A Counterfactual Framework for Directional Cell–Cell Interaction Analysis in Spatial Transcriptomics

Understanding how neighboring cells influence cellular states is central to spatial transcriptomics, yet most existing methods rely on correlation or predefined ligand–receptor (LR) pairs and do not explicitly test directionality. We introduce a counterfactual, intervention-based framework for quantifying directional dependence within a predictive model of spatial transcriptomics, enabling interpretability analysis without ligand–receptor priors.

A neighborhood-conditioned graph model predicts receiver cell state from local spatial context. Directional influence is quantified by counterfactually replacing neighbors of a candidate sender type and measuring the resulting displacement in predicted receiver state. We define a Counterfactual Directionality Score (CDS) that quantifies directional influence, and compute pair-level CDS by aggregating across receiver cells and test cores for each ordered sender–receiver pair.

Applied to Xenium cholangiocarcinoma tissue microarrays (38 cores), the framework identified reproducible, asymmetric interactions between tumor, immune, and stromal compartments, most prominently Tumor- EMT → Macrophage (CDS = 0.0828) and Fibroblast → Macrophage (CDS = 0.0582). Effects exceeded label-permutation and spatial-shuffle null models ( p < 0.001, FDR-controlled) and remained stable under core-level bootstrap resampling. Inferred directional strengths correlated strongly with matched LR scores ( r = 0.758, p = 0.0027), supporting biological concordance.

These results demonstrate counterfactual testing as a statistically rigorous and scalable approach for model-based directional dependence analysis in spatial transcriptomics.