Statistical Methods for Network-based Integrative Analysis of Microbiome Data

Fred Hutchinson Cancer Center, Seattle WA

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Abstract

Project Summary The past decade has seen substantial progress in the discovery of microbiome biomarkers associated with hu- man health and diseases. However, despite the exciting prior work, we currently still lack an understanding of the mechanism by which the gut microbiome impacts human health. An outstanding challenge is how to integrate microbiome and other -omics data types generated in microbiome multi-omics profiling studies to elucidate mi- crobial functional pathways. Unfortunately, available statistical methods for integrative analysis do not adequately address the analytical challenges specific to microbiome data. Microbiome data are compositional, zero-inflated, high-dimensional, and highly structured where samples are related by ecologically defined distances and taxa are related by their phylogeny. We propose to use our expertise in network analysis and high-dimensional statistical inference to tackle these challenges unique to microbiome data analysis. Our overall objective is to develop rigor- ous statistical methods that yield reliable and powerful inferences relating microbial functional pathways with host health conditions. The proposed methodologies are motivated by our collaboration with the Study of Latinos and the Dog Aging Project, and include a novel inference procedure for joint analysis of microbial and metabolomic networks (Aim 1), a novel method for joint dimensionality reduction which incorporates prior biological knowledge about the relationships between samples and between variables (Aim 2), and a powerful framework for jointly associating microbiome and other -omics data types with health outcomes (Aim 3). We will develop efficient and easy-to-use software tools for the proposed methods (Aim 4). This work is innovative and significant, because it will provide systems biology insights into the role of the microbiome and has the potential to make a major impact on the identification of novel microbiome biomarkers. Successful completion of this proposal will generate impor- tant community resources, including new methodologies for integrative analysis and their user-friendly software tools. With these analytical tools, the longer-term goal of this project is to hasten the discovery of microbiome- based therapeutic targets and contribute to the development of microbiome-based precision therapies.