BIC_criterion_FLASH

Numeric vector with objective function values

Mut_num

the number of clusters (in the same order as Obj)

The function to minimize for the model selection: can be "AIC", "BIC", or numeric. In numeric, the function

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_FLASH: Compute criterion FLASH

Description

Usage

Arguments