Mercurial > repos > melpetera > intensity_checks
changeset 7:00181e66c50f draft default tip
planemo upload for repository https://github.com/workflow4metabolomics/tools-metabolomics commit 689ab2a3db06eeed6a190f3cfa1618b9d9b7b07b
| author | workflow4metabolomics |
|---|---|
| date | Wed, 09 Jul 2025 15:43:29 +0000 |
| parents | ec75de7f1e08 |
| children | |
| files | Intchecks/README.md Intchecks/RcheckLibrary.R Intchecks/Script_intensity_check.R Intchecks/parseargs.R Intchecks/static/images/int_check.png Intchecks/test-data/input_IC_DM.txt Intchecks/test-data/input_IC_SM.txt Intchecks/test-data/input_IC_VM.txt Intchecks/test-data/output_IC_VM.tabular Intchecks/wrapper_intensity_check.R Intchecks/xml_intensity_check.xml README.md static/images/int_check.png test-data/ic_in_dataMatrix.tabular test-data/ic_in_sampleMetadata.tabular test-data/ic_in_variableMetadata.tabular test-data/ic_out_graphs.pdf test-data/ic_out_graphs_classes.pdf test-data/ic_out_variableMetadata.tabular test-data/ic_out_variableMetadata_classes.tabular wrapper_intensity_check.R xml_intensity_check.xml |
| diffstat | 22 files changed, 4904 insertions(+), 1119 deletions(-) [+] |
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--- a/Intchecks/README.md Mon Dec 11 12:56:20 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,51 +0,0 @@ -Intensity check -======= - -Metadata ------------ - - * **@name**: Intensity check - * **@version**: 1.3.0 - * **@authors**: Original code: Anthony Fernandes (PFEM - INRA) - Maintainer: Melanie Petera (PFEM - INRAE - MetaboHUB) - * **@init date**: 2018, September - * **@main usage**: This tool computes some statistical measures, number of missing values and mean fold change. - - -Context ------------ - -This tool is provided as one of the [Workflow4Metabolomics](http://workflow4metabolomics.org) Galaxy instance statistical tools. -W4M is a French infrastructure providing software tools to process, analyse and annotate metabolomics data. - -User interface is based on the Galaxy platform (homepage: https://galaxyproject.org/). It is an open, web-based platform for data intensive biomedical research. -Whether on the free public server or your own instance, you can perform, reproduce, and share complete analyses. - - -Configuration ------------ - -### Requirement: - * R software: version > 4.0.0 recommended - * Specific R libraries: none - - -Technical description ------------ - -Main files: - -- Script_intensity_check.R: R function (core script) -- wrapper_intensity_check.R: R script to link the main R function to inputs -- xml_intensity_check.xml: XML wrapper (interface for Galaxy) - - -Services provided ------------ - - * Help and support: support@workflow4metabolomics.org - - -License ------------ - - * Cea Cnrs Inria Logiciel Libre License, version 2.1 (CECILL-2.1)
--- a/Intchecks/RcheckLibrary.R Mon Dec 11 12:56:20 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,124 +0,0 @@ -###################################################### -# R check library -# Coded by: M.Petera, -# - - -# R functions to use in R scripts -# (management of various generic subroutines) -# - - -# V0: script structure + first functions -# V1: More detailed error messages in match functions -###################################################### - - -# Generic function to return an error if problems have been encountered - - - - - -check.err <- function(err.stock){ - - # err.stock = vector of results returned by check functions - - if(length(err.stock)!=0){ stop("\n- - - - - - - - -\n",err.stock,"\n- - - - - - - - -\n") } - -} - - - - -# Table match check functions - - - - - - - - - - - - - - - - - - - - - - - - - - -# To check if dataMatrix and (variable or sample)Metadata match regarding identifiers -match2 <- function(dataMatrix, Metadata, Mtype){ - - # dataMatrix = data.frame containing dataMatrix - # Metadata = data.frame containing sampleMetadata or variableMetadata - # Mtype = "sample" or "variable" depending on Metadata content - - err.stock <- NULL # error vector - - id2 <- Metadata[,1] - if(Mtype=="sample"){ id1 <- colnames(dataMatrix)[-1] } - if(Mtype=="variable"){ id1 <- dataMatrix[,1] } - - if( length(which(id1%in%id2))!=length(id1) || length(which(id2%in%id1))!=length(id2) ){ - err.stock <- c("\nData matrix and ",Mtype," metadata do not match regarding ",Mtype," identifiers.") - if(length(which(id1%in%id2))!=length(id1)){ - if(length(which(!(id1%in%id2)))<4){ err.stock <- c(err.stock,"\n The ") - }else{ err.stock <- c(err.stock,"\n For example, the ") } - err.stock <- c(err.stock,"following identifiers found in the data matrix\n", - " do not appear in the ",Mtype," metadata file:\n") - identif <- id1[which(!(id1%in%id2))][1:min(3,length(which(!(id1%in%id2))))] - err.stock <- c(err.stock," ",paste(identif,collapse="\n "),"\n") - } - if(length(which(id2%in%id1))!=length(id2)){ - if(length(which(!(id2%in%id1)))<4){ err.stock <- c(err.stock,"\n The ") - }else{ err.stock <- c(err.stock,"\n For example, the ") } - err.stock <- c(err.stock,"following identifiers found in the ",Mtype," metadata file\n", - " do not appear in the data matrix:\n") - identif <- id2[which(!(id2%in%id1))][1:min(3,length(which(!(id2%in%id1))))] - err.stock <- c(err.stock," ",paste(identif,collapse="\n "),"\n") - } - err.stock <- c(err.stock,"\nPlease check your data.\n") - } - - return(err.stock) - -} - -# To check if the 3 standard tables match regarding identifiers -match3 <- function(dataMatrix, sampleMetadata, variableMetadata){ - - # dataMatrix = data.frame containing dataMatrix - # sampleMetadata = data.frame containing sampleMetadata - # variableMetadata = data.frame containing variableMetadata - - err.stock <- NULL # error vector - - id1 <- colnames(dataMatrix)[-1] - id2 <- sampleMetadata[,1] - id3 <- dataMatrix[,1] - id4 <- variableMetadata[,1] - - if( length(which(id1%in%id2))!=length(id1) || length(which(id2%in%id1))!=length(id2) ){ - err.stock <- c(err.stock,"\nData matrix and sample metadata do not match regarding sample identifiers.") - if(length(which(id1%in%id2))!=length(id1)){ - if(length(which(!(id1%in%id2)))<4){ err.stock <- c(err.stock,"\n The ") - }else{ err.stock <- c(err.stock,"\n For example, the ") } - err.stock <- c(err.stock,"following identifiers found in the data matrix\n", - " do not appear in the sample metadata file:\n") - identif <- id1[which(!(id1%in%id2))][1:min(3,length(which(!(id1%in%id2))))] - err.stock <- c(err.stock," ",paste(identif,collapse="\n "),"\n") - } - if(length(which(id2%in%id1))!=length(id2)){ - if(length(which(!(id2%in%id1)))<4){ err.stock <- c(err.stock,"\n The ") - }else{ err.stock <- c(err.stock,"\n For example, the ") } - err.stock <- c(err.stock,"following identifiers found in the sample metadata file\n", - " do not appear in the data matrix:\n") - identif <- id2[which(!(id2%in%id1))][1:min(3,length(which(!(id2%in%id1))))] - err.stock <- c(err.stock," ",paste(identif,collapse="\n "),"\n") - } - } - - if( length(which(id3%in%id4))!=length(id3) || length(which(id4%in%id3))!=length(id4) ){ - err.stock <- c(err.stock,"\nData matrix and variable metadata do not match regarding variable identifiers.") - if(length(which(id3%in%id4))!=length(id3)){ - if(length(which(!(id3%in%id4)))<4){ err.stock <- c(err.stock,"\n The ") - }else{ err.stock <- c(err.stock,"\n For example, the ") } - err.stock <- c(err.stock,"following identifiers found in the data matrix\n", - " do not appear in the variable metadata file:\n") - identif <- id3[which(!(id3%in%id4))][1:min(3,length(which(!(id3%in%id4))))] - err.stock <- c(err.stock," ",paste(identif,collapse="\n "),"\n") - } - if(length(which(id4%in%id3))!=length(id4)){ - if(length(which(!(id4%in%id3)))<4){ err.stock <- c(err.stock,"\n The ") - }else{ err.stock <- c(err.stock,"\n For example, the ") } - err.stock <- c(err.stock,"following identifiers found in the variable metadata file\n", - " do not appear in the data matrix:\n") - identif <- id4[which(!(id4%in%id3))][1:min(3,length(which(!(id4%in%id3))))] - err.stock <- c(err.stock," ",paste(identif,collapse="\n "),"\n") - } - } - - if(length(err.stock)!=0){ err.stock <- c(err.stock,"\nPlease check your data.\n") } - - return(err.stock) - -} \ No newline at end of file
--- a/Intchecks/Script_intensity_check.R Mon Dec 11 12:56:20 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,473 +0,0 @@ -######################################################################### -# SCRIPT INTENSITY CHECK # -# # -# Input: Data Matrix, VariableMetadata, SampleMetadata # -# Output: VariableMetadata, Graphics # -# # -# Dependencies: RcheckLibrary.R # -# # -######################################################################### - - -# Parameters (for dev) -if(FALSE){ - - rm(list = ls()) - setwd("Y:\\Developpement\\Intensity check\\Pour tests\\Tests_global") - - DM.name <- "DM_NA.tabular" - SM.name <- "SM_NA.tabular" - VM.name <- "vM_NA.tabular" - method <- "one_class" - chosen.stat <- "mean,sd,quartile,decile,NA" - class.col <- "uv" - test.fold <- "Yes" - class1 <- "Pools" - fold.frac <- "Top" - logarithm <- "log10" - VM.output <- "new_VM.txt" - graphs.output <- "Barplots_and_Boxplots.pdf" -} - - - - -intens_check <- function(DM.name, SM.name, VM.name, method, chosen.stat, class.col, test.fold, class1, fold.frac, - logarithm, VM.output, graphs.output){ - - # This function allows to check the intensities with various statistics, number of missing values and mean fold change - # - # Three methods proposed: - # - global: tests for each variable without distinction between samples - # - one class: one class versus all the remaining samples - # - each class: if the class columns contains at least three classes and you want to test each of them - # - # Parameters: - # DM.name, SM.name, VM.name: dataMatrix, sampleMetadata, variableMetadata files access - # method: "global", "one_class", "each_class" - # chosen.stat: character listing the chosen analysis (comma-separated) - # class.col: number of the sampleMetadata's column with classes (if method = one_class or each_class) - # test.fold: "yes" or "no" (if method = one_class or each_class) - # class1: name of the class (if method = one_class) - # fold.frac: "Top" -> class1/other or "Bottom" -> other/class1 (if method = one_class) - # logarithm: "log2", "log10" or "none" (if method = one_class or each_class) - # VM.output: output file's access (VM with new columns) - # graphs.output: pdf file's access with barplots for the proportion of NA and boxplots with the folds values - - - - - # Input --------------------------------------------------------------------------------------------------- - - DM <- read.table(DM.name, header=TRUE, sep="\t", check.names=FALSE) - SM <- read.table(SM.name, header=TRUE, sep="\t", check.names=FALSE) - VM <- read.table(VM.name, header=TRUE, sep="\t", check.names=FALSE) - - - - # Table match check with Rchecklibrary - table.check <- match3(DM, SM, VM) - check.err(table.check) - - # Transposing the dataMatrix - rownames(DM) <- DM[,1] - var_names <- DM[,1] - DM <- DM[,-1] - DM <- data.frame(t(DM)) - - # Re-ordering the dataMatrix to match the sampleMetadata file order - DM <- merge(x=cbind(1:nrow(SM),SM), y=DM, by.x=2, by.y=0) - DM <- DM[order(DM[,2]),] - rownames(DM) <- DM[,1] - DM <- DM[,-c(1:(ncol(SM)+1))] - - - stat.list <- strsplit(chosen.stat,",")[[1]] - - - # check class.col, class1 and the number of classes --------------------------------------------------------- - - #set 1 class for all samples in case of method = no_class - if(method=="no_class"){ - c_class <- rep("global", length=nrow(DM)) - classnames <- "global" - nb_class=1 - test.fold <- "No" - } - - - if(method != "no_class"){ - - if(!(class.col %in% colnames(SM))){ - stop("\n- - - - - - - - -\n", "The ",class.col, " column is not found in the specified sample metadata file.","\n- - - - - - - - -\n") - } - - c_class <- SM[,class.col] - c_class <- as.factor(c_class) - nb_class <- nlevels(c_class) - classnames <- levels(c_class) - - if((nb_class < 2)&&(test.fold=="Yes")){ - err.1class <- c("\n The ",class.col, " column contains only one class, fold calculation could not be executed. \n") - cat(err.1class) - } - - if((nb_class > (nrow(SM))/3)&&(method == "each_class")){ - class.err <- c("\n There are too many classes, think about reducing the number of classes and excluding those - with few samples. \n") - cat(class.err) - } - - - if(method == "one_class"){ - if(!(class1 %in% classnames)){ - list.class1 <- c("\n Classes:",classnames,"\n") - cat(list.class1) - err.class1 <- c("The ",class1, " class does not appear in the ",class.col," column.") - stop("\n- - - - - - - - -\n", err.class1,"\n- - - - - - - - -\n") - } - - #If method is "one_class", change others classes in "other" - for(i in 1:length(c_class)){ - if(c_class[i]!=class1){ - c_class <- as.character(c_class) - c_class[i] <- "Other" - c_class <- as.factor(c_class) - nb_class <- nlevels(c_class) - classnames <- c(class1,"Other") - } - } - } - - } - - - # Statistics ------------------------------------------------------------------------------------------------ - - # Check whether the dataMatrix contains non-numeric values - if(!(is.numeric(as.matrix(DM)))){ - # findchar definition - findchar <- function(myval){ - if(is.na(myval)){ - return("ok") - }else{ - mytest <- as.character(myval) - if(is.na(as.numeric(mytest[1]))){ - return("char") - }else{ - return("ok") - } - } - } - # findchar application - chardiag <- suppressWarnings(apply(DM,2,vapply,findchar,"character")) - charlist <- which(chardiag == "char") - err.stock <- paste("\n- - - - - - - - -\nYour dataMatrix contains", - length(charlist), - "non-numeric value(s). To help you check your data, please find below a short overview:\n") - charover <- 1 - while((length(err.stock)<10)&(length(err.stock)<(length(charlist)+1))){ - charex <- paste("variable",colnames(DM)[ceiling(charlist[charover]/nrow(DM))], - "- sample",rownames(DM)[charlist[charover]-floor(charlist[charover]/nrow(DM))*nrow(DM)], - "~> value:",as.matrix(DM)[charlist[charover]],"\n") - err.stock <- c(err.stock,charex) - charover <- charover + 1 - } - stop(c(err.stock,"The dataMatrix file is supposed to contain only numeric values.\n- - - - - - - - -\n")) - } - - ### Initialization - - DM <- cbind(c_class,DM) - - stat.res <- t(DM[0,-1,drop=FALSE]) - names <- NULL - - mean.res <- NULL - mean.names <- NULL - - sd.res <- NULL - sd.names <- NULL - - med.res <- NULL - med.names <- NULL - - quart.res <- NULL - quart.names <- NULL - - dec.res <- NULL - dec.names <- NULL - - NA.res <- NULL - NA.names <- NULL - pct_NA.res <- NULL - pct_NA.names <- NULL - - fold.res <- NULL - fold.names <- NULL - - if(("NA" %in% stat.list)||(test.fold=="Yes")){ - graphs <- 1 - }else{ - graphs=0 - } - - data_bp <- data.frame() #table for NA barplot - - - - ### Computation - - - for(j in 1:nb_class){ - - # Mean --------- - - if("mean" %in% stat.list){ - mean.res <- cbind(mean.res, colMeans(DM[which(DM$c_class==classnames[j]),-1],na.rm=TRUE)) - mean.names <- cbind(mean.names, paste("Mean",classnames[j], sep="_")) - if(j == nb_class){ - stat.res <- cbind(stat.res, mean.res) - names <- cbind(names, mean.names) - } - } - - # Standard deviation ----- - - if("sd" %in% stat.list){ - sd.res <- cbind(sd.res, apply(DM[which(DM$c_class==classnames[j]),-1],2,sd,na.rm=TRUE)) - sd.names <- cbind(sd.names, paste("Sd",classnames[j], sep="_")) - if(j == nb_class){ - stat.res <- cbind(stat.res, sd.res) - names <- cbind(names, sd.names) - } - } - - # Median --------- - - if(("median" %in% stat.list)&&(!("quartile" %in% stat.list))){ - med.res <- cbind(med.res, apply(DM[which(DM$c_class==classnames[j]),-1],2,median,na.rm=TRUE)) - med.names <- cbind(med.names, paste("Median",classnames[j], sep="_")) - if(j == nb_class){ - stat.res <- cbind(stat.res, med.res) - names <- cbind(names, med.names) - } - } - - # Quartiles ------ - - if("quartile" %in% stat.list){ - quart.res <- cbind(quart.res,t(apply(DM[which(DM$c_class==classnames[j]),-1],2,quantile,na.rm=TRUE))) - quart.names <- cbind(quart.names, paste("Min",classnames[j], sep="_"),paste("Q1",classnames[j], sep="_"), - paste("Median",classnames[j],sep="_"),paste("Q3",classnames[j],sep="_"), - paste("Max",classnames[j],sep="_")) - if(j == nb_class){ - stat.res <- cbind(stat.res, quart.res) - names <- cbind(names, quart.names) - } - } - - # Deciles ------ - - if("decile" %in% stat.list){ - dec.res <- cbind(dec.res,t(apply(DM[which(DM$c_class==classnames[j]),-1],2,quantile,na.rm=TRUE,seq(0,1,0.1)))) - dec.names <- cbind(dec.names, t(matrix(paste((paste("D",seq(0,10,1),sep="")),classnames[j],sep="_")))) - if(j == nb_class){ - stat.res <- cbind(stat.res, dec.res) - names <- cbind(names, dec.names) - } - } - - # Missing values ------------ - - if("NA" %in% stat.list){ - - nb_NA <- apply(DM[which(DM$c_class==classnames[j]),-1],2,function(x) sum(is.na(x))) - pct_NA <- round(nb_NA/nrow(DM[which(DM$c_class==classnames[j]),-1])*100,digits=4) - NA.res <- cbind(NA.res,nb_NA) - pct_NA.res <- cbind(pct_NA.res,pct_NA) - NA.names <- cbind(NA.names, paste("NA",classnames[j], sep="_")) - pct_NA.names <- cbind(pct_NA.names,paste("Pct_NA", classnames[j], sep="_")) - if(j == nb_class){ - stat.res <- cbind(stat.res, NA.res,pct_NA.res) - names <- cbind(names, NA.names,pct_NA.names) - } - - #for barplots - Nb_NA_0_20 <- 0 - Nb_NA_20_40 <- 0 - Nb_NA_40_60 <- 0 - Nb_NA_60_80 <- 0 - Nb_NA_80_100 <- 0 - - for (i in 1:length(pct_NA)){ - - if ((0<=pct_NA[i])&(pct_NA[i]<20)){ - Nb_NA_0_20=Nb_NA_0_20+1} - - if ((20<=pct_NA[i])&(pct_NA[i]<40)){ - Nb_NA_20_40=Nb_NA_20_40+1} - - if ((40<=pct_NA[i])&(pct_NA[i]<60)){ - Nb_NA_40_60=Nb_NA_40_60+1} - - if ((60<=pct_NA[i])&(pct_NA[i]<80)){ - Nb_NA_60_80=Nb_NA_60_80+1} - - if ((80<=pct_NA[i])&(pct_NA[i]<=100)){ - Nb_NA_80_100=Nb_NA_80_100+1} - } - data_bp[1,j] <- Nb_NA_0_20 - data_bp[2,j] <- Nb_NA_20_40 - data_bp[3,j] <- Nb_NA_40_60 - data_bp[4,j] <- Nb_NA_60_80 - data_bp[5,j] <- Nb_NA_80_100 - rownames(data_bp) <- c("0%-20%", "20%-40%", "40%-60%", "60%-80%", "80%-100%") - - if(j == nb_class){ - - # Alert message if there is no missing value in data matrix - sum_total <- sum(NA.res) - alerte <- NULL - if(sum_total==0){ - alerte <- c(alerte, "Data Matrix contains no NA.\n") - } - if(length(alerte) != 0){ - cat(alerte,"\n") - } - - - colnames(data_bp) <- classnames - data_bp <- as.matrix(data_bp) - } - } - - - # Mean fold change ------------ - - if(test.fold=="Yes"){ - if(nb_class >= 2){ - if(j!=nb_class){ - ratio1 <- NULL - ratio2 <- NULL - if(method=="each_class"){ - fold.frac <- "Top" - } - for(k in (j+1):nb_class) { - if(fold.frac=="Bottom"){ - ratio1 <- classnames[k] - ratio2 <- classnames[j] - }else{ - ratio1 <- classnames[j] - ratio2 <- classnames[k] - } - fold <- colMeans(DM[which(DM$c_class==ratio1),-1],na.rm=TRUE)/ - colMeans(DM[which(DM$c_class==ratio2),-1],na.rm=TRUE) - if(logarithm=="log2"){ - fold.res <- cbind(fold.res,log2(fold)) - }else if(logarithm=="log10"){ - fold.res <- cbind(fold.res,log10(fold)) - }else{ - fold.res <- cbind(fold.res, fold) - } - if(logarithm == "none"){ - fold.names <- cbind(fold.names,paste("fold",ratio1,"VS", ratio2, sep="_")) - }else{ - fold.names <- cbind(fold.names,paste(logarithm, "fold", ratio1, "VS", ratio2, sep="_")) - } - } - - }else{ - stat.res <- cbind(stat.res,fold.res) - names <- cbind(names, fold.names) - } - } - } - - } - - ############ - - - # check columns names in variableMetadata - - VM.names <- colnames(VM) - for (i in 1:length(VM.names)){ - for (j in 1:length(names)){ - if (VM.names[i]==names[j]){ - names[j] <- paste(names[j], "2", sep="_") - } - } - } - - colnames(stat.res) <- names - - - - - - - # Output --------------------------------------------------------------------------------------------------- - - VM <-cbind(VM,stat.res) - - write.table(VM, VM.output,sep="\t", quote=FALSE, row.names=FALSE) - - - ### graphics pdf - - if(graphs == 1){ - - pdf(graphs.output) - - - #Barplots for NA - if("NA" %in% stat.list){ - graph.colors <- c("green3","palegreen3","lightblue","orangered","red") - par(mar=c(5.1, 4.1, 4.1, 8.1), xpd=TRUE) - - bp=barplot(data_bp, col=graph.colors, main="Proportion of NA", xlab="Classes", ylab="Variables") - legend("topright", fill=graph.colors,rownames(data_bp), inset=c(-0.3,0)) - - stock=0 - for (i in 1:nrow(data_bp)){ - text(bp, stock+data_bp[i,]/2, data_bp[i,], col="white", cex=0.7) - stock <- stock+data_bp[i,] - } - - } - - # Boxplots for fold test - - if((test.fold=="Yes")&&(nb_class >= 2)){ - - clean_fold <- fold.res - for(i in 1:nrow(clean_fold)){ - for(j in 1:ncol(clean_fold)){ - if(is.infinite(clean_fold[i,j])){ - clean_fold[i,j] <- NA - } - } - } - for (j in 1:ncol(clean_fold)){ - title <- paste(fold.names[j]) - boxplot(clean_fold[,j], main=title) - } - } - - dev.off() - - }else{ - pdf(graphs.output) - plot.new() - legend("center","You did not select any option with graphical output.") - dev.off() - } - - } - - - - - - \ No newline at end of file
