viterbi_joint

Viterbi algorithm for joint HMM, can only handle one set of observations

internal

Haplotype-aware Hidden Markov Model for RNA (HaHMMR) is a method for detecting copy number variations (CNVs) from bulk RNA-seq data. Additional examples, documentations, and details on the method are available at <https://github.com/kharchenkolab/hahmmr/>.

Teng Gao

hahmmr

Haplotype-Aware Hidden Markov Model for RNA

Evan Biederstedt

Peter Kharchenko

viterbi_joint function

<dl><dt>hmm</dt>
<dd>HMM object; expect variables x (allele depth), d (total depth), y (expression count), l (cell total library size),
lambda (reference expression rate), mu (global expression mean), sig (global expression standard deviation),
logPi (log transition prob matrix), phi (expression fold change for each state), delta (prior for each state),
alpha (alpha for each state), beta (beta for each state), states (states), p_s (phase switch probs)</dd></dl>

Arguments

Viterbi algorithm for joint HMM, can only handle one set of observations — viterbi_joint

<dl>

<dt>hmm</dt>
<dd>HMM object; expect variables x (allele depth), d (total depth), y (expression count), l (cell total library size),
lambda (reference expression rate), mu (global expression mean), sig (global expression standard deviation),
logPi (log transition prob matrix), phi (expression fold change for each state), delta (prior for each state),
alpha (alpha for each state), beta (beta for each state), states (states), p_s (phase switch probs)</dd>

</dl>

viterbi_joint: Viterbi algorithm for joint HMM, can only handle one set of observations

Description

Usage

Value

Arguments