comparison lib.r @ 9:ee29f0a6e361 draft

planemo upload for repository https://github.com/workflow4metabolomics/xcms commit 08e7f269a5c59687a7768be8db5fcb4e4d736093

8:cce30b3398e0

# lib.r version="2.0.1"
#Authors ABiMS TEAM
#Lib.r for Galaxy Workflow4Metabo
#version 2.2
#Based on lib.r 2.1
#Modifications made by Guitton Yann 
#correct bug in Base Peak Chromatogram (BPC) option, not only TIC when scanrange used in xcmsSet
#Note if scanrange is used a warning is prompted in R console but do not stop PDF generation




#@author Y. Guitton
getBPC <- function(file,rtcor=NULL, ...) {
  object <- xcmsRaw(file)
  sel <- profRange(object, ...)

  for (j in 1:N) {

    TIC[[j]] <- getBPC(files[j])
    #good for raw 
    # seems strange for corrected
    #errors if scanrange used in xcmsSetgeneration
    if (!is.null(xcmsSet) && rt == "corrected")
    rtcor <- xcmsSet@rt$corrected[[j]] else
    rtcor <- NULL
    
    TIC[[j]] <- getBPC(files[j],rtcor=rtcor)
    # TIC[[j]][,1]<-rtcor
  }

  ylim = range(sapply(TIC, function(x) range(x[,2])))
  ylim = c(-ylim[2], ylim[2])


  ##plot start
  
  if (length(class)>2){
    for (k in 1:(length(class)-1)){
      for (l in (k+1):length(class)){
        #print(paste(class[k],"vs",class[l],sep=" ")) 
        plot(0, 0, type="n", xlim = xlim/60, ylim = ylim, main = paste("Base Peak Chromatograms \n","BPCs_",class[k]," vs ",class[l], sep=""), xlab = "Retention Time (min)", ylab = "BPC")
        colvect<-NULL
        for (j in 1:length(classnames[[k]])) {
          tic <- TIC[[classnames[[k]][j]]]
          # points(tic[,1]/60, tic[,2], col = cols[i], pch = pch[i], type="l")

    }
    legend("topright",paste(basename(files[c(classnames[[k]],classnames[[l]])])), col = colvect, lty = lty, pch = pch)

  }#end length ==2

  dev.off() #pdf(pdfname,w=16,h=10)

  invisible(TIC)
}

  classnames<-vector("list",length(class))
  for (i in 1:length(class)){
    classnames[[i]]<-which( xcmsSet@phenoData[,1]==class[i])
  }
  
  N <- length(files)
  TIC <- vector("list",N)

  for (i in 1:N) {
    if (!is.null(xcmsSet) && rt == "corrected")

  ##plot start
  if (length(class)>2){
    for (k in 1:(length(class)-1)){
      for (l in (k+1):length(class)){
        #print(paste(class[k],"vs",class[l],sep=" ")) 
        plot(0, 0, type="n", xlim = xlim/60, ylim = ylim, main = paste("Total Ion Chromatograms \n","TICs_",class[k]," vs ",class[l], sep=""), xlab = "Retention Time (min)", ylab = "TIC")
        colvect<-NULL
        for (j in 1:length(classnames[[k]])) {

          tic <- TIC[[classnames[[k]][j]]]

      colvect<-append(colvect,cols[classnames[[l]][j]])
    }
    legend("topright",paste(basename(files[c(classnames[[k]],classnames[[l]])])), col = colvect, lty = lty, pch = pch)

  }#end length ==2
  dev.off() #pdf(pdfname,w=16,h=10)

  invisible(TIC)
}

  #Create the sampleMetada dataframe
  sampleMetadata=xset@phenoData
  sampleNamesOrigin=rownames(sampleMetadata)
  sampleNamesMakeNames=make.names(sampleNamesOrigin)
    
  if (any(duplicated(sampleNamesMakeNames))) {
    write("\n\nERROR: Usually, R has trouble to deal with special characters in its column names, so it rename them using make.names().\nIn your case, at least two columns after the renaming obtain the same name, thus XCMS will collapse those columns per name.", stderr())
    for (sampleName in sampleNamesOrigin) {
      write(paste(sampleName,"\t->\t",make.names(sampleName)),stderr())
    }

      library(tools)
      samplename=file_path_sans_ext(filename)

      #Set the polarity attribute
      sampleMetadata$polarity[sampleMetadata$sampleMetadata==samplename]=polarity
      
