DPfitSamples: MCMC fitting of sAGP values

In CHAT: Clonal Heterogeneity Analysis Tool

Description

This function is a wrapper for getDPfit() by analyzing batch samples for sAGP values.

Usage

DPfitSamples(dd, alpha = 0.05, low.thr = 0.05, min.peaksize = 10, prior, mcmc, nt=FALSE)

Arguments

dd	numeric matrix, returned from getsAGP or getSegPurity()
alpha	significant level.
low.thr	values below this threshold in sAGP will be omitted.
min.peaksize	minimum number of segments each peak must contain.
prior	a list of prior parameters required for DPdensity. An example is data(prior).
mcmc	a list of parameters required to run MCMC for DPdensity. An example is data(mcmc).
nt	logical. If TRUE, multi-thread processing is performed.

Value

A list containing the following elements:

DD	input dd with additional peak information.
Labels	a vector assigning each sample to model 0, 1 or 2. See getDPfit() for more details.
Pval	P value
par	parameters fitted for the distribution of sAGP value from each sample.

Author(s)

Bo Li

Examples

data(A0SD.BAF)
data(A0SD.LRR)
## DNA segmentation
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'
save(dd.dat,file='A0SDseg.Rdata')
para=getPara()
para$datafile='A0SDseg.Rdata'
para$savefile='A0SD-AGP.txt'
para$is.normalize=FALSE
## AGP estimation
getAGP(para=para)
para.s=getPara.sAGP()
para.s$inputdata='A0SDseg.Rdata'
para.s$purityfile='A0SD-AGP.txt'
para.s$savedata='A0SD-sAGP.Rdata'
## sAGP estimation
getsAGP(para=para.s)
## Perform MCMC fitting
load('A0SD-sAGP.Rdata')
data(mcmc)
data(prior)
temp=DPfitSamples(new.dd,prior=prior,mcmc=mcmc)

CHAT documentation built on May 29, 2017, 10:32 p.m.