# Statistical Methods for Network-based Integrative Analysis of Microbiome Data

> **NIH NIH R01** · FRED HUTCHINSON CANCER CENTER · 2022 · $409,904

## 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.

## Key facts

- **NIH application ID:** 10423788
- **Project number:** 1R01GM145772-01
- **Recipient organization:** FRED HUTCHINSON CANCER CENTER
- **Principal Investigator:** Jing Ma
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $409,904
- **Award type:** 1
- **Project period:** 2022-09-20 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10423788

## Citation

> US National Institutes of Health, RePORTER application 10423788, Statistical Methods for Network-based Integrative Analysis of Microbiome Data (1R01GM145772-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10423788. Licensed CC0.

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