hmm {VanillaICE}R Documentation

Wrapper for fitting the HMM

Description

A wrapper for fitting the HMM.

Usage

hmm(object, states, mu = NULL, probs = NULL, takeLog = FALSE, initialP, returnSegments = TRUE, TAUP = 1e+08, verbose = FALSE, ice = FALSE, envir)

Arguments

object SnpCallSet, SnpCopyNumberSet, or oligoSnpSet object
states Labels for the hidden states. See details for order.
mu The latent copy number. See details for order.
probs See details.
takeLog Whether to take the log of the copy number before computing emission probabilities and standard deviations
initialP Initial state probabilities
returnSegments Logical: whether to return the segments or the loci x sample matrix of predicted states
TAUP Scaling parameter for transition probabilities.
verbose Logical: Verbose output?
ice Whether to use CRLMM confidence scores of the genotype calls.
envir Optional. An environment for storing intermediate files created for fitting the HMM.

Details

For oligoSnpSet objects, the hidden state labels are assumed to be 1: hemizygous deletion 2: normal 3: region of homozygosity (ROH) 4: amplification

The argument mu should have copy number values corresponding to the above states. For instance on the absolute scale, the copy number states should be 1, 2, 2, and 4.

probs: If ice is FALSE, the elements in probs should correspond to the probability of a homozygous genotype in each of the above states. If ice is TRUE, the elements in probs should correspond to 1. Pr(homozygous call | truth is heterozyous) 2. Pr(heterozygous call | truth is heterozygous) 3. Pr(homozygous call | truth is ROH) 4. Pr(homozygous call | truth is normal) . 'Normal' meaning copy number 2 and a typical frequency of heterozygosity for autosomes.

Value

If returnSegments is TRUE, a data.frame containing the coordinates of the predicted segments is returned. Otherwise, a loci X sample matrix is returned. The elements of the matrix correspond to the predict hidden state for a specific locus and sample.

Author(s)

R. Scharpf

References

RB Scharpf et al. (2008) Hidden Markov Models for the assessment of chromosomal alterations using high-throughput SNP arrays, Annals of Applied Statistics


[Package VanillaICE version 1.6.0 Index]