genoCN (version 1.24.0)

genoCNV: Copy Number Variation

Description

extract genotype and copy number calls for copy number variation, which are inheritable DNA polymorphisms and are observed in normal tissues

Usage

genoCNV(snpNames, chr, pos, LRR, BAF, pBs, sampleID,  Para=NULL, fixPara=FALSE, cnv.only=NULL, estimate.pi.r=TRUE,  estimate.pi.b=FALSE, estimate.trans.m=FALSE, normLRR=TRUE,  outputSeg=TRUE, outputSNP=3, outputTag=sampleID, outputViterbi=FALSE,  Ds = c(1e6, 1e6, rep(1e5, 4)),  pBs.alpha=0.001, loh=FALSE, output.loh=FALSE,  min.tp=5e-5, max.diff=0.1, distThreshold=5000,  transB = c(0.995, 0.005*c(.01, .09, .8, .09, .01)),  epsilon=0.005, K=5, maxIt=200, seg.nSNP=3, traceIt=5)

Arguments

snpNames

a vector of SNP names. SNPs must be ordered by chromosome locations

chr

chromosomes of all the SNPs specified in snpNames

pos

positions of all the SNPs specified in snpNames

LRR

Log R Ratio of all the SNPs specified in snpNames

BAF

B Allele Frequency of all the SNPs specified in snpNames

pBs

population frequency of of all the SNPs specified in snpNames

sampleID

symbol/name of the studied sample. Only one sample is studied each time

Para

a list of initial parameters for the HMM. If Para is NULL, The default initial parameters: init.Para.CNV is used

fixPara

if fixPara is TRUE, the parameters in Para are fixed, and are used directly to calculate posterior probabilities

cnv.only

a vector indicating those CNV-only probes, for which we only consider their Log R ratio. If it is NULL, there is no CNV-only probes

estimate.pi.r

to estimate pi.r (proportion of uniform component for LRR) or not. By default, estimate.pi.r=FALSE, and the initial value of pi.r is used to estimate other parameters

estimate.pi.b

to estimate pi.b (proportion of uniform component for BAF) or not. By default, estimate.pi.b=FALSE, and the initial value of pi.b is used to estimate other parameters

estimate.trans.m

to estimate transition probability matrix or not. By default, estimate.trans.m=FALSE, and the initial value of estimate.trans.m is used to estimate other parameters

normLRR

If normLRR is TRUE, we normalize the LRR data by subtracting the median LRR for those LRR between -2 and 2. This strategy has been used by PennCNV.

outputSeg

wether to output the information of copy number altered segments

outputSNP

if outputSNP is 0, do not output SNP specific information; if outputSNP is 1, output the most likely copy number and genotype state of the SNPs that are within copy number altered regions; if outputSNP is 2, output the most likely copy number and genotype state of all the SNPs (whether it is within CNV regions or not), if outputSNP is 3, output the posterior probability for all the copy number and genotype states for the SNPs.

outputTag

the prefix of the output files, output of copy number altered segments is written into file outputTag\_segment.txt, and output of SNP information is written into file outputTag\_SNP.txt

outputViterbi

whether to output the copy altered regions identified by the viterbi algorithm. see details

Ds

Parameter to for transition probability of the HMM. A vector of length N, where N is the number of states in the HMM

pBs.alpha

pBs.alpha is the lower limit of population B allele frequency, and the upper limit is 1 - pBs.alpha

loh

Whether we use the copy-number-neutral loss of heterozygosity state for CNV studies.

output.loh

Whether we output the loh information.

min.tp

the minimum of transition probability.

max.diff

Due to normalization procedure, the BAF may not be symmetric. Let's use state (AAA, AAB, ABB, BBB) as an example. Ideally, mean values of normal components AAB and ABB, denoted by mu1 and mu2, respectively, should have the relation mu1 = 1-mu2 if BAF is symmetric. However, this may not be true due to normalization procedures. We restrict the difference of mu1 and (1-mu2) by this parameter max.diff.

distThreshold

If distance between adjacent probes is larger than distThreshold, restart the transition probability by the default values in transB.

transB

The default transition probability.

epsilon

see explanation of K

K

epsilon and K are used to specify the convergence criteria. We say the estimate.para is converged if for K consecutive updates, the maximum change of parameter estimates in every adjacent step is smaller than epsilon

maxIt

the maximum number of iterations of the EM algorithm to estimate parameters

seg.nSNP

the minimum number of SNPs per segment

traceIt

if traceIt is a integer n, then the running time is printed out in every n iterations of the EM algorithm. if traceIt is 0 or negative, no tracing information is printed out.

Value

results are written into output files

Examples

data(snpData)
data(snpInfo)

dim(snpData)
dim(snpInfo)

snpData[1:2,]
snpInfo[1:2,]

snpInfo[c(1001,1100,10001,10200),]

plotCN(pos=snpInfo$Position, LRR=snpData$LRR, BAF=snpData$BAF, 
main = "simulated data on Chr22")

snpNames = snpInfo$Name
chr = snpInfo$Chr
pos = snpInfo$Position
LRR = snpData$LRR
BAF = snpData$BAF
pBs = snpInfo$PFB
cnv.only=(snpInfo$PFB>1)
sampleID="simu1"

Theta = genoCNV(snpNames, chr, pos, LRR, BAF, pBs, 
            sampleID, cnv.only=cnv.only, outputSeg = TRUE, 
            outputSNP = 1, outputTag = "simu1")