Assessment of Bayesian Hierarchical Covariance Structures for Modeling Spatial Protein Imaging Data

Music City Center 

Examining the tumor immune microenvironment (TIME) has been revolutionized by advancements in spatial proteomic imaging techniques. These techniques assess multiple markers simultaneously to differentiate different immune cell populations in the TIME. The analysis of these immune profiles has become increasingly significant with the progress of immunotherapy treatments. The over-dispersed nature of the cell count data is accounted for by modeling the count data using a beta-binomial distribution. To account for the correlation between the different cell populations in the TIME (i.e., T cells and Cytotoxic T cells), we developed a Bayesian hierarchical beta-binomial model. The Bayesian model can incorporate different covariance (or relationship) structures between the different immune cell populations to incorporate immune differentiation paths. To illustrate the Bayesian model and different covariance structures that are possible, the model is applied to spatial proteomic data from three large epidemiologic cohorts (N = 486) looking at the TIME of ovarian cancer.

Bayesian

beta-binomial model

covariance structures

hierarchical

spatial protein imaging data

tumor immune microenvironment 

Biometrics Section