TitanCNA (version 1.10.0)

removeEmptyClusters: Post-process TitanCNA results by removing clusters with proportion of data points altered lower than a threshold. The number of clonal clusters, cellular prevalence, and normal contamination will be adjusted to reflect the remaining clonal clusters.

Description

Filters all vectors in list based on specified chromosome(s) of interest, minimum and maximum read depths, missing data, mappability score threshold

Usage

removeEmptyClusters(convergeParams, results, proportionThreshold = 0.001, proportionThresholdClonal = 0.3)

Arguments

convergeParams

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

results

data.frame returned by outputTitanResults. Each row corresponds to a genomic SNP position in the analysis.

proportionThreshold

Minimum proportion of the genome altered (by SNPs) for a cluster to be retained. Clonal clusters having lower proportion of alteration are removed.

proportionThresholdClonal

Minimum proportion of genome altered by clonal events (by SNPs) for the highest cellular prevalence cluster.

Value

list with components:

convergeParams

The same data structure and format as the input convergeParams with removed clusters and adjusted parameters.

results

The same data structure and format as the input results with removed clusters and adjusted clonal cluster and cellular prevalence values.

Details

All vectors in the input data list object, and map, must all have the same number of rows.

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

Examples

data(EMresults)

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

#### FORMAT RESULTS ####
results <- outputTitanResults(data, convergeParams, optimalPath, 
                              filename = NULL, posteriorProbs = FALSE,
                              subcloneProfiles = TRUE)

#### REMOVE EMPTY CLONAL CLUSTERS ####
corrResults <- removeEmptyClusters(convergeParams, results, proportionThreshold = 0.001,
																		proportionThresholdClonal = 0.3)
convergeParams <- corrResults$convergeParams
results <- corrResults$results