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 inferring directional cell-cell influence that is LR-agnostic and tests sender specificity. 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 directional cell-cell communication analysis in spatial transcriptomics.