aneuHMM: Hidden Markov Model

In ataudt/aneufinder: Analysis of Copy Number Variation in Single-Cell-Sequencing Data

Hidden Markov Model

Description

The aneuHMM object is output of the function findCNVs and is basically a list with various entries. The class() attribute of this list was set to "aneuHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' and 'hmm$'.

Value

ID	An identifier that is used in various AneuFinder functions.
bins	A GRanges-class object containing the genomic bin coordinates, their read count and state classification.
segments	A GRanges-class object containing regions and their state classification.
weights	Weight for each component.
transitionProbs	Matrix of transition probabilities from each state (row) into each state (column).
transitionProbs.initial	Initial transitionProbs at the beginning of the Baum-Welch.
startProbs	Probabilities for the first bin
startProbs.initial	Initial startProbs at the beginning of the Baum-Welch.
distributions	Estimated parameters of the emission distributions.
distributions.initial	Distribution parameters at the beginning of the Baum-Welch.
convergenceInfo	Contains information about the convergence of the Baum-Welch algorithm.
convergenceInfo$eps	Convergence threshold for the Baum-Welch.
convergenceInfo$loglik	Final loglikelihood after the last iteration.
convergenceInfo$loglik.delta	Change in loglikelihood after the last iteration (should be smaller than eps)
convergenceInfo$num.iterations	Number of iterations that the Baum-Welch needed to converge to the desired eps.
convergenceInfo$time.sec	Time in seconds that the Baum-Welch needed to converge to the desired eps.

See Also

findCNVs

ataudt/aneufinder documentation built on April 18, 2023, 4:20 a.m.