 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.
<fluency>  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.
 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 of controlling false discoveries in a general setting.