POLYspace reveals domain-specific cellular neighborhoods through topology-aware spatial analysis

Thomas M. Menino Convention & Exhibition Center 

Identifying cellular neighborhoods is essential for understanding cell–cell interactions in a spatial context. However, existing approaches often overlook the complexity of multicellular interactions and the organization of spatial domains. We present POLYspace, a general and efficient framework for discovering and analyzing cellular neighborhoods of arbitrary topology while accounting for spatial domains. POLYspace formulates neighborhood identification as a subgraph searching problem and leverages C3G, a fast graph canonization algorithm we developed, to achieve scalability. Applied to one in-house dataset and three publicly available datasets spanning diverse platforms and tissues, POLYspace uncovers domain-specific cellular neighborhoods that are not captured by existing methods. These neighborhoods reveal key biological mechanisms and improve phenotype prediction.

Spatial gene profiling

Graph theory

Graph canonization

Statistical genomics

Subgraph mining

Cellular microenvironments 

Section on Statistics in Genomics and Genetics