{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T21:10:21Z","timestamp":1782421821981,"version":"3.54.5"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Prism \u2013 National Precision Medicine Center in Oncology"},{"DOI":"10.13039\/501100001665","name":"French National Research Agency","doi-asserted-by":"publisher","award":["ANR-18-IBHU-0002"],"award-info":[{"award-number":["ANR-18-IBHU-0002"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Cytometry enables precise single-cell phenotyping within heterogeneous populations. These cell types are traditionally annotated via manual gating, but this method lacks reproducibility and sensitivity to batch effect. Also, the most recent cytometers\u2014spectral flow or mass cytometers\u2014create rich and high-dimensional data whose analysis via manual gating becomes challenging and time-consuming. To tackle these limitations, we introduce Scyan https:\/\/github.com\/MICS-Lab\/scyan, a Single-cell Cytometry Annotation Network that automatically annotates cell types using only prior expert knowledge about the cytometry panel. For this, it uses a normalizing flow\u2014a type of deep generative model\u2014that maps protein expressions into a biologically relevant latent space. We demonstrate that Scyan significantly outperforms the related state-of-the-art models on multiple public datasets while being faster and interpretable. In addition, Scyan overcomes several complementary tasks, such as batch-effect correction, debarcoding and population discovery. Overall, this model accelerates and eases cell population characterization, quantification and discovery in cytometry.<\/jats:p>","DOI":"10.1093\/bib\/bbad260","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T09:45:53Z","timestamp":1690451153000},"source":"Crossref","is-referenced-by-count":9,"title":["A biology-driven deep generative model for cell-type annotation in cytometry"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3836-2889","authenticated-orcid":false,"given":"Quentin","family":"Blampey","sequence":"first","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, CentraleSup\u00e9lec, Laboratory of Mathematics and Computer Science (MICS) , 3 rue Joliot Curie, 91190,Gif-sur-Yvette , 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