Informed spatially aware patterns for multiplexed immunofluorescence data

Multiplexed immunofluorescence (mIF) imaging has revolutionized the study of cellular interactions within tissue microenvironments, enabling complex pattern analysis critical to understanding disease biology. However, current analytical methods assume uniform cellular patterns across tissues, overlooking the spatial heterogeneity that characterizes tumor microenvironments. Here, we introduce ISPat (Informed Spatially aware Patterns), a fully Bayesian framework that identifies both shared and region-specific interaction patterns while integrating domain knowledge to enhance spatial pattern estimation. ISPat models spatial cellular densities through kernel density estimation, then constructs interaction networks from precision matrices that capture conditional dependencies between cell types while controlling for confounding effects. The resulting networks reveal direct cellular relationships, with non-zero precision matrix entries indicating significant interactions. We applied ISPat to analyze 119 pancreatic ductal adenocarcinoma (PDAC) and 53 intraductal papillary mucinous neoplasm (IPMN) patients, partitioning tissues into five regions based on tumor intensity gradients. Our analysis revealed fundamentally distinct immune architectures: PDAC maintains a rigid, stable immunosuppressive microenvironment across tumor heterogeneity gradients, whereas IPMN exhibits dynamic spatial remodeling with marked regional variability. Critically, we identified multiple ligand-receptor (LR) interactions that consistently differ between disease conditions specifically in intermediate tumor intensity regions, while extreme conditions showed no significant differences. These include interactions spanning multiple functional axes of anti-tumor immunity: antigen presentation and T cell activation (APC↔CTL, THelper↔APC, Epithelial↔APC), effector function and tumor cell killing (Epithelial↔CTL, CTL↔Treg), and immune regulation (Epithelial↔Treg, Treg↔APC, THelper↔Treg). Notably, the APC↔CTL interaction, fundamental for adaptive immunity activation, differs significantly in high tumor density regions, alongside Epithelial↔CTL interactions critical for direct tumor elimination. These spatially resolved signatures provide quantitative evidence for distinct immune evasion mechanisms and represent promising biomarker candidates for disease classification and risk stratification. Through simulation studies, we demonstrated ISPat's accuracy in pattern recovery and its computational efficiency, achieving 8-10 fold speedup over comparable methods through variational Bayesian inference. The framework exhibits robust scalability and handles naturally occurring partition size imbalances, making it well-suited for analyzing heterogeneous tissues. Our findings demonstrate that spatial context fundamentally shapes cellular interactions in pancreatic cancer, with critical implications for understanding immune evasion mechanisms and developing spatially informed therapeutic strategies. The identification of differential interactions across antigen presentation, effector function, and immune regulation pathways suggests that therapeutic interventions must address multiple axes of immune dysfunction rather than single targets. ISPat provides a generalizable framework for spatial analysis applicable to emerging technologies, enabling precision oncology approaches guided by spatially resolved biomarkers.