A sparse negative binomial classifier with covariate adjustment for RNA-seq data
Tanbin Rahman, Hsin-En Huang, Yujia Li, An-Shun Tai, Wen-Ping Hseih, Colleen A. McClung, George Tseng
The Annals of Applied Statistics · 2022 · Volume 16 · Issue 2
Abstract
Supervised machine learning methods have been increasingly used in biomedical research and clinical practice. In transcriptomic applications, RNA-seq data have become dominating and have gradually replaced traditional microarray, due to their reduced background noise and increased digital precision. Most existing machine learning methods are, however, designed for continuous intensities of microarray and are not suitable for RNA-seq count data. In this paper we develop a negative binomial model via generalized linear model framework with double regularization for gene and covariate sparsity to accommodate three key elements: adequate modeling of count data with overdispersion, gene selection and adjustment for covariate effect. The proposed sparse negative binomial classifier (snbClass) is evaluated in simulations and two real applications of multidisease postmortem brain tissue RNA-seq data and cervical tumor miRNA-seq data to demonstrate its superior performance in prediction accuracy and feature selection.
Keywords
classification, count data, covariate adjustment, RNA-seq data
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