Clomial.likelihood {Clomial} | R Documentation

## 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.

### 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.

### 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.

### Note

The likelihood is computed assuming the heterozygosity is 2.

### Author(s)

Habil Zare

### 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)

[Package Clomial version 1.26.0 Index]