segmentSeq (version 2.6.0)

lociLikelihoods: Evaluates the posterior likelihoods of each region defined by a segmentation map as a locus.

Description

An empirical Bayesian approach that takes a segmentation map and uses this to bootstrap posterior likelihoods on each region being a locus for each replicate group.

Usage

lociLikelihoods(cD, aD, newCounts = FALSE, bootStraps = 3, inferNulls = TRUE, nasZero = FALSE, usePosteriors =
TRUE, cl)

Arguments

cD

A lociData object that defines a segmentation map.

aD

An alignmentData object.

newCounts

Should new counts be evaluated for the segmentation map in `cD' before calculating loci likelihoods? Defaults to FALSE

bootStraps

What level of bootstrapping should be carried out on the inference of posterior likelihoods? See the baySeq function getLikelihoods.NB for a discussion of bootstrapping.

inferNulls

Should null regions be inferred from the gaps between segments defined by the `cD' object?

nasZero

If FALSE, any locus with a posterior likelihood `NA' in the existing segmentation map is treated as a null region for the first bootstrap; If TRUE, it is ignored for the first bootstrap.

usePosteriors

If TRUE, the function uses the existing likelihoods to weight the prior estimation of parameters. Defaults to TRUE.

cl

A SNOW cluster object, or NULL. See Details.

Value

A lociData object.

Details

A 'cluster' object (package: snow) may be used for parallelisation of this function when examining large data sets. Passing NULL to this variable will cause the function to run in non-parallel mode.

Examples

# Define the chromosome lengths for the genome of interest.

chrlens <- c(2e6, 1e6)

# Define the files containing sample information.

datadir <- system.file("extdata", package = "segmentSeq")
libfiles <- c("SL9.txt", "SL10.txt", "SL26.txt", "SL32.txt")

# Establish the library names and replicate structure.

libnames <- c("SL9", "SL10", "SL26", "SL32")
replicates <- c(1,1,2,2)

# Process the files to produce an `alignmentData' object.

alignData <- readGeneric(file = libfiles, dir = datadir, replicates =
replicates, libnames = libnames, chrs = c(">Chr1", ">Chr2"), chrlens =
chrlens)

# Process the alignmentData object to produce a `segData' object.

sD <- processAD(alignData, gap = 100, cl = NULL)

# Use the segData object to produce a segmentation of the genome, but
# without evaluating posterior likelihoods.

segD <- heuristicSeg(sD = sD, aD = alignData,
    subRegion = data.frame(chr= ">Chr1", start = 1, end = 1e5),
    getLikes = FALSE, cl = NULL) 

# Use the lociData function to evaluate the posterior likelihoods directly.

lociData <- lociLikelihoods(segD, aD = alignData, bootStraps = 5,
inferNulls = TRUE, cl = NULL)