--- a/Intchecks/parseargs.R Mon Dec 11 12:56:20 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,53 +0,0 @@ -# parseCommandArgs function -parse_args <- function( - args = NULL, - convert_booleans = TRUE, - convert_numerics = TRUE -) { - if (is.null(args)) { - args <- commandArgs() - } - start <- which(args == "--args")[1] + 1 - if (is.na(start)) { - return(list()) - } - seq_by2 <- seq(start, length(args), by = 2) - result <- as.list(args[seq_by2 + 1]) - names(result) <- args[seq_by2] - converters <- c() - if (convert_booleans) { - converters <- c( - converters, - function(x) { - return(if (x == "TRUE") TRUE else if (x == "FALSE") FALSE else x) - } - ) - } - if (convert_numerics) { - converters <- c( - converters, - function(x) { - return(if (is.na(y <- as.numeric(x))) x else y) - } - ) - } - return(convert_parameters(result, converters)) -} - -convert_parameters <- function(args, converters) { - suppressWarnings( - for (param in names(args)) { - for (converter in converters) { - old_value <- args[[param]] - args[[param]] <- converter(args[[param]]) - if (!identical(args[[param]], old_value)) { - ## The value has been modified by the converter, and - ## we don't want values to be converted multiple times, - ## so we pass to the next value. - break - } - } - } - ) - return(args) -} \ No newline at end of file
--- a/Intchecks/test-data/input_IC_DM.txt Mon Dec 11 12:56:20 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,18 +0,0 @@ -data j 785 y54j 68y4j6 5-6 4 hrh 5h - 3 t564 t54 66 y6y t6 5h (5y g51 ( 6 98 j7-0 06654h 60 -5d_-kkcùf 0.356426723610756 0.380152310071702 0.0306944207412024 0.334137638848017 0.298147485608469 0.975073793297568 0.129099504745855 0.973451663994064 0.334792719284834 0.0751565260418877 0.322897933010729 0.129033714279026 0.722966330196726 0.580654367692078 0.644502528016206 0.352827235332827 0.511778286438001 -npèt 0.801750949101246 0.535593104115636 0.258351312473067 0.599475573145688 0.0763319665667417 0.504558103160623 0.367963830290116 0.440540211857668 0.158066366765388 0.609574253877002 0.550590276177951 0.232864384033621 0.637065775537391 0.424098276140436 0.662053737304139 0.0217541227637467 0.299053946391647 -5PY4(*3 0.875199970420953 NA 0.253659010703906 0.507762246807195 0.856444177031262 NA 0.616538655402613 0.388475672176377 0.914254939740854 0.449607330807756 0.628469388494906 0.436558212905713 0.951198610265672 0.956459387015831 0.0113483457928278 0.611559621193868 0.127393349941781 -k 0.235214515530521 0.50917637648828 0.0303152651414629 0.305595242012361 0.470145996588881 0.356776179776951 0.569840649303474 0.720070419680388 0.875026990189028 0.97246313990283 0.936423978578332 0.810603455355391 0.903046784946495 0.667091730522105 0.0282601148338295 0.276666638740528 0.747503427028951 -"t v^ptok!R/; " 0.251801918471682 0.871383805557867 0.285183681272061 0.552739436194454 0.863766301215097 0.182656918760194 0.977603710051043 0.204845724382198 0.751690583305025 0.751475804910098 0.938399878874186 0.218163132174348 0.523463794277572 0.987156468237196 0.630938391969655 0.495061607430081 0.815474132321848 -ojt* 0.907412839750932 0.857498273276021 0.370137361785406 0.76678484779183 0.213299689296413 0.521214352529346 0.0851731095099456 0.868227767553325 0.0711706993343434 NA 0.700568035764899 NA 0.570332959002042 0.23270290523468 NA NA 0.466363859676536 -lmtki 0.344288940919619 0.308219131305663 0.218449898667526 0.700869668247368 0.932538878977527 0.396198994630489 0.710306820874376 0.124235068279782 0.56608643523601 0.887647287670482 0.0375011938139768 0.282378766400105 0.757492159558571 0.506567258021703 0.990910788272054 0.326205008250378 0.61003856679129 -1h6-(587 t 0.308709890328272 0.717202505490233 0.777934118812417 NA 0.588458968776734 0.940112596977594 0.788662314418432 0.133046934771924 0.891405172730758 0.915119678712577 0.401136527896346 0.996068607925829 0.049077648050296 0.157657286859101 0.598183619914656 0.439531332356748 0.11995300831499 -r154 (54 86 0.0861033910635205 0.243889357950567 0.848801588667359 0.972183114768299 0.311829870889657 0.434854970574638 0.0240613698880671 0.273335646139236 0.902686361056168 0.73479376111027 0.232132726965829 0.471097756642369 0.511949019774828 0.934158039531663 0.60125450357768 0.972297241140637 0.63149263296722 -(h4 0.336373953934809 0.521412085356266 0.281632887739949 0.67004516763179 0.0199332367594847 0.867732114278491 0.973477443505332 0.883197938525362 0.334509105447652 0.793947578560618 0.465167375556954 0.508757674191867 0.983758014396725 0.100582744588671 0.501301982240787 0.789637338939369 0.967382850752139 -2 0.65083934802851 NA 0.0376667088550816 0.514003854683931 0.274761574286474 0.652108809243866 0.689393057486628 0.262205073769319 0.871958663956254 0.709490850507154 0.854477673591409 0.672918329289054 0.141693817710263 0.00543057257281332 0.273084303937691 0.39570748778882 0.303196826092465 - m(yk 0.798331012769621 0.806128809071498 0.519974578571807 0.0237856823099186 0.396426673475168 0.75979784245693 0.0589336105092753 0.763144123705766 0.16086904822931 0.301000258395631 0.321186481311644 0.0091434029302796 0.0346149352313591 0.276366137673702 NA NA NA - -546 -3 -3 0.981561422839383 0.755973696318293 0.328878596436459 0.233341270249666 0.14569201922932 0.458105216271765 NA 0.775603857680909 0.146769440956804 0.86812372109824 0.601678275863019 0.782019307745586 0.955614654474782 0.836058192741214 0.454550085076576 0.181788519803465 0.325277631599655 -nz^à t 0.920089977342539 NA 0.105577675363886 0.805216649434891 0.505774902707566 0.619612431793644 0.337662179244335 0.672829320507873 0.387228581340173 0.209795853201976 0.0403285073621635 0.540614252108851 0.43362855410287 0.160204078494507 0.924783730297701 0.116837628312753 0.58763473928668 -kzjù 0.418651623550921 0.668434005412235 0.845525871017258 0.567468950919516 0.111441846749533 0.411424602517146 0.0743349602270699 0.583730221710347 0.0105008018078939 0.918789674284249 0.808643595994088 0.33578972219198 0.536848200622412 0.898842910125025 0.333954421105444 0.0390509357521919 0.159857705429294 -. Glk(ôepjg$ 0.3266193089274 0.806668730531573 0.87265879590923 0.934940869880726 0.548967615908753 0.250539517744596 0.75436043048449 0.597444122305604 0.343955002787363 0.560987092777944 0.134929631392942 0.958047956861493 0.870429681664852 0.66286672214456 0.441523248613101 0.437073092741238 0.265031623006715 -rmlgj*lr , 0.462672931523031 0.74679446664976 0.151929368261195 0.165870989887449 0.90931621240424 0.423706631494886 0.337742032589001 0.184993498717115 0.0792523932129463 0.071353352835237 0.525247607449536 0.647744088759121 0.158725526449032 0.203927461340644 0.20055683200405 0.897053444943847 0.506954538974894
--- a/Intchecks/test-data/input_IC_SM.txt Mon Dec 11 12:56:20 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,18 +0,0 @@ -data ABCD E AAA fp uv -j 785 1 91 a a s -y54j 68y4j6 2 60 b z d -5-6 4 2 43 c e f -hrh 3 24 d r s -5h - 4 29 b t d -3 56 71 a y f -t564 4 4 r u s -t54 66 23 91 c i d -y6y 5 61 f o f -t6 5h 8 21 s p s -(5y 98 77 b q d -g51 67 1 b s f -( 98 48 r d s -6 2 10 a f d -98 j7-0 1 36 a g f -06654h 34 100 k h s -60 67 38 d j d
--- a/Intchecks/test-data/input_IC_VM.txt Mon Dec 11 12:56:20 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,18 +0,0 @@ -variable A B C D -5d_-kkcùf 0 a ze$ùj 0 -npèt 1 b g 0 -5PY4(*3 2 c 15j-è, 0 -k 3 a 6jè 1 -t v^ptok!R/; 4 b 8j4, 1 -ojt* 5 c 5èj1 1 -lmtki 6 a j84 2 -1h6-(587 t 7 b j4e54 2 -r154 (54 86 8 c j 2 -(h4 9 a 54j6@44èu6(è4 3 -2 10 b (51h 3 - m(yk 11 c 8-1 3 - -546 -3 -3 12 a hy-81u 4 -nz^à t 13 b zs5-hj1 4 -kzjù 14 c 861h 4 -. Glk(ôepjg$ 15 a (8168(h1 9 -rmlgj*lr , 16 b 8hjè1 9
