  Differential abundance tests in the compositional data are essential and
fundamental tasks in various biomedical applications, such as single-cell, bulk
RNA-seq, and microbiome data analysis. Despite the recent developments in the
fields, differential abundance analysis in the compositional data is still a
complicated and unsolved statistical problem because of the compositional
constraint and prevalent zero counts in the dataset. A new differential
abundance test is introduced in this paper to address these challenges,
referred to as the robust differential abundance (RDB) test. 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 to
control 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. To demonstrate its practical merits, we apply the new test to
several numerical examples using both simulated and real datasets.
