Spatial transcriptomics stops picking one scale: a bank of graph low-pass filters reads cells, niches and tissue domains at once
Spatial transcriptomics analysis has long clustered at a single scale, so cells, neighboring cell groups and tissue regions were handled separately. SpatialZoomer applies a set of spectral graph low-pass filters to extract spatial molecular features at the single-cell, niche and domain scales from one dataset, and locates critical scales by partitioning a cross-scale similarity map. In Alzheimer's disease and tumor microenvironment data it identified disease-progression signals at specific scales and spatial context-dependent cell subtypes, matching or exceeding existing methods on single-scale tasks with lower compute and hardware demands.