McCarthy, D.J., Chen, Y. and Smyth, G.K. (2021) Differential Expression Analysis of RNA-Seq Experiments with Respect to Biological Variation. Nucleic Acids Research, 40, 4288-4297.
https://doi.org/10.1093/nar/gks042

has been cited by the following article:

• TITLE: Quasi-Negative Binomial: Properties, Parametric Estimation, Regression Model and Application to RNA-SEQ Data

  AUTHORS: Mohamed M. Shoukri, Maha M. Aleid

  KEYWORDS: Queuing Models, Overdispersion, Moment Estimators, Delta Method, Bootstrap, Maximum Likelihood Estimation, Fisher’s Information, Orthogonal Polynomials, Regression Models, RNE-Seq Data

  JOURNAL NAME: Open Journal of Statistics, Vol.12 No.2, April 19, 2022

  ABSTRACT: Background: The Poisson and the Negative Binomial distributions are commonly used to model count data. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial has a variance larger than the mean and therefore both models are appropriate to model over-dispersed count data. Objectives: A new two-parameter probability distribution called the Quasi-Negative Binomial Distribution (QNBD) is being studied in this paper, generalizing the well-known negative binomial distribution. This model turns out to be quite flexible for analyzing count data. Our main objectives are to estimate the parameters of the proposed distribution and to discuss its applicability to genetics data. As an application, we demonstrate that the QNBD regression representation is utilized to model genomics data sets. Results: The new distribution is shown to provide a good fit with respect to the “Akaike Information Criterion”, AIC, considered a measure of model goodness of fit. The proposed distribution may serve as a viable alternative to other distributions available in the literature for modeling count data exhibiting overdispersion, arising in various fields of scientific investigation such as genomics and biomedicine.