clac.preparenormal.R {clac}

A function to prepare normal reference in CLAC(Cluster Along Chromosome) Analysis

Package:

clac

Version:

0.1-1

Description

CLAC is a method for calling gains and losses in CGH array data. This function is to prepare normal references in CLAC(Cluster Along Chromosome) Analysis

Usage

clac.preparenormal.R(CANCER, NORMAL, Normal.Type, chromosome.number, nucleotide.position, windowsize=5, targetFDR=0.01, chromosomeOption=FALSE, centromere=NULL)

Arguments

CANCER: data matrix. It's the result of a group of CGH experiments, which are the target disease arrays to analysis. Each column corresponds to one sample (one target array), and each row corresponds to one gene/clone. The (i, j) entry should be the log fluorescence ratio of the ith gene/clone in the jth sample. Missing value should be coded as either NA or 999.
NORMAL: data matrix. It's also the result of a group of CGH experiment. But these results are from normal reference arrays. Again, each column corresponds to one sample, and each row corresponds to one gene/clone. Missing value should be coded as either NA or 999.
Normal.Type: a vector specifying the normal array type. Length should be the same as the column number of NORMAL. Code 0 for normal reference arrays from the same gender hybridization, while 1 for arrays from opposite gender hybridization.
chromosome.number: numeric vector . Length should be the same as the row number of NORMAL. It's the chromosome number of each gene/clone.
nucleotide.position: numeric vector. Length should be the same as the row number of NORMAL. It's the nucleotide position of each gene/clone.
windowsize: numeric value, specifying the window size to carry out the average smooth.
targetFDR: numeric value between 0 and 1, specifying the desired fianl FDR for CLAC analysis.
chromosomeOption: a boolean variable. If False, the chromosome arms will be considered seperately. If true, two chormosome arms of one chromosome would be dealed together.
centromere: numeric vector specifying the centromere positions. If missing, the default centromere value of human genome will be used.

Details

clac.preparenormal.R builds cluster trees on normal reference arrays, the result is reported to the next step of CLAC analysis.

Values

A list with components

normal.result: An object containing the information about the normal reference arrays.
CANCER.sm: The result of average smooth for data matrix CANCER
NORMAL.sm: The result of average smooth for data matrix NORMAL

References

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/

Examples

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")

Author(s)

Pei Wang

Documentation reproduced from package clac, version 0.1-1. License: GPL (>= 2)