# DMS/NIGMS 2: Bayesian Statistical Methods for Comprehensive Inferences on Microbial Community Dynamics Using High-Throughput Sequencing Data

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA SANTA CRUZ · 2024 · $133,246

## Abstract

We propose to develop flexible Bayesian statistical methods to gain a comprehensive understanding of
microbial community dynamics using high-throughput sequencing data. The emergence of large-scale
microbiome studies provides new opportunities for understanding how various microbial communities
function and relate to their environment. However, the analytical methodology required to model complex
microbiome data is still lacking. One of the key objectives is to develop a general method for inferring
microbial community dynamics that vary with host and environmental factors. We also aim to extend this
method to complex scenarios, such as longitudinal microbiome studies, which investigate the evolution of
microbial communities, and multi-omics microbiome studies that integrate various types of omics data. Our
proposed methods rigorously address the unique challenges of microbiome data analysis and achieve
more accurate inferences about the underlying biological processes with honest uncertainty quantification.
The proposed methods will provide an opportunity to attain a deeper understanding of the microbiome’s
role, potentially paving the way for intervention strategies that enhance health and disease management.
The proposed research involves synthesizing innovative concepts to tackle statistical challenges in
microbiome data analysis within complex study settings, with a particular focus on multivariate count data
presenting unique statistical complexities. The research agenda is broad and widely applicable, consisting
of methodological development and theoretical examination of model properties, along with a challenging
computational component aimed at achieving computational feasibility for big data. Our semiparametric
methods offer significantly improved accuracy compared to existing methods. Our innovative approach to
imposing a joint sparsity structure on the covariance matrix enhances the ability to infer microbial
interactions. This approach improves robustness against large signals and reduces noise in complex high-
dimensional data.
These models are developed in close collaboration with biologists at UC Los Angeles and UC Santa Cruz,
incorporating domain-specific biological knowledge from microbiome research, and consequently, they
yield biologically interpretable inferences. Our findings, integrated into microbiome research through
collaboration, will advance our understanding of how microbes are functionally related to the host, the
environment, and other microbes. This understanding can ultimately lead to improvements in human health
or the environment through microbiome monitoring or manipulation. Another key aspect of the project
involves disseminating the proposed methods through user-friendly software for public use.

## Key facts

- **NIH application ID:** 11043535
- **Project number:** 1R01GM157612-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA SANTA CRUZ
- **Principal Investigator:** Ju Hee Lee
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $133,246
- **Award type:** 1
- **Project period:** 2024-09-20 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11043535

## Citation

> US National Institutes of Health, RePORTER application 11043535, DMS/NIGMS 2: Bayesian Statistical Methods for Comprehensive Inferences on Microbial Community Dynamics Using High-Throughput Sequencing Data (1R01GM157612-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/11043535. Licensed CC0.

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