BIC_criterion

list of outputs from EM.algo or FullEM

EM_out_list

The function to minimize for the model selection: can be "AIC", 
"BIC", or numeric. In numeric, the BIC function is modified. If variance: returns max(abs(1 - Var(cluster)/expected(Var)))

model.selection

Computes BIC from a list of outputs of EM algorithm, then returns the position with minimal BIC

clustering

number

Using HTS data, clusters mutations in order to recreate putative
clones from the data provided. It requires genotype at the location of the
variant as well as the depth of coverage and number of reads supporting the
mutation. Additional information may be provided, such as the contamination
in the tumor sample. This package also provides a function QuantumCat() which
simulates data obtained from tumor sequencing.

BIC_criterion: Bayesian Information Criterion

Description

Usage

Arguments