6 March 2015
ShearwaterML is a maximum-likelihood adaptation of the original Shearwater algorithm. Unlike the original algorithm, ShearwaterML does not use prior information and yields p-values, instead of Bayes factors, using a Likelihood-Ratio Test. This allows using standard multiple testing correction methods to obtain a list of significant variants with a controlled false discovery rate.
For a detailed description of the algorithm see:
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).
Load data from deepSNV example
library(deepSNV)
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=30)
ShearwaterML: “betabinLRT” calculates p-values for each possible mutation
pvals <- betabinLRT(counts, rho=1e-4, maxtruncate = 1)$pvals
qvals <- p.adjust(pvals, method="BH")
dim(qvals) = dim(pvals)
vcfML = qvals2Vcf(qvals, counts, regions, samples = files, mvcf = TRUE)
Original Shearwater: “bbb” computes the original Bayes factors
bf <- bbb(counts, model = "OR", rho=1e-4)
vcfBF <- bf2Vcf(bf, counts, regions, samples = files, prior = 0.5, mvcf = TRUE)
plot(pvals[1,,], bf[1,,]/(1+bf[1,,]), log="xy")