SpatialZoomer: multi-scale feature analysis of spatial transcriptomics.

Abstract

Single-cell resolution spatial transcriptomics provides a great opportunity to explore the complex cellular contents in different tissues. Here we introduce SpatialZoomer, a spectral graph-based method that applies a set of low-pass filters to efficiently extract spatial molecular features from spatial transcriptomics 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 tumour microenvironment. The extracted multi-scale features also uncovered spatially heterogeneous niches in cancer. SpatialZoomer achieves comparable or superior performance to existing methods for single-scale tasks, and it also has the advantages of high computational efficiency and low hardware requirements.