      #Delete xcmsRaw object because it creates a bug for the fillpeaks step
      rm(xcmsRaw)
    }

  }

  # WHAT IS ON THE FILESYSTEM
  filesystem_filepaths=system(paste("find $PWD/",directory," -not -name '\\.*' -not -path '*conda-env*' -type f -name \"*\"", sep=""), intern=T)
  filesystem_filepaths=filesystem_filepaths[grep(filepattern, filesystem_filepaths, perl=T)]

  # COMPARISON
  if (!is.na(table(filesystem_filepaths %in% files)["FALSE"])) { 
    write("\n\nERROR: List of the files which will not be imported by xcmsSet",stderr())
    write(filesystem_filepaths[!(filesystem_filepaths %in% files)],stderr())
    stop("\n\nERROR: One or more of your files will not be import by xcmsSet. It may due to bad characters in their filenames.")

  }

    #message=paste("The following mzXML or mzML file is incorrect, please check these files first:",capture)
    write("\n\nERROR: The following mzXML or mzML file(s) are incorrect, please check these files first:", stderr())
    write(capture, stderr())
    stop("ERROR: xcmsSet cannot continue with incorrect mzXML or mzML files")
  }
   
}


##
## This function check if XML contain special characters

#@author Misharl Monsoor [e-mail] ABiMS TEAM
deleteXmlBadCharacters<- function (directory) {
  cat("Checking Non ASCII characters in the XML...\n")

  processed=F
  l=system( paste("find",directory, "-not -name '\\.*' -not -path '*conda-env*' -type f -iname '*.*ml*'"),intern=TRUE) 
  for (i in l){
    cmd=paste("LC_ALL=C grep '[^ -~]' \"",i,"\"",sep="")
    capture=suppressWarnings(system(cmd,intern=TRUE))
    if (length(capture)>0){
      cmd=paste("perl -i -pe 's/[^[:ascii:]]//g;'",i)
      print( paste("WARNING: Non ASCII characters have been removed from the ",i,"file") )
      c=system(cmd,intern=TRUE)
      capture=""
      processed=T 
    }
  }
  if (processed) cat("\n\n")
  return(processed)
}


## 
## This function will compute MD5 checksum to check the data integrity
##
#@author Gildas Le Corguille [e-mail]
getMd5sum <- function (directory) {
  cat("Compute md5 checksum...\n")

  #cat("\n\n")

  return(as.matrix(md5sum(files)))
}

9:ee29f0a6e361

#Authors ABiMS TEAM
#Lib.r for Galaxy Workflow4Metabolomics xcms tools
#
#version 2.4: lecorguille
#   add getPeaklistW4M
#version 2.3: yguitton
#   correction for empty PDF when only 1 class
#version 2.2
#   correct bug in Base Peak Chromatogram (BPC) option, not only TIC when scanrange used in xcmsSet
#   Note if scanrange is used a warning is prompted in R console but do not stop PDF generation
#version 2.1: yguitton
#   Modifications made by Guitton Yann


#@author G. Le Corguille
#This function convert if it is required the Retention Time in minutes
RTSecondToMinute <- function(variableMetadata, convertRTMinute) {
    if (convertRTMinute){
        #converting the retention times (seconds) into minutes
        print("converting the retention times into minutes in the variableMetadata")
        variableMetadata[,"rt"]=variableMetadata[,"rt"]/60
        variableMetadata[,"rtmin"]=variableMetadata[,"rtmin"]/60
        variableMetadata[,"rtmax"]=variableMetadata[,"rtmax"]/60
    }
    return (variableMetadata)
}

#@author G. Le Corguille
#This function format ions identifiers
formatIonIdentifiers <- function(dataData, numDigitsRT=0, numDigitsMZ=0) {
    return(make.unique(paste0("M",round(dataData[,"mz"],numDigitsMZ),"T",round(dataData[,"rt"],numDigitsRT))))
}

#@author G. Le Corguille
# value: intensity values to be used into, maxo or intb
getPeaklistW4M <- function(xset, intval="into",convertRTMinute=F,numDigitsMZ=4,numDigitsRT=0,variableMetadataOutput,dataMatrixOutput) {
    groups <- xset@groups
    values <- groupval(xset, "medret", value=intval)
    
