Heterogeneity-driven adaptive scale graph learning for subcellular spatial transcriptomics

MotivationSpatial transcriptomics enables gene expression profiling within intact tissue sections, providing an important basis for analyzing tissue organization, cellular heterogeneity, and microenvironmental interactions. However, existing spatial structure identification methods often integrate spatial information using fixed neighborhoods or predefined smoothing scales, which limits their ability to adapt to region-specific structural heterogeneity. In homogeneous regions, broader spatial smoothing can help preserve continuous tissue structures, whereas in regions with complex boundaries or mixed cell populations, excessive smoothing may obscure local expression differences and fine-scale structural changes. Therefore, it is necessary to develop an adaptive graph learning framework that can adjust the range of spatial information integration according to tissue structural heterogeneity. ResultsIn this study, we propose HAST, a heterogeneity-driven adaptive-scale graph learning framework for spatial transcriptomics. HAST adaptively determines graph filtering scales according to spatial structural heterogeneity, enabling flexible information aggregation across different tissue regions. It further decomposes gene expression signals into low-frequency structural components and high-frequency residual components, thereby jointly modeling global spatial continuity and local expression variations. Experiments on high-resolution spatial transcriptomics datasets show that HAST improves spatial structure identification and cross-section generalization. Tumor-enriched cluster identification and neighborhood enrichment analysis further demonstrate its ability to characterize tumor-associated spatial regions and microenvironmental organization.