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

Area of Science:

• Computational Biology
• Biostatistics
• Pathology

Background:

• Spatial dependencies in tissue microenvironments are crucial for 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.

Purpose of the Study:

• To introduce SHADE (Spatial Hierarchical Asymmetry via Directional Estimation), a Bayesian hierarchical framework for modeling asymmetric spatial associations and multilevel structure in multiplexed imaging data.
• To capture directional relationships and provide interpretable, distance-resolved summaries of cell-cell interactions.
• To support multiscale inference across different biological scales.

Main Methods:

• Developed SHADE, a Bayesian hierarchical framework.
• Utilized smooth spatial interaction curves (SICs) to capture directional relationships.
• Applied the framework to multiplexed imaging data, including colorectal cancer imaging data.

Main Results:

• SHADE demonstrates improved inference quality and robustness in simulation studies.
• Application to colorectal cancer data revealed biologically meaningful differences in immune and stromal organization.
• The method effectively models asymmetric cell-cell interactions and multilevel structures.

Conclusions:

• SHADE offers a powerful new approach for analyzing spatial asymmetry in biological systems.
• The framework enhances biological interpretability and inference quality in multiplexed imaging.
• Freely available code facilitates broader adoption and application in research.