A multi-scale graph frequency network for structural and functional region analysis in spatial transcriptomics
E evaluation across diverse biological systems shows that SGFN achieves improved or competitive performance relative to representative baseline methods in reference-based benchmarks, and identifies biologically coherent spatial or functional regions in unlabeled datasets supported by marker-gene, spatial-autocorrelation, cell-type-colocalization, and pathway-enrichment evidence.