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
Note
Copy number altered regions are identified, by default, based on the SNP level copy number calls. A CNV 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.