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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Source code and data are available at: https:\/\/github.com\/Little-Eel\/STAHD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf619","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T12:29:45Z","timestamp":1762777785000},"source":"Crossref","is-referenced-by-count":0,"title":["STAHD: a scalable and accurate method to detect spatial domains in high-resolution spatial transcriptomics data"],"prefix":"10.1093","volume":"42","author":[{"given":"Zhihua","family":"Du","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, ShenZhen 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