{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:22:50Z","timestamp":1774830170304,"version":"3.50.1"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T00:00:00Z","timestamp":1713398400000},"content-version":"vor","delay-in-days":20,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ghent University Special Research Fund","award":["BOF18-GOA-024"],"award-info":[{"award-number":["BOF18-GOA-024"]}]},{"DOI":"10.13039\/501100003130","name":"FWO","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Foundation\u2014Flanders","award":["T000119N"],"award-info":[{"award-number":["T000119N"]}]},{"name":"ISAC Marylou Ingram Scholar","award":["1272823N"],"award-info":[{"award-number":["1272823N"]}]},{"name":"Research Foundation\u2014Flanders"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>We describe a new Python implementation of FlowSOM, a clustering method for cytometry data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>This implementation is faster than the original version in R, better adapted to work with single-cell omics data including integration with current single-cell data structures and includes all the original visualizations, such as the star and pie plot.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The FlowSOM Python implementation is freely available on GitHub: https:\/\/github.com\/saeyslab\/FlowSOM_Python.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae179","type":"journal-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T02:13:59Z","timestamp":1713406439000},"source":"Crossref","is-referenced-by-count":12,"title":["Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7858-6521","authenticated-orcid":false,"given":"Artuur","family":"Couckuyt","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, Computer Science and