estimateRho

index indicating the set of samples to use (typically indicating those with relative frequency smaller than 0.1).

a pseudo count added to each sample to avoid problems with zeros. Default = .Machine$double.eps

pseudo.rho


It uses a method of moments approximation to estimate rho from the variances of the relative frequencies nu across samples


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.

estimateRho function

It uses a method of moments approximation to estimate rho from the variances of the relative frequencies nu across samples

estimateRho: Helper function for estimating the dispersion factor rho

Description

Usage

Arguments

Value