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Activity Number: 652 - Genomics, Metabolomics, Microbiome and NextGen Sequencing
Type: Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #305352
Title: Multivariate Spatial Point Process Models for the Analysis of Spectral Imaging Data
Author(s): Kyu Ha Lee* and Brent A. Coull and Jacqueline R Starr
Companies: Harvard T.H. Chan School of Public Health and Harvard T. H. Chan School of Public Health and The Forsyth Institute
Keywords: biofilms; image analysis; Markov chain Monte Carlo; multivariate analysis; spatial point processes

Clarifying the community organization, as reflected in the spatial distribution of microbes, is required to understand the role of biofilm in human and environmental health. Continuing advance in spectral imaging technologies has facilitated the collection of data displaying how multiple taxa are located relative to each other. However, widely used quantitative methods are limited to describing spatial patterns of one or two taxa at a time and thus are not able to capture complex architecture of biofilm. In this paper, we propose a multivariate spatial point process model that can quantify spatial relationships among multiple taxa in biofilm image. An efficient Markov chain Monte Carlo algorithm is developed to implement the methodology. We have successfully applied the proposed model to data from banked images of tongue biofilm.

Authors who are presenting talks have a * after their name.

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