betaparametVP: Estimation of parameters V and P in count data of RNA reads

In NBBttest: Negative Binomial Beta t-Test

Estimation of parameters V and P in count data of RNA reads

Description

This function is used to estimate parameters P and V by optimalizing estimates 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 sequence reads are assumed to follow binomial distribution with parameters (P) and (n) or negative binomial distribution with parameters (P) and (r) , while P is frequency of a gene or an isoform in RNA sequence population and assumed to follow beta distribution with parameters alpha (a) and beta(b). Parameters P and V are estimated by using optimal estimation of parameters a and b. The optimal method is an iteration algorithm 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 [e-mail]

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[1:100,]
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)

NBBttest documentation built on May 30, 2022, 1:05 a.m.