--- a/Intchecks/test-data/output_IC_VM.tabular Mon Dec 11 12:56:20 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,18 +0,0 @@ -variable A B C D Mean_s Mean_Other Sd_s Sd_Other NA_s NA_Other Pct_NA_s Pct_NA_Other log2_fold_Other_VS_s -5d_-kkcùf 0 a ze$ùj 0 0.328435659796011 0.471016292948534 0.228451584966275 0.306556034161105 0 0 0 0 0.520166189269251 -npèt 1 b g 0 0.506264084119198 0.376554698635174 0.274830786331717 0.183936635993605 0 0 0 0 -0.427030691648967 -5PY4(*3 2 c 15j-è, 0 0.668644405816343 0.50811805375594 0.201164644457448 0.349587008290475 0 2 0 18.1818 -0.396075445063365 -k 3 a 6jè 1 0.543804495072701 0.559217630380327 0.327662996370251 0.314841391119243 0 0 0 0 0.040321768397534 -t v^ptok!R/; 4 b 8j4, 1 0.592024378555822 0.613605365279061 0.247181366605864 0.324224837252577 0 0 0 0 0.0516545128160351 -ojt* 5 c 5èj1 1 0.582425939013687 0.477909216050108 0.359170947930419 0.286185546056577 2 2 33.3333 18.1818 -0.285338020130744 -lmtki 6 a j84 2 0.621134980920132 0.452102270945102 0.231326066781317 0.308002376571479 0 0 0 0 -0.458257654955689 -1h6-(587 t 7 b j4e54 2 0.500220172773265 0.574650849860962 0.353280826805041 0.328577697095778 1 0 16.6667 0 0.200122420718524 -r154 (54 86 8 c j 2 0.550231316290937 0.535048495905671 0.420471482717973 0.267498028870002 0 0 0 0 -0.0403685917338988 -(h4 9 a 54j6@44èu6(è4 3 0.757873249494774 0.495600908676148 0.238828322527637 0.310985735881046 0 0 0 0 -0.612777752412226 -2 10 b (51h 3 0.516854736034218 0.420780853559443 0.219207900924983 0.318004326311928 0 1 0 9.0909 -0.296689802273714 - m(yk 11 c 8-1 3 0.243333099843161 0.445893010825123 0.330573629082567 0.285737925809767 1 2 16.6667 18.1818 0.873765007290379 - -546 -3 -3 12 a hy-81u 4 0.644085917693107 0.510055119992691 0.40111184763981 0.256342273516465 1 0 16.6667 0 -0.336599986295532 -nz^à t 13 b zs5-hj1 4 0.470538473606561 0.454458821926304 0.3244119263237 0.280532541853305 0 1 0 9.0909 -0.0501631106824151 -kzjù 14 c 861h 4 0.425857390892727 0.46983142764184 0.331238344963174 0.311088298141582 0 0 0 0 0.141772831906062 -. Glk(ôepjg$ 15 a (8168(h1 9 0.647401746079442 0.534784815200539 0.244322481422829 0.27318926988152 0 0 0 0 -0.275702754775479 -rmlgj*lr , 16 b 8hjè1 9 0.348903046371266 0.41640209993349 0.303393826442722 0.274707725036023 0 0 0 0 0.255151150062007
--- a/Intchecks/wrapper_intensity_check.R Mon Dec 11 12:56:20 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,82 +0,0 @@ -#!/usr/bin/Rscript --vanilla --slave --no-site-file - - -############################################################################# -# WRAPPER for INTENSITY CHECK # -# # -#Script: Script_intensity_check.R # -#Xml: xml_intensity_check.xml # -# # -# # -# Input: Data Matrix, VariableMetadata, SampleMetadata # -# Output: VariableMetadata, Graphics # -# # -# # -############################################################################# - -source_local <- function(...){ - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - for(i in 1:length(list(...))){source(paste(base_dir, list(...)[[i]], sep="/"))} -} -#Import the different functions -source_local("parseargs.R", "Script_intensity_check.R", "RcheckLibrary.R") - -args = parse_args() #interpretation of arguments given in command line as an R list of objects - - -if(length(args) < 7){ stop("NOT enough argument !!!") } - -cat('\nJob starting time:\n',format(Sys.time(), "%a %d %b %Y %X"), -'\n\n--------------------------------------------------------------------', -'\nIntensity Check parameters:\n\n') -print(args) -cat('--------------------------------------------------------------------\n\n') - -class_col <- NULL -test_fold <- NULL -class1 <- NULL -fold_frac <- NULL -logarithm <- NULL -if(args$method == "each_class"){ - class_col <- args$class_col - test_fold <- args$test_fold - if(args$test_fold == "Yes"){ - logarithm <- args$logarithm - } -} -if(args$method == "one_class"){ - class_col <- args$class_col - class1 <- args$class1 - test_fold <- args$test_fold - if(args$test_fold == "Yes"){ - fold_frac <- args$fold_frac - logarithm <- args$logarithm - } -} - -err_no_option <- NULL - -if(((args$method == "no_class")&&(args$chosen_stat == "None"))|| - ((args$method != "no_class") && (args$chosen_stat == "None") && (test_fold == "No"))){ - err_no_option<- "You did not select any computational option. Program can not be executed." - stop("\n- - - - - - - - -\n",err_no_option,"\n- - - - - - - - -\n") -} - - -if(is.null(err_no_option)){ - - intens_check(args$dataMatrix_in, args$sampleMetadata_in, args$variableMetadata_in, args$method, args$chosen_stat, - class_col, test_fold, class1, fold_frac, logarithm, args$variableMetadata_out, args$graphs_out) -} - - -cat('\n--------------------------------------------------------------------', -'\nInformation about R (version, Operating System, attached or loaded packages):\n\n') -sessionInfo() -cat('--------------------------------------------------------------------\n', -'\nJob ending time:\n',format(Sys.time(), "%a %d %b %Y %X")) - - -#delete the parameters to avoid the passage to the next tool in .RData image -rm(args)
--- a/Intchecks/xml_intensity_check.xml Mon Dec 11 12:56:20 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,264 +0,0 @@ -<tool id="intens_check" name="Intensity Check" version="1.3.0"> - <description>Statistical measures, number of missing values and mean fold change</description> - <requirements> - <requirement type="package" version="4.2.1">r-base</requirement> - </requirements> - <command interpreter="Rscript"> - - wrapper_intensity_check.R - - dataMatrix_in "$dataMatrix_in" - sampleMetadata_in "$sampleMetadata_in" - variableMetadata_in "$variableMetadata_in" - - method "${method_cond.method}" - - chosen_stat "${method_cond.chosen_stat}" - - #if $method_cond.method == "each_class" : - class_col "${method_cond.class_col}" - test_fold "${method_cond.test_fold_cond.test_fold}" - #if $method_cond.test_fold_cond.test_fold == "Yes" : - logarithm "${method_cond.test_fold_cond.logarithm}" - #end if - #end if - - #if $method_cond.method == "one_class" : - class_col "${method_cond.class_col}" - class1 "${method_cond.class1}" - test_fold "${method_cond.test_fold_cond.test_fold}" - #if $method_cond.test_fold_cond.test_fold == "Yes" : - fold_frac "${method_cond.test_fold_cond.fold_frac}" - logarithm "${method_cond.test_fold_cond.logarithm}" - #end if - #end if - - variableMetadata_out "$variableMetadata_out" - graphs_out "$graphs_out" - - - - </command> - - <inputs> - <param name="dataMatrix_in" type="data" label="Data matrix file" help="" format="tabular" /> - <param name="sampleMetadata_in" type="data" label="Sample metadata file" help="" format="tabular" /> - <param name="variableMetadata_in" type="data" label="Variable metadata file" help="" format="tabular" /> - - <conditional name="method_cond"> - <param name="method" type="select" label="Computation method" help="Select the first method if you don't want to take into account any class of samples"> - <option value="no_class">Without distinction between samples </option> - <option value="each_class">For each class of samples </option> - <option value="one_class">Between one class and all the remaining samples </option> - - </param> - - <when value="no_class"> - <param name="chosen_stat" type="select" display="checkboxes" multiple="True" label="Statistics"> - <option value="mean">Mean</option> - <option value="sd">Standard deviation</option> - <option value="median">Median</option> - <option value="quartile">Quartile</option> - <option value="decile">Decile</option> - <option value="NA">Missing values</option> - <validator type="no_options" message="Please choose at least one statistic representation" /> - </param> - </when> - - <when value="each_class"> - <param name="class_col" type="text" label="Class column" help="Name of the column in Sample metadata" /> - <param name="chosen_stat" type="select" display="checkboxes" multiple="True" label="Statistics"> - <option value="mean">Mean</option> - <option value="sd">Standard deviation</option> - <option value="median">Median</option> - <option value="quartile">Quartile</option> - <option value="decile">Decile</option> - <option value="NA">Missing values</option> - </param> - <conditional name="test_fold_cond"> - <param name="test_fold" type="select" display="radio" label="Calculate the mean fold change"> - <option value="Yes">Yes</option> - <option value="No">No</option> - </param> - <when value="Yes"> - <param name="logarithm" type="select" label="Logarithm" help="Choose if you want the mean fold change to be converted into a log mean fold change (log2 or log10)"> - <option value="none">none </option> - <option value="log2">log2 </option> - <option value="log10">log10 </option> - </param> - </when> - <when value="No"> - </when> - </conditional> - </when> - - <when value="one_class"> - <param name="class_col" type="text" label="Class column" help="Name of the column in Sample metadata" /> - <param name="class1" type="text" label="Selected class" help="Name of the class to test versus all the remaining samples." /> - <param name="chosen_stat" type="select" display="checkboxes" multiple="True" label="Statistics"> - <option value="mean">Mean</option> - <option value="sd">Standard deviation</option> - <option value="median">Median</option> - <option value="quartile">Quartile</option> - <option value="decile">Decile</option> - <option value="NA">Missing values</option> - </param> - <conditional name="test_fold_cond"> - <param name="test_fold" type="select" display="radio" label="Calculate the mean fold change"> - <option value="Yes">Yes</option> - <option value="No">No</option> - </param> - <when value="Yes"> - <param name="fold_frac" type="select" label="Where should the class be placed for the mean fold change calculation?" display="radio"> - <option value="Top">Numerator (Top) </option> - <option value="Bottom">Denominator (Bottom) </option> - </param> - <param name="logarithm" type="select" label="Logarithm" help="Choose if you want the mean fold change to be converted into a log mean fold change (log2 or log10)"> - <option value="none">none </option> - <option value="log2">log2 </option> - <option value="log10">log10 </option> - </param> - </when> - <when value="No"> - </when> - </conditional> - </when> - </conditional> - </inputs> - - - <outputs> - <data name="variableMetadata_out" label="IC_${variableMetadata_in.name}" format="tabular" /> - <data name="graphs_out" label="IC_Graphs" format="pdf" /> - </outputs> - - - <tests> - <test> - <param name="dataMatrix_in" value="input_IC_DM.txt"/> - <param name="sampleMetadata_in" value="input_IC_SM.txt"/> - <param