{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T23:31:44Z","timestamp":1772148704663,"version":"3.50.1"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":8,"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":["62102294"],"award-info":[{"award-number":["62102294"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Single-cell multi-omics data integration is essential for understanding cellular states and disease mechanisms, yet integrating heterogeneous data modalities remains a challenge. We present scMGCL, a graph contrastive learning framework for robust integration of single-cell ATAC-seq and RNA-seq data. Our approach leverages self-supervised learning on cell\u2013cell similarity graphs, in which each modality\u2019s graph structure serves as an augmentation for the other. This cross-modality contrastive paradigm enables the learning of biologically meaningful, shared representations while preserving modality-specific features.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Benchmarking against state-of-the-art methods demonstrates that scMGCL outperforms others in cell-type clustering, label transfer accuracy, and preservation of marker-gene correlations. Additionally, scMGCL significantly improves computational efficiency, reducing runtime and memory usage. The method\u2019s effectiveness is further validated through extensive analyses of cell-type similarity and functional consistency, providing a powerful tool for multi-omics data exploration.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Code and datasets are released at https:\/\/github.com\/zlCreator\/scMGCL.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf392","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T07:34:49Z","timestamp":1751960089000},"source":"Crossref","is-referenced-by-count":2,"title":["scMGCL: accurate and efficient integration representation of single-cell multi-omics data"],"prefix":"10.1093","volume":"41","author":[{"given":"Zhenglong","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xi\u2019an, Shaanxi 710126,","place":["China"]}]},{"given":"Risheng","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xi\u2019an, Shaanxi 710126,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0314-9199","authenticated-orcid":false,"given":"Shixiong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xi\u2019an, Shaanxi 710126,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"2025073004363465700_btaf392-B1","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1038\/s41592-019-0654-x","article-title":"Orchestrating single-cell analysis with bioconductor","volume":"17","author":"Amezquita","year":"2020","journal-title":"Nature 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