fitGP: Fit Generalized Poisson Mixture Model The function fits a...

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

Description

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

Usage

fitGP(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.
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 Generalized Poisson 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 Generalized Poisson mixture, a mixture of point mass at 0 and Generalized Poisson is fitted.

Details

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

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.

nickytong/SIBER documentation built on May 23, 2019, 5:08 p.m.