A flexible mixed-membership model for community and enterotype detection for microbiome data

Abstract

Understanding how the human gut microbiome affects host health is challenging due to the wide interindividual variability, sparsity, and high dimensionality of microbiome data. Mixed-membership models have been previously applied to these data to detect latent communities of bacterial taxa that are expected to co-occur. The most widely used mixed-membership model is latent Dirichlet allocation (LDA). However, LDA is limited by the rigidity of the Dirichlet distribution imposed on the community proportions, which hinders its ability to model dependencies and account for overdispersion. To address this limitation, a generalization of LDA is proposed that introduces greater flexibility into the covariance matrix by incorporating the flexible Dirichlet (FD), a specific identifiable mixture with Dirichlet components. In addition to identifying communities, the new model enables the detection of enterotypes, i.e., clusters of samples with similar microbe composition. For inferential purposes, a computationally efficient collapsed Gibbs sampler that exploits the conjugacy of the FD distribution with respect to the multinomial model is proposed. A simulation study demonstrates the model's ability to accurately recover true parameter values by minimizing appropriate compositional discrepancy measures between the true and estimated values. Additionally, the model correctly identifies the number of communities, as evidenced by perplexity scores. Moreover, an application to the COMBO dataset highlights its effectiveness in detecting biologically significant and coherent communities and enterotypes, revealing a broader range of correlations between community abundances. These results underscore the new model as a definite improvement over LDA.