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B0604
Title: Spatial point process models for multivariate microbiome image data Authors:  Kyu Ha Lee - Harvard T.H. Chan School of Public Health (United States) [presenting]
Brent Coull - Harvard University (United States)
Gary Borisy - The Forsyth Institute (United States)
Floyd Dewhirst - The Forsyth Institute (United States)
Jessica Mark Welch - Marine Biological Laboratory (United States)
Jacqueline Starr - Brigham and Womens Hospital (United States)
Abstract: The spatial distribution of microbes is investigated to understand the role of biofilms in human and environmental health. Advances in spectral imaging technologies enable us to display how different taxa (e.g. species or genera) are located relative to one another and host cells. However, most commonly used quantitative methods are limited to describing spatial patterns of bivariate data. Therefore, we propose a flexible multivariate spatial point process model that can quantify spatial relationships among the multiple taxa observable in biofilm images. We have developed an efficient computational scheme based on the Hamiltonian Monte Carlo algorithm, implemented in the R package. We applied the proposed model to tongue biofilm image data.