    # renamming of the column rtmed to rt to fit with camera peaklist function output
    colnames(groups)[colnames(groups)=="rtmed"] <- "rt"
    colnames(groups)[colnames(groups)=="mzmed"] <- "mz"
    
    ids <- formatIonIdentifiers(groups, numDigitsRT=numDigitsRT, numDigitsMZ=numDigitsMZ)
    groups = RTSecondToMinute(groups, convertRTMinute)

    rownames(groups) = ids
    rownames(values) = ids

    #@TODO: add "name" as the first column name
    #colnames(groups)[1] = "name"
    #colnames(values)[1] = "name"

    write.table(groups, file=variableMetadataOutput,sep="\t",quote=F,row.names = T,col.names = NA)
    write.table(values, file=dataMatrixOutput,sep="\t",quote=F,row.names = T,col.names = NA)
}

#@author Y. Guitton
getBPC <- function(file,rtcor=NULL, ...) {
  object <- xcmsRaw(file)
  sel <- profRange(object, ...)

  for (j in 1:N) {

    TIC[[j]] <- getBPC(files[j])
    #good for raw
    # seems strange for corrected
    #errors if scanrange used in xcmsSetgeneration
    if (!is.null(xcmsSet) && rt == "corrected")
    rtcor <- xcmsSet@rt$corrected[[j]] else
    rtcor <- NULL

    TIC[[j]] <- getBPC(files[j],rtcor=rtcor)
    # TIC[[j]][,1]<-rtcor
  }

  ylim = range(sapply(TIC, function(x) range(x[,2])))
  ylim = c(-ylim[2], ylim[2])


  ##plot start

  if (length(class)>2){
    for (k in 1:(length(class)-1)){
      for (l in (k+1):length(class)){
        #print(paste(class[k],"vs",class[l],sep=" "))
        plot(0, 0, type="n", xlim = xlim/60, ylim = ylim, main = paste("Base Peak Chromatograms \n","BPCs_",class[k]," vs ",class[l], sep=""), xlab = "Retention Time (min)", ylab = "BPC")
        colvect<-NULL
        for (j in 1:length(classnames[[k]])) {
          tic <- TIC[[classnames[[k]][j]]]
          # points(tic[,1]/60, tic[,2], col = cols[i], pch = pch[i], type="l")

    }
    legend("topright",paste(basename(files[c(classnames[[k]],classnames[[l]])])), col = colvect, lty = lty, pch = pch)

  }#end length ==2

  #case where only one class
  if (length(class)==1){
    k=1
		ylim = range(sapply(TIC, function(x) range(x[,2])))
    colvect<-NULL
    plot(0, 0, type="n", xlim = xlim/60, ylim = ylim, main = paste("Base Peak Chromatograms \n","BPCs_",class[k], sep=""), xlab = "Retention Time (min)", ylab = "BPC")

    for (j in 1:length(classnames[[k]])) {
      tic <- TIC[[classnames[[k]][j]]]
      # points(tic[,1]/60, tic[,2], col = cols[i], pch = pch[i], type="l")
      points(tic[,1]/60, tic[,2], col = cols[classnames[[k]][j]], pch = pch[classnames[[k]][j]], type="l")
      colvect<-append(colvect,cols[classnames[[k]][j]])
    }

    legend("topright",paste(basename(files[c(classnames[[k]])])), col = colvect, lty = lty, pch = pch)

  }#end length ==1

  dev.off() #pdf(pdfname,w=16,h=10)

  invisible(TIC)
}

  classnames<-vector("list",length(class))
  for (i in 1:length(class)){
    classnames[[i]]<-which( xcmsSet@phenoData[,1]==class[i])
  }

  N <- length(files)
  TIC <- vector("list",N)

  for (i in 1:N) {
    if (!is.null(xcmsSet) && rt == "corrected")

  ##plot start
  if (length(class)>2){
    for (k in 1:(length(class)-1)){
      for (l in (k+1):length(class)){
        #print(paste(class[k],"vs",class[l],sep=" "))
        plot(0, 0, type="n", xlim = xlim/60, ylim = ylim, main = paste("Total Ion Chromatograms \n","TICs_",class[k]," vs ",class[l], sep=""), xlab = "Retention Time (min)", ylab = "TIC")
        colvect<-NULL
        for (j in 1:length(classnames[[k]])) {

          tic <- TIC[[classnames[[k]][j]]]

      colvect<-append(colvect,cols[classnames[[l]][j]])
    }
    legend("topright",paste(basename(files[c(classnames[[k]],classnames[[l]])])), col = colvect, lty = lty, pch = pch)

