callMethylationSeparate: Call methylation status

Description Usage Arguments Details Value Examples

View source: R/callMethylation.R

Description

Call methylation status of cytosines (or bins) with a separate Hidden Markov Model for each context.

Usage

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callMethylationSeparate(data, fit.on.chrom = NULL, transDist = Inf,
  eps = 1, max.time = Inf, max.iter = Inf, count.cutoff = 500,
  verbosity = 1, num.threads = 2 + include.intermediate,
  initial.params = NULL, include.intermediate = FALSE,
  update = "independent", min.reads = 0)

Arguments

data

A methimputeData object.

fit.on.chrom

A character vector specifying the chromosomes on which the HMM will be fitted.

transDist

The decaying constant for the distance-dependent transition matrix. Either a single numeric or a named numeric vector, where the vector names correspond to the transition contexts. Such a vector can be obtained from estimateTransDist.

eps

Convergence threshold for the Baum-Welch algorithm.

max.time

Maximum running time in seconds for the Baum-Welch algorithm.

max.iter

Maximum number of iterations for the Baum-Welch algorithm.

count.cutoff

A cutoff for the counts to remove artificially high counts from mapping artifacts. Set to Inf to disable this filtering (not recommended).

verbosity

An integer from 1 to 5 specifying the verbosity of the fitting procedure. Values > 1 are only for debugging.

num.threads

Number of CPU to use for the computation. Parallelization is implemented on the number of states, which is 2 or 3, so setting num.threads > 3 will not give additional performance increase.

initial.params

A methimputeBinomialHMM object. This parameter is useful to continue the fitting procedure for a methimputeBinomialHMM object.

include.intermediate

A logical specifying wheter or not the intermediate component should be included in the HMM.

update

One of c("independent", "constrained"). If update="independent" probability parameters for the binomial test will be updated independently. If update="constrained" the probability parameter of the intermediate component will be constrained to the mean of the unmethylated and the methylated component.

min.reads

The minimum number of reads that a position must have to contribute in the Baum-Welch fitting procedure.

Details

The Hidden Markov model uses a binomial test for the emission densities. Transition probabilities are modeled with a distance dependent decay, specified by the parameter transDist.

Value

A methimputeBinomialHMM object.

Examples

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## Get some toy data
file <- system.file("data","arabidopsis_toydata.RData", package="methimpute")
data <- get(load(file))
print(data)
model <- callMethylationSeparate(data)
print(model)

methimpute documentation built on Nov. 8, 2020, 5:47 p.m.