{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:08:31Z","timestamp":1777547311495,"version":"3.51.4"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,12,21]],"date-time":"2024-12-21T00:00:00Z","timestamp":1734739200000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472239"],"award-info":[{"award-number":["62472239"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)\u2013based methods have been proposed to annotate cell types of scRNA-seq data and have achieved impressive results. However, there are several limitations to these methods. First, they do not fully exploit cell-to-cell differential features. Second, they are developed based on shallow features and lack of flexibility in integrating high-order features in the data. Finally, the low-dimensional gene features may lead to overfitting in neural networks. To overcome those limitations, we propose a novel DL-based model, cell type annotation of single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning (scRGCL), based on residual graph convolutional neural network and contrastive learning for cell type annotation of single-cell RNA-seq data. scRGCL mainly consists of a residual graph convolutional neural network, contrastive learning, and weight freezing. A residual graph convolutional neural network is utilized to extract complex high-order features from data. Contrastive learning can help the model learn meaningful cell-to-cell differential features. Weight freezing can avoid overfitting and help the model discover the impact of specific gene expression on cell type annotation. To verify the effectiveness of scRGCL, we compared its performance with six methods (three shallow learning algorithms and three state-of-the-art DL-based methods) on eight single-cell benchmark datasets from two species (seven in human and one in mouse). Experimental results not only show that scRGCL outperforms competing methods but also demonstrate the generalizability of scRGCL for cell type annotation. scRGCL is available at https:\/\/github.com\/nathanyl\/scRGCL.<\/jats:p>","DOI":"10.1093\/bib\/bbae662","type":"journal-article","created":{"date-parts":[[2024,12,21]],"date-time":"2024-12-21T22:51:36Z","timestamp":1734821496000},"source":"Crossref","is-referenced-by-count":43,"title":["scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-8191","authenticated-orcid":false,"given":"Lin","family":"Yuan","sequence":"first","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences) , 3501 Daxue Road, 250353, Shandong ,","place":["China"]},{"name":"Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences) , 3501 Daxue Road, 250353, Shandong ,","place":["China"]},{"name":"Shandong Provincial Key Laboratory of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science , 3501 Daxue Road, 250353, Shandong ,","place":["China"]}]},{"given":"Shengguo","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences) , 3501 Daxue Road, 250353, Shandong ,","place":["China"]},{"name":"Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences) , 3501 Daxue Road, 250353, Shandong ,","place":["China"]},{"name":"Shandong Provincial Key Laboratory of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science , 3501 Daxue Road, 250353, Shandong ,","place":["China"]}]},{"given":"Yufeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences) , 3501 Daxue Road, 250353, Shandong ,","place":["China"]},{"name":"Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences) , 3501 Daxue Road, 250353, Shandong ,","place":["China"]},{"name":"Shandong Provincial Key Laboratory of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science , 3501 Daxue Road, 250353, Shandong ,","place":["China"]}]},{"given":"Qinhu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ningbo Institute of Digital Twin, Eastern Institute of Technology , 568 Tongxin Road, 315201, Zhejiang ,","place":["China"]}]},{"given":"Lan","family":"Ye","sequence":"additional","affiliation":[{"name":"Cancer Center, The Second Hospital of Shandong University , 247 Beiyuan Street, 250033, Shandong ,","place":["China"]}]},{"given":"Chun-Hou","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University , 111 Jiulong Road, 230601, Anhui ,","place":["China"]}]},{"given":"De-Shuang","family":"Huang","sequence":"additional","affiliation":[{"name":"Ningbo Institute of Digital Twin, Eastern Institute of Technology , 568 Tongxin Road, 315201, Zhejiang ,","place":["China"]},{"name":"Institute for Regenerative Medicine, 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recognition","author":"He","year":"2016"},{"key":"2024122307353734500_ref31","first-page":"148","article-title":"ResGraphNet: GraphSAGE with embedded residual module for prediction of global monthly mean temperature","volume":"3","author":"Chen","year":"2022","journal-title":"Artif Intell Geosci"},{"key":"2024122307353734500_ref32","first-page":"18661","article-title":"Supervised contrastive learning","volume":"33","author":"Khosla","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024122307353734500_ref33","doi-asserted-by":"crossref","first-page":"eabf1970","DOI":"10.1126\/science.abf1970","article-title":"Single-cell RNA-seq reveals cell type\u2013specific molecular and genetic associations to lupus","volume":"376","author":"Perez","year":"2022","journal-title":"Science"},{"key":"2024122307353734500_ref34","doi-asserted-by":"crossref","first-page":"eabf3041","DOI":"10.1126\/science.abf3041","article-title":"Single-cell eQTL mapping identifies cell type\u2013specific genetic control of autoimmune disease","volume":"376","author":"Yazar","year":"2022","journal-title":"Science"},{"key":"2024122307353734500_ref35","volume-title":"Weight Freezing: A Regularization Approach for Fully Connected Layers with an Application in EEG Classification","author":"Miao"},{"key":"2024122307353734500_ref36","first-page":"10890","article-title":"R-drop: regularized dropout for neural networks","volume":"34","author":"Wu","year":"2021","journal-title":"Adv Neural Inf Proces Syst"},{"key":"2024122307353734500_ref37","volume-title":"Adam: A method for stochastic optimization","author":"Kingma"},{"key":"2024122307353734500_ref38","volume-title":"Sgdr: Stochastic gradient descent with warm restarts","author":"Loshchilov"},{"key":"2024122307353734500_ref39","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip Rev Comput Stat"},{"key":"2024122307353734500_ref40","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: a search space odyssey","volume":"28","author":"Greff","year":"2016","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2024122307353734500_ref41","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.neucom.2013.09.055","article-title":"Autoencoder for words","volume":"139","author":"Liou","year":"2014","journal-title":"Neurocomputing"},{"key":"2024122307353734500_ref42","first-page":"2024.2006. 2018.599579","article-title":"Comparative analysis of commercial single-cell RNA sequencing technologies","author":"De Simone","year":"2024","journal-title":"bioRxiv"},{"issue":"385\u2013394","key":"2024122307353734500_ref43","first-page":"e383","article-title":"A single-cell transcriptome atlas of the human pancreas","volume":"3","author":"Muraro","year":"2016","journal-title":"Cell Syst"},{"key":"2024122307353734500_ref44","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/j.cmet.2016.08.020","article-title":"Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes","volume":"24","author":"Segerstolpe","year":"2016","journal-title":"Cell Metab"},{"key":"2024122307353734500_ref45","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1038\/s41586-020-2157-4","article-title":"Construction of a human cell landscape at single-cell level","volume":"581","author":"Han","year":"2020","journal-title":"Nature"},{"key":"2024122307353734500_ref46","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nprot.2014.006","article-title":"Full-length RNA-seq from single cells using Smart-seq2","volume":"9","author":"Picelli","year":"2014","journal-title":"Nat Protoc"},{"key":"2024122307353734500_ref47","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1038\/s41592-019-0392-0","article-title":"Multiplexed 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