SHADE: A multilevel Bayesian framework for modeling directional spatial interactions in tissue microenvironments.

Area of Science:

• Computational Biology
• Bioinformatics
• Spatial Omics

Background:

• Understanding cell-cell spatial interactions in tissue microenvironments is crucial for immunology, oncology, and developmental biology.
• Current spatial analysis methods often lack interpretability and statistical power due to assumptions of symmetry and isolated patient data analysis.

Purpose of the Study:

• To introduce SHADE (Spatial Hierarchical Asymmetry via Directional Estimation), a novel Bayesian framework for modeling asymmetric spatial interactions.
• To enhance the biological interpretability and statistical power of spatial analysis by integrating data across multiple scales and cohorts.

Main Methods:

• Developed a multilevel Bayesian framework (SHADE) to model asymmetric and directional cell-cell associations.
• Utilized smooth spatial interaction curves (SICs) to quantify direction-specific interactions.
• Integrated data across tissue sections, patients, and cohorts for robust analysis.

Main Results:

• SHADE demonstrated superior accuracy, robustness, and interpretability compared to existing methods in simulation studies.
• Applied to colorectal cancer data, SHADE quantified directional spatial patterns while accounting for tissue architecture and patient heterogeneity.
• Identified significant patient-level variations in local microenvironmental structures within molecular subtypes.

Conclusions:

• SHADE provides a powerful new tool for analyzing complex spatial relationships in biological tissues.
• The framework reveals that local tissue microenvironments exhibit considerable patient-specific heterogeneity, even within defined molecular subtypes.
• This approach advances the understanding of spatial organization in diseases like cancer.