{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T10:38:16Z","timestamp":1769596696599,"version":"3.49.0"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172087"],"award-info":[{"award-number":["62172087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172129"],"award-info":[{"award-number":["62172129"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science Foundation for Distinguished Young Scholars of China","award":["62225109"],"award-info":[{"award-number":["62225109"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The rapid advancement of spatial transcriptomics (ST) sequencing technology has made it possible to capture gene expression with spatial coordinate information at the cellular level. Although many methods in ST data analysis can detect spatially variable genes (SVGs), these methods often fail to identify genes with explicit spatial expression patterns due to the lack of consideration for spatial domains. Considering spatial domains is crucial for identifying SVGs as it focuses the analysis of gene expression changes on biologically relevant regions, aiding in the more accurate identification of SVGs associated with specific cell types. Existing methods for identifying SVGs based on spatial domains predefine spot similarity before training, which prevents adaptive learning and limits generalizability across different tissues or samples. This limitation may also lead to inaccurate identification of specific genes at boundary regions. To address these issues, we present GAADE, an unsupervised neural network architecture based on graph-structured data representation learning. GAADE stacks encoder\/decoder layers and integrates a self-attention mechanism to reconstruct node attributes and graph structure, effectively capturing spatial domain structures of different sections. Consequently, we confine the identification of SVGs within spatial domains. By performing differential expression analysis on spots within the target spatial domain and their multi-order neighbors, GAADE detects genes with enriched expression patterns within defined domains. Comparative evaluations with five other popular methods on ST datasets across four different species, regions and tissues demonstrate that GAADE exhibits superior performance in detecting SVGs and capturing the extent of spatial gene expression variation.<\/jats:p>","DOI":"10.1093\/bib\/bbae669","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:26:29Z","timestamp":1734654389000},"source":"Crossref","is-referenced-by-count":2,"title":["GAADE: identification spatially variable genes based on adaptive graph attention network"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9807-8620","authenticated-orcid":false,"given":"Tianjiao","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , No. 26 Hexing Road, Xiangfang District, Harbin 150040,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6935-2905","authenticated-orcid":false,"given":"Hao","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , No. 26 Hexing Road, Xiangfang District, Harbin 150040,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3774-8660","authenticated-orcid":false,"given":"Zhenao","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , No. 26 Hexing Road, Xiangfang District, Harbin 150040,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6754-7109","authenticated-orcid":false,"given":"Zhongqian","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , No. 26 Hexing Road, Xiangfang District, Harbin 150040,","place":["China"]}]},{"given":"Xingjie","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , No. 26 Hexing Road, Xiangfang District, Harbin 150040,","place":["China"]}]},{"given":"Hongfei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , No. 26 Hexing Road, Xiangfang District, Harbin 150040,","place":["China"]}]},{"given":"Bo","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Harbin Medical University , No. 246 Xuefu Road, Nangang District, Harbin 150081,","place":["China"]}]},{"given":"Guohua","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , No. 26 Hexing Road, Xiangfang District, Harbin 150040,","place":["China"]},{"name":"Faculty of Computing, Harbin Institute of Technology , No. 92 West Da Zhi Street, Nangang District, Harbin 150001,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"2024122606161022000_ref1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae371","article-title":"scLEGA: An attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data","volume":"25","author":"Liu","year":"2024","journal-title":"Brief Bioinform"},{"key":"2024122606161022000_ref2","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1038\/s41576-022-00515-3","article-title":"The emerging landscape of spatial profiling technologies","volume":"23","author":"Moffitt","year":"2022","journal-title":"Nat Rev Genet"},{"key":"2024122606161022000_ref3","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1038\/s41592-022-01409-2","article-title":"Museum of spatial 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