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

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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 tradi-tional 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 general-ized linear model framework with double regularization for gene and covari-ate 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.

原文English
頁(從 - 到)1071-1089
頁數19
期刊Annals of Applied Statistics
16
發行號2
DOIs
出版狀態Published - 2022 6月

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 建模與模擬
  • 統計、概率和不確定性

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