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Spatially Varying Graphical Models For Cell-cell Interaction Networks In Multiplexed Tissue Imaging.

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Spatially Varying Graphical Models For Cell-cell Interaction Networks In Multiplexed Tissue Imaging.

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Spatially Varying Graphical Models for Cell-Cell Interaction Networks in Multiplexed Tissue Imaging.

Sagnik Bhadury¹, Jeremy T Gaskins², Arvind Rao^1,3,4

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Biorxiv : the Preprint Server for Biology

|April 10, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

GP-GHS, a new Bayesian framework, reveals spatially varying cell-cell interactions in tumor microenvironments. It identifies a Treg-centered immunosuppressive network in colorectal cancer, crucial for understanding immune responses.

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Area of Science:

Computational Biology
Immunology
Bioinformatics

Background:

Multiplexed imaging provides single-cell resolution of tumor immune microenvironments.
Current methods for analyzing cell interactions lack spatial context and conditional analysis.
Understanding cell-cell networks is vital for characterizing complex biological systems.

Purpose of the Study:

To develop a Bayesian framework (GP-GHS) for inferring spatially varying cell-cell interaction networks from multiplexed imaging data.
To accurately model spatial heterogeneity in cell interactions within tissue compartments.
To identify differential cell-cell interactions associated with specific disease subtypes.

Main Methods:

Utilized a Bayesian nodewise regression framework with Gaussian Processes (GP) over the tissue domain.

Employed a Hilbert Space Gaussian Process (HSGP) expansion for computational scalability.

Incorporated a group horseshoe prior for robust edge inference and spatial smoothness.

Implemented a block Gibbs sampler for efficient posterior inference.

Main Results:

GP-GHS significantly outperformed existing methods in simulation studies across various metrics (F1, MCC).
The group shrinkage prior was identified as a critical component for accurate network recovery.
Analysis of colorectal cancer data revealed 13 differentially active cell-cell interactions.
Identified a Treg-centered immunosuppressive network, particularly active in the diffuse inflammatory subtype.

Conclusions:

GP-GHS provides a powerful and scalable method for inferring spatially structured cell-cell interaction networks.
The identified Treg-centered network offers insights into colorectal cancer immunosuppression mechanisms.
This approach enhances the characterization of tumor immune microenvironments and disease subtypes.