mcChunk

split

mc.cores


Little helper function to split the count objects into a smaller digestible chunks and run function FUN on each subset


This package provides provides quantitative variant callers for
detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing
experiments. The deepSNV algorithm is used for a comparative setup with a
control experiment of the same loci and uses a beta-binomial model and a
likelihood ratio test to discriminate sequencing errors and subclonal SNVs.
The shearwater algorithm computes a Bayes classifier based on a
beta-binomial model for variant calling with multiple samples for
precisely estimating model parameters such as local error rates and
dispersion and prior knowledge, e.g. from variation data bases such as
COSMIC.

Moritz Gerstung

deepSNV

Detection of subclonal SNVs in deep sequencing data.

mcChunk function

Little helper function to split the count objects into a smaller digestible chunks and run function FUN on each subset

mcChunk: Little helper function to split the count objects into a smaller digestible chunks and run function FUN on each subset

Description

Usage

Arguments

Value