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TitanCNA (version 1.10.0)

Subclonal copy number and LOH prediction from whole genome sequencing of tumours

Description

Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalenece of clonal clusters in tumour whole genome sequencing data.

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Version

Version

1.10.0

License

GPL-3

Issues

25

Pull Requests

2

Stars

97

Forks

36

Maintainer

Gavin Ha

Last Published

February 15th, 2017

Functions in TitanCNA (1.10.0)

TitanCNA-package

TITAN: Subclonal copy number and LOH prediction whole genome sequencing of tumours
getPositionOverlap

Function to assign values to given chromosome-position that overlaps a list of chromosomal segments
extractAlleleReadCounts

Function to extract allele read counts from a sequence alignment (BAM) file
correctReadDepth

Correct GC content and mappability biases in sequencing data read counts
filterData

Filter list object based on read depth and missing data
runEMclonalCN

Function to run the Expectation Maximization Algorithm in TitanCNA.
viterbiClonalCN

Function to run the Viterbi algorithm for TitanCNA.
Formatting and output of Titan results

Formatting and printing TitanCNA results.
computeSDbwIndex

Compute the S_Dbw Validity Index for TitanCNA model selection
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.
WIG Import Functions

WIG Import Functions
loadDefaultParameters

Load TITAN parameters
loadAlleleCounts

Function to load tumour allele counts from a text file or data.frame
Plotting TITAN results

Plotting functions for TitanCNA results.
TitanCNA trained dataset

TITAN EM trained results for an example dataset