betabinLRT

The array of counts typically generated by loadAllData.

counts

Use this variable to fix the dispersion parameter to a value of interest. Default: NULL, rho will be estimated from the data.

Samples with variant allele frequencies higher than "truncate" will be excluded from the background error model.

truncate

If rho=NULL, rho will be estimated from the data in the interval [rho.min,rho.max].

rho.min

rho.max

Sites with an average rate of mimatches higher than maxvaf will not be considered (e.g. SNPs or reference sites).

maxvaf

Minimum coverage required to test a site.

mindepth

Maximum number of samples that can be excluded from the background error model by truncate for a site to be tested.

maxtruncate


Maximum likelihood version of Shearwater producing p-values instead of Bayes factors.


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.

betabinLRT function

Maximum likelihood version of Shearwater producing p-values instead of Bayes factors.

betabinLRT: ShearwaterML

Description

Usage

Arguments

Value

References

Examples