Quantitative Characterization Of Cell Niches In Spatially Resolved Omics Data Sebastian Birk 1 by Sebastian Birk  1, 2, 3, 4, Irene Bonafonte-pardàs5, 6, Adib Miraki Feriz4, instant download after payment.

Spatial omics enable the characterization of colocalized cell communities that coordinate specifc functions within tissues. These communities, or niches, are shaped by interactions between neighboring cells, yet existing computational methods rarely leverage such interactions for their identifcation and characterization. To address this gap, here we introduce NicheCompass, a graph deep-learning method that models cellular communication to learn interpretable cell embeddings that encode signaling events, enabling the identifcation of niches and their underlying processes. Unlike existing methods, NicheCompass quantitatively characterizes niches based on communication pathways and consistently outperforms alternatives. We show its versatility by mapping tissue architecture during mouse embryonic development and delineating tumor niches in human cancers, including a spatial reference mapping application. Finally, we extend its capabilities to spatial multi-omics, demonstrate cross-technology integration with datasets from diferent sequencing platforms and construct a whole mouse brain spatial atlas comprising 8.4 million cells, highlighting NicheCompass’ scalability. Overall, NicheCompass provides a scalable framework for identifying and analyzing niches through signaling events.