# Novel Statistical Methods for Analyzing Complex Microbiome Data

> **NIH NIH R01** · EMORY UNIVERSITY · 2024 · $304,484

## Abstract

Project Summary/Abstract
It is imperative to elucidate the roles that different microbes play in human health and diseases. However,
microbiome data from (either 16S rRNA gene or shotgun metagenomic) sequencing studies have unique and
complex features, including high-dimensionality, sparsity, overdispersion, compositionality, and experimental
bias. Existing statistical methods for hypothesis testing often fail to account for these features in full and thus
tend to yield false-positive results. The goal of this application is to develop robust and flexible statistical
methods that perform well in the presence of all data complexities, allow testing of various hypotheses (e.g.,
differential abundance, dynamic changes, mediation effects), and accommodate a wide range of datasets (e.g.,
continuous or discrete traits of interest, longitudinal data). To these ends, we propose the following specific
aims: (Aim 1) to develop a new framework for compositional analysis of differential abundance; (Aim 2) to
develop methods for controlling Monte-Carlo error rate in resampling-based multiple-hypotheses testing; (Aim
3) to develop methods for analyzing longitudinal data; (Aim 4) to develop a new framework for mediation
analysis of the microbiome; and (Aim 5) to develop and support a user-friendly software program implementing
the methods developed in Aims 1-4. We will evaluate these methods using extensive simulation studies and
multiple datasets from real microbiome studies at Emory University that we are actively involved in.

## Key facts

- **NIH application ID:** 10813127
- **Project number:** 5R01GM141074-04
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Chang Su
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $304,484
- **Award type:** 5
- **Project period:** 2021-06-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10813127

## Citation

> US National Institutes of Health, RePORTER application 10813127, Novel Statistical Methods for Analyzing Complex Microbiome Data (5R01GM141074-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10813127. Licensed CC0.

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