CHAT (version 1.1)

getSampleAGP: AGP inference by sample

Description

This function performs AGP inference based on canonical positions on BAF-LRR plot for one sample.

Usage

getSampleAGP(sam.dat, oo, para)

Arguments

sam.dat

numeric matrix, as returned from getSeg() or getSegChr()

oo

Origin Cluster, as returned from getOrigin()

para

list, parameters returned from getPara()

Value

A list inherited from the output of getSumDist() with additional elements:

percent.on.track

For quality control, percent of genome close to at least one regression line.

percent.on.point

For quality control, percent of genome close to at least one canonical point.

percent.change

For quanlity control, percent of genome with CNA.

sam.p

estimated AGP of the sample

type

estimated ploidy type of the sample

If is.perm is set TRUE in para, additional elements are included:

conf.int

95 percent confident interval of estimated AGP

std

standard deviation of estimated AGP

Details

AGP values and possible ploidy types of the input sample are screened for best combination which minimizes the total summation of distances from observed data points to the grid of theoretical canonical positions.

Examples

data(A0SD.BAF)
data(A0SD.LRR)
seg.dat=c()
for(CHR in c(8,9,10)){
	baf=A0SD.BAF[A0SD.BAF[,2]==CHR,]
	lrr=A0SD.LRR[A0SD.LRR[,2]==CHR,]
	x=getSegChr(baf,lrr)
	seg.dat=rbind(seg.dat,x)
}
dd.dat=seg.dat[,2:8]
rownames(dd.dat)=seg.dat[,1]
mode(dd.dat)='numeric'
para=getPara()
oo=getOrigin(dd.dat,para=para)
getSampleAGP(dd.dat,oo,para=para)