Parsimonious cell co-localization scoring for spatial transcriptomics

Abstract Spatial transcriptomics (ST) preserves tissue architecture while profiling gene expression, motivating methods that quantify whether annotated labels (such as cell types) preferentially co-occur in local neighborhoods. We introduce the Neighborhood Product Co-localization (NPC) score, a simple per-cell metric computed on a pruned spatial neighbor graph: for a set of m ≥ 2 labels, NPC is the product of their neighborhood proportions, optionally normalized by expected co-occurrence under independence and paired with permutation-based significance testing. NPC is interpretable (maximized under balanced neighborhoods), efficient to compute, and extends naturally from pairwise to multivariate microenvironment definitions. Using a mouse ovary MERFISH dataset, we show that NPC complements established Squidpy co-occurrence and neighborhood enrichment analyses by localizing co-localization hotspots in tissue space, recapitulating prominent global associations, and highlighting spatially restricted niches such as follicle boundaries; we further demonstrate multivariate NPC scoring by identifying coordinated endothelial–stroma–theca co-localization. Overall, NPC provides a practical framework for interpretable, single-cell resolution co-localization analysis in ST cohorts.