Robust differential abundance test in compositional data
成果类型：
Article
署名作者：
Wang, Shulei
署名单位：
University of Illinois System; University of Illinois Urbana-Champaign
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/asac029
发表日期：
2023
页码：
169185
关键词：
摘要：
Differential abundance tests for compositional data are essential and fundamental in various biomedical applications, such as single-cell, bulk RNA-seq and microbiome data analysis. However, because of the compositional constraint and the prevalence of zero counts in the data, differential abundance analysis on compositional data remains a complicated and unsolved statistical problem. This article proposes a new differential abundance test, the robust differential abundance test, to address these challenges. Compared with existing methods, the robust differential abundance test is simple and computationally efficient, is robust to prevalent zero counts in compositional datasets, can take the data's compositional nature into account, and has a theoretical guarantee of controlling false discoveries in a general setting. Furthermore, in the presence of observed covariates, the robust differential abundance test can work with covariate-balancing techniques to remove potential confounding effects and draw reliable conclusions. The proposed test is applied to several numerical examples, and its merits are demonstrated using both simulated and real datasets.