Robust Differential Abundance Test in Compositional Data
时间：2022-02-21
阅读量：517次
- 演讲人： 王树磊
- 时间：2022年03月01日 周二上午9:30
- 地点：腾讯会议 569 229 249
- 主办单位：数据科学研究中心
摘要：Differential abundance tests in compositional data are essential and fundamental tasks in various biomedical applications, such as microbiome data analysis. However, despite the recent developments in these fields, differential abundance analysis in compositional data remains a complicated and unsolved statistical problem, because of the compositional constraint and prevalent zero counts in the dataset. This study introduces a new differential abundance test, the robust differential abundance (RDB) test, to address these challenges. Compared with existing methods, the RDB test (i) is simple and computationally efficient, (ii) is robust to prevalent zero counts in compositional datasets, (iii) can take the data's compositional nature into account, and (iv) has a theoretical guarantee of controlling false discoveries in a general setting. Furthermore, in the presence of observed covariates, the RDB test can work with the covariate balancing techniques to remove the potential confounding effects and lead to reliable conclusions. Finally, we apply the new test to several numerical examples using simulated and real datasets to demonstrate its practical merits.
个人简介：Dr. Wang an Assistant Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign. Previously, he was a postdoc researcher at the University of Pennsylvania. Dr. Wang received my Ph.D. in Statistics from the University of Wisconsin-Madison and my B.S. in Mathematics from Zhejiang University.