Clomial.likelihood {Clomial} | R Documentation |
Computes the expected complete data log-likelihood of a Clomial model over all possible values of the hidden variables.
Clomial.likelihood(Dc, Dt, Mu, P)
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. |
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.
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. |
The likelihood is computed assuming the heterozygosity is 2.
Habil Zare
Inferring clonal composition from multiple sections of a breast cancer, Zare et al., Submitted.
Clomial
,
choose.best
,
compute.bic
, breastCancer
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)