lociLikelihoods {segmentSeq} | R Documentation |
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.
lociLikelihoods(cD, aD, newCounts = FALSE, bootStraps = 3, inferNulls = TRUE, nasZero = FALSE, usePosteriors = TRUE, tail = 0.1, subset = NULL, cl)
cD |
A |
aD |
An |
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
|
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
|
subset |
A subset of the data on which to calculate the likelihoods. |
cl |
A SNOW cluster object, or NULL. See 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.
A lociData
object.
Thomas J. Hardcastle
# 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)