Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/Clomial.likelihood.R

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

1 | ```
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`

1 2 3 4 5 6 7 8 | ```
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)
``` |

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