  It is largely taken for granted that differential abundance analysis is, by
default, the best first step when analyzing genomic data. We argue that this is
not necessarily the case. In this article, we identify key limitations that are
intrinsic to differential abundance analysis: it is (a) dependent on
unverifiable assumptions, (b) an unreliable construct, and (c) overly
reductionist. We formulate an alternative framework called ratio-based
biomarker analysis which does not suffer from the identified limitations.
Moreover, ratio-based biomarkers are highly flexible. Beyond replacing DAA,
they can also be used for many other bespoke analyses, including dimension
reduction and multi-omics data integration.
