SHADE: A Multilevel Bayesian Approach to Modeling Directional Spatial Associations in Tissues.

ABSTRACT:

Motivation

Spatial dependencies in tissue microenvironments, particularly asymmetric interactions between cell types, are central to understanding immune dynamics, tumor behavior, and tissue organization. Existing spatial statistical methods often assume symmetric associations or analyze images independently, limiting biological interpretability and inference quality.

Results

We introduce SHADE (Spatial Hierarchical Asymmetry via Directional Estimation), a Bayesian hierarchical framework that models asymmetric spatial associations and multilevel structure in multiplexed imaging data. SHADE captures directional relationships via smooth spatial interaction curves (SICs), provides interpretable distance-resolved summaries of cell-cell interactions, and supports multiscale infer

SUBMITTER: Eliason J 

PROVIDER: S-EPMC12262264 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature