  Scientific studies in the last two decades have established the central role
of the microbiome in disease and health. Differential abundance analysis seeks
to identify microbial taxa associated with sample groups defined by a factor
such as disease subtype, geographical region, or environmental condition. The
results, in turn, help clinical practitioners and researchers diagnose disease
and develop treatments more effectively. However, microbiome data analysis is
uniquely challenging due to high-dimensionality, sparsity, compositionally, and
collinearity. There is a critical need for unified statistical approaches for
differential analysis in the presence of covariates. We develop a zero-inflated
Bayesian nonparametric (ZIBNP) methodology that meets these multipronged
challenges. The proposed technique flexibly adapts to the unique data
characteristics, casts the high proportion of zeros in a censoring framework,
and mitigates high-dimensionality and collinearity by utilizing the
dimension-reducing property of the semiparametric Chinese restaurant process.
Additionally, the ZIBNP approach relates the microbiome sampling depths to
inferential precision while accommodating the compositional nature of
microbiome data. Through simulation studies and analyses of the CAnine
Microbiome during Parasitism (CAMP) and Global Gut microbiome datasets, we
demonstrate the accuracy of ZIBNP compared to established methods for
differential abundance analysis in the presence of covariates.
