| clac.tumorarray.R {clac} | R Documentation |
A function to do CLAC(Cluster Along Chromosome) Analysis
clac.tumorarray.R(NormalResult, tumorarrayIndex)
NormalResult |
result object from function clac.preparenormal.R in package clac. |
tumorarrayIndex |
a vector serving as a subscript index to take out a subset of the disease arrays for analysis |
clac.tumorarray.R do CLAC analysis on selected arrays.
A list with components
RegionMean |
data matrix reporting the CLAC result. It has the same dimension as the original input data matrix CANCER of function clac.preparenormal.R. For each entry of the matrix, 0 is reported if this genes/clone is not being called to have any copy number alteration, otherwise the amplificaiton/deletion region mean of the genes/clone is reported. |
fdr |
numeric vector reporting the result FDR for each target disease array. |
Pei Wang
P. Wang, Y. Kim, J. Pollack, B. Narasimhan and R. Tibshirani, ¡°A method for calling gains and losses in array CGH data¡±, Biostatistics (accepted for publication 4/5/2004), available at http://www-stat.stanford.edu/~wp57/CGH-Miner/
library(clac)
data(BACarray)
attach(BACarray)
############ prepare the normal reference arrays
NormalResult<-clac.preparenormal.R(DiseaseArray, NormalArray, Normal.Type=rep(0,3), chromosome.number=chromosome, nucleotide.position=nucposition, windowsize=5, targetFDR=0.01, chromosomeOption=FALSE)
############ clac on selected tumor arrays
clac.result<-clac.tumorarray.R(NormalResult, tumorarrayIndex=1:4)
############ Plot for the first arrays
i<-1
clac.PlotSingleArray.R(i, NormalResult, clac.result)
title(main=paste("CLAC Plot for the ", i ,"th BAC array; FDR=", round(clac.result$fdr[i],3), sep=""))
############ consensus plot
clac.PlotConsensus.R(clac.result, chromosome, nucposition, 1:4)
title(main="Consensus Plot for 4 BAC arrays")
############ Plot all arrays
clac.PlotAllArray.R(NormalResult, clac.result)
title(main="Plot for all 4 arrays")