NBLDA: Negative Binomial Linear Discriminant Analysis for RNA-Seq Data

TL;DR

This paper introduces NBLDA, a novel classification method for RNA-Seq data that models overdispersion using negative binomial distribution, outperforming previous Poisson-based approaches in simulations and real data.

Contribution

The paper develops a negative binomial linear discriminant analysis method specifically for RNA-Seq data, addressing overdispersion and improving classification accuracy.

Findings

NBLDA outperforms Poisson-based classifiers in simulations.

NBLDA achieves higher accuracy on real RNA-Seq datasets.

The method effectively models overdispersion in count data.

Abstract

RNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays. Although statistical methods that have been developed for microarray data can be applied to RNA-Seq data, they are not ideal due to the discrete nature of RNA-Seq data. The Poisson distribution and negative binomial distribution are commonly used to model count data. Recently, Witten (2011) proposed a Poisson linear discriminant analysis for RNA-Seq data. The Poisson assumption may not be as appropriate as negative binomial distribution when biological replicates are available and in the presence of overdispersion (i.e., when the variance is larger than the mean). However, it is more complicated to model negative binomial variables because they involve a dispersion parameter that needs to be estimated. In this paper, we propose a negative binomial linear discriminant analysis for RNA-Seq data. By Bayes' rule, we construct the classifier by fitting a negative binomial model, and propose some plug-in rules to estimate the unknown parameters in the classifier. The relationship between the negative binomial classifier and the Poisson classifier is explored, with a numerical investigation of the impact of dispersion on the discriminant score. Simulation results show the superiority of our proposed method. We also analyze four real RNA-Seq data sets to demonstrate the advantage of our method in real-world applications.