ArticleBioinformatics (Oxford, England)2026
STAHD: a scalable and accurate method to detect spatial domains in high-resolution spatial transcriptomics data.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
motivationSpatial transcriptomics (ST) enables the study of spatial heterogeneity in tissues. However, current methods struggle with large-scale, high-resolution data, leading to reduced efficiency and accuracy in detecting spatial domains. A scalable, precise solution is urgently needed.
resultsWe present STAHD, a scalable and efficient framework for spatial domain detection in ST data. Combining a graph attention autoencoder with multilevel k-way graph partitioning, STAHD decomposes large graphs into compact subgraphs and generates low-dimensional embeddings. This improves computational efficiency and clustering accuracy. Benchmarks on human and mouse datasets show STAHD outperforms existing methods and accurately reveals spatially distinct tumor microenvironments and functional regions. AVAILABILITY AND IMPLEMENTATION: Source code and data are available at: https://github.com/Little-Eel/STAHD.
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