{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:07:01Z","timestamp":1785402421735,"version":"3.56.0"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"22","license":[{"start":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T00:00:00Z","timestamp":1664841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Nature Science Foundation of China","award":["62032007"],"award-info":[{"award-number":["62032007"]}]},{"name":"Nature Science Foundation of China","award":["61873089"],"award-info":[{"award-number":["61873089"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Cell-type annotation plays a crucial role in single-cell RNA-seq (scRNA-seq) data analysis. As more and more well-annotated scRNA-seq reference data are publicly available, automatical label transference algorithms are gaining popularity over manual marker gene-based annotation methods. However, most existing methods fail to unify cell-type annotation with dimensionality reduction and are unable to generate deep latent representation from the perspective of data generation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we propose scSemiGAN, a single-cell semi-supervised cell-type annotation and dimensionality reduction framework based on a generative adversarial network, to overcome these challenges, modeling scRNA-seq data from the aspect of data generation. Our proposed scSemiGAN is capable of performing deep latent representation learning and cell-type label prediction simultaneously. Through extensive comparison with four state-of-the-art annotation methods on diverse simulated and real scRNA-seq datasets, scSemiGAN achieves competitive or superior performance in multiple downstream tasks including cell-type annotation, latent representation visualization, confounding factor removal and enrichment analysis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code and data of scSemiGAN are available on GitHub: https:\/\/github.com\/rafa-nadal\/scSemiGAN.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac652","type":"journal-article","created":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T12:50:50Z","timestamp":1664887850000},"page":"5042-5048","source":"Crossref","is-referenced-by-count":16,"title":["scSemiGAN: a single-cell semi-supervised annotation and dimensionality reduction framework based on generative adversarial network"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1759-9665","authenticated-orcid":false,"given":"Zhongyuan","family":"Xu","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6549-9610","authenticated-orcid":false,"given":"Zehao","family":"Xiong","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,10,4]]},"reference":[{"key":"2022112014192978000_btac652-B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1795-z","article-title":"A comparison of automatic cell identification methods for single-cell rna sequencing data","volume":"20","author":"Abdelaal","year":"2019","journal-title":"Genome Biol"},{"key":"2022112014192978000_btac652-B2","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1038\/376074a0","article-title":"Activity-dependent long-term enhancement of transmitter release by presynaptic 3\u2019,5\u2019-cyclic GMP in cultured hippocampal neurons","volume":"376","author":"Arancio","year":"1995","journal-title":"Nature"},{"key":"2022112014192978000_btac652-B3","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1016\/j.cell.2018.05.060","article-title":"Single-cell map of diverse immune phenotypes in the breast tumor microenvironment","volume":"174","author":"Azizi","year":"2018","journal-title":"Cell"},{"key":"2022112014192978000_btac652-B4","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 adaptation","volume":"37","author":"Chen","year":"2021","journal-title":"Bioinformatics"},{"key":"2022112014192978000_btac652-B5","doi-asserted-by":"crossref","first-page":"1258367","DOI":"10.1126\/science.1258367","article-title":"Combinatorial labeling of single cells for gene expression cytometry","volume":"347","author":"Fan","year":"2015","journal-title":"Science"},{"key":"2022112014192978000_btac652-B6","article-title":"Generative adversarial nets. 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