{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:43:07Z","timestamp":1753875787315,"version":"3.41.2"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92249303","61872395"],"award-info":[{"award-number":["92249303","61872395"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023A1515011907"],"award-info":[{"award-number":["2023A1515011907"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities, Sun Yat-sen University","award":["23xkjc003"],"award-info":[{"award-number":["23xkjc003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Recent advances in single-cell technologies enable the rapid growth of multi-omics data. Cell type annotation is one common task in analyzing single-cell data. It is a challenge that some cell types in the testing set are not present in the training set (i.e. unknown cell types). Most scATAC-seq cell type annotation methods generally assign each cell in the testing set to one known type in the training set but neglect unknown cell types. Here, we present OVAAnno, an automatic cell types annotation method which utilizes open-set domain adaptation to detect unknown cell types in scATAC-seq data. Comprehensive experiments show that OVAAnno successfully identifies known and unknown cell types. Further experiments demonstrate that OVAAnno also performs well on scRNA-seq data. Our codes are available online at https:\/\/github.com\/lisaber\/OVAAnno\/tree\/master.<\/jats:p>","DOI":"10.1093\/bib\/bbae370","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T23:32:13Z","timestamp":1722295933000},"source":"Crossref","is-referenced-by-count":0,"title":["Detecting novel cell type in single-cell chromatin accessibility data via open-set domain adaptation"],"prefix":"10.1093","volume":"25","author":[{"given":"Yuefan","family":"Lin","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering , Sun Yat-sen University, Guangzhou, 510006, China"}]},{"given":"Zixiang","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering , Sun Yat-sen University, Guangzhou, 510006, China"}]},{"given":"Yuansong","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering , Sun Yat-sen University, Guangzhou, 510006, China"}]},{"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering , Sun Yat-sen University, Guangzhou, 510006, China"}]},{"given":"Zhiming","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering , Sun Yat-sen University, Guangzhou, 510006, China"}]}],"member":"286","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"key":"2024072915241736700_ref1","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1038\/s41580-022-00466-x","article-title":"Deep learning shapes single-cell data analysis","volume":"23","author":"Ma","year":"2022","journal-title":"Nat Rev Mol Cell Biol"},{"key":"2024072915241736700_ref2","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","volume":"562","author":"Tabula Muris Consortium","year":"2018","journal-title":"Nature"},{"key":"2024072915241736700_ref3","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1109\/BIBM49941.2020.9313138","article-title":"Assessment of machine learning methods for classification in single cell atac-seq","volume-title":"2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Cui","year":"2020"},{"key":"2024072915241736700_ref4","first-page":"223","article-title":"Transformer for one stop interpretable cell type annotation. 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