R: Copy number variation inference through a Hidden Markov Model

runHMM {CNOGpro} | R Documentation

## Copy number variation inference through a Hidden Markov Model

### Description

Implements a Viterbi algorithm for assigning most likely copy number to each chromosomal position in the chromosome.

### Usage

    runHMM(experiment, nstates = 5, changeprob = 1e-04, includeZeroState = T,
    	errorRate = 0.001)

### Arguments

`experiment` | An object of class `CNOGpro`
`nstates` | The possible number of states, not including state 0. The returned copy numbers will be in the range 0, 1, 2, ... , nstates
`changeprob` | The probability of transitioning from one state to another, used to set up the transition matrix.
`includeZeroState` | Whether or not to allow the copy number state 0 in the results
`errorRate` | The presumed fraction of erroneously mapped reads. Only needed when includeZeroState is set to TRUE. This numbers is used for setting the probability distribution of each observation in copy number state 0.

### Details

For each read count observation the algorithm computes the probability of that observation in each possible state. The minimum path through the trellis is then calculated at the end.

### Value

An object of class `CNOGpro`, with a HMMtable listing the breakpoints of different copy number states. The most probable states of each genetic element are also listed in the `genes` table of the object.

### Author(s)

Ola Brynildsrud

### Examples

    data(carsonella)
    carsonella_normalized <- normalizeGC(carsonella)
    carsonella_hmm <- runHMM(carsonella_normalized)
    plotCNOGpro(carsonella_hmm)

[Package CNOGpro version 1.1 Index]