EBSeqHMM (version 1.6.0)

EBSeqHMMTest: Identify DE genes and classify them into their most likely path in an RNA-seq experiment with ordered conditions

Description

Identify DE genes and classify them into their most likely path in an RNA-seq experiment with ordered conditions

Usage

EBSeqHMMTest(Data,
	NgVector=NULL, Conditions, AllTran=NULL,
	AllPi0=NULL, Terms=NULL,
	sizeFactors, NumTranStage=c(3,2),FCV=2,
	homo=FALSE, UpdateRd=10, PIBound=.9, UpdatePI=FALSE,
	Print=FALSE,WithinCondR=TRUE,Qtrm=.75,QtrmCut=10,
	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

FCV

candidate values for expected FC. Default is 2. If user wants to search through a list of candidate FCs, he/she may define FCV as a vector. e.g. By defining FCV=seq(1.4,2,.2), the function will search over (1.4 1.6 1.8 2.0). Note that searching over a number of candidate FCs will increase the running time.

sizeFactors

a vector indicates library size factors

NumTranStage

number of states in two chains

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

Qtrm,QtrmCut

Transcripts with Qtrm th quantile < = QtrmCut will be removed before testing. The default value is Qtrm = 0.75 and QtrmCut=10. By default setting, transcripts that have >75% of the samples with expression less than 10 won't be tested.

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

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.FCLikelihood: log likelihood of each FC.

Details

EBSeqHMMTest() function applies EBSeqHMM model with differentexpected FC's and select the optimal FC that maximizes the log likelohood. EBSeqHMMTest() calls EBHMMNBMultiEM_2chain() function which implements the EBSeqHMM model to perform statistical analysis in an RNA-seq experiment with ordered conditions based on a fixed expected FC. EBSeqHMMTest() runs EBHMMNBMultiEM_2chain() with varying FCs (default is seq(1.4,2,.2)). And it will return the results of the model with optimal FC. 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.

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