In this paper, researchers from Hong Kong Baptist University propose a negative binomial linear discriminant analysis for RNA-Seq data. By Bayes’ rule, they 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 this proposed method. The researchers also analyze two real RNA-Seq data sets to demonstrate the advantages of our method in real-world applications.

*Mean misclassification rates for real data sets*

**Availability** – R code is available at http://www.comp.hkbu.edu.hk/~xwan/NBLDA.R

or https://github.com/yangchadam/NBLDA

Dong K, Zhao H, Tong T, Wan X. (2016) **NBLDA: negative binomial linear discriminant analysis for RNA-Seq data.** *BMC Bioinformatics* 17(1):369. [article]