choose.best: Chooses the best trained Clomial model.
In Clomial: Infers clonal composition of a tumor

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

Given the output of Clomial function, the likelihoods of all models are compared, and the best model is determined.

1 2	choose.best(models, U = NULL, PTrue = NULL, compareTo = NULL, upto = "All", doTalk=FALSE)

`models`	The models trained by `Clomial` function.
`U`	The optional genotype matrix used for comparison.
`PTrue`	The optional clone frequency matrix used for comparison.
`compareTo`	The index of the model against which all other models are compared. Set to `NULL` to disable.
`upto`	The models with index less than this value are considered. Set to "All" to include every model.
`doTalk`	If TRUE, information on number of analyzed models is reported.

If compareTo, U, and PTrue are NULL no comparison will be done, and the function runs considerably faster.

A list will be made with the following entries:

`err`	A list with 2 entries; err$P and err$U the vectors of clonal frequency errors, and genotype errors, accordingly.
`Li`	A vector of the best obtained log-likelihood for each model.
`bestInd`	The index of the best model in terms of log-likelihood.
`comparison`	If `compareTo` is not `NULL`, the result of comparison with the corresponding model is reported.
`bestModel`	The best model in terms of log-likelihood.
`seconds`	A vector of the time taken, in seconds, to train each model.

When the number of assumed clones, C, is greater than 6, the comparison will be time taking because all possible permutations of clones should be considered. The running time will be slowed down by C!.

Habil Zare

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

Clomial, Clomial.likelihood, Clomial.iterate

set.seed(4)
data(breastCancer)
Dc <- breastCancer$Dc
Dt <- breastCancer$Dt
ClomialResult <-Clomial(Dc=Dc,Dt=Dt,maxIt=20,C=4,doParal=FALSE,binomTryNum=5)
chosen <- choose.best(models=ClomialResult$models)
M1 <- chosen$bestModel
print("Genotypes:")
round(M1$Mu)
print("Clone frequencies:")
M1$P
bestInd <- chosen$bestInd
plot(chosen$Li,ylab="Log-likelihood",type="l")
points(x=bestInd,y=chosen$Li[bestInd],col="red",pch=19)