R/pelt.R

Defines functions PELTaroma PELT

#
# This function launches the segmentation PELT (from package changepoint) with a penalty value of rho * log(n) for a range of value for rho.
# Then an optimal penalty value is chosen by looking for a stabilization in the number of segments according to the penalty values.
#
# @title segmentation function 
#
# @param signal a vector containing the signal.
# @param Rho A vector containing all the penalization values to test for the segmentation. If no values are provided, default values will be used
# @param position A vector containing the position of all elements of the signal (not necessary)
# @param plot if TRUE, plot the segmentation results
# @param verbose if TRUE print some informations
#
# @return a list containing
# \describe{
#   \item{signal}{A vector containing the signal.}
#   \item{segmented}{A vector of the same size as signal containing the segmented values.}
#   \item{startPos}{The position of each probe.}
#   \item{segment}{A data.frame that summarizes the results of the segmentation. Each row is a different segment with the start position, end position, number of points in the signal and the value of the segment.}
# }
#
# @export
# 
# @author Quentin Grimonprez
# 
PELT=function(signal,Rho,position=NULL,plot=TRUE,verbose=TRUE)
{
  #package for PELT method
  allpkg=TRUE
  
  if(!allpkg)
    stop("You have to install some packages : Follow the printed informations.")
  
  #signal
  if(missing(signal))
    stop("signal is missing.") 
  if(!is.numeric(signal) )#|| !is.vector(signal))
    stop("signal must be a vector of real.")
    
  #position
  if(is.null(position))
    position=1:length(signal)
  if(!is.numeric(position) || !is.vector(position))
    stop("position must be a vector of real.")
  
  #order signal
  ord=order(position)
  position=position[ord]
  signal=signal[ord]
  
  

  #Rho
  if(missing(Rho)||is.null(Rho))
    Rho=c(seq(0.1,2,by=0.1),seq(2.2,5,by=0.2),seq(5.5,10,by=0.2),seq(11,16,by=1),seq(18,36,by=2),seq(40,80,by=4))
  else
  {
    if(!is.numeric(Rho) || !is.vector(Rho))
      stop("Rho must be a vector of positive real.")
    if(length(Rho[Rho>0])!=length(Rho))
      stop("Rho must be a vector of positive real.")
    Rho=unique(Rho)
    Rho=sort(Rho)
  }
  
  #PELT doesn't tolerate NA values
  noNA=which(!is.na(signal))
  
  #Test if there is at least 2 valid points.
  if (length(noNA) < 2)
  {
    warning("Not enough point to segment signal")
    return(NULL)
   }
  
  #list for store the results
  allBreakpoints=list()
  allSegmentedValue=list()
  
  for(i in 1:length(Rho))
  {
    seg=cpt.mean(signal[noNA],method="PELT",penalty="Manual",pen.value=paste0(Rho[i],"*log(n)"))
    allBreakpoints[[i]]=seg@cpts
    allSegmentedValue[[i]]=seg@param.est$mean
    if(length(seg@cpts)==1)
      break;
  }
  
  #find the best rho
  segmentation=findPlateau(allBreakpoints,Rho,plot=plot,verbose=verbose)
  
  ind=which(Rho==segmentation$rho)#index of the best rho
  cpt=c(0,allBreakpoints[[ind]])
  
  if(plot)
  {    
    #plot data
    plot(position,signal,pch=".",xlab="Position",ylab="signal")
    
    #plot segments 
    for(i in 1:length(allBreakpoints[[ind]]))
      lines(c((position[noNA])[cpt[i]+1],(position[noNA])[cpt[i+1]]),rep(allSegmentedValue[[ind]][i],2),col="red",lwd=3)  
  }
  
