A0629
Title: Bayesian analysis of spatial point patterns in multivariate microbiome imaging data
Authors: Kyu Ha Lee - Harvard T.H. Chan School of Public Health (United States) [presenting]
Abstract: Advances in cellular imaging technologies, especially those based on fluorescence in situ hybridization, now allow detailed visualization of the spatial organization of human or bacterial cells. Quantifying this spatial organization is crucial for understanding the function of multicellular tissues or biofilms, with implications for human health and disease. To address the need for better methods to achieve such quantification, flexible multivariate point process models are proposed that characterizes and estimates complex spatial interactions among multiple cell types. The proposed Bayesian framework is appealing due to its unified estimation process and the ability to directly quantify uncertainty in key estimates of interest, such as those of inter-type correlation and the proportion of variance due to inter-type relationships. To ensure stable and interpretable estimation, we consider shrinkage priors for coefficients associated with latent processes. Model selection and comparison are conducted by using a deviance information criterion designed for models with latent variables, effectively balancing the risk of overfitting with that of oversimplifying key quantities. Furthermore, a hierarchical modeling approach is developed to integrate multiple image-specific estimates from a given subject, allowing inference at both the global and subject-specific levels.
