hiddenMarkovAnalysis: Run Multi-Step HMM Analysis on tLOHCalcUpdate output

In USCDTG/tLOH: Assessment of evidence for LOH in spatial transcriptomics pre-processed data using Bayes factor calculations

Run Multi-Step HMM Analysis on tLOHCalcUpdate output

Description

Applies the depmixS4 method of HMM Analysis on tLOHCalcUpdate output to obtain segments, noted by the output 'state' column

Usage

hiddenMarkovAnalysis(df, initProbs, trProbs)

Arguments

df	A dataframe output by tLOHCalcUpdate
initProbs	Dataframe containing 22 rows and two columns, initProb1 and initProb2. Each row represents a chromosome in sequential order, with initProb1 being the probability of state1 and initProb2 being the probability of state2.
trProbs	Matrix of transition start probabilities for HMM

Value

Output is a dataframe containing HMM analysis output and tLOHCalcUpdate output summary

Author(s)

Michelle Webb

Examples

data('humanGBMsampleAC')
data('initialStartProbabilities')
df <- tLOHCalcUpdate(humanGBMsampleAC,1.25,1.25,500,500,4)
trProbs <- cbind(c(0.8999,0.1001),c(0.1001,0.8999))
output <- hiddenMarkovAnalysis(df,initialStartProbabilities,trProbs)

USCDTG/tLOH documentation built on Oct. 23, 2022, 8:05 p.m.