Nothing
### Model selection function with C constant
# @note used in bestSegmentationBM
modelSelection <- function(ERM, n, C)
{
D <- length(ERM)
pen <- C*(1:D)*(2.5+log(n/1:D))/n
DHat <- which.min(ERM/n+pen)
return(DHat)
}
# Method from Birge Massart to find an optimal segmentation of cghseg results.
#
# @param res output from cghseg segmentation (segmeanCO)
# @param n length of signal
#
# @return a list containing
# \describe{
# \item{D}{number of segments}
# \item{m}{breakpoints}
# \item{C}{penalty parameter}
#}
#
# @examples
# profil=c(rnorm(20,0,0.5),rnorm(10,-1,0.5),rnorm(40,1,0.5),rnorm(20,0,0.5))
# Kmax=10
# rescghseg <- cghseg:::segmeanCO(profil, Kmax=Kmax)
# bestSegmentationBM(rescghseg,length(profil))
#
bestSegmentationBM <- function(res,n)
{
ERM=res$J.est
Dmax = length(ERM)
highD = (Dmax%/%2):Dmax
model <- lm(ERM[highD]~highD)
lambda <- -model$coefficient["highD"]
DhatBest <- modelSelection(ERM, n ,lambda)
mhatBest <- res$t.est[DhatBest, 1: DhatBest]
return(list(D = DhatBest, m = mhatBest, C = lambda))
}
# Second Method from Birge Massart to find an optimal segmentation of cghseg results.
#
# @param lambdaList vector of constants in decreasing order
# @param resCGHSeg output from cghseg segmentation (segmeanCO)
# @param n length of signal
#
# @return a list containing
# \describe{
# \item{D}{number of segments}
# \item{m}{breakpoints}
# \item{C}{penalty parameter}
#}
#
# @examples
# profil=c(rnorm(20,0,0.3),rnorm(10,-1,0.3),rnorm(40,1,0.3),rnorm(20,0,0.3))
# Kmax=10
# rescghseg <- cghseg:::segmeanCO(profil, Kmax=Kmax)
# bestSegmentation(seq(10,0,length=1000),rescghseg,length(profil))
#
bestSegmentation <- function(lambdaList,resCGHSeg,n)
{
Dhat <- sapply(lambdaList, modelSelection, ERM = resCGHSeg$J.est, n = n)
maxJumpInDhat <- which.max(diff(Dhat))+1
DhatBest <- modelSelection(resCGHSeg$J.est, n ,lambdaList[maxJumpInDhat]*2)
mhatBest <- resCGHSeg$t.est[DhatBest, 1:DhatBest]
return(list(D = DhatBest, m = mhatBest, C = lambdaList[maxJumpInDhat]*2))
}
#
# This function launches the segmentation cghseg (from package cghseg).
#
# @title segmentation function
#
# @param signal a vector containing the signal.
# @param Kmax maximal number of segments
# @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
#
cghseg=function(signal,Kmax=10,position=NULL,plot=TRUE,verbose=TRUE)
{
#signal
if(missing(signal))
stop("signal is missing.")
if(!is.numeric(signal) )#|| !is.vector(signal))
stop("signal must be a vector of real.")
#Kmax
if(!is.numeric(Kmax) || (length(Kmax)>1))
if(!is.wholenumber(Kmax))
stop("Kmax must be a positive integer.")
if(Kmax<=0)
stop("Kmax must be a positive integer.")
#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]
#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)
}
#segmentation
if(!suppressPackageStartupMessages(requireNamespace("cghseg", quietly=TRUE)))
{
cat("cghseg package not found. To install cghseg:\n")
cat("drat::addRepo('sblanck')\n")
cat("install.packages('cghseg')")
stop("Error Loading cghseg package. Please follow the printed instructions to install cghseg or use PELT method")
} else {
segmentation=cghseg::segmeanCO(signal, Kmax=Kmax)
}
#find best segmentation
#best=bestSegmentationBM(segmentation,length(signal))
lambdalist=seq(var(signal)*10,0,length=1000)
best=bestSegmentation(lambdalist,segmentation,length(signal))
cpt=c(0,best$m)
means=unlist(lapply(1:length(best$m),FUN=function(i){return(mean(signal[(cpt[i]+1):cpt[i+1]]))}))
if(plot)
{
#plot data
plot(position,signal,pch=".",xlab="Position",ylab="signal",ylim=c(0,6))
#plot segments
for(i in 1:length(best$m))
lines(c((position[noNA])[cpt[i]+1],(position[noNA])[cpt[i+1]]),rep(means[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(means[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=means)))
}
#########################################################################
#
# This function launches the segmentation cghseg.
#
# @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 Kmax maximum number of segments.
# @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
#
CGHSEGaroma=function(dataSetName,normalTumorArray,chromosome=1:22,Kmax=10,listOfFiles=NULL,onlySNP=TRUE,savePlot=TRUE,verbose=TRUE)
{
requireNamespace("R.devices")
allpkg=TRUE
if(!suppressPackageStartupMessages(requireNamespace("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(requireNamespace("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.")
requireNamespace("aroma.core")
requireNamespace("R.filesets")
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.")
