BZINB model-based pathway analysis and module identification facilitates integration of microbiome and metabolome data.

ABSTRACT: Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZO

SUBMITTER: Lin B 

PROVIDER: S-EPMC9915478 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature