Biology subjects

Bhadury, S.

Publications and source records attributed to Bhadury, S..

2 recordsLinked to original sources

bioRxiv2026

ISPAT-3D: Spatially Varying Conditional Volumetric Network Estimation for 3D Tumor Imaging

The spatial organization of the tumor microenvironment shapes immune function and disease progression, yet existing methods for cell-type interaction networks from multiplexed tissue images operate in two dimensions and ignore spatial auto-correlation. We introduce ISPat-3D (Informed Spatially Aware Patterns in 3D), a hierarchical Bayesian framework that recovers spatially varying, zone-specific interaction networks from 3D multiplexed cancer imaging data. The method partitions the tissue volume into tumor intensity zones, fits an anisotropic Gaussian process per cell type and zone with separate lengthscales for the tissue plane and axial direction, decomposes the residuals via multi-study factor analysis, and extracts partial correlation networks from the resulting precision matrices. Simulations demonstrate accurate recovery of shared and zone-specific structure with high power and controlled FDR. We apply ISPat-3D to two 3D datasets: the colorectal cancer atlas (CRC1) 3D CyCIF specimen and a HER2-positive ductal breast carcinoma (BC) specimen from a 3D IMC. In CRC1, zone-specific networks reveal a T cell module intensifying with tumor burden, with the dominant regulatory association shifting from CD4+{leftrightarrow}Treg at intermediate density to CD8+{leftrightarrow}Treg at maximal density, consistent with cytotoxic suppression at the tumor core. In BC, the shared network shows near-perfect conditional coupling between cancer-associated fibroblasts and the myoepithelial layer, while zone-specific networks reveal CAF{leftrightarrow}endothelial co-localisation at intermediate and high burden, consistent with angiogenic remodeling, and a B cell{leftrightarrow}CAF association confined to high-density zones, consistent with tertiary lymphoid structure formation. Across both tumors, ISPat-3D identifies volumetric spatial conditional interactions not recoverable from 2D sections.

bioinformatics↗

bioRxiv2026

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

Multiplexed tissue imaging platforms resolve dozens of cell types at single-cell spatial resolution, enabling characterization of conditional interaction networks governing tumor immune microenvironments. Existing methods rely on marginal pairwise co-occurrence statistics without conditioning on third cell types, or estimate a global interaction coefficient per cell type pair that ignores spatial heterogeneity across tissue compartments. We present GP-GHS, a Bayesian nodewise regression framework for inferring spatially varying cell-cell interaction networks from multi-plexed imaging data. Each regression coefficient is modeled as a Gaussian process over the tissue domain, approximated via a Hilbert Space Gaussian Process (HSGP) expansion for scalability. A group horseshoe prior assigns a single local shrinkage parameter across all spectral basis coefficients for each candidate edge, enforcing edge inclusion as a group decision rather than independent coefficient level decisions. This separation of roles, where group shrinkage governs edge existence and the spectral prior governs spatial smoothness conditional on existence, enables recovery of spatially structured graphs with high sensitivity. Posterior inference uses a closed form block Gibbs sampler with nodewise regressions parallelized across cores. In simulation studies, GP-GHS dominates all competitors on F1 and MCC across sparsity levels and problem sizes, with ablations isolating group shrinkage as the critical modeling ingredient. Applied to a 140 image CODEX dataset from advanced colorectal cancer patients stratified by pathology subtype, GP-GHS identifies 13 differentially active edges at FDR < 0.05, forming a Treg-centered immunosuppressive network amplified in the diffuse inflammatory subtype, consistent with known mechanisms of Treg recruitment and macrophage-mediated immunosuppression in colorectal cancer.

bioinformatics↗