Scalable discovery of spatial multicellular patterns via neighborhood-to-sequence transformation

Mining multi-cellular spatial patterns associated with biological events from high-resolution spatial omics data remains a fundamental challenge. While current computational methods have advanced from pairwise associations to identifying higher-order spatial domains, they often lack the granularity to resolve subtle local architectural shifts or the statistical framework to quantify condition-specificity. Here, we present FDPMining (Frequent and Distinctive spatial Patterns Mining), a computational framework that reformulates the biological problem of pattern discovery into a scalable data mining task through a Neighborhood-to-Sequence (N2S) encoding strategy. This transformation uniquely converts spatial grid neighborhoods for each cell into lossless and reversible numerical sequences, enabling efficient and scalable discovery of FDPs (Frequent and Distinctive spatial Patterns) via data mining algorithms. Our approach systematically explores the vast combinatorial space of cellular arrangements to identify FDPs associated with specific biological conditions. To enable spatial traceability, we further develop FDPs-Mapping, a spatial reconstruction component that maps identified patterns back to their original tissue context. This advancement allows researchers to examine and interpret patterns directly in situ. In extensive benchmarking, FDPMining demonstrates superior sensitivity in capturing subtle and condition-specific differences, outperforming state-of-the-art pairwise and higher-order pattern discovery methods. We applied our framework across diverse biological systems and spatial omics technologies, successfully identifying biologically meaningful spatial multicellular patterns in axolotl brain regeneration, brain aging, liver zonation, Alzheimer’s disease, and colorectal cancer. Notably, FDPMining enables landmark-anchored pattern discovery around specific anatomical or pathological features such as blood vessels or amyloid plaques, among which applications to Alzheimer’s disease revealed previously inaccessible insights into the multicellular organization of these microenvironments. FDPMining offers a paradigm for quantitatively dissecting spatial heterogeneity in complex tissues, enabling more systematic mining, visualization, and interpretation of cellular organization across diverse biological conditions. By converting local spatial neighborhoods into reversible sequences, FDPMining enables scalable discovery of condition-specific higher-order multicellular patterns and provides insights into tissue organization in spatial omics data