getViterbi

The observations. A list of one or more entries containing the observation matrix (<code>numeric</code>) for the samples (e.g. chromosomes).

Successive positions having NAs longer than this threshold are masked in the viterbi path.

NAtol

List of precalculated emission probabilities of emission function is of type 'null'.

emissionProbs

<code>logical</code> for printing algorithm status or not.

verbose

Library size factors for Emissions PoissonLogNormal or NegativeBinomial as a length(obs) x ncol(obs[[1]]) matrix.

sizeFactors


Given a Hidden Markov Model, the function calculates the most likely state path (viterbi) for one or more observation sequence.


Genome segmentation with hidden Markov models has become a useful tool to annotate genomic elements, such as promoters and enhancers. STAN (genomic STate ANnotation) implements (bidirectional) hidden Markov models (HMMs) using a variety of different probability distributions, which can model a wide range of current genomic data (e.g. continuous, discrete, binary). STAN de novo learns and annotates the genome into a given number of 'genomic states'. The 'genomic states' may for instance reflect distinct genome-associated protein complexes (e.g. 'transcription states') or describe recurring patterns of chromatin features (referred to as 'chromatin states'). Unlike other tools, STAN also allows for the integration of strand-specific (e.g. RNA)  and non-strand-specific data (e.g. ChIP).

Benedikt Zacher

STAN

The genomic STate ANnotation package

getViterbi function

Given a Hidden Markov Model, the function calculates the most likely state path (viterbi) for one or more observation sequence.

getViterbi: Calculate the most likely state path

Description

Usage

Arguments

Value

Examples