genoCNA: Copy Number Aberration
In genoCN: genotyping and copy number study tools

Description Usage Arguments Value Note Author(s) Examples

View source: R/genoCNA.R

extract genotype and copy number calls for copy number aberrations, which are often observed in tumor tissues

genoCNA(snpNames, chr, pos, LRR, BAF, pBs, sampleID, 
  Para=NULL, fixPara=FALSE, cnv.only=NULL, estimate.pi.r=TRUE, 
  estimate.pi.b=TRUE, estimate.trans.m=TRUE, outputSeg = TRUE, 
  outputSNP=3, outputTag=sampleID, outputViterbi=FALSE, 
  Ds=c(1e10, 1e10, rep(1e8, 7)), pBs.alpha=0.001, contamination=TRUE, 
  normalGtp=NULL, geno.error=0.01, min.tp=1e-4, max.diff=0.1, 
  distThreshold=1e6, transB=c(0.5,.05,.05,0.1,0.1,.05,.05,.05,.05), 
  epsilon=0.005, K=5, maxIt=200, seg.nSNP=3, traceIt=5)

`snpNames`	a vector of SNP names. SNPs must be ordered by chromosme 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.CNA is used
`fixPara`	if fixPara is TRUE, the parameters in Para are fixed, and are used directly to calculate posterior probabilities. It is not recommended to set fixPara as TRUE for CNA studies.
`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
`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
`contamination`	whether tissue contamination is considered
`normalGtp`	`normalGtp` is specified only if paired tumor-normal SNP array is availalble. It is the normal tissue genotype for all the SNPs specified in `snpNames`, which can only take four different values: -1, 0, 1, and 2. Values 0, 1, 2 correspond to the number of B alleles, and value -1 indicates the normal genotype is missing. By default, it is NULL, then all the normal genotype are set missing (-1)
`geno.error`	probability of genotyping error in normal tissue genotypes
`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.

results are written into output files

Copy number altered regions are identified, by default, based on the SNP level copy number calls. A CNA region boundary is declared simply when the adjacent SNPs have different copy numbers. An alternative approach is to use viterbi algorithm to output the “best path”. Most time the results based on the SNP level copy number calls are the same as the results from viterbi algorithm. For the following up association studies, the SNP level information is more relevant if we examine the association SNP by SNP.

Wei Sun and Zhengzheng Tang

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"

# Note this simulated data is more of CNV rather than CNA. 
# For example, there is no tissue contamination. 
# We just use it to illustrate the usage of genoCNA. 

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

[1] 17348     3
[1] 17348     4
       Name        LRR         BAF
1 rs2334386 -0.2440655 1.000000000
2 rs9617528 -0.1422038 0.007824231
             Name Chr Position   PFB
1075853 rs2334386  22 14430353 0.956
1075854 rs9617528  22 14441016 0.176
              Name Chr Position   PFB
1076853  rs1934895  22 17460150 0.140
1076952  rs1206549  22 17595860 0.740
1085853 rs17750152  22 35827972 0.856
1086052  rs8141057  22 36007895 0.780
5 Fri Jun 25 05:55:15 2021 
10 Fri Jun 25 05:55:16 2021 
15 Fri Jun 25 05:55:18 2021 
20 Fri Jun 25 05:55:19 2021 
converges after 20 iterations
estimate copy number states for chromosome:

22