23: Uncovering Hidden Spatial Structure Across 3D Biofilm Images

Thomas M. Menino Convention & Exhibition Center 

Microscopic images of microbial communities often reveal intricate spatial organization, where different species form structured spatial "neighborhoods" that are likely to reflect cooperation, competition, and shared function. A striking example is the biofilm community on the human tongue, where microbes arrange themselves in highly organized patterns at micrometer scales. Understanding these spatial relationships is key to understanding biological function—but statistically modeling them is challenging, especially when each sample gives rise to many biofilm images or slices.

Multivariate log-Gaussian Cox processes are flexible models for the analysis of multivariate point patterns. However, they have so far been focused on single realizations only (i.e., single images), ignoring similarity and dissimilarity across images. In this work, we develop a hierarchical Bayesian multivariate log-Gaussian Cox process framework that allows us to investigate both the common spatial interaction patterns shared across repeated images and how individual image slices differ from one another. By modeling all images jointly and borrowing information across slices, the approach improves the ability to detect meaningful interaction patterns while accounting for image-to-image variability.

Using simulation studies, we demonstrate accurate recovery of multitype spatial relationships and effective sharing of information across images. We also apply the framework to 3D Z-stack images of human tongue dorsum biofilms. By moving beyond post hoc slice-by-slice comparisons, the proposed approach enables integrated inference on shared spatial structure and image-specific variation in multivariate, multilevel biofilm imaging data.