EBSeqHMM (version 1.6.0)

EBHMMNBMultiEM_2chain: Run EBSeqHMM model with a fixed expected FC

Description

Run EBSeqHMM model with a fixed expected FC

Usage

EBHMMNBMultiEM_2chain(Data,
	NgVector=NULL, Conditions, AllTran=NULL,
	AllPi0=NULL, Terms=NULL,
	sizeFactors, NumTranStage=c(3,2),PriorFC=2,
	StateNames=c("Up","Down"),homo=FALSE,
	UpdateRd=5, PIBound=.9, UpdatePI=FALSE,Print=FALSE,
	WithinCondR=TRUE,
	PenalizeLowMed=TRUE, PenalizeLowMedQt=.1,PenalizeLowMedVal=10)

Arguments

Data

input data, rows are genes 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.

AllTran

initial values for transition matrices

AllPi0

initial values for starting probabilities

Terms

Terms

sizeFactors

a vector indicates library size factors

StateNames

names of the hidden states

NumTranStage

number of states in two chains

PriorFC

target FC for gridient change

homo

whether the chain is assumed to be homogenious

UpdateRd

max number of iteration

UpdatePI

whether update the mixture proportion of two chains

PIBound

upper bound of the mixture proportion of the two chains

Print

Whether print the elapsed-time while running the test.

WithinCondR

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

PenalizeLowMed,PenalizeLowMedQt,PenalizeLowMedVal

Transcripts with median quantile < = PenalizeLowMedQt will be penalized

Value

Pi0Out: estimated starting probabilities of each iteration.TranOut: estimated transition probabilities of each iteration.Pi: estimated probability of being each chain.Alpha, Beta: estimated alpha and beta(s).LLSum: log likelihood of the model.QList: estimated q's.MgAllPP: marginal PP for all paths.MgAllMAPChar: Most likely path based on MgAllPP.MgAllMaxVal: Highest PP based on MgAllPP.PPMatW: Posterior probabilities of being each of the chains.

Details

EBHMMNBMultiEM_2chain() function implements the EBSeqHMM model to perform statistical analysis in an RNA-seq experiment with ordered conditions. EBHMMNBMultiEM_2chain() calls EBHMMNBfunForMulti() function to perform Balm-Welch algorithm that estimates the starting probabilities and transition probabilities. 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. Function EBSeqHMMTest() runs several models with varying FCs and returns the model with maximum likelihood.

Examples

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 <- EBHMMNBMultiEM_2chain(Data=GeneExampleData,sizeFactors=Sizes, Conditions=Conditions,
          UpdateRd=2)