  }#end length ==2

  #case where only one class
  if (length(class)==1){
	  k=1
	  ylim = range(sapply(TIC, function(x) range(x[,2])))

	  plot(0, 0, type="n", xlim = xlim/60, ylim = ylim, main = paste("Total Ion Chromatograms \n","TICs_",class[k], sep=""), xlab = "Retention Time (min)", ylab = "TIC")
    colvect<-NULL
		for (j in 1:length(classnames[[k]])) {
      tic <- TIC[[classnames[[k]][j]]]
			# points(tic[,1]/60, tic[,2], col = cols[i], pch = pch[i], type="l")
			points(tic[,1]/60, tic[,2], col = cols[classnames[[k]][j]], pch = pch[classnames[[k]][j]], type="l")
      colvect<-append(colvect,cols[classnames[[k]][j]])
	  }

		legend("topright",paste(basename(files[c(classnames[[k]])])), col = colvect, lty = lty, pch = pch)

	}#end length ==1

  dev.off() #pdf(pdfname,w=16,h=10)

  invisible(TIC)
}

  #Create the sampleMetada dataframe
  sampleMetadata=xset@phenoData
  sampleNamesOrigin=rownames(sampleMetadata)
  sampleNamesMakeNames=make.names(sampleNamesOrigin)

  if (any(duplicated(sampleNamesMakeNames))) {
    write("\n\nERROR: Usually, R has trouble to deal with special characters in its column names, so it rename them using make.names().\nIn your case, at least two columns after the renaming obtain the same name, thus XCMS will collapse those columns per name.", stderr())
    for (sampleName in sampleNamesOrigin) {
      write(paste(sampleName,"\t->\t",make.names(sampleName)),stderr())
    }

      library(tools)
      samplename=file_path_sans_ext(filename)

      #Set the polarity attribute
      sampleMetadata$polarity[sampleMetadata$sampleMetadata==samplename]=polarity

      #Delete xcmsRaw object because it creates a bug for the fillpeaks step
      rm(xcmsRaw)
    }

  }

  # WHAT IS ON THE FILESYSTEM
  filesystem_filepaths=system(paste("find $PWD/",directory," -not -name '\\.*' -not -path '*conda-env*' -type f -name \"*\"", sep=""), intern=T)
  filesystem_filepaths=filesystem_filepaths[grep(filepattern, filesystem_filepaths, perl=T)]

  # COMPARISON
  if (!is.na(table(filesystem_filepaths %in% files)["FALSE"])) {
    write("\n\nERROR: List of the files which will not be imported by xcmsSet",stderr())
    write(filesystem_filepaths[!(filesystem_filepaths %in% files)],stderr())
    stop("\n\nERROR: One or more of your files will not be import by xcmsSet. It may due to bad characters in their filenames.")

  }

    #message=paste("The following mzXML or mzML file is incorrect, please check these files first:",capture)
    write("\n\nERROR: The following mzXML or mzML file(s) are incorrect, please check these files first:", stderr())
    write(capture, stderr())
    stop("ERROR: xcmsSet cannot continue with incorrect mzXML or mzML files")
  }

}


##
## This function check if XML contain special characters

#@author Misharl Monsoor [e-mail] ABiMS TEAM
deleteXmlBadCharacters<- function (directory) {
  cat("Checking Non ASCII characters in the XML...\n")

  processed=F
  l=system( paste("find",directory, "-not -name '\\.*' -not -path '*conda-env*' -type f -iname '*.*ml*'"),intern=TRUE)
  for (i in l){
    cmd=paste("LC_ALL=C grep '[^ -~]' \"",i,"\"",sep="")
    capture=suppressWarnings(system(cmd,intern=TRUE))
    if (length(capture)>0){
      cmd=paste("perl -i -pe 's/[^[:ascii:]]//g;'",i)
      print( paste("WARNING: Non ASCII characters have been removed from the ",i,"file") )
      c=system(cmd,intern=TRUE)
      capture=""
      processed=T
    }
  }
  if (processed) cat("\n\n")
  return(processed)
}


##
## This function will compute MD5 checksum to check the data integrity
##
#@author Gildas Le Corguille [e-mail]
getMd5sum <- function (directory) {
  cat("Compute md5 checksum...\n")

  #cat("\n\n")

  return(as.matrix(md5sum(files)))
}