EBSeqHMMTest: Identify DE genes and classify them into their most likely...

In lengning/EBSeqHMM: Bayesian analysis for identifying gene or isoform expression changes in ordered RNA-seq experiments

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

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.

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.

Author(s)

Ning Leng

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)

lengning/EBSeqHMM documentation built on May 21, 2019, 4:02 a.m.