{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T06:19:08Z","timestamp":1785392348438,"version":"3.55.0"},"reference-count":59,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:00:00Z","timestamp":1730505600000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in the analysis of scRNA-seq data to identify the groups of cells with similar expression profiles. However, cell clustering is technically challenging, as raw scRNA-seq data have various analytical issues, including high dimensionality and dropout values. Existing research has developed deep learning models, such as graph machine learning models and contrastive learning-based models, for cell clustering using scRNA-seq data and has summarized the unsupervised learning of cell clustering into a human-interpretable format. While advances in cell clustering have been profound, we are no closer to finding a simple yet effective framework for learning high-quality representations necessary for robust clustering. In this study, we propose scSimGCL, a novel framework based on the graph contrastive learning paradigm for self-supervised pretraining of graph neural networks. This framework facilitates the generation of high-quality representations crucial for cell clustering. Our scSimGCL incorporates cell-cell graph structure and contrastive learning to enhance the performance of cell clustering. Extensive experimental results on simulated and real scRNA-seq datasets suggest the superiority of the proposed scSimGCL. Moreover, clustering assignment analysis confirms the general applicability of scSimGCL, including state-of-the-art clustering algorithms. Further, ablation study and hyperparameter analysis suggest the efficacy of our network architecture with the robustness of decisions in the self-supervised learning setting. The proposed scSimGCL can serve as a robust framework for practitioners developing tools for cell clustering. The source code of scSimGCL is publicly available at https:\/\/github.com\/zhangzh1328\/scSimGCL.<\/jats:p>","DOI":"10.1093\/bib\/bbae558","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T07:18:43Z","timestamp":1729063123000},"source":"Crossref","is-referenced-by-count":19,"title":["Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis"],"prefix":"10.1093","volume":"25","author":[{"given":"Zhenhao","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Life Sciences, Northwest A&F University , Yangling, 712100 Shaanxi ,","place":["China"]},{"name":"College of Information Engineering, Northwest A&F University , Yangling, 712100 Shaanxi ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Medicine, University of Florida , Gainesville, FL 32610 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meichen","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Life Sciences, Northwest A&F University , Yangling, 712100 Shaanxi ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Life Sciences, Northwest A&F University , Yangling, 712100 Shaanxi ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Medicine, University of Florida , Gainesville, FL 32610 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Bian","sequence":"additional","affiliation":[{"name":"College of Medicine, University of Florida , Gainesville, FL 32610 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruolin","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Life Sciences, Northwest A&F University , Yangling, 712100 Shaanxi ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&F University , Yangling, 712100 Shaanxi ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"2024110201483085000_ref1","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.omtm.2018.07.003","article-title":"An introduction to the analysis of single-cell RNA-sequencing data","volume":"10","author":"AlJanahi","year":"2018","journal-title":"Mol Ther Methods Clin 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