runHMM_1: Step 1 of HMM process

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

Step 1 of HMM process

Description

Applies the depmixS4 method depmix on normalized K values.

Usage

runHMM_1(dataframeList, initProbs, trProbs)

Arguments

dataframeList	List of dataframes separated by cluster and chromosome from the prepareHMMdataframes function.
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 list of depmixS4 depmix class objects for each input dataframe

Author(s)

Michelle Webb

Examples

data('humanGBMsampleAC')
data('initialStartProbabilities')
df <- tLOHCalcUpdate(humanGBMsampleAC,1.25,1.25,500,500,4)
dataframeList <- prepareHMMdataframes(df)
trProbs <- cbind(c(0.8999,0.1001),c(0.1001,0.8999))
output <- runHMM_1(dataframeList, initialStartProbabilities, trProbs)

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