EBTest_ext {EBSeqHMM} | R Documentation

## Extented EBTest function

### Description

Extented EBTest function

### Usage

    EBTest_ext(Data,NgVector=NULL,Conditions,
    	sizeFactors, maxround, Pool=FALSE, NumBin=1000,
    	ApproxVal=10^-10, Alpha=NULL, Beta=NULL,
    	PInput=NULL,RInput=NULL,PoolLower=.25,
    	PoolUpper=.75,OnlyCalcR=FALSE,Print=TRUE)

### Arguments

`Data` | Input data, rows are genes/isoforms and columns are samples. Data should come from a two condition experiment
`NgVector` | Ng vector; NULL for gene level data
`Conditions` | A factor indicates the condition (time/spatial point) which each sample belongs to. Only two levels are allowed.
`sizeFactors` | a vector indicates library size factors
`maxround` | number of iteration
`Pool` | While working without replicates, user could define the Pool = TRUE in the EBTest function to enable pooling.
`NumBin` | By defining NumBin = 1000, EBSeq will group the genes with similar means together into 1,000 bins.
`PoolLower,PoolUpper` | With the assumption that only subset of the genes are DE in the data set, we take genes whose FC are in the PoolLower - PoolUpper quantile of the FCs as the candidate genes (default is 25 bin, the bin-wise variance estimation is defined as the median of the cross condition variance estimations of the candidate genes within that bin. We use the cross condition variance estimations for the candidate genes and the bin-wise variance estimations of the host bin for the non-candidate genes.
`ApproxVal` | The variances of the transcripts with mean < var will be approximated as mean/(1-ApproxVal).
`Alpha,Beta,PInput,RInput` | If the parameters are known and the user doesn't want to estimate them from the data, user may specify them here.
`Print` | Whether print the elapsed-time while running the test.
`OnlyCalcR` | if OnlyCalcR=TRUE, the function will only return estimation of r's.

### Details

EBSeq_ext() function is an extension of EBTest() function, which is used to calculate the conditional probability P(X_g,t | X_g,t-1). In EBSeqHMM, we assume the conditional distribution is Beta-Negative Binomial.

### Value

See `EBTest`

### Author(s)

Ning Leng

### Examples

    data(GeneExampleData)
    Data=GeneExampleData[,1:6]
    CondVector <- rep(paste("t",1:2,sep=""),each=3)
    Conditions <- factor(CondVector, levels=c("t1","t2"))
    Sizes <- MedianNorm(Data[1:10,])
    Out <- EBTest_ext(Data=Data[1:10,], sizeFactors=Sizes, Conditions=Conditions,
             maxround=1)

[Package EBSeqHMM version 1.14.0 Index]