{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:42:18Z","timestamp":1772905338589,"version":"3.50.1"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T00:00:00Z","timestamp":1708128000000},"content-version":"vor","delay-in-days":16,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["21623341"],"award-info":[{"award-number":["21623341"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangzhou Basic and Applied Basic Research Foundation","award":["2024A04J4225"],"award-info":[{"award-number":["2024A04J4225"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Cell-type annotation is fundamental in revealing cell heterogeneity for single-cell data analysis. Although a host of works have been developed, the low signal-to-noise-ratio single-cell RNA-sequencing data that suffers from batch effects and dropout still poses obstacles in discovering grouped patterns for cell types by unsupervised learning and its alternative\u2013semi-supervised learning that utilizes a few labeled cells as guidance for cell-type annotation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a robust cell-type annotation method scSemiGCN based on graph convolutional networks. Built upon a denoised network structure that characterizes reliable cell-to-cell connections, scSemiGCN generates pseudo labels for unannotated cells. Then supervised contrastive learning follows to refine the noisy single-cell data. Finally, message passing with the refined features over the denoised network structure is conducted for semi-supervised cell-type annotation. Comparison over several datasets with six methods under extremely limited supervision validates the effectiveness and efficiency of scSemiGCN for cell-type annotation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Implementation of scSemiGCN is available at https:\/\/github.com\/Jane9898\/scSemiGCN.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae091","type":"journal-article","created":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T22:31:28Z","timestamp":1707949888000},"source":"Crossref","is-referenced-by-count":3,"title":["scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision"],"prefix":"10.1093","volume":"40","author":[{"given":"Jue","family":"Yang","sequence":"first","affiliation":[{"name":"School of Mathematics, Sun Yat-sen University , Guangzhou 510000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5435-2680","authenticated-orcid":false,"given":"Weiwen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mathematics, School of Information Science and Technology, Jinan University , Guangzhou 510000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, College of Medical Information Engineering, Guangdong Pharmaceutical University , Guangzhou 510000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"2024022922523972200_btae091-B1","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1093\/bioinformatics\/btaa908","article-title":"Single-cell RNA-seq data semi-supervised clustering and annotation via structural regularized domain 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