Multivariate Spatial LGCP Modeling using INLA-SPDE, with Application to Microbiome Image Data

Oregon Convention Center 

Human microbiome data exhibit complex spatial structures. Understanding the spatial dependence structures can often enhance inference about microbes' functions. In this work, we propose a novel parsimonious multivariate spatial log Gaussian Cox process (LGCP) model using the concept of the linear model of regionalization, which can explicitly capture within-species and cross-species dependence structures and interactions. The model is inherently latent Gaussian, thus we adopt the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE) method to efficiently speed up the computation using an approximate Bayesian approach. We apply the model to study human oral microbiome biofilm image data from samples of multiple patients obtained using spectral imaging fluorescence in situ hybridization (FISH), where the spatial information of how taxa's cells are located relative to each other and to host cells are preserved.

Multivariate Spatial LGCP

INLA-SPDE

Microbiome Image Data 

Biometrics Section