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

LinDA: linear models for differential abundance analysis of microbiome compositional data