runHMM_3: Step 3 of HMM process

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

Step 3 of HMM process

Description

Applies the depmixS4 method posterior on normalized K values.

Usage

runHMM_3(dataframeList)

Arguments

dataframeList

List of depmixS4 depmix.fitted class output generated from the runHMM_2 step

Value

Output is a list of depmixS4 posterior state classifications 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))
dataframeList2 <- runHMM_1(dataframeList, initialStartProbabilities, trProbs)
dataframeList3 <- runHMM_2(dataframeList2)
output <- runHMM_3(dataframeList3)

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