CNOGpro (version 1.1)

runHMM: 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.

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.

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.

Examples

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