betabinLRT: ShearwaterML

In gerstung-lab/deepSNV: Detection of subclonal SNVs in deep sequencing data.

ShearwaterML

Description

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

Usage

betabinLRT(
  counts,
  rho = NULL,
  truncate = 0.05,
  rho.min = 1e-04,
  rho.max = 0.8,
  maxvaf = 0.3,
  mindepth = 10,
  maxtruncate = 0.5
)

Arguments

counts	The array of counts typically generated by loadAllData.
rho	Use this variable to fix the dispersion parameter to a value of interest. Default: NULL, rho will be estimated from the data.
truncate	Samples with variant allele frequencies higher than "truncate" will be excluded from the background error model.
rho.min	If rho=NULL, rho will be estimated from the data in the interval [rho.min,rho.max].
rho.max	If rho=NULL, rho will be estimated from the data in the interval [rho.min,rho.max].
maxvaf	Sites with an average rate of mimatches higher than maxvaf will not be considered (e.g. SNPs or reference sites).
mindepth	Minimum coverage required to test a site.
maxtruncate	Maximum number of samples that can be excluded from the background error model by truncate for a site to be tested.

Value

A list with two arrays for P- and Q-values.

Author(s)

Inigo Martincorena and Moritz Gerstung

References

Martincorena I, Roshan A, Gerstung M, et al. (2015). High burden and pervasive positive selection of somatic mutations in normal human skin. _Science_ (Under consideration).

Examples

# code to be added

gerstung-lab/deepSNV documentation built on June 3, 2022, 3:05 p.m.