# Bayesian multivariate 3D spatial modeling for microbiome image analysis

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2022 · $551,098

## Abstract

Bacteria play critical beneficial and harmful roles in human health. Living in biofilm communities, one
species may attack, protect, or provide nutrients for neighboring species. These interactions determine the
community's net effects. Clarifying community organization is needed to understand how biofilm affects health.
 To begin to meet this need, we developed an imaging technique, Combinatory Labeling and Spectral
Imaging Fluorescence in Situ Hybridization (CLASI-FISH), which displays how taxa's cells are located relative
to each other and to host cells. Yet biofilm's complex, three-dimensional (3D) architecture is poorly captured by
commonly used measures, such as intercellular distances or global biofilm volume for one or two taxa.
 Here, we propose to extend Log Gaussian Cox process models (LGCP) to describe and test hypotheses
about human biofilm architecture, a novel application. Computational burden limits existing LGCP models for
geostatistical data to datasets with thousands of observations. These methods cannot be applied to biofilm
image data typically containing millions of observations. In preliminary work on two-dimensional (2D) biofilm
images, we have successfully scaled up multivariate LGCPs for six taxa. Estimated pairwise cross-correlation
functions differ in univariate analyses, which ignore other taxa's locations, versus multivariate analyses, which
leverage taxa's joint spatial distribution. We propose statistical innovations to address challenges raised by, but
not unique to, 3D biofilm images. Comparing biofilm across sample groups defined experimentally or based on
exposure history requires integrating data across subjects' images that lack true spatial correspondence.
Further, 3D spatial analyses have not been applied to multivariate data with millions of observations.
 The goal of this proposal is therefore to build a Bayesian multivariate 3D LGCP that incorporates different
images—thereby allowing for non-spatial covariate factors—by applying a separate coordinate system to each
image. This proposal has three parts: (a) the development of novel multivariate 3D spatial analysis methods
(aims 1-3), (b) evaluation of a hypothesis regarding the spatial structure of human tongue microbiome (aim 4),
and (c) software development and dissemination, based on best practices (aim 5). The interdisciplinary team
has a deep skill set and experience developing Bayesian high-dimensional multivariate analysis methods.
 The core innovation proposed is to integrate non-spatial covariates with multivariate spatial data across 3D
images lacking a common coordinate system. Sample accessibility and prior biological knowledge make the
oral cavity the best starting point to develop a flexible modeling framework that will allow testing of hypotheses
regarding microbial interactions and associations with host characteristics. This is a fundamental shift for how
such images will be analyzed, potentially providing new insight into the role of ora...

## Key facts

- **NIH application ID:** 10401247
- **Project number:** 5R01GM126257-02
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** KYU HA LEE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $551,098
- **Award type:** 5
- **Project period:** 2021-05-04 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10401247

## Citation

> US National Institutes of Health, RePORTER application 10401247, Bayesian multivariate 3D spatial modeling for microbiome image analysis (5R01GM126257-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10401247. Licensed CC0.

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