betaparametVP {MBttest} | R Documentation

## Estimation of Binomal Parameters V And P in Count Data of RNA Reads

### Description

This function is used to estimate parameters P and V by optimalizing estimation of parameters: alpha and beta.

### Usage

    betaparametVP(X, NX)

### Arguments

`X` | count dataset derived from m replicate libraries in one condition.
`NX` | vector of m library sizes. Library size is sum of counts over the whole library.

### Details

Count data of RNA reads are assumed to follow binomial distribution with parameters (`P`) and (`V`), while `P` is assumed to follow beta distribution with parameters `alpha` (`a`) and `beta`(`b`). Parameters `P and V` are estimated by optimal estimation of parameters a and b. The optimal method is an iteration method drived by weighting proportion of gene or isoform in each replicate library. This is a large-scale method for estimating these parameters. Estimation of parameters `P and V` is core of the multiple beta t-test method because `P` and `V` will be used to calculate t-value.

### Value

return a list:

`P` | N proportions estimated.
`V` | N variances estimated.

### Note

betaparametVP requres functions betaparametab and betaparametw.

### Author(s)

Yuan-DE Tan tanyuande@gmail.com

### References

Baggerly KA, Deng L, Morris JS, Aldaz CM (2003) Differential expression in SAGE: accounting for normal between-library variation. Bioinformatics, 19: 1477-1483.
Yuan-De Tan, Anita M. Chandler, Arindam Chaudhury, and Joel R. Neilson(2015) A Powerful Statistical Approach for Large-scale Differential Transcription Analysis.Plos One,10.1371/journal.pone.0123658.

### See Also

`betaparametab`, `betaparametw`

### Examples

    data(jkttcell)
    X<-jkttcell[1:500,]
    na<-3
    nb<-3
    cn<-length(X[1,])
    rn<-length(X[,1])
    XC<-X[,1:(cn-na-nb)]
    XX<-X[,(cn-na-nb+1):cn]
    n<-na+nb
    XA<-XX[,1:na]
    SA<-apply(XA,2,sum)
    PA<-betaparametVP(XA,SA)

[Package MBttest version 1.18.0 Index]