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The human microbiome plays critical roles in human health and has been linkedto many diseases. While advanced sequencing technologies can characterize thecomposition of the microbiome in unprecedented detail, it remains challengingto disentangle the complex interplay between human microbiome and disease riskfactors due to the complicated nature of microbiome data. Excessive numbers ofzero values, high dimensionality, the hierarchical phylogenetic tree andcompositional structure are compounded and consequently make existing methodsinadequate to appropriately address these issues. We propose a multivariatetwo-part zero-inflated logistic normal (MZILN) model to analyze the associationof disease risk factors with individual microbial taxa and overall microbialcommunity composition. This approach can naturally handle excessive numbers ofzeros and the compositional data structure with the discrete part and thelogistic-normal part of the model. For parameter estimation, an estimatingequations approach is employed that enables us to address the complexinter-taxa correlation structure induced by the hierarchical phylogenetic treestructure and the compositional data structure. This model is able toincorporate standard regularization approaches to deal with highdimensionality. Simulation shows that our model outperforms existing methods.Our approach is also compared to others using the analysis of real microbiomedata.

Conditional regression based on a multivariate zero-inflated logistic normal model for microbiome relative abundance data