lociLikelihoods: Evaluates the posterior likelihoods of each region defined by...

Description Usage Arguments Details Value Author(s) Examples

View source: R/lociLikelihoods.R View source: R/lociLikelihoods.R

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

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lociLikelihoods(cD, aD, newCounts = FALSE, bootStraps = 3,
                inferNulls = TRUE, nasZero = FALSE, usePosteriors =
TRUE, tail = 0.1, subset = NULL, 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.

tail

The cutoff for the tail of the distribution to be used in pre-calculating data for methylation analysis. See methObservables.

subset

A subset of the data on which to calculate the likelihoods.

cl

A SNOW cluster object, or NULL. See Details.

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.

Value

A lociData object.

Author(s)

Thomas J. Hardcastle

Examples

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# 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, gap = 100)

# 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)

segmentSeq documentation built on Nov. 1, 2018, 5:07 a.m.