Description Usage Arguments Details Value Author(s) Examples
Under the empirical Bayes approach (and assuming a uniform prior for the methylation level) the shape and scale parameters for the gamma prior of the regionspecific read density are derived. The parameters are thereby determined in a CpGdependent manner.
1 
x 
Object of class 
ngroups 
Number of CpG density groups you would like to consider. The bins are
classified based on its CpG density into one of 
ncomp 
Number of components of beta distributions in the prior distribution for
the methylation level when method is equal to 
maxBins 
Maximum number of bins in one CpG density group used to derive the
parameter estimates. If maxBins is smaller than the number of bins
that are in one groups than 
method 
Either 
controlMethod 
list defining settings if the DiracBetaDirac mixture is chosen.

ncpu 
Number of CPUs on your machine you would like to use in parallel.
If 
verbose 
Boolean indicating whether the empirical Bayes function should run in a verbose mode (default 'FALSE'). 
BayMeth takes advantage of the relationship between CpGdensity and read
depth to formulate a CpGdensitydependent gamma prior distribution for the
regionspecific read density. Taking CpGdensity into account the prior should
stabilise the methylation estimation procedure for low counts and in the
presence of sampling variability. The shape and scale parameter of the gamma
prior distribution are determined in a CpGdensitydependent manner using
empirical Bayes. For each genomic bin the CpG density is provided in the
BayMethList
object. Each bin is classified based on its CpGdensity into
one of ngroups
nonoverlapping CpGdensity intervals. For each class
separately, we derive the values for the shape and scale parameter under an
empirical Bayes framework using maximum likelihood. For CpG classes which
contain more than maxBins
bins, a random sample drawn with replacement of size
maxBins
is used to derive these prior parameters. Note that both read depths,
from the SssI control and the sample of interest, are thereby taken into
account. We end up with ngroups
parameter sets for shape and rate.
A BayMethList
object where the slot priorTab
is filled. priorTab
represent a list. The first list entry contains the CpG group a bin is assigned to. The second entry contains the number of components that have been used for the prior (at the moment 1). The following list entries correspond to one sample of interest, respectively, and contain a matrix with the optimal shape and scale parameters for all CpG classes. The first row contains the optimal shape parameter and the second row the optimal scale parameter. The number of columns corresponds to the number of CpG classes specified in ngroups
.
Andrea Riebler
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  if(require(BSgenome.Hsapiens.UCSC.hg18)){
windows < genomeBlocks(Hsapiens, chrs="chr21", width=100, spacing=100)
cpgdens < cpgDensityCalc(windows, organism=Hsapiens,
w.function="linear", window=700)
co < matrix(rnbinom(length(windows), mu=10, size=2), ncol=1)
sI < matrix(rnbinom(2*length(windows), mu=5, size=2), ncol=2)
bm < BayMethList(windows=windows, control=co,
sampleInterest=sI, cpgDens=cpgdens)
bm < determineOffset(bm)
# mask out unannotated high copy number regions
# see Pickrell et al. (2011), Bioinformatics 27: 21442146.
# should take about 3 minutes for both sample of interests with 2 CPUs.
bm < empBayes(bm, ngroups=20)
}

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