TitanCNA (version 1.10.0)

TitanCNA-package: TITAN: Subclonal copy number and LOH prediction whole genome sequencing of tumours

Description

TITAN is a software tool for inferring subclonal copy number alterations (CNA) and loss of heterozygosity (LOH). The algorithm also infers clonal group cluster membership for each event and the tumour proportion, or cellular prevalence, for each event.

Details

Package:

TitanCNA

Type:

Package

Version:

1.9.0

Date:

2016-02-17

License:

see LICENSE

example("TitanCNA-package") for quick tour of functionality and visualization

vignette("TitanCNA") for detailed example

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)

Examples

message('Running TITAN ...')
#### LOAD DATA ####
infile <- system.file("extdata", "test_alleleCounts_chr2.txt", package = "TitanCNA")
data <- loadAlleleCounts(infile)

#### LOAD PARAMETERS ####
message('titan: Loading default parameters')
numClusters <- 2
params <- loadDefaultParameters(copyNumber = 5, 
                                numberClonalClusters = numClusters, skew = 0.1)

#### READ COPY NUMBER FROM HMMCOPY FILE ####
message('titan: Correcting GC content and mappability biases...')
tumWig <- system.file("extdata", "test_tum_chr2.wig", package = "TitanCNA")
normWig <- system.file("extdata", "test_norm_chr2.wig", package = "TitanCNA")
gc <- system.file("extdata", "gc_chr2.wig", package = "TitanCNA")
map <- system.file("extdata", "map_chr2.wig", package = "TitanCNA")
cnData <- correctReadDepth(tumWig, normWig, gc, map)
logR <- getPositionOverlap(data$chr, data$posn, cnData)
data$logR <- log(2^logR) #transform to natural log

#### FILTER DATA FOR DEPTH, MAPPABILITY, NA, etc ####
data <- filterData(data, c(1:22,"X"), minDepth = 10, maxDepth = 200, map = NULL)

#### EM (FWD-BACK) TO TRAIN PARAMETERS ####
#### Can use parallelization packages ####
K <- length(params$genotypeParams$alphaKHyper)
params$genotypeParams$alphaKHyper <- rep(500, K)
params$ploidyParams$phi_0 <- 1.5 
convergeParams <- runEMclonalCN(data, gParams = params$genotypeParams, 
                                nParams = params$normalParams, 
                                pParams = params$ploidyParams, 
                                sParams = params$cellPrevParams, 
                                maxiter = 3, maxiterUpdate = 500, 
                                txnExpLen = 1e9, txnZstrength = 1e9, 
                                useOutlierState = FALSE, 
                                normalEstimateMethod = "map", 
                                estimateS = TRUE, estimatePloidy = TRUE)                                
#### 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 CLUSTERS ####
corrResults <- removeEmptyClusters(convergeParams, results, proportionThreshold = 0.001,
																		proportionThresholdClonal = 0.3)
convergeParams <- corrResults$convergeParams
results <- corrResults$results

#### PLOT RESULTS ####
norm <- tail(convergeParams$n, 1)
ploidy <- tail(convergeParams$phi, 1)

par(mfrow=c(4, 1))    
plotCNlogRByChr(results, chr = 2, ploidy = ploidy, normal = norm, geneAnnot = NULL, 
                ylim = c(-2, 2), cex = 0.5, xlab = "", main = "Chr 2")
plotAllelicRatio(results, chr = 2, geneAnnot = NULL, ylim = c(0, 1), cex = 0.5, 
                xlab = "", main = "Chr 2")
plotClonalFrequency(results, chr = 2, normal = norm, geneAnnot = NULL, 
                    ylim = c(0, 1), cex = 0.5, xlab = "", main = "Chr 2")
plotSubcloneProfiles(results, chr = 2, cex = 2, main = "Chr 2")