hmm2 {VanillaICE} | R Documentation

## Fit a 6-state HMM to log R ratios and B allele frequencies estimated from SNP arrays

### Description

This function is intended for estimating the integer copy number from germline or DNA of clonal origin using a 6-state HMM. The states are homozygous deletion, hemizygous deletion, diploid copy number, diploid region of homozygosity, single copy gain, and two+ copy gain. Because heterozygous markers are more informative for copy number than homozygous markers and regions of homozgosity are common in normal genomes, we currently computed a weighted average of the BAF emission matrix with a uniform 0,1 distribution by the probability that the marker is heterozygous, thereby downweighting the contribution of homozygous SNPs to the likelihood. In addition to making the detection of copy-neutral regions of homozgosity less likely, it also helps prevent confusing hemizygous deletions with copy neutral regions of homozygosity – the former would be driven mostly by the log R ratios. This is experimental and subject to change.

### Usage

    hmm2(
      object,
      emission_param = EmissionParam(),
      transition_param = TransitionParam(),
      ...
    )

    ## S4 method for signature 'SnpArrayExperiment'
    hmm2(
      object,
      emission_param = EmissionParam(),
      transition_param = TransitionParam(),
      ...
    )

    ## S4 method for signature 'oligoSnpSet'
    hmm2(
      object,
      emission_param = EmissionParam(),
      transition_param = TransitionParam(),
      ...
    )

    ## S4 method for signature 'ArrayViews'
    hmm2(
      object,
      emission_param = EmissionParam(),
      transition_param = TransitionParam(),
      tolerance = 2,
      verbose = FALSE,
      ...
    )

### Arguments

`object` | A `SnpArrayExperiment`
`emission_param` | A `EmissionParam` object
`transition_param` | A `TransitionParam` object
`...` | currently ignored
`tolerance` | length-one numeric vector. When the difference in the log-likelihood of the Viterbi state path between successive models (updated by Baum Welch) is less than the tolerance, no additional model updates are performed.
`verbose` | logical. Whether to display messages indicating progress.

### Details

The `hmm2` method allows parallelization across samples using the foreach paradigm. Parallelization is automatic when enabled via packages such as snow/doSNOW.

### Examples

    tp <- TransitionParam()
    TransitionParam(taup=1e12)
    data(snp_exp)
    emission_param <- EmissionParam(temper=1/2)
    fit <- hmm2(snp_exp, emission_param)
    unlist(fit)
    cnvSegs(fit)
    ## There is too little data to infer cnv reliably in this trivial example.
    ## To illustrate filtering options on the results, we select
    ## CNVs for which
    ## - the CNV call has a posterior probability of at least 0.5
    ## - the number of features is 2 or more
    ## - the HMM states are 1 (homozygous deletion) or 2 (hemizygous deletion)
    fp <- FilterParam(probability=0.5, numberFeatures=2, state=c("1", "2"))
    cnvSegs(fit, fp)
    ## for parallelization
    ## Not run:
       library(snow)
       library(doSNOW)
       cl <- makeCluster(2, type = "SOCK")
       registerDoSNOW(cl)
       fit <- hmm2(snp_exp, emission_param)

    ## End(Not run)

[Package VanillaICE version 1.50.0 Index]