EBHMMNBfun: Baum-Welch algorithm for a single hidden markov chain

Description Usage Arguments Details Value Author(s) Examples

View source: R/EBHMMNBfun.R

Description

Baum-Welch algorithm for a single hidden markov chain

Usage

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EBHMMNBfun(Data,NgVector=NULL,Conditions, sizeFactors,
	PriorFC=1.5,homo=TRUE, maxround=5,
	Pi0=NULL, Tran=NULL,NoTrend=FALSE, NumTranStage=3,
	FCParam=NULL, AlphaIn=NULL,BetaIn=NULL,
	StateNames=c("Up","NC","Down"),
	EM=TRUE, UpdateParam=TRUE, Print=TRUE,
	OnlyQ=FALSE,WithinCondR=TRUE,
	PenalizeLowMed=TRUE, PenalizeLowMedQt=.2,PenalizeLowMedVal=10)

Arguments

Data

input data, rows are genes/isoforms and columns are samples

NgVector

Ng vector; NULL for gene level data

Conditions

A factor indicates the condition (time/spatial point) which each sample belongs to.

sizeFactors

a vector indicates library size factors

Tran

initial values for transition matrices

Pi0

initial values for starting probabilities

NumTranStage

number of states

PriorFC

target FC for gridient change

StateNames

name of the hidden states

homo

whether the chain is assumed to be homogenious

maxround

max number of iteration

AlphaIn,BetaIn

If the parameters are known and the user doesn't want to estimate them from the data, user may specify them here.

NoTrend

if NoTrend=TRUE, initial transition probabilities will be set to be equal

FCParam

not in use

EM

Whether estimate the prior parameters of the beta distribution by EM

UpdateParam

Whether update starting probabilities and transition probabilities

OnlyQ

If OnlyQ=TRUE, the function will only return estimated q's.

WithinCondR

By defining WithinCondR=TRUE, estimation of r's are obtained within each condition. (WithinCondR=FALSE is not suggested here)

Print

Whether print the elapsed-time while running the test.

PenalizeLowMed,PenalizeLowMedQt,PenalizeLowMedVal

Transcripts with median quantile < = PenalizeLowMedQt will be penalized

Details

EBHMMNBfun() function implements the Balm-Welch algorithm that estimates the starting probabilities and transition probabilities of a single hidden Markov model. Here the emission distribution of each gene is assumed to be a Beta-Negative Binomial distribution with parameters (r_g, alpha, beta) , in which alpha and beta are shared by all the genes and r_g is gene specific. If not specified, r_g, alpha and beta will be estimated using method of moments. For isoform data, we assume isoforms from the same Ig group share the same beta^Ig. alpha is shared by all the isoforms and r_gi is isoform specific. The user also needs to specify an expected FC.

Value

MAPTerm: the most likely path of each gene/isoform. MAPTermNum: numeric version of MAPTerm.

AllTerm: all possible expression paths considered in the model. PP: posterior probability of being each expression path.

WhichMax: index of the most likely path. Allf: prior probability of being each path.

Pi0Track: estimated starting probabilities of each iteration.

TranTrack: estimated transition probabilities of each iteration.

AlphaTrack, BetaTrack: estimated alpha and beta(s).

LLAll=PostSumForLL.Sum: log likelihood of the model.

Author(s)

Ning Leng

Examples

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data(GeneExampleData)
CondVector <- rep(paste("t",1:5,sep=""),each=3)
Conditions <- factor(CondVector, levels=c("t1","t2","t3","t4","t5"))
Sizes <- MedianNorm(GeneExampleData)
tmp <- EBHMMNBfun(Data=GeneExampleData, sizeFactors=Sizes, Conditions=Conditions,
          maxround=2, OnlyQ=TRUE)

EBSeqHMM documentation built on May 2, 2018, 2:58 a.m.