  #create segmented signals
  nbPtsSeg=diff(cpt)
  segmentedSignal=rep(NA,length(signal))  
  segmentedSignal[noNA]=unlist(lapply(1:length(nbPtsSeg),FUN=function(i)
    {
      rep(allSegmentedValue[[ind]][i],nbPtsSeg[i])
    }))
  
  return(list(signal=as.matrix(signal),
              segmented=as.matrix(segmentedSignal),
              startPos=position,
              segment=data.frame(start=(position[noNA])[cpt[-length(cpt)]+1],
                                 end=(position[noNA])[cpt[-1]],
                                 points=nbPtsSeg,
                                 means=allSegmentedValue[[ind]])))
}

           
#########################################################################


#
# This function launches the segmentation PELT (from package changepoint) with a penalty value of rho * log(n) for a range of value for rho.
# Then an optimal penalty value is chosen by looking for a stabilization in the number of segments according to the penalty values.
#
# @title segmentation function 
#
# @param dataSetName The name of the data-set folder (it must correspond to a folder name in rawData folder.).
# @param normalTumorArray Only in the case of normal-tumor study. A csv file or a data.frame containing the mapping between normal and tumor files
# The first column contains the name of normal files and the second the names of associated tumor files.
# @param chromosome A vector with the chromosomes to be segmented. 
# @param Rho A vector containing all the penalization values to test for the segmentation. If no values are provided, default values will be used.
# @param onlySNP If TRUE, only the copy-number for SNPs positions will be returned (default=TRUE).
# @param listOfFiles A vector containing the names of the files in dataSetName folder for which the copy number profiles will be segmented (default is all the files).
# @param savePlot if TRUE, graphics of the segmented CN signal will be saved in the figures/dataSetName/segmentation/CN folder. (default=TRUE).
# @param verbose if TRUE print some informations
# 
# @return a list containing
# \describe{
#   \item{copynumber}{A vector containing  the copynumber signal.}
#   \item{segmented}{A vector of the same size as copynumber containing the segmented values.}
#   \item{startPos}{The position of each probes.}
#   \item{chromosome}{A vector of the same size as copynumber containing the chromosome number.}
#   \item{featureNames}{Names of the probes.}
#   \item{sampleNames}{The name of the signal.}
#   \item{segment}{A data.frame that summarizes the results of the segmentation. Each row is a different segment with the chromosome, start position, end position, number of probes in the signal and the value of the segment.}
# }
#
# @export
# 
# @author Quentin Grimonprez
# 
PELTaroma=function(dataSetName,normalTumorArray,chromosome=1:22,Rho=NULL,listOfFiles=NULL,onlySNP=TRUE,savePlot=TRUE,verbose=TRUE)
{
  
  allpkg=TRUE
  if(!suppressPackageStartupMessages(require("aroma.affymetrix", quietly=TRUE) ) )
  {
    cat("Package not found: aroma.affymetrix. For download it:\n")
    cat("source(\"http://www.braju.com/R/hbLite.R\")\n")
    cat(" hbLite(\"sfit\")\n")
    cat("source(\"http://bioconductor.org/biocLite.R\")\n")
    cat("biocLite(\"affxparser\")\n")
    cat("biocLite(\"DNAcopy\")\n")
    cat("biocLite(\"aroma.light\")\n")
    #     cat("source(\"http://aroma-project.org/hbLite.R\")\n")
    cat("install.packages(\"aroma.affymetrix\")\n")
    allpkg=FALSE
  }
  
  if(!suppressPackageStartupMessages(require("aroma.cn", quietly=TRUE) ) )
  {
    cat("Package not found: aroma.cn. For download it:\n")
    cat("install.packages(\"aroma.cn\")\n") 
    allpkg=FALSE
  }

  
  if(!allpkg)
    stop("You have to install some packages : Follow the printed informations.")
  
  require(aroma.core)
  require(R.filesets)
  require(R.devices)
  
  if(!("totalAndFracBData"%in%list.files()))
    stop("There is no \"totalAndFracBData\", check if you are in the good working directory or if you have run the signalPreProcess function before.")
  
