B0414
Title: Multivariate cluster point process model
Authors: Suman Majumder - University of Missouri (United States) [presenting]
Abstract: A common challenge in spatial statistics is to quantify the spatial distributions of clusters of objects. Frequently used approaches treat the central object of each cluster as latent, but often cells of one or more types cluster around cells of another type. Quantifying these spatial relationships in biofilms may provide clues to disease pathogenesis. Even when clustering arrangements are not strictly parent-offspring relationships, treating the central object as a parent can enable the use of parent-offspring clustering frameworks. A novel multivariate spatial point process model is proposed to quantify multi-cellular arrangements with parent-offspring statistical approaches. The proposed model is used to analyze data from a human dental plaque biofilm image containing spatial locations of Streptococcus, Porphyromonas, Corynebacterium, and Pasteurellaceae, among other species and investigate any possible relationships between them. The proposed multivariate cluster point process (MCPP) model departs from commonly used approaches in that it exploits the locations of the central parent object in clusters. It also accounts for possibly multilayered, multivariate parent-offspring clustering. In simulated datasets, the MCPP outperforms the classical Neyman-Scott process model. Applied to the motivating biofilm data, the simultaneous clustering of Streptococcus and Porphyromonas around Corynebacterium and of Pasteurellaceae are quantified around Streptococcus.
