TitanCNA (version 1.10.0)

viterbiClonalCN: Function to run the Viterbi algorithm for TitanCNA.

Description

Function to run the Viterbi algorithm to find the optimal state path in the TitanCNA hidden Markov model (HMM). The states returned will indicate the optimal copy number and LOH state as well as the most likely clonal cluster for each data point. After running EM, use the converge parameters and the input data to infer the optimal state for each position. This function makes calls to a C subroutine that allows the algorithm to be run more efficiently.

Usage

viterbiClonalCN(data, convergeParams, genotypeParams = NULL)

Arguments

data

list object that contains the components for the data to be analyzed. chr, posn, ref, and tumDepth that can be obtained using loadAlleleCounts, and logR that can be obtained using correctReadDepth and getPositionOverlap (see Example).

convergeParams

list object that is returned from the function runEMclonalCN in TitanCNA.

genotypeParams

If convergeParams does not contain a genotypeParams element, then the user can pass this as an argument.

Value

numeric array containing the integer states corresponding to each data point in data.

Details

It is difficult to interpret the output of this function directly. The user should use the function outputTitanResults after.

References

Ha, G., Roth, A., Khattra, J., Ho, J., Yap, D., Prentice, L. M., Melnyk, N., McPherson, A., Bashashati, A., Laks, E., Biele, J., Ding, J., Le, A., Rosner, J., Shumansky, K., Marra, M. A., Huntsman, D. G., McAlpine, J. N., Aparicio, S. A. J. R., and Shah, S. P. (2014). TITAN: Inference of copy number architectures in clonal cell populations from tumour whole genome sequence data. Genome Research, 24: 1881-1893. (PMID: 25060187)

See Also

outputTitanResults, loadAlleleCounts

Examples

data(EMresults)

#### COMPUTE OPTIMAL STATE PATH USING VITERBI ####
optimalPath <- viterbiClonalCN(data, convergeParams)