Bayesian Inference for Point Patterns in Space, applied to Multiplex Imaging data

3122 

Contributed Abstract 

Paper 

Sagnik Bhadury (1), Joel Eliason (1), Michele Peruzzi (1), Arvind Rao (1)

(1) University of Michigan, N/A

Joel Eliason  
University of Michigan

Michele Peruzzi  
University of Michigan

Arvind Rao  
University of Michigan

Sagnik Bhadury  
University of Michigan

Sagnik Bhadury  
University of Michigan

Multi-subject models of the tumor microenvironment (TME) typically rely on global features of imaged tissues and model TME dynamics ignoring specific local spatial changes in cell interactions during tumor development. We introduce a novel method of Bayesian Inference for Point Patterns in Space (BIPPS) for interpretable analyses of the complex spatial dynamics of the TME with multi-subject, multi-tissue multiplexed immunofluorescence (mIF) data. Unlike scope limited black-box methods requiring significant computational resources and posing interpretability issues, BIPPS can model the spatial dynamics of the TME using localized tissue features in an interpretable log-linear spatial factor model for multivariate counts. Each tissue image is expressed as a linear combination of a few subject-specific scalable Gaussian Processes. Covariates characterizing the TME can be integrated into the factor loadings, facilitating efficient estimation of spatial intensities across subjects. We demonstrate BIPPS on an internal cohort of mIF images from patients belonging to six different pancreatic diseases. An open source R package for implementing BIPPS will be available on github.

Bayesian|Spatial|Point Pattern|Multi-subject | Imaging|

Section on Statistics in Imaging

Imaging