  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, because of the compositional
constraint and the prevalence of zero counts in the data, differential
abundance analysis in compositional data remains a complicated and unsolved
statistical problem. 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 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 RDB test can work with the
covariate balancing techniques to remove the potential confounding effects and
draw reliable conclusions. Finally, we apply the new test to several numerical
examples using simulated and real datasets to demonstrate its practical merits.