#Kmax
if(!is.numeric(Kmax) || (length(Kmax)>1))
if(!is.wholenumber(Kmax))
stop("Kmax must be a positive integer.")
if(Kmax<=0)
stop("Kmax must be a positive integer.")
#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$byName(dataSet, chipType="*", paths=rootPath);
################### check normalTumorArray
if(!singleStudy)
{
#normalTumorArray
if(is.character(normalTumorArray))
normalTumorArray=read.csv(normalTumorArray)
else
{
if(!is.data.frame(normalTumorArray))
stop("normalTumorArray must be either the path to the normalTumorArray csv file or a data.frame containing the data.\n")
}
#check normalTumorArray
if(!("normal"%in%colnames(normalTumorArray)) || !("tumor"%in%colnames(normalTumorArray)))
stop("normalTumorArray doesn't contain a column \"normal\" or \"tumor\".\n")
# isArrayComplete=sapply(getNames(dsC),FUN=function(name,listOfNames){name%in%listOfNames},c(as.character(normalTumorArray$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$tumor)))
if(sum(isArrayComplete)!=length(isArrayComplete))
stop("normalTumorArray doesn't contain all the filenames you specified in listOfFiles parameter.")
}
#if single array study, we just reduce dsC to pos
if(!singleStudy)
{
#if normal-tumor study, we need the tumor and normal files
#we obtain the complementary files
compFiles=getComplementaryFile(listOfFiles,normalTumorArray)
allFiles=unique(c(listOfFiles,compFiles))
#index of the files
pos=sapply(allFiles,FUN=function(x,dsC){which(R.filesets::getNames(dsC)==x)},dsC)
tag=getStatus(allFiles,normalTumorArray)
pos=pos[which(tag=="tumor")]
}
######################### END CHECK PARAMETERS
###bed files output
#results are stored in the segmentation folder
#check the existence of the segmentation folder
if(!("segmentation"%in%list.files()))
dir.create("segmentation");
#check the existence of th dataSet folder in segmentation folder
if(!(dataSetName%in%list.files("segmentation")))
dir.create(paste0("segmentation/",dataSetName));
if(!("CN"%in%list.files(paste0("segmentation/",dataSetName))))
dir.create(paste0("segmentation/",dataSetName,"/CN"));
figPath <- Arguments$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=cghseg(as.vector(CN[,3]),Kmax,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)
}
######################################
#'
#' This function launches the segmentation of a signal.
#'
#' @title segmentation function
#'
#' @param signal a vector containing the signal.
#' @param method method of segmentation, either "PELT" or "cghseg".
#' @param Rho For method="PELT", vector containing all the penalization values to test for the segmentation. If no values are provided, default values will be used.
#' @param Kmax For method="cghseg", maximal number of segments.
#' @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
#'
segmentation=function(signal,method=c("PELT","cghseg"),Rho=NULL,Kmax=10,position=NULL,plot=TRUE,verbose=TRUE)
{
method <- match.arg(method)
seg=switch(method,
PELT=PELT(signal,Rho,position,plot,verbose),
cghseg=cghseg(signal,Kmax,position,plot,verbose))
return(seg)
}
######################################
#'
#' This function launches the segmentation process using the aroma architecture.
#'
#' @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 method method of segmentation, either "PELT" or "cghseg".
#' @param Rho For method="PELT", vector containing all the penalization values to test for the segmentation. If no values are provided, default values will be used.
#' @param Kmax For method="cghseg", maximal number of segments.
#' @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
#'
segmentationAroma=function(dataSetName,normalTumorArray,chromosome=1:22,method=c("PELT","cghseg"),Kmax,Rho=NULL,listOfFiles=NULL,onlySNP=TRUE,savePlot=TRUE,verbose=TRUE)
{
method <- match.arg(method)
seg=switch(method,
PELT=PELTaroma(dataSetName,normalTumorArray,chromosome,Rho,listOfFiles,onlySNP,savePlot,verbose),
cghseg=CGHSEGaroma(dataSetName,normalTumorArray,chromosome,Kmax,listOfFiles,onlySNP,savePlot,verbose))
return(seg)
}
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