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  .
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. Finally , we apply the new test to several numerical examples using simulated and real datasets to demonstrate its practical merits .
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