Webinar: Robust Differential Abundance Test in Compositional Data (20211020)


This seminar marks the launch of the new CoDa-Association Seasonal Seminar series initiative:

To get access to the slides of the talk, please, login and go to menu Members area -> Video Recording & slides

  • Day: 20-10-2021
  • Time: 17:30h (CEST: UTC/GMT +2 h)
  • Link: --closed--
  • Speaker: Shulei Wang (Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, U.S.A.)
  •  Moderator: Gianna Monti
  • Title: Robust Differential Abundance Test in Compositional Data
  • Abstract:  Differential abundance tests in compositional data are essential and fundamental tasks in various biomedical applications, such as single-cell, bulk RNA-seq, and 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 1) is simple and computationally efficient, 2) is robust to prevalent zero counts in compositional datasets, 3) can take the data’s compositional nature into account, and 4) 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 draw reliable conclusions.
  • Keywords:  Compositional Data, Differential Abundance Test, Multiple Testing, Covariate Balancing