{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T08:49:43Z","timestamp":1782204583316,"version":"3.54.5"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":47,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG083039"],"award-info":[{"award-number":["R01AG083039"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["RF1AG084178"],"award-info":[{"award-number":["RF1AG084178"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["RF1AG077820"],"award-info":[{"award-number":["RF1AG077820"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG080991"],"award-info":[{"award-number":["R01AG080991"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG080624"],"award-info":[{"award-number":["R01AG080624"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG076234"],"award-info":[{"award-number":["R01AG076234"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Australia National Health and Medical Research Council (NHMRC) Investigator Fellowship","award":["GNT2041439"],"award-info":[{"award-number":["GNT2041439"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of cellular heterogeneity, but its rich, complementary structure across cells and genes remains underexploited, especially in the presence of technical noise and sparsity. Effectively leveraging this multi-scale structure is essentially an information fusion problem that requires integrating heterogeneous graph-based views of cells and genes into robust low-dimensional representations. In this paper, we introduce GatorSC, a unified representation learning framework that models scRNA-seq data through multi-scale cell and gene graphs and fuses them with a mixture-of-experts architecture. GatorSC constructs a global cell\u2013cell graph, a global gene\u2013gene graph, and a local gene\u2013gene graph derived from neighborhood-specific subgraphs, and learns graph neural network embeddings that are adaptively fused by a gating network. To learn noise-robust and structure-preserving embeddings without labels, we couple graph reconstruction and graph contrastive learning in a unified self-supervised objective applied to both cell- and gene-level graphs. We evaluate GatorSC on 19 publicly available scRNA-seq datasets covering diverse tissues, species, and sequencing platforms. Experiments showed that GatorSC consistently outperforms state-of-the-art deep generative, graph-based, and contrastive methods for cell clustering, gene expression imputation, and cell-type annotation. The learned embeddings are used for accurate trajectory inference, recovery of canonical marker gene programs, and cell-type-specific pathway signatures in an Alzheimer\u2019s disease single-nucleus dataset. GatorSC provides a flexible foundation for comprehensive single-cell transcriptomic analysis and can be readily extended to multi-omic and spatial modalities.<\/jats:p>","DOI":"10.1093\/bib\/bbag330","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:48:00Z","timestamp":1780055280000},"source":"Crossref","is-referenced-by-count":0,"title":["GatorSC: multi-scale cell and gene graphs with mixture-of-experts fusion for single-cell transcriptomics"],"prefix":"10.1093","volume":"27","author":[{"given":"Yuxi","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Health Data Science, Indiana University School of Medicine , Indianapolis, 410 W 10th St, IN 46202 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Life Sciences, Northwest A&F University , No. 3 Taicheng Road, Yangling, Shaanxi 712100 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mufan","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of North Carolina at Chapel Hill , 232 S Columbia St, NC 27599 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Central Florida , 4000 Central Florida Blvd., FL 32816 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Flora D","family":"Salim","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales , High St, Kensington, NSW 2052 ,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong , Wollongong, NSW 2522 ,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianlong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of North Carolina at Chapel Hill , 232 S Columbia St, NC 27599 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imran","family":"Razzak","sequence":"additional","affiliation":[{"name":"Division of Biology and Life Science, Muhammad bin Zayed University of Artificial Intelligence , Building 1B, Masdar City 20302, Abu Dhabi ,","place":["UAE"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyi","family":"Li","sequence":"additional","affiliation":[{"name":"South Australian immunoGENomics Cancer Institute (SAiGENCI) , The University of Adelaide, AHMS Building, North Terrace, SA 5005 ,","place":["Australia"]},{"name":"College of Information Engineering, Northwest A&F University , Yangling, Shaanxi 712100 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Bian","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Data Science, Indiana University School of Medicine , Indianapolis, 410 W 10th St, IN 46202 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"2026062304162289600_ref1","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1038\/s41586-018-0590-4","article-title":"Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris: the Tabula Muris 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