The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq

Abstract

We propose a new statistical test for assessing differential gene expression using RNA sequencing (RNA-Seq) data. Commonly used probability distributions, such as binomial or Poisson, cannot appropriately model the count variability in RNA-Seq data due to overdispersion. The small sample size that is typical in this type of data also prevents the uncritical use of tools derived from large-sample asymptotic theory. The test we propose is based on the NBP parameterization of the negative binomial distribution. It extends an exact test proposed by Robinson and Smyth (2007, 2008). In one version of Robinson and Smyth’s test, a constant dispersion parameter is used to model the count variability between biological replicates. We introduce an additional parameter to allow the dispersion parameter to depend on the mean. Our parametric method complements nonparametric regression approaches for modeling the dispersion parameter. We apply the test we propose to an Arabidopsis data set and a range of simulated data sets. The results show that the test is simple, powerful and reasonably robust against departures from model assumptions.