shearwater: Bayesian beta-binomal test, codename shearwater

In deepSNV: Detection of subclonal SNVs in deep sequencing data.

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
rho	Disperision factor. If NULL, estimated from the data.
alternative	The alternative. Currently only "greater" is implemented.
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.
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.
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.
mu.min	Minimum of the error rate mu.
mu.max	Maximal error rate mu.

Value

An array of Bayes factors

Note

Experimental code, subject to changes

Author(s)

mg14

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")

deepSNV documentation built on Nov. 8, 2020, 8:01 p.m.