Clomial (version 1.8.0)

Clomial.likelihood: Computes the complete data log-likelihood of a Clomial model.

Description

Computes the expected complete data log-likelihood of a Clomial model over all possible values of the hidden variables.

Usage

Clomial.likelihood(Dc, Dt, Mu, P)

Arguments

Dt

A matrix which contains the counts of the alternative allele where rows correspond to the genomic loci, and columns correspond to the samples.

Dc

A matrix which contains the counts of the total number of mapped reads where rows correspond to the genomic loci, and columns correspond to the samples.

Mu

The matrix which models the genotypes, where rows and columns correspond to genomic loci and clones, accordingly.

P

The matrix of clonal frequency where rows and columns correspond to clones and samples, accordingly.

Value

A list will be made with the following entries:

ll

The expectation of complete log-likelihood over the hidden variables.

llS

A vector of computed log-likelihoods at all loci.

Details

By assuming that the genomic loci and the samples are independent given the model parameters, the computation is simplified by first summing over the samples for a locus, and then summing over all the loci. This strategy avoids exploring the exponentially huge probability space.

References

Inferring clonal composition from multiple sections of a breast cancer, Zare et al., Submitted.

See Also

Clomial, choose.best, compute.bic, breastCancer

Examples

set.seed(1)
data(breastCancer)
Dc <- breastCancer$Dc
Dt <- breastCancer$Dt
ClomialResult <-Clomial(Dc=Dc,Dt=Dt,maxIt=20,C=4,doParal=FALSE,binomTryNum=1)
model1 <- ClomialResult$models[[1]]
likelihood <- Clomial.likelihood(Dc=Dc, Dt=Dt, Mu=model1$Mu, P=model1$P)$ll
print(likelihood)