# Robust Statistical Methods for Longitudinal Microbiome Studies

> **NIH NIH R01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2024 · $427,368

## Abstract

PROJECT SUMMARY
Large-scale longitudinal microbiome studies are increasingly common as they allow investigators to study
temporal patterns of the microbiome, elucidating the forces that shape the microbiome and enabling the
development of microbiota-based interventions. Unfortunately, despite the potential of longitudinal
microbiome studies, few methods exist for analyzing these studies. Also, the few extant tailored methods are
limited, either failing to accommodate the characteristics of microbiome data or failing to properly
accommodate the longitudinal structure, leading to potentially spurious findings. This proposal aims to fill the
critical gaps in methodology by addressing four major areas. Specifically, we aim to develop a comprehensive
and coherent suite of statistical tools for (1) addressing batch effects in longitudinal microbiome data – a
pressing problem as studies are getting bigger or integrated; (2) improved identification of individual taxa
associated with crucial biomedical exposures or outcomes over time; (3) identifying microbiome interaction
network dynamics between taxa within longitudinal microbiome data; (4) visualizing the longitudinal
microbiome data. These approaches are all based on rigorous prior data emphasizing the importance of the
problems as well as the limitations or absence of existing strategies. Our work is motivated by and will directly
enable analyses within three important longitudinal microbiome profiling studies: few studies have the
combination of large cohorts of patients with numerous follow-ups and rich covariates. Consequently, our
methods have the potential to accelerate understanding of the roles of the microbiome in diseases and
therapeutics, especially in the motivating studies that include populations of infants with diabetes and
immunocompromised individuals (patients undergoing kidney or bone marrow transplant). Ultimately, the
deployment of our methods could guide the design and development of novel microbiota-based preventive
and therapeutic interventions.

## Key facts

- **NIH application ID:** 10943343
- **Project number:** 1R01GM155734-01
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Wodan Ling
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $427,368
- **Award type:** 1
- **Project period:** 2024-09-25 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10943343

## Citation

> US National Institutes of Health, RePORTER application 10943343, Robust Statistical Methods for Longitudinal Microbiome Studies (1R01GM155734-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10943343. Licensed CC0.

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