fitNB: Fit Negative Binomial Mixture Model

In SIBER: Systematic Identification of Bimodally Expressed Genes Using RNAseq Data

Description

The function fits a two-component Negative Binomial mixture model.

Usage

fitNB(y, d=NULL, inits=NULL, model='V', zeroPercentThr=0.2)

Arguments

y	A vector representing the RNAseq raw count.
d	A vector of the same length as y representing the normalization constant to be applied to the data.
inits	Initial value to fit the mixture model. A vector with elements mu1, mu2, phi1, phi2 and pi1. For 0-inflated model, only mu2, phi2, pi1 are used while the other elements can be arbitrary.
model	Character specifying E or V model. E model fits the mixture model with equal dispersion phi while V model doesn't put any constraint.
zeroPercentThr	A scalar specifying the minimum percent of zero counts needed when fitting a zero-inflated Negative Binomial model. This parameter is used to deal with zero-inflation in RNAseq count data. When the percent of zero exceeds this threshold, rather than fitting a 2-component negative binomial mixture, a mixture of point mass at 0 and negative binomial is fitted.

Details

This function directly maximize the log likelihood function through optimization. With this function, three models can be fitted: (1) negative binomial mixture with equal dispersion (E model); (2) negative binomial mixture with unequal dispersion (V model); (3) 0-inflated negative binomial model. The 0-inflated negative binomial has the following density function:

P(Y=y)=π D(y) + (1-π)NB(μ, φ) where D is the point mass at 0 while NB(μ, φ) is the density of negative binomial distribution with mean μ and dispersion φ. The variance is μ+φ μ^2.

The rule to fit 0-inflated model is that the observed percentage of count exceeds the user specified threshold. This rule overrides the model argument when observed percentae of zero count exceeds the threshold.

Value

A vector consisting parameter estimates of mu1, mu2, phi1, phi2, pi1, logLik and BIC. For 0-inflated model, mu1=phi1=0.

Author(s)

Pan Tong ([e-mail]), Kevin R Coombes ([e-mail])

References

Tong, P., Chen, Y., Su, X. and Coombes, K. R. (2012). Systematic Identification of Bimodally Expressed Genes Using RNAseq Data. Bioinformatics, submitted.

See Also

SIBER fitLN fitGP fitNL

Examples

# artificial RNAseq data from negative binomial distribution
set.seed(1000)
dat <- rnbinom(100, mu=1000, size=1/0.2)
fitNB(y=dat)

SIBER documentation built on May 2, 2019, 5:20 p.m.