A tail-based test to detect differential expression in RNA-sequencing data

Jiong Chen, Xinlei Mi, Jing Ning, Xuming He, Jianhua Hu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

RNA sequencing data have been abundantly generated in biomedical research for biomarker discovery and other studies. Such data at the exon level are usually heavily tailed and correlated. Conventional statistical tests based on the mean or median difference for differential expression likely suffer from low power when the between-group difference occurs mostly in the upper or lower tail of the distribution of gene expression. We propose a tail-based test to make comparisons between groups in terms of a specific distribution area rather than a single location. The proposed test, which is derived from quantile regression, adjusts for covariates and accounts for within-sample dependence among the exons through a specified correlation structure. Through Monte Carlo simulation studies, we show that the proposed test is generally more powerful and robust in detecting differential expression than commonly used tests based on the mean or a single quantile. An application to TCGA lung adenocarcinoma data demonstrates the promise of the proposed method in terms of biomarker discovery.

Original languageEnglish (US)
Pages (from-to)261-276
Number of pages16
JournalStatistical Methods in Medical Research
Volume30
Issue number1
DOIs
StatePublished - Jan 2021
Externally publishedYes

Keywords

  • Correlated data
  • differential expression analysis
  • quantile regression
  • RNA sequencing
  • robust tail-based test

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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