name="variableMetadata_in" value="input_IC_VM.txt"/> - <param name="method" value="one_class"/> - <param name="chosen_stat" value="mean,sd,NA"/> - <param name="class_col" value="uv"/> - <param name="class1" value="s"/> - <param name="test_fold" value="Yes"/> - <param name="fold_frac" value="Bottom"/> - <param name="logarithm" value="log2"/> - <output name="variableMetadata_out" file="output_IC_VM.tabular"/> - </test> - </tests> - - <help> - -.. class:: infomark - -**Author:** -Anthony Fernandes for original code (PFEM - INRA) -**Maintainer:** Melanie Petera (PFEM - INRAE - MetaboHUB) - ---------------------------------------------------- - -======================== -Intensity Check -======================== - ------------ -Description ------------ - -This tool performs various metrics: mean fold change calculation, number and proportion of missing values, and mean, sd and decile calculation. -You can choose to perform these metrics according to sample groups defined in the sample metadata file given as input. - - ------------------ -Workflow position ------------------ - - -.. image:: int_check.png - :width: 800 - - ------------ -Input files ------------ - -+----------------------------+------------+ -| Parameter | Format | -+============================+============+ -| 1 : Data matrix file | tabular | -+----------------------------+------------+ -| 2 : Sample metadata file | tabular | -+----------------------------+------------+ -| 3 : Variable metadata file | tabular | -+----------------------------+------------+ - - | - - ----------- -Parameters ----------- - -**Computation method** - | - **Without distinction between samples:** calculates chosen statistic(s) for each variable. - | - **For each class of samples:** separates samples between each class (class column to specified). Chosen statistic(s) and/or mean fold change are calculated for each of them. - | - **Between one class versus all the remaining samples:** If you want to focus only on one class versus all the remaining samples without class distinction. - - | In the case of two classes: "each class" and "one class" give the same results for statistical measures. We recommend to choose "one class" for mean fold change calculation in order to select the class you want to put as numerator or denominator (see below). - -**Statistics** - | Select the statistical measures you want to add in the variable metadata table. If the method is "each class" or "one class", you can choose to leave this section blank if you only want to calculate the mean fold change (see below). - -**Class column** (*only if "each class" or "one class"*) - | Fill the name of the class column in sample metadata table; it is case-sensitive. - -**Selected class** (*only if "one class"*) - | If the method is "one class", specify it. Remaining samples will be named "Other". - -**Calculate the mean fold change** (*only if "each class" or "one class"*) - | Choose if you want to calculate the mean fold change. If the method is "each class": mean fold change will be calculated for all combinations of classes. If the method is "one class": it will be calculated between the selected class (see above) and the remaining samples. - -**Where should the class be placed for the mean fold change calculation?** (*only if "one class"*) - | If the method is "one class", choose "top" or "bottom" to put the selected class as numerator or denominator (respectively) for the mean fold change calculation. - -**Logarithm** (*only if "each class" or "one class"*) - | Choose if you want to transform the mean fold change with a log2 or log10. - ------------- -Output file ------------- - -**Variable metadata file** - | Contains the previous columns in variable metadata file and the new ones. - | In the column names for fold, the first class specified is the one used as numerator in the ratio. - -**Graphs file** - | Contains barplots with the proportion of NA considering classes and boxplots with the fold values. - - - ---------------------------------------------------- - ----------------------- -Additional information ----------------------- - - -.. class:: warningmark - -For more information about input files, refer to the corresponding "W4M HowTo" page: - | `W4M table format for Galaxy <https://workflow4metabolomics.org/sites/default/files/fichiers/documents/w4m_TableFormatForGalaxy_150908.pdf>`_ - | - - - - - - - </help> - - -</tool> - \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.md Wed Jul 09 15:43:29 2025 +0000 @@ -0,0 +1,52 @@ +Intensity check +======= + +Metadata +----------- + + * **@name**: Intensity check + * **@version**: 2.0.0 + * **@authors**: Original code: Anthony Fernandes (PFEM - INRA) - Maintainer: Melanie Petera (PFEM - INRAE - MetaboHUB) + * **@contributors**: Wrapper update/refactoring: Helge Hecht + * **@init date**: 2018, September + * **@main usage**: This tool computes some statistical measures, number of missing values and mean fold change. + + +Context +----------- + +This tool is provided as one of the [Workflow4Metabolomics](http://workflow4metabolomics.org) Galaxy instance statistical tools. +W4M is a French infrastructure providing software tools to process, analyse and annotate metabolomics data. + +User interface is based on the Galaxy platform (homepage: https://galaxyproject.org/). It is an open, web-based platform for data intensive biomedical research. +Whether on the free public server or your own instance, you can perform, reproduce, and share complete analyses. + + +Configuration +----------- + +### Requirement: + * R software: version > 3.0.0 recommended + * Specific R libraries: 'argparse' (for the Galaxy wrapper only) + + +Technical description +----------- + +Main files: + +- Script_intensity_check.R: R function (core script) +- wrapper_intensity_check.R: R script to link the main R function to inputs +- xml_intensity_check.xml: XML wrapper (interface for Galaxy) + + +Services provided +----------- + + * Help and support: https://community.france-bioinformatique.fr/c/workflow4metabolomics/10 + + +License +----------- + + * Cea Cnrs Inria Logiciel Libre License, version 2.1 (CECILL-2.1)
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ic_in_sampleMetadata.tabular Wed Jul 09 15:43:29 2025 +0000 @@ -0,0 +1,30 @@ +sampleMetadata sampleType injectionOrder Treatment Cell_Type +Blanc2 Blanc 1 Blanc Blanc +QC1 QC 2 QC bacterie +WTp.2 Ech 3 WTp bacterie +D.EV.2 Ech 4 D.EV vesicule +WTp.1 Ech 5 WTp bacterie +Dp.EV.2 Ech 6 Dp.EV vesicule +WTp.3 Ech 7 WTp bacterie +QC2 QC 8 QC bacterie +Dp.EV.1 Ech 9 Dp.EV vesicule +WT.3 Ech 10 WT bacterie +Dp.2 Ech 11 Dp bacterie +Blanc3 Blanc 12 Blanc Blanc +Dp.EV.3 Ech 13 Dp.EV vesicule +WT.2 Ech 14 WT bacterie +QC3 QC 15 QC bacterie +WTp.EV.2 Ech 16 WTp.EV vesicule +D.2 Ech 17 D bacterie +D.1 Ech 18 D bacterie +Dp.3 Ech 19 Dp bacterie +D.EV.1 Ech 20 D.EV vesicule +QC4 QC 21 QC bacterie +Dp.1 Ech 22 Dp bacterie +Blanc4 Blanc 23 Blanc Blanc +WTp.EV.1 Ech 24 WTp.EV vesicule +WT.1 Ech 25 WT bacterie +D.EV.3 Ech 26 D.EV vesicule +D.3 Ech 27 D bacterie +QC5 Ech 28 QC bacterie +WTp.EV.3 Ech 29 WTp.EV vesicule
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ic_in_variableMetadata.tabular Wed Jul 09 15:43:29 2025 +0000 @@ -0,0 +1,1000 @@ +name namecustom mz mzmin mzmax rt rtmin rtmax npeaks Blanc Ech QC peakidx ms_level +M501T868 M501.356T868 501.355960969943 501.355896357243 501.356025582642 868.148189755057 865.705746643383 870.590632866731 2 0 2 0 6347,22443 1 +M503T691 M503.2863T691 503.286262695626 503.285988707119 503.286414649374 690.831528403268 689.833362434444 691.103189702095 4 0 4 0 23855,26261,34980,37767 1 +M503T865 M503.3719T865 503.371944510852 503.371745256575 503.372326048482 865.195017714887 862.786266465101 866.383967591779 4 0 4 0 10402,11750,35797,40584 1 +M505T559 M505.3447T559 505.344658878565 505.344116378743 505.344845737255 559.312189532128 558.130684756397 560.594160227732 4 0 4 0 80,23142,31953,36996 1 +M506T1091 M506.2357T1091 506.23571984884 506.235472427375 506.235877726525 1090.97947935389 1087.48145474369 1091.67337833551 3 0 3 0 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662.474689963475 662.475159525057 819.736317639184 815.806517236665 827.06623320117 10 0 8 2 7233,13269,14597,15871,17187,18479,31553,40650,41857,43066 1 1374723.00478468 808464.2507633 592773.96607296 748111.027714266 1133236.81390923 1572918.20968756 2928240.53444629 592773.96607296 661473.121609856 677527.159631553 871372.630930231 971742.449450396 1133236.81390923 1352137.09150932 1503032.11043346 1807249.24992309 2623167.92471512 2928240.53444629 19 65.5172 +M662T759 M662.4751T759 662.475113591151 662.474916638045 662.47545899343 758.52692398715 756.860475723021 761.777842894845 7 0 4 3 15869,17185,18477,19747,31551,40648,41855 1 302031.142835485 103520.654222187 132801.352246351 255785.650983719 275430.249167951 384001.69299984 426411.710466978 132801.352246351 200701.316159809 249895.035654756 261676.266312682 269534.105586375 275430.249167951 323741.577837433 367170.155370189 400833.23062949 417797.237616346 426411.710466978 22 75.8621 +M662T877_12 M662.4834T877_12 662.483409367439 662.483391424546 662.484494258429 876.680758100062 870.871936054063 899.59255492849 3 0 3 0 6423,14382,24507 1 543131.686277344 471530.592122011 161258.687921401 279607.747144746 397956.806368091 734068.185455316 1070179.56454254 161258.687921401 208598.311610739 255937.935300077 303277.558989415 350617.182678753 397956.806368091 532401.358002981 666845.909637871 801290.461272761 935735.01290765 1070179.56454254 26 89.6552 +M662T878_5 M662.4859T878_5 662.485873978155 662.484866591432 662.486723268219 877.805880400154 871.003598042524 907.477630139606 6 0 4 2 10415,11872,16967,20696,28769,42612 1 559319.594139967 306528.340423233 106981.715175358 