  Differential abundance analysis is at the core of statistical analysis of
microbiome data. The compositional nature of microbiome sequencing data makes
false positive control challenging. Here, we show that the compositional
effects can be addressed by a simple, yet highly flexible and scalable,
approach. The proposed method, LinDA, only requires fitting linear regression
models on the centered log-ratio transformed data, and correcting the bias due
to compositional effects. We show that LinDA enjoys asymptotic FDR control and
can be extended to mixed-effect models for correlated microbiome data. Using
simulations and real examples, we demonstrate the effectiveness of LinDA.
