{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T05:26:55Z","timestamp":1783574815791,"version":"3.55.0"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin","doi-asserted-by":"publisher","award":["23JCYBJC00790"],"award-info":[{"award-number":["23JCYBJC00790"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chinese State Scholarship Fund","award":["202108130108"],"award-info":[{"award-number":["202108130108"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Single-cell multi-omics clustering confronts noise and heterogeneity barriers. Current multi-view anchor graph approaches, though successful in noise reduction, inadequately model higher order feature interactions. To address this issue, we propose scAGCI, a cell clustering method based on anchor graphs that integrates both scRNA-seq and scATAC-seq data. Our method captures specific and shared anchor graphs representing the properties of omics data in the process of dynamic anchor unification, and mines high-order shared information to complete the omics representation. Subsequently, clustering results are obtained by integrating the specific and shared omics representation. Benchmarking against 13 state-of-the-art methods confirms scAGCI\u2019s superior clustering performance and computational efficiency in cell-type identification and subtype resolution. The method preserves biologically meaningful omics patterns, as evidenced by marker gene enrichment and functional analyses, establishing it as a robust tool for elucidating cellular heterogeneity in single-cell multi-omics data.<\/jats:p>","DOI":"10.1093\/bib\/bbaf244","type":"journal-article","created":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T07:29:22Z","timestamp":1751786962000},"source":"Crossref","is-referenced-by-count":5,"title":["scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0994-8394","authenticated-orcid":false,"given":"Yao","family":"Dong","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology , No. 5340, Xiping Road, Beichen District, Tianjin 300401 ,","place":["China"]},{"name":"Hebei Engineering Research Center of Data-Driven Industrial Intelligent , No. 5340, Xiping 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