384738.445001281 576995.946651292 808694.674016242 888363.762106023 106981.715175358 217559.33596219 328136.956749023 441339.933253539 554542.909758056 576995.946651292 599448.983544527 738946.11052567 878443.237506814 883403.499806419 888363.762106023 23 79.3103 +M662T877_13 M662.4891T877_13 662.489091602147 662.487978314839 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662.494382378734 877.277926513975 875.783832062597 880.292623144434 5 0 3 2 3808,11785,18147,20550,40236 1 404295.515440121 125834.180170416 222886.718270714 324650.179482015 461199.226706378 504408.24867994 508333.20406156 222886.718270714 263592.102755234 304297.487239755 351959.988926888 406579.607816633 461199.226706378 478482.835495803 495766.444285228 505193.239756264 506763.221908912 508333.20406156 24 82.7586 +M662T878_6 M662.4991T878_6 662.49908150903 662.498431964179 662.499731053881 878.36483681063 875.013505680451 881.716167940809 2 0 2 0 8071,36050 1 309768.234746139 119077.405892821 225567.793553222 267668.01414968 309768.234746139 351868.455342597 393968.675939055 225567.793553222 242407.881791805 259247.970030389 276088.058268972 292928.146507555 309768.234746139 326608.322984722 343448.411223305 360288.499461888 377128.587700472 393968.675939055 27 93.1034 +M663T877_1 M662.5005T877_1 662.500482140811 662.499882924067 662.501399384827 876.680758100062 873.436484610673 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ic_out_variableMetadata_classes.tabular Wed Jul 09 15:43:29 2025 +0000 @@ -0,0 +1,1000 @@ +name namecustom mz mzmin mzmax rt rtmin rtmax npeaks Blanc Ech QC peakidx ms_level Mean_Blanc Mean_Ech Mean_QC Sd_Blanc Sd_Ech Sd_QC Min_Blanc Q1_Blanc Median_Blanc Q3_Blanc Max_Blanc Min_Ech Q1_Ech Median_Ech Q3_Ech Max_Ech Min_QC Q1_QC Median_QC Q3_QC Max_QC D_0_Blanc D_1_Blanc D_2_Blanc D_3_Blanc D_4_Blanc D_5_Blanc D_6_Blanc D_7_Blanc D_8_Blanc D_9_Blanc D_10_Blanc D_0_Ech D_1_Ech D_2_Ech D_3_Ech D_4_Ech D_5_Ech D_6_Ech D_7_Ech D_8_Ech D_9_Ech D_10_Ech D_0_QC D_1_QC D_2_QC D_3_QC D_4_QC D_5_QC D_6_QC D_7_QC D_8_QC D_9_QC D_10_QC NA_Blanc NA_Ech NA_QC Pct_NA_Blanc Pct_NA_Ech Pct_NA_QC +M501T868 M501.356T868 501.355960969943 501.355896357243 501.356025582642 868.148189755057 865.705746643383 870.590632866731 2 0 2 0 6347,22443 1 NA 50447.7362113025 NA NA 18903.1834705623 NA NA NA NA NA NA 37081.1669932544 43764.4516022784 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/wrapper_intensity_check.R Wed Jul 09 15:43:29 2025 +0000 @@ -0,0 +1,535 @@ +suppressPackageStartupMessages(library(argparse)) + +check.err <- function(err.stock) { + # err.stock = vector of results returned by check functions + if (length(err.stock) != 0) { + stop("\n- - - - - - - - -\n", err.stock, "\n- - - - - - - - -\n") + } +} + + +# To check if the 3 standard tables match regarding identifiers +table_match <- function(dataMatrix, sampleMetadata, variableMetadata) { + err.stock <- character() + + # Sample identifiers + dm_samples <- colnames(dataMatrix)[-1] + sm_samples <- as.character(sampleMetadata[[1]]) + # Variable identifiers + dm_vars <- as.character(dataMatrix[[1]]) + vm_vars <- as.character(variableMetadata[[1]]) + + # Check sample IDs + missing_in_sm <- setdiff(dm_samples, sm_samples) + missing_in_dm <- setdiff(sm_samples, dm_samples) + if (length(missing_in_sm) > 0 || length(missing_in_dm) > 0) { + err.stock <- c( + err.stock, + "\nData matrix and sample metadata do not match regarding sample identifiers." + ) + if (length(missing_in_sm) > 0) { + prefix <- if (length(missing_in_sm) < 4) { + "\n The " + } else { + "\n For example, the " + } + err.stock <- c( + err.stock, + prefix, + "following identifiers found in the data matrix\n", + " do not appear in the sample metadata file:\n", + " ", + paste(head(missing_in_sm, 3), collapse = "\n "), + "\n" + ) + } + if (length(missing_in_dm) > 0) { + prefix <- if (length(missing_in_dm) < 4) { + "\n The " + } else { + "\n For example, the " + } + err.stock <- c( + err.stock, + prefix, + "following identifiers found in the sample metadata file\n", + " do not appear in the data matrix:\n", + " ", + paste(head(missing_in_dm, 3), collapse = "\n "), + "\n" + ) + } + } + + # Check variable IDs + missing_in_vm <- setdiff(dm_vars, vm_vars) + missing_in_dm_var <- setdiff(vm_vars, dm_vars) + if (length(missing_in_vm) > 0 || length(missing_in_dm_var) > 0) { + err.stock <- c( + err.stock, + "\nData matrix and variable metadata do not match regarding variable identifiers." + ) + if (length(missing_in_vm) > 0) { + prefix <- if (length(missing_in_vm) < 4) { + "\n The " + } else { + "\n For example, the " + } + err.stock <- c( + err.stock, + prefix, + "following identifiers found in the data matrix\n", + " do not appear in the variable metadata file:\n", + " ", + paste(head(missing_in_vm, 3), collapse = "\n "), + "\n" + ) + } + if (length(missing_in_dm_var) > 0) { + prefix <- if (length(missing_in_dm_var) < 4) { + "\n The " + } else { + "\n For example, the " + } + err.stock <- c( + err.stock, + prefix, + "following identifiers found in the variable metadata file\n", + " do not appear in the data matrix:\n", + " ", + paste(head(missing_in_dm_var, 3), collapse = "\n "), + "\n" + ) + } + } + + if (length(err.stock) > 0) { + err.stock <- c(err.stock, "\nPlease check your data.\n") + } + + return(err.stock) +} + + +intens_check <- function( + DM.name, + SM.name, + VM.name, + method, + chosen.stat, + class.col, + test.fold, + class1, + fold.frac, + logarithm, + VM.output, + graphs.output) { + # Read input tables + DM <- read.table(DM.name, header = TRUE, sep = "\t", check.names = FALSE) + SM <- read.table(SM.name, header = TRUE, sep = "\t", check.names = FALSE) + VM <- read.table(VM.name, header = TRUE, sep = "\t", check.names = FALSE) + + # Table match check + table.check <- table_match(DM, SM, VM) + check.err(table.check) + + # Transpose and align DM + rownames(DM) <- DM[, 1] + DM <- data.frame(t(DM[, -1])) + DM <- merge(x = cbind(1:nrow(SM), SM), y = DM, by.x = 2, by.y = 0) + DM <- DM[order(DM[, 2]), ] + rownames(DM) <- DM[, 1] + DM <- DM[, -c(1:(ncol(SM) + 1))] + + stat.list <- strsplit(chosen.stat, ",")[[1]] + + # Class assignment + if (method == "no_class") { + c_class <- rep("global", nrow(DM)) + classnames <- "global" + nb_class <- 1 + test.fold <- "No" + } else { + class.col <- colnames(SM)[as.numeric(class.col)] + if (!(class.col %in% colnames(SM))) { + stop("The column ", class.col, " is not in sample metadata") + } + c_class <- as.factor(SM[, class.col]) + nb_class <- nlevels(c_class) + classnames <- levels(c_class) + if (nb_class < 2 && test.fold == "Yes") { + cat( + "The '", + class.col, + "' column contains only one class, fold calculation could not be executed.\n" + ) + } + if (nb_class > (nrow(SM)) / 3 && method == "each_class") { + cat("There are too many classes, consider reducing the number.\n") + } + if (method == "one_class") { + if (!(class1 %in% classnames)) { + stop("The '", class1, "' class does not appear in the '", class.col, "'column.") + } + c_class <- factor( + ifelse(c_class == class1, class1, "Other"), + levels = c(class1, "Other") + ) + nb_class <- nlevels(c_class) + classnames <- levels(c_class) + } + } + + # Check numeric + if (!is.numeric(as.matrix(DM))) { + findchar <- function(myval) { + ifelse( + is.na(myval), + "ok", + ifelse(is.na(as.numeric(as.character(myval))), "char", "ok") + ) + } + chardiag <- suppressWarnings(apply(DM, 2, vapply, findchar, "character")) + charlist <- which(chardiag == "char") + err.stock <- paste( + "\n- - - - - - - - -\nYour dataMatrix contains", + length(charlist), + "non-numeric value(s).\n" + ) + stop(c( + err.stock, + "The dataMatrix file is supposed to contain only numeric values.\n- - - - - - - - -\n" + )) + } + + DM <- cbind(c_class, DM) + stat.res <- t(DM[0, -1, drop = FALSE]) + names <- NULL + + mean.res <- sd.res <- med.res <- quart.res <- dec.res <- NA.res <- pct_NA.res <- fold.res <- NULL + mean.names <- sd.names <- med.names <- quart.names <- dec.names <- NA.names <- pct_NA.names <- fold.names <- NULL + graphs <- if (("NA" %in% stat.list) || (test.fold == "Yes")) 1 else 0 + data_bp <- data.frame() + + for (j in 1:nb_class) { + idx <- which(DM$c_class == classnames[j]) + # Mean + if ("mean" %in% stat.list) { + mean.res <- cbind(mean.res, colMeans(DM[idx, -1], na.rm = TRUE)) + mean.names <- cbind(mean.names, paste("Mean", classnames[j], sep = "_")) + if (j == nb_class) { + stat.res <- cbind(stat.res, mean.res) + names <- cbind(names, mean.names) + } + } + # SD + if ("sd" %in% stat.list) { + sd.res <- cbind(sd.res, apply(DM[idx, -1], 2, sd, na.rm = TRUE)) + sd.names <- cbind(sd.names, paste("Sd", classnames[j], sep = "_")) + if (j == nb_class) { + stat.res <- cbind(stat.res, sd.res) + names <- cbind(names, sd.names) + } + } + # Median + if (("median" %in% stat.list) && (!("quartile" %in% stat.list))) { + med.res <- cbind(med.res, apply(DM[idx, -1], 2, median, na.rm = TRUE)) + med.names <- cbind(med.names, paste("Median", classnames[j], sep = "_")) + if (j == nb_class) { + stat.res <- cbind(stat.res, med.res) + names <- cbind(names, med.names) + } + } + # Quartiles + if ("quartile" %in% stat.list) { + quart.res <- cbind( + quart.res, + t(apply(DM[idx, -1], 2, quantile, na.rm = TRUE)) + ) + quart.names <- cbind( + quart.names, + paste("Min", classnames[j], sep = "_"), + paste("Q1", classnames[j], sep = "_"), + paste("Median", classnames[j], sep = "_"), + paste("Q3", classnames[j], sep = "_"), + paste("Max", classnames[j], sep = "_") + ) + if (j == nb_class) { + stat.res <- cbind(stat.res, quart.res) + names <- cbind(names, quart.names) + } + } + # Deciles + if ("decile" %in% stat.list) { + dec.res <- cbind( + dec.res, + t(apply(DM[idx, -1], 2, quantile, na.rm = TRUE, seq(0, 1, 0.1))) + ) + dec.names <- cbind( + dec.names, + t(matrix(paste("D", seq(0, 10, 1), sep = "_", classnames[j]))) + ) + if (j == nb_class) { + stat.res <- cbind(stat.res, dec.res) + names <- cbind(names, dec.names) + } + } + # Missing values + if ("NA" %in% stat.list) { + nb_NA <- apply(DM[idx, -1], 2, function(x) sum(is.na(x))) + pct_NA <- round(nb_NA / nrow(DM[idx, -1]) * 100, 4) + NA.res <- cbind(NA.res, nb_NA) + pct_NA.res <- cbind(pct_NA.res, pct_NA) + NA.names <- cbind(NA.names, paste("NA", classnames[j], sep = "_")) + pct_NA.names <- cbind( + pct_NA.names, + paste("Pct_NA", classnames[j], sep = "_") + ) + if (j == nb_class) { + stat.res <- cbind(stat.res, NA.res, pct_NA.res) + names <- cbind(names, NA.names, pct_NA.names) + } + # Barplot data + bins <- cut(pct_NA, breaks = c(-1, 20, 40, 60, 80, 100), labels = FALSE) + for (b in 1:5) data_bp[b, j] <- sum(bins == b) + rownames(data_bp) <- c( + "0%-20%", + "20%-40%", + "40%-60%", + "60%-80%", + "80%-100%" + ) + if (j == nb_class) { + if (sum(NA.res) == 0) cat("Data Matrix contains no NA.