  ###check the arguments
  #dataSetName
  if(missing(dataSetName))
    stop("dataSetName is missing.")
  if(!is.character(dataSetName))
    stop("dataSetName must be the name of a folder in \"rawData\" folder.")
  if(!(dataSetName%in%list.files("rawData")))
    stop("dataSetName must be the name of a folder in \"rawData\" folder.")
  #onlySNP
  if(!is.logical(onlySNP))
    stop("onlySNP must be a boolean.")
  
  #check if we are in a normal-tumor study or in a single array study
  singleStudy=TRUE
  if(!missing(normalTumorArray))
    singleStudy=FALSE
  
  #################### import the dataSet to have the name of all the files
  
  #path where find the CN data
  rootPath <- "totalAndFracBData";
  rootPath <- Arguments$getReadablePath(rootPath);
  dataSet <- paste0(dataSetName,",ACC,ra,-XY,BPN,-XY,AVG,FLN,-XY");
  
  #load CN
  dsC <- aroma.core::AromaUnitTotalCnBinarySet$byName(dataSet, chipType="*", paths=rootPath);
  
  ################### check normalTumorArray
  if(!singleStudy)
  {
    #normalTumorArray
    if(is.character(normalTumorArray))
      normalTumorArray=read.csv(normalTumorArray)
    else
    {
      if(!is.data.frame(normalTumorArray))
        stop("normalTumorArray must be either the path to the normalTumorArray csv file or a data.frame containing the data.\n")
    }
    
    #check normalTumorArray
    if(!("normal"%in%colnames(normalTumorArray)) || !("tumor"%in%colnames(normalTumorArray)))
      stop("normalTumorArray doesn't contain a column \"normal\" or \"tumor\".\n")
    
#     isArrayComplete=sapply(R.filesets::getNames(dsC),FUN=function(name,listOfNames){name%in%listOfNames},c(as.character(normalTumorArray$normal),as.character(normalTumorArray$tumor)))
#     if(sum(isArrayComplete)!=length(isArrayComplete))
#       stop("normalTumorArray doesn't contain all the filenames of dataSetName.")
  }
  
  ###### check listOfFiles
  pos=c()
  if(is.null(listOfFiles) || missing(listOfFiles))
  {
    listOfFiles=R.filesets::getNames(dsC)
    pos=1:length(dsC)
  }
  else
  {
    #check the format
    if(!is.vector(listOfFiles) || !is.character(listOfFiles))
      stop("listOfFiles must be a vector of string.")
    listOfFiles=unique(listOfFiles)
    #check if all the files of listOfFiles are in the folder
    pos=sapply(listOfFiles,match,R.filesets::getNames(dsC))#position of the files of listOfFiles in the folder
    if(length(which(pos>0))!=length(pos))
      stop("Wrong name of files in listOfFiles")
  }  
  
  #check if file of listOfFiles are in normalTumorArray
  if(!singleStudy)
  {
    isArrayComplete=sapply(listOfFiles,FUN=function(name,listOfNames){name%in%listOfNames},c(as.character(normalTumorArray$normal),as.character(normalTumorArray$tumor)))
    if(sum(isArrayComplete)!=length(isArrayComplete))
      stop("normalTumorArray doesn't contain all the filenames you specified in listOfFiles parameter.")
  }
  
  #if single array study, we just reduce dsC to pos  
  if(!singleStudy)
  {      
    #if normal-tumor study, we need the tumor and normal files
    
    #we obtain the complementary files  
    compFiles=getComplementaryFile(listOfFiles,normalTumorArray)
    allFiles=unique(c(listOfFiles,compFiles))
    
    #index of the files
    pos=sapply(allFiles,FUN=function(x,dsC){which(R.filesets::getNames(dsC)==x)},dsC)
    tag=getStatus(allFiles,normalTumorArray)
    
    pos=pos[which(tag=="tumor")]
  }
  
  #Rho
  if(missing(Rho) || is.null(Rho))
    Rho=c(seq(0.1,2,by=0.1),seq(2.2,5,by=0.2),seq(5.5,10,by=0.2),seq(11,16,by=1),seq(18,36,by=2),seq(40,80,by=4))
  
