Spatial omics maps cellular landscapes, yet current tools might conflate stochastic proximity with organized niches. We present ClusToRa (Cluster-to-Randomization), a framework that identifies high-density cellular territories and quantifies cell-type recruitment using a fixed-position null model. Benchmarked against graph-based neighborhood-enrichment and point-pattern statistics, ClusToRa reduced false-positive enrichment in simulations and resolved core-vs-boundary interactions. Applied to cirrhotic MASH liver, ClusToRa identifies stellate-cell territories with immune/endothelial infiltration and stress-, Notch-, and PPAR-associated programs, providing a niche-centric framework for distinguishing structural cellular infiltration from boundary adjacency or density-driven colocalization.
Githaka, J. M., Lerner, E. P.
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