\n") + colnames(data_bp) <- classnames + data_bp <- as.matrix(data_bp) + } + } + # Mean fold change + if (test.fold == "Yes" && nb_class >= 2 && j != nb_class) { + if (method == "each_class") fold.frac <- "Top" + for (k in (j + 1):nb_class) { + ratio1 <- if (fold.frac == "Bottom") classnames[k] else classnames[j] + ratio2 <- if (fold.frac == "Bottom") classnames[j] else classnames[k] + fold <- colMeans(DM[which(DM$c_class == ratio1), -1], na.rm = TRUE) / + colMeans(DM[which(DM$c_class == ratio2), -1], na.rm = TRUE) + if (logarithm == "log2") { + fold <- log2(fold) + } else if ( + logarithm == "log10" + ) { + fold <- log10(fold) + } + fold.res <- cbind(fold.res, fold) + fname <- if (logarithm == "none") { + paste("fold", ratio1, "VS", ratio2, sep = "_") + } else { + paste(logarithm, "fold", ratio1, "VS", ratio2, sep = "_") + } + fold.names <- cbind(fold.names, fname) + } + if (j == nb_class) { + stat.res <- cbind(stat.res, fold.res) + names <- cbind(names, fold.names) + } + } + } + + # Ensure unique column names + VM.names <- colnames(VM) + for (i in seq_along(VM.names)) { + for (j in seq_along(names)) { + if (VM.names[i] == names[j]) names[j] <- paste(names[j], "2", sep = "_") + } + } + colnames(stat.res) <- names + + # Output + VM <- cbind(VM, stat.res) + write.table(VM, VM.output, sep = "\t", quote = FALSE, row.names = FALSE) + + # Graphics + pdf(graphs.output) + if (graphs == 1) { + if ("NA" %in% stat.list) { + graph.colors <- c("green3", "palegreen3", "lightblue", "orangered", "red") + par(mar = c(5.1, 4.1, 4.1, 8.1), xpd = TRUE) + bp <- barplot( + data_bp, + col = graph.colors, + main = "Proportion of NA", + xlab = "Classes", + ylab = "Variables" + ) + legend( + "topright", + fill = graph.colors, + rownames(data_bp), + inset = c(-0.3, 0) + ) + stock <- 0 + for (i in 1:nrow(data_bp)) { + text( + bp, + stock + data_bp[i, ] / 2, + data_bp[i, ], + col = "white", + cex = 0.7 + ) + stock <- stock + data_bp[i, ] + } + } + if ((test.fold == "Yes") && (nb_class >= 2)) { + clean_fold <- fold.res + clean_fold[is.infinite(clean_fold)] <- NA + for (j in 1:ncol(clean_fold)) { + boxplot(clean_fold[, j], main = fold.names[j]) + } + } + } else { + plot.new() + legend("center", "You did not select any option with graphical output.") + } + dev.off() +} + + +parser <- ArgumentParser(description = "Intensity Check Tool") + +parser$add_argument( + "--dataMatrix_in", + required = TRUE, + help = "Input data matrix file" +) +parser$add_argument( + "--sampleMetadata_in", + required = TRUE, + help = "Input sample metadata file" +) +parser$add_argument( + "--variableMetadata_in", + required = TRUE, + help = "Input variable metadata file" +) +parser$add_argument("--method", required = TRUE, help = "Computation method") +parser$add_argument( + "--chosen_stat", + required = TRUE, + help = "Statistics to compute" +) +parser$add_argument( + "--class_col", + default = NULL, + help = "Class column (optional)" +) +parser$add_argument( + "--test_fold", + default = NULL, + help = "Test fold (optional)" +) +parser$add_argument("--class1", default = NULL, help = "Class1 (optional)") +parser$add_argument( + "--fold_frac", + default = NULL, + help = "Fold fraction (optional)" +) +parser$add_argument( + "--logarithm", + default = NULL, + help = "Logarithm (optional)" +) +parser$add_argument( + "--variableMetadata_out", + required = TRUE, + help = "Output variable metadata file" +) +parser$add_argument( + "--graphs_out", + required = TRUE, + help = "Output graphs file" +) + +args <- parser$parse_args() + +print(args) + +if (length(args) < 7) { + stop("NOT enough argument !!!") +} + +cat( + "\nJob starting time:\n", + format(Sys.time(), "%a %d %b %Y %X"), + "\n\n--------------------------------------------------------------------", + "\nIntensity Check parameters:\n\n" +) +print(args) +cat("--------------------------------------------------------------------\n\n") + +class_col <- NULL +test_fold <- NULL +class1 <- NULL +fold_frac <- NULL +logarithm <- NULL +if (args$method == "each_class") { + class_col <- args$class_col + test_fold <- args$test_fold + if (args$test_fold == "Yes") { + logarithm <- args$logarithm + } +} +if (args$method == "one_class") { + class_col <- args$class_col + class1 <- args$class1 + test_fold <- args$test_fold + if (args$test_fold == "Yes") { + fold_frac <- args$fold_frac + logarithm <- args$logarithm + } +} + +err_no_option <- NULL + +if ( + ((args$method == "no_class") && (args$chosen_stat == "None")) || + ((args$method != "no_class") && + (args$chosen_stat == "None") && + (test_fold == "No")) +) { + err_no_option <- "You did not select any computational option. Program can not be executed." + stop("\n- - - - - - - - -\n", err_no_option, "\n- - - - - - - - -\n") +} + + +if (is.null(err_no_option)) { + intens_check( + args$dataMatrix_in, + args$sampleMetadata_in, + args$variableMetadata_in, + args$method, + args$chosen_stat, + class_col, + test_fold, + class1, + fold_frac, + logarithm, + args$variableMetadata_out, + args$graphs_out + ) +} + + +cat( + "\n--------------------------------------------------------------------", + "\nInformation about R (version, Operating System, attached or loaded packages):\n\n" +) +sessionInfo() +cat( + "--------------------------------------------------------------------\n", + "\nJob ending time:\n", + format(Sys.time(), "%a %d %b %Y %X") +) + + +# delete the parameters to avoid the passage to the next tool in .RData image +rm(args)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/xml_intensity_check.xml Wed Jul 09 15:43:29 2025 +0000 @@ -0,0 +1,287 @@ +<tool id="intensity_check" name="Intensity Check" version="@TOOL_VERSION@" profile="22.05"> + <description>Statistical measures, number of missing values and mean fold change</description> + <macros> + <token name="@TOOL_VERSION@">2.0.0</token> + </macros> + + <edam_topics> + <edam_topic>topic_3172</edam_topic> + </edam_topics> + <edam_operations> + <edam_operation>operation_2428</edam_operation> + <edam_operation>operation_2428</edam_operation> + </edam_operations> + + <requirements> + <requirement type="package" version="4">r-base</requirement> + <requirement type="package" version="2.2.5">r-argparse</requirement> + </requirements> + + <required_files> + <include path="wrapper_intensity_check.R"/> + </required_files> + + <command detect_errors="aggressive"><![CDATA[ +Rscript '$__tool_directory__/wrapper_intensity_check.R' + --dataMatrix_in '$dataMatrix_in' + --sampleMetadata_in '$sampleMetadata_in' + --variableMetadata_in '$variableMetadata_in' + --method '${method_cond.method}' + --chosen_stat '${method_cond.chosen_stat}' +#if $method_cond.method == "each_class" + --class_col '${method_cond.class_col}' + --test_fold '${method_cond.test_fold_cond.test_fold}' + #if $method_cond.test_fold_cond.test_fold == "Yes" + --logarithm '${method_cond.test_fold_cond.logarithm}' + #end if +#end if +#if $method_cond.method == "one_class" + --class_col '${method_cond.class_col}' + --class1 '${method_cond.class1}' + --test_fold '${method_cond.test_fold_cond.test_fold}' + #if $method_cond.test_fold_cond.test_fold == "Yes" + --fold_frac '${method_cond.test_fold_cond.fold_frac}' + --logarithm '${method_cond.test_fold_cond.logarithm}' + #end if +#end if + --variableMetadata_out '$variableMetadata_out' + --graphs_out '$graphs_out' + ]]></command> + + <inputs> + <param name="dataMatrix_in" type="data" format="tabular,tsv" label="Data matrix file" + help="Main data matrix. Rows are variables (features), columns are samples. The first column must contain variable identifiers, and the first row must contain sample identifiers." /> + <param name="sampleMetadata_in" type="data" format="tabular,tsv" label="Sample metadata file" + help="Sample metadata table. Each row corresponds to a sample, and columns provide sample annotations (e.g., class, batch, group). The first column must contain sample identifiers matching the data matrix columns." /> + <param name="variableMetadata_in" type="data" format="tabular,tsv" label="Variable metadata file" + help="Variable metadata table. Each row corresponds to a variable (feature), and columns provide variable annotations (e.g., m/z, retention time). The first column must contain variable identifiers matching the data matrix rows." /> + <conditional name="method_cond"> + <param name="method" type="select" label="Computation method" + help="Choose how to group samples for the calculation of statistics and fold changes. + • Without distinction between samples: All samples are treated as a single group. + • For each class of samples: Samples are grouped by a class column in the sample metadata. + • Between one class and all the remaining samples: Focus on one class versus all others."