  ######################### END CHECK PARAMETERS
  
  ###bed files output
  #results are stored in the segmentation folder
  #check the existence of the segmentation folder
  if(!("segmentation"%in%list.files()))
    dir.create("segmentation");
  #check the existence of th dataSet folder in segmentation folder
  if(!(dataSetName%in%list.files("segmentation")))
    dir.create(paste0("segmentation/",dataSetName));
  if(!("CN"%in%list.files(paste0("segmentation/",dataSetName))))
    dir.create(paste0("segmentation/",dataSetName,"/CN"));
  
  figPath <- Arguments$getWritablePath(paste0("figures/",dataSetName,"/segmentation/CN/"));
  
  #names of the files to segment
  names=R.filesets::getNames(dsC)[pos]
  
  output=lapply(names,FUN=function(name)
  {
    cghArg=list()
    for(chr in chromosome)
    {
      #get the copy-number for 1 chr
      if(!singleStudy)
        CN=getCopyNumberSignal(dataSetName,chr,normalTumorArray,onlySNP,name,verbose=FALSE)
      else
        CN=getCopyNumberSignal(dataSetName,chr,onlySNP=onlySNP,listOfFiles=name,verbose=FALSE)
      
      CN=CN[[paste0("chr",chr)]]
      
      gc()
      
      #segmentation
      if (length(which(!is.na(as.vector(CN[,3]))))<2)
      {
        if (chr==24)
        {
          cat(paste0("Cannot segment file ",name," chromosome Y (24) : gender = XX\n"))
        } else {
          cat(paste0("Cannot segment file ",name," chromosome ",chr,  ": less than 2 points in the signal\n"))
        }
      } else {
      
        cat(paste0("Segmentation of file ",name," chromosome ",chr,"..."))
        seg=PELT(as.vector(CN[,3]),Rho,CN$position,plot=savePlot,verbose=FALSE)
        cat("OK\n")
        
        if(savePlot)
        {
          figName <- sprintf("%s,%s", name, chr);
          pathname <- filePath(figPath, sprintf("%s.png", figName));
          width <- 1280;
          aspect <- 0.6*1/3;
          fig <- R.devices::devNew("png", pathname, label=figName, width=width, height=2*aspect*width);
          plot(NA,xlim=c(min(CN$position),max(CN$position)), ylim=c(0,6),xlab="Position", main=figName,ylab="CN", pch=".")
          points(CN$position, seg$signal, pch=".");
          for(i in 1:nrow(seg$segment))
            lines(c(seg$segment$start[i],seg$segment$end[i]),rep(seg$segment$means[i],2),col="red",lwd=3)
          R.devices::devDone();
        }
  
        
        
        #concatenate the results
        cghArg=list(copynumber=rbind(cghArg$copynumber,seg$signal),
                    segmented=rbind(cghArg$segmented,seg$segmented),
                    startPos=c(cghArg$startPos,CN$position),
                    chromosome=c(cghArg$chromosome,CN$chromosome),
                    sampleNames=name,
                    featureNames=c(cghArg$featureNames,CN$featureNames),
                    segment=data.frame(chrom=c(as.character(cghArg$segment$chrom),rep(paste0("chr",CN$chromosome[1]),length(seg$segment$start))),
                               chromStart=c(cghArg$segment$chromStart,seg$segment$start),
                               chromEnd=c(cghArg$segment$chromEnd,seg$segment$end),
                               probes=c(cghArg$segment$probes,seg$segment$points),
                               means=c(cghArg$segment$means,seg$segment$means)))
      }
    }

    #write in .bed
    write.table(cghArg$segment,file=paste0("segmentation/",dataSetName,"/CN/",name,",segmentation.bed"),sep="\t",row.names=FALSE)
    
    return(cghArg)
  })
          
  names(output)=names  
  return(output)            
}

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MPAgenomics documentation built on May 29, 2017, 9:34 p.m.