments = parseCommandArgs(evaluate=FALSE) #interpretation of arguments given in command line as an R list of objects
-
-#Redirect all stdout to the log file
-sink(listArguments$information)
-
-# ----- PACKAGE -----
-cat("\tPACKAGE INFO\n")
-sessionInfo()
-
-source_local <- function(fname) {
-    argv <- commandArgs(trailingOnly = FALSE)
-    base_dir <- dirname(substring(argv[grep("--file=", argv)], 8))
-    source(paste(base_dir, fname, sep="/"))
-}
-
-#load asca_w4m function
-source_local("asca_w4m.R")
-source_local("ASCA.Calculate_w4m.R")
-source_local("ASCA.PlotScoresPerLevel_w4m.R")
-print("first loadings OK")
-
-## libraries
-##----------
-
-cat('\n\nRunning ASCA.calculate\n');
-options(warn=-1);
-#remove rgl warning
-options(rgl.useNULL = TRUE);
-
-
-## constants
-##----------
-
-modNamC <- "asca" ## module name
-
-topEnvC <- environment()
-flgC <- "\n"
-
-## functions
-##----------For manual input of function
-##--end function
-
-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
-##---------
-cat("\nStart of the '", modNamC, "' Galaxy module call: ",
-    format(Sys.time(), "%a %d %b %Y %X"), "\n", sep="")
-
-
-## arguments
-##----------
-## loading files and checks
-xMN <- t(as.matrix(read.table(listArguments[["dataMatrix_in"]],
-                              check.names = FALSE,
-                              header = TRUE,
-                              row.names = 1,
-                              sep = "\t")))
-
-samDF <- read.table(listArguments[["sampleMetadata_in"]],
-                    check.names = FALSE,
-                    header = TRUE,
-                    row.names = 1,
-					sep = "\t")
-
-varDF <- read.table(listArguments[["variableMetadata_in"]],
-                    check.names = FALSE,
-                    header = TRUE,
-                    row.names = 1,
-					sep = "\t")
-
-result <- asca_w4m(xMN, samDF, c(listArguments[["factor1"]],listArguments[["factor2"]]), varDF, as.numeric(listArguments[["threshold"]]), 
-                   scaling=listArguments[["scaling"]], listArguments[["nPerm"]])
-
-
-##saving
-
-if (exists("result")) {
-	## writing output files
-	cat("\n\nWriting output files\n\n");
-	write.table(result[[4]],
-			file = listArguments$sampleMetadata_out,
-			quote = FALSE,
-			row.names = TRUE,
-			sep = "\t")
-
-	write.table(result[[5]],
-			file = listArguments$variableMetadata_out,
-			quote = FALSE,
-			row.names = TRUE,
-			sep = "\t")
-
-	# Graphical display for each significant parameter
-	print(result[[3]])
-	cat("\n p-value of Residuals must not be taken into account\n")
-	
-	if (any(result[[2]] < as.numeric(listArguments[["threshold"]])))
-	{
-		data.asca.permutation <- result[[2]]
-		design <- data.matrix(samDF[, colnames(samDF) %in% c(listArguments[["factor1"]],listArguments[["factor2"]])])
-		
-		pdf(listArguments$figure, onefile=TRUE)
-		par(mfrow=c(1,3))
-		if (data.asca.permutation[1] < as.numeric(listArguments[["threshold"]]))
-		{
-			eigenvalues <- data.frame(1:length(unique(design[,1])), result[[1]]$'1'$svd$var.explained[1:length(unique(design[,1]))])
-			colnames(eigenvalues) <- c("PC", "explainedVariance")
-			barplot(eigenvalues[,2], names.arg=eigenvalues[,1], ylab="% of explained variance", xlab="Principal component")
-			noms <- levels(as.factor(samDF[, listArguments$factor1]))
-			ASCA.PlotScoresPerLevel_w4m(result[[1]], ee="1", interaction=0, factorName=listArguments$factor1, factorModalite=noms)
-			f1.loadings <- data.matrix(result[[5]][,2:3])
-			f1.loadings.leverage <- diag(f1.loadings%*%t(f1.loadings))
-			names(f1.loadings.leverage) <- colnames(xMN)
-			f1.loadings.leverage <- sort(f1.loadings.leverage, decreasing=TRUE)
-			barplot(f1.loadings.leverage[f1.loadings.leverage > 0.001], main="PC1 loadings")
-		}
-		if (data.asca.permutation[2] < as.numeric(listArguments[["threshold"]]))
-		{
-			eigenvalues <- data.frame(1:length(unique(design[,2])), result[[1]]$'2'$svd$var.explained[1:length(unique(design[,2]))])
-			colnames(eigenvalues) <- c("PC", "explainedVariance")
-			barplot(eigenvalues[,2], names.arg=eigenvalues[,1], ylab="% of explained variance", xlab="Principal component")    
-			noms <- levels(as.factor(samDF[, listArguments$factor2]))
-			ASCA.PlotScoresPerLevel_w4m(result[[1]], ee="2", interaction=0, factorName=listArguments$factor2, factorModalite=noms)
-			f2.loadings <- data.matrix(result[[5]][,4:5])
-			f2.loadings.leverage <- diag(f2.loadings%*%t(f2.loadings))
-			names(f2.loadings.leverage) <- colnames(xMN)
-			f2.loadings.leverage <- sort(f2.loadings.leverage, decreasing=TRUE)
-			barplot(f2.loadings.leverage[f2.loadings.leverage > 0.001], main="PC1 loadings")
-		}
-  	if (data.asca.permutation[3] < as.numeric(listArguments[["threshold"]]))
-  	{
-  	  eigenvalues <- data.frame(1:(length(unique(design[,1]))*length(unique(design[,2]))), result[[1]]$'12'$svd$var.explained[1:(length(unique(design[,1]))*length(unique(design[,2])))])
-  	  colnames(eigenvalues) <- c("PC", "explainedVariance")
-  	  barplot(eigenvalues[,2], names.arg=eigenvalues[,1], ylab="% of explained variance", xlab="Principal component")
-  	  noms1 <- data.matrix(levels(as.factor(samDF[, listArguments$factor1])))
-  	  noms2 <- data.matrix(levels(as.factor(samDF[, listArguments$factor2])))
-  	  noms <- apply(noms1, 1, FUN=function(x){paste(x, "-", noms2, sep="")})
-  	  noms <- apply(noms, 1, FUN=function(x){c(noms)})
-  	  ASCA.PlotScoresPerLevel_w4m(result[[1]], ee="12", interaction=1, factorModalite=noms[,1])
-  	  f1f2.loadings <- data.matrix(result[[5]][,6:7])
-  	  f1f2.loadings.leverage <- diag(f1f2.loadings%*%t(f1f2.loadings))
-  	  names(f1f2.loadings.leverage) <- colnames(xMN)
-  	  f1f2.loadings.leverage <- sort(f1f2.loadings.leverage, decreasing=TRUE)
-  	  barplot(f1f2.loadings.leverage[f1f2.loadings.leverage > 0.001], main="PC1 loadings")
-  	}
-    dev.off()
-	}
-
-	tryCatch({
-	save(result, file="asca.RData");
-	}, warning = function(w) {
-	print(paste("Warning: ", w));
-	}, error = function(err) {
-	stop(paste("ERROR saving result RData object:", err));
-	});
-}
-
-## ending
-##-------
-
-cat("\nEnd of the '", modNamC, "' Galaxy module call: ",
-    format(Sys.time(), "%a %d %b %Y %X"), "\n", sep = "")
-
-sink()
-
-# options(stringsAsFactors = strAsFacL)
-
-
-rm(list = ls())