> + <option value="no_class">Without distinction between samples</option> + <option value="each_class">For each class of samples</option> + <option value="one_class">Between one class and all the remaining samples</option> + </param> + <when value="no_class"> + <param name="chosen_stat" type="select" display="checkboxes" multiple="true" label="Statistics" + help="Select one or more statistics to calculate for each variable across all samples. At least one must be selected."> + <option value="mean">Mean</option> + <option value="sd">Standard deviation</option> + <option value="median">Median</option> + <option value="quartile">Quartile</option> + <option value="decile">Decile</option> + <option value="NA">Missing values</option> + <validator type="no_options" message="Please choose at least one statistic representation" /> + </param> + </when> + <when value="each_class"> + <param name="class_col" type="data_column" data_ref="sampleMetadata_in" use_header_names="true" + label="Class column" + help="Select the column in the sample metadata file that defines the sample classes (groups) for statistics and fold change calculations." /> + <param name="chosen_stat" type="select" display="checkboxes" multiple="true" label="Statistics" + help="Select one or more statistics to calculate for each variable within each class. Leave blank if you only want to calculate mean fold change."> + <option value="mean">Mean</option> + <option value="sd">Standard deviation</option> + <option value="median">Median</option> + <option value="quartile">Quartile</option> + <option value="decile">Decile</option> + <option value="NA">Missing values</option> + </param> + <conditional name="test_fold_cond"> + <param name="test_fold" type="select" display="radio" label="Calculate the mean fold change" + help="Select 'Yes' to calculate the mean fold change between all pairs of classes."> + <option value="Yes">Yes</option> + <option value="No" selected="true">No</option> + </param> + <when value="Yes"> + <param name="logarithm" type="select" label="Logarithm" + help="Choose a logarithmic transformation for the mean fold change values. + • none: No transformation + • log2: Log2 transformation + • log10: Log10 transformation"> + <option value="none" selected='true'>none</option> + <option value="log2">log2</option> + <option value="log10">log10</option> + </param> + </when> + <when value="No"> + </when> + </conditional> + </when> + <when value="one_class"> + <param name="class_col" type="data_column" data_ref="sampleMetadata_in" use_header_names="true" + label="Class column" + help="Select the column in the sample metadata file that defines the sample classes (groups). The selected class will be compared to all other samples." /> + <param name="class1" type="text" label="Selected class" + help="Enter the name of the class to compare against all other samples. This class will be used as numerator or denominator in fold change calculations." /> + <param name="chosen_stat" type="select" display="checkboxes" multiple="true" label="Statistics" + help="Select one or more statistics to calculate for the selected class and the remaining samples. Leave blank if you only want to calculate mean fold change."> + <option value="mean">Mean</option> + <option value="sd">Standard deviation</option> + <option value="median">Median</option> + <option value="quartile">Quartile</option> + <option value="decile">Decile</option> + <option value="NA">Missing values</option> + </param> + <conditional name="test_fold_cond"> + <param name="test_fold" type="select" display="radio" label="Calculate the mean fold change" + help="Select 'Yes' to calculate the mean fold change between the selected class and all other samples."> + <option value="Yes">Yes</option> + <option value="No">No</option> + </param> + <when value="Yes"> + <param name="fold_frac" type="select" label="Where should the class be placed for the mean fold change calculation?" display="radio" + help="Choose whether the selected class should be used as the numerator (Top) or denominator (Bottom) in the fold change calculation."> + <option value="Top">Numerator (Top)</option> + <option value="Bottom">Denominator (Bottom)</option> + </param> + <param name="logarithm" type="select" label="Logarithm" + help="Choose a logarithmic transformation for the mean fold change values. + • none: No transformation + • log2: Log2 transformation + • log10: Log10 transformation"> + <option value="none">none</option> + <option value="log2">log2</option> + <option value="log10">log10</option> + </param> + </when> + <when value="No"> + </when> + </conditional> + </when> + </conditional> + </inputs> + + <outputs> + <data name="variableMetadata_out" format="tabular" label="IC_${variableMetadata_in.name}" /> + <data name="graphs_out" format="pdf" label="IC_Graphs" /> + </outputs> + + <tests> + <test> + <param name="dataMatrix_in" value="ic_in_dataMatrix.tabular" /> + <param name="sampleMetadata_in" value="ic_in_sampleMetadata.tabular" /> + <param name="variableMetadata_in" value="ic_in_variableMetadata.tabular" /> + <conditional name="method_cond"> + <param name="method" value="no_class" /> + <param name="chosen_stat" value="mean,sd,median,quartile,decile,NA" /> + </conditional> + <output name="variableMetadata_out" value="ic_out_variableMetadata.tabular" /> + <output name="graphs_out" value="ic_out_graphs.pdf" /> + </test> + <test> + <param name="dataMatrix_in" value="ic_in_dataMatrix.tabular" /> + <param name="sampleMetadata_in" value="ic_in_sampleMetadata.tabular" /> + <param name="variableMetadata_in" value="ic_in_variableMetadata.tabular" /> + <conditional name="method_cond"> + <param name="chosen_stat" value="mean,sd,median,quartile,decile,NA" /> + <param name="method" value="each_class" /> + <param name="class_col" value="2" /> + </conditional> + <output name="variableMetadata_out" value="ic_out_variableMetadata_classes.tabular" /> + <output name="graphs_out" value="ic_out_graphs_classes.pdf" /> + </test> + </tests> + + <help><![CDATA[ + +.. class:: infomark + +**Author:** +Anthony Fernandes for original code (PFEM - INRA) +**Maintainer:** Melanie Petera (PFEM - INRAE - MetaboHUB) + +--------------------------------------------------- + +==================== +Intensity Check +==================== + +----------- +Description +----------- + +This tool performs various metrics: mean fold change calculation, number and proportion of missing values, and mean, sd and decile calculation. +You can choose to perform these metrics according to sample groups defined in the sample metadata file given as input. + +----------------- +Workflow position +----------------- + +.. image:: int_check.png + :width: 800 + +----------- +Input files +----------- + ++----------------------------+------------+ +| Parameter | Format | ++============================+============+ +| 1 : Data matrix file | tabular | ++----------------------------+------------+ +| 2 : Sample metadata file | tabular | ++----------------------------+------------+ +| 3 : Variable metadata file | tabular | ++----------------------------+------------+ + +---------- + +Parameters +---------- + +**Computation method** + - **Without distinction between samples:** calculates chosen statistic(s) for each variable. + - **For each class of samples:** separates samples between each class (class column to be specified). Chosen statistic(s) and/or mean fold change are calculated for each of them. + - **Between one class versus all the remaining samples:** If you want to focus only on one class versus all the remaining samples without class distinction. + + In the case of two classes: "each class" and "one class" give the same results for statistical measures. We recommend to choose "one class" for mean fold change calculation in order to select the class you want to put as numerator or denominator (see below). + +**Statistics** + Select the statistical measures you want to add in the variable metadata table. If the method is "each class" or "one class", you can choose to leave this section blank if you only want to calculate the mean fold change (see below). + +**Class column** (*only if "each class" or "one class"*) + Select the class column in sample metadata table. + +**Selected class** (*only if "one class"*) + If the method is "one class", specify it. Remaining samples will be named "Other". + +**Calculate the mean fold change** (*only if "each class" or "one class"*) + Choose if you want to calculate the mean fold change. If the method is "each class": mean fold change will be calculated for all combinations of classes. If the method is "one class": it will be calculated between the selected class (see above) and the remaining samples. + +**Where should the class be placed for the mean fold change calculation?** (*only if "one class"*) + If the method is "one class", choose "top" or "bottom" to put the selected class as numerator or denominator (respectively) for the mean fold change calculation. + +**Logarithm** (*only if "each class" or "one class"*) + Choose if you want to transform the mean fold change with a log2 or log10. + +------------ +Output files +------------ + +**Variable metadata file** + Contains the previous columns in variable metadata file and the new ones. + In the column names for fold, the first class specified is the one used as numerator in the ratio. + +**Graphs file** + Contains barplots with the proportion of NA considering classes and boxplots with the fold values. + +--------------------------------------------------- + +---------------------- +Additional information +---------------------- + +.. class:: warningmark + +For more information about input files, refer to the corresponding "W4M HowTo" page: +`W4M table format for Galaxy <https://nextcloud.inrae.fr/s/qLkNZRf84QQ5YLY>`_ + + ]]></help> + <citations> + <citation type="doi">10.1002/cpz1.70095</citation> + </citations> +</tool>