eval.fik.m

Schrod

centers

weights

The adjusting factor, taking into account contamination, copy number, number of copies

adj.factor

Should it compute the log distribution (TRUE) or probability (FALSE)
between two optimization steps. If NULL, will take 1/(median depth).

Eval probability for M step
Computes the log directly as log density is faster to compute

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.

eval.fik.m: Eval probability for M step Computes the log directly as log density is faster to compute

Description

Usage

Arguments