deepSNV (version 1.18.3)

bbb: Bayesian beta-binomal test, codename shearwater

Description

This is the workhorse of the shearwater test. It computes the Bayes factor for each sample, nucleotide and position of the null-model vs. the alternative of a real variant.

Usage

bbb(counts, rho = NULL, alternative = "greater", truncate = 0.1, rho.min = 1e-04, rho.max = 0.1, pseudo = .Machine$double.eps, return.value = c("BF", "P0", "err"), model = c("OR", "AND", "adaptive"), min.cov = NULL, max.odds = 10, mu.min = 1e-06, mu.max = 1 - mu.min)

Arguments

counts

An array of nucleotide counts (samples x positions x 10 nucleotides in forward and reverse orientation), typically from loadAllData

truncate

The model uses a compound control sample which is the sum of all samples with a relative nucleotide frequency below truncate at this locus. Default = 0.1.

alternative

The alternative. Currently only "greater" is implemented.

rho

Disperision factor. If NULL, estimated from the data.

rho.min

Lower bound for the method of moment estimate of the dispersion factor rho.

rho.max

Upper bound for the method of moment estimate of the dispersion factor rho.

mu.min

Minimum of the error rate mu.

mu.max

Maximal error rate mu.

pseudo

A pseudo count to be added to the counts to avoid problems with zeros.

return.value

Return value. Either "BF" for Bayes Factor of "P0" for the posterior probability (assuming a prior of 0.5).

model

The null model to use. For "OR" it requires the alternative model to be violated on either of the strands, for "AND" the null is specified such that the error rates of the sample of interest and the compound control sample are identical on both strands. "AND" typically yield many more calls. The most recent addition is "adaptive", which switches from "OR" to "AND", if the coverage is less than min.cov, or if the odds of forward and reverse coverage is greater than max.odds. Default = "OR".

min.cov

Minimal coverage to swith from OR to AND, if model is "adaptive"

max.odds

Maximal odds before switching from OR to AND if model is "adaptive" and min.cov=NULL.

Value

An array of Bayes factors

Examples

## Load data from deepSNV example
regions <- GRanges("B.FR.83.HXB2_LAI_IIIB_BRU_K034", IRanges(start = 3120, end=3140))
files <- c(system.file("extdata", "test.bam", package="deepSNV"), system.file("extdata", "control.bam", package="deepSNV"))
counts <- loadAllData(files, regions, q=10)

## Run (bbb) computes the Bayes factor
bf <- bbb(counts, model = "OR", rho=1e-4)
vcf <- bf2Vcf(bf, counts, regions, samples = files, prior = 0.5, mvcf = TRUE) 

## Compare to deepSNV
bf <- bbb(counts, model = "AND", rho=1e-4)
dpSNV <- deepSNV(test = files[1], control = files[2], regions=regions, q=10)
plot(p.val(dpSNV), bf[1,,]/(1+bf[1,,]), log="xy")