Robust Differential Abundance Test in Compositional Data

Speaker: Shulei Wang (University of Illinois at Urbana-Champaign)

Date: March 1 Tues. 9:30am

Venue: Tencent Meeting Room: 569 229 249    

Abstract: 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.