\name{genomewidePlots}
\alias{genomewidePlots}
\title{
Plot of the genomewide copy number profiles of a pair of tumors.
}
\description{
Plot contains genomewide profiles from a pair of tumors. It uses the output from the function clonality.analysis().
}
\usage{
genomewidePlots(data.seg1, classall, ptlist, ptpair, ptLR, plot.as.in.analysis = TRUE)
}

\arguments{
  \item{data.seg1}{
Output of one-step segmentation - output OneStepSeg of clonality.analysis().  The chromosomes should be in the format "chr01p", "chr01q" etc.
}
  \item{classall}{
Classifications of the chromosomes  - output ChromClass of clonality.analysis()
}

  \item{ptlist}{
Vector of the patient IDs in the order of the samples appearing in the data. 
}
  \item{ptpair}{
Two sample names for which the plot is desired}
  \item{ptLR}{
Matrix with the likelihood ratios -  output LR of clonality.analysis()
}
  \item{plot.as.in.analysis}{
If TRUE then the gain/loss  patterns  will be highlighted in accordance with the chromosome classification.  For example, if there are three segments in a chromosome, then the middle one determines the chromosome status. If it is normal, no color will be plotted in the chromosome even if the 1st and 3rd segments are gains or losses. Another example: if there are 2 or 3 different segments of gains, they will be combined and only one segment will be plotted. If plot.as.in.analysis is equal to FALSE, the original one-step CBS segmentation will be plotted.
}
}
\details{
Function produces genomewide plots of a pair of tumors.  The log-ratios are plotted in grey in the order of their genomic locations, gains are plotted in blue, and losses are plotted in red.
}


\examples{
 # Same example as in clonality.analysis()
   #Analysis of paired breast samples from study
#Hwang ES, Nyante SF, Chen YY, Moore D, DeVries S, Korkola JE, Esserman LJ, and Waldman FM. 
#Clonality of lobular carcinoma in situ and synchronous invasive lobular cancer. Cancer 100(12):2562-72, 2004.
#library(gdata)    #needed to read .xls files
#library(DNAcopy) 
#arrayinfo<-read.xls("http://waldman.ucsf.edu/Colon/nakao.data.xls")  #needed to extract  genomic locations
#data<-read.xls("http://waldman.ucsf.edu/Breast/Hwang.data.xls")
#data<-data[!is.na(data[,2]),]
#data<-data[apply(is.na(data),1,sum)<=50,]
#data<-data[,apply(is.na(data),2,sum)<=1000]
#data$Position<-arrayinfo$Mb[match(toupper(as.character(data[,1])),toupper(as.character(arrayinfo[,1])))]
#data<-data[!is.na(data$Position),]
#dim(data)
#length(unique(paste(data$Chromosome, data$Position))) #there are repeated genomic locations
#data<-data[c(TRUE,data$Position[-1]!=data$Position[-1864]),] #discard probes with repeated genomic locations
#data<-data[data$Chromosome<=22,] #getting rid of X and Y chromosomes
#dataCNA<-CNA(as.matrix(data[,c(4:6,28:30)]),maploc=data$Position,chrom=data$Chromosome,sampleid=names(data)[c(4:6,28:30)]) #taking the first 3 patients only to shorten the computation time; use c(4:51) for the full dataset

#dataCNA$maploc<-dataCNA$maploc*1000 #transforming maploc to Kb scale
#dataCNA$chrom<- splitChromosomes( dataCNA$chrom,dataCNA$maploc)  #splits the chromosomes into arms


#ptlist<-substr(names(dataCNA)[-c(1,2)],1,4)
#samnms<-names(dataCNA)[-c(1,2)]

#results<-clonality.analysis(dataCNA, ptlist,  pfreq = NULL, refdata = NULL, nmad = 1.25, 
# reference = TRUE, allpairs = FALSE)
              
#genomewide plots of pairs of tumors from the same patient
#pdf("genomewideplots.pdf",height=7,width=11)
#for (i in unique(ptlist))
#{
#w<-which(ptlist==i) 
#ns<- length(w)
#if (ns>1)
#{
#for (p1 in c(1:(ns-1)))
#for (p2 in c((p1+1):ns))
#genomewidePlots(results$OneStepSeg, results$ChromClass,ptlist , ptpair=samnms[c(w[p1],w[p2])],results$LR,  plot.as.in.analysis = TRUE) 
#}
#}
#dev.off()



#pdf("hist.pdf",height=7,width=11)
#histogramPlot(results$LR[,4], results$refLR[,4])
#dev.off()


#for (i in unique(ptlist))
#{
#pdf(paste("pt",i,".pdf",sep=""),height=7,width=11)
#chromosomePlots(results$OneStepSeg, ptlist,ptname=i,nmad=1.25)
#dev.off()
#}
 
}