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

In segmentSeq: Methods for identifying small RNA loci from high-throughput sequencing data

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, 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

# 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. 8, 2020, 5:18 p.m.