Latent Interacting Variable-Effects Modeling of Gut Microbiome Multi-Omics in Inflammatory Bowel Disease

ABSTRACT Latent Interacting Variable Effects (LIVE) modeling is a framework to integrate different types of microbiome multi-omics data by combining latent variables from single-omic models into a structured meta-model to determine discriminative, interacting multi-omics features driving disease status. We implemented and tested LIVE modeling in publicly available metagenomics and metabolomics datasets from Crohn’s Disease and Ulcerative Colitis patients. Here, LIVE modeling reduced the number of feature correlations from the original data set for CD and UC to tractable numbers and facilitated prioritization of biological associations between microbes, metabolites, enzymes and IBD status through the application of stringent thresholds on generated inferential statistics. We determined LIVE modeling confirmed previously reported IBD biomarkers and uncovered potentially novel disease mechanisms in IBD. LIVE modeling makes a distinct and complementary contribution to the current methods to integrate microbiome data to predict IBD status because of its flexibility to adapt to different types of microbiome multi-omics data, scalability for large and small cohort studies via reliance on latent variables and dimensionality reduction, and the intuitive interpretability of the linear meta-model integrating -omic data types. The results of LIVE modeling and the biological relationships can be represented in networks that connect local correlation structure of single omic data types with global community and omic structure in the latent variable VIP scores. This model arises as novel tool that allows researchers to be more selective about omic feature interaction without disrupting the structural correlation framework provided by sPLS-DA interaction effects modeling. It will lead to form testable hypothesis by identifying potential and unique interactions between metabolome and microbiome that must be considered for future studies. AUTHOR SUMMARYLatent Interacting Variable Effects (LIVE) modeling integrates microbiome multiomics features by encoding them in a set of latent variables (LVs) from single-omic sparse Partial Lease Squares models, and then combine these LVs into structured metamodel to determine the most discriminative features driving IBD. We used publicly available metagenomic and metabolomics data from Crohn’s Disease and Ulcerative Colitis patients to develop LIVE modeling. LIVE modeling reduced data dimensionality efficiently and identified statistical interactions among microbiome multi-omics data, which can be visualized as a mineable network data structure. LIVE modeling confirmed features previously reported and revealed novel microbiome interactions in IBD. LIVE offers a flexible framework for multi-omic modeling that may aid in interpretation of complex microbiome datasets.