callMethylationSeparate {methimpute} | R Documentation

## Call methylation status

### Description

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

### Usage

    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

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

[Package methimpute version 1.9.0 Index]