{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T11:14:24Z","timestamp":1776424464861,"version":"3.51.2"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"vor","delay-in-days":2,"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":["62402145"],"award-info":[{"award-number":["62402145"]}],"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":["62272094"],"award-info":[{"award-number":["62272094"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The development of spatial transcriptomics (ST) technologies has enabled researchers to better understand cells\u2019 spatial organization and functional heterogeneity within their native tissue context. Spatial domain identification plays a crucial role in ST data analysis. However, most existing spatial domain identification methods do not fully exploit spatial information, and often fail to adequately integrate both local and global features, resulting in suboptimal spatial domain identification. We propose SLGCA, a novel method based on cross-level graph contrastive learning to address these challenges. SLGCA adopts a dual-channel learning mechanism, combining local-level contrastive learning based on spatial neighborhood information and global information contrastive learning across views, thereby significantly enhancing the accuracy of spatial domain identification. SLGCA can integrate multiple tissue sections without needing pre-alignment or external tools, eliminating batch effects and accurately identifying spatial domains across multiple slices. Experimental results show that SLGCA significantly outperforms the benchmark methods in spatial domain identification accuracy on ST data generated by multiple techniques. Moreover, SLGCA enables accurate dissection of tumor heterogeneity in human breast cancer datasets and effectively uncovers the heterogeneous tumor microenvironment in liver cancer, revealing two distinct fibroblast subtypes.<\/jats:p>","DOI":"10.1093\/bib\/bbaf574","type":"journal-article","created":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T11:57:10Z","timestamp":1760183830000},"source":"Crossref","is-referenced-by-count":2,"title":["SLGCA: spatial cross-level graph contrastive autoencoder for multislice spatial domain identification and microenvironment exploration"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1666-9466","authenticated-orcid":false,"given":"Xin","family":"Lu","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9634-8164","authenticated-orcid":false,"given":"Murong","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Life Science, Northeast Forestry University , Harbin 150040 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7381-2374","authenticated-orcid":false,"given":"Guohua","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040 ,","place":["China"]},{"name":"Department of Computer Science and Technology, Faculty of Computing, Harbin Institute of Technology , Harbin 150001 ,","place":["China"]}]},{"given":"Qiaoming","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Henan University , Zhengzhou 450000 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