Li, X.; Fan, Y.; Han, Y.; Guo, W.; Huang, J.; Yan, N.; Chen, Z.; Wu, Y.; Miao, Y.; Hou, L.; Zhang, X.; Chen, Z.; Gu, J.

Single-cell resolution spatial transcriptomics (ST) provides a great opportunity to explore the complex cellular contents in different tissues. In this study, we introduce SpatialZoomer, a spectral graph-based method that applies a set of low-pass filters to efficiently extract spatial molecular features from ST data at multiple resolutions or scales, including the single cell scale, the niche scale with dozens of closely interacting cells, and the domain scale with spatially-organized cell contents. The corresponding "critical" scales can be automatically identified by partitioning a cross-scale similarity map. Results show that SpatialZoomer can identify disease-progression signals at specific scales in Alzheimer's Disease, and spatial context dependent cell subtypes in tumor microenvironment. The extracted multi-scale features also uncovered spatially heterogeneous niches in cancer. SpatialZoomer also takes advantage of high computational efficiency and low hardware requirements.