compute.errors: Computes the error of a Clomial model.
In Clomial: Infers clonal composition of a tumor

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

Given the true genotype and frequency matrices, finds the permutation of genotypes matrix which best matches the true genotypes and returns the corresponding errors.

1	compute.errors(Mu, U, P, PTrue)

`Mu`	The matrix which models the genotypes, where rows and columns correspond to genomic loci and clones, accordingly.
`U`	The true genotype matrix defined similar to `Mu`.
`P`	The matrix of clonal frequency where rows and columns correspond to clones and samples, accordingly.
`PTrue`	The true clonal frequency matrix defined similar to `P`.

Computing the error is useful for estimating the performance of inference on simulated, and for comparing different trained models. Genotype and frequency errors are defined as the normalized l1-error in reconstructing the genotype, and the clone frequency matrices, accordingly, where by normalized l1-error we mean the sum of absolute values of an error matrix divided by the size of the matrix.

A list will be made with the following entries:

`UError`	The `l1`-error of the genotype matrix normalized by the size of matrix.
`discretizedUError`	The `l1`-error of the rounded genotype matrix, i.e. the number of mismatching genotypes, normalized by the size of matrix

`PErrorAbsolute`	The normalized `l1`-error of the clone frequency matrix.
`PErrorRelative`	Each entry of the error clone frequency matrix is normalized by the corresponding entry in `PTrue`, and then the normalized `l1` norm is computed.

The use of UError and PErrorAbsolute is recommended. Computing the error is not feasible for more than 7 clones because the number of all possible permutations is factorial in the number of clones which grows super fast. Such input will trigger an error message.

Habil Zare

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

Clomial

set.seed(1)
data(breastCancer)
Dc <- breastCancer$Dc
Dt <- breastCancer$Dt
bics <- c()
ClomialResult <-Clomial(Dc=Dc,Dt=Dt,maxIt=20,C=3,doParal=FALSE,binomTryNum=2)
model1 <- ClomialResult$models[[1]]
model2 <- ClomialResult$models[[2]]
## Comparing 2 trained models:
compute.errors(Mu=model1$Mu,U=model2$Mu,P=model1$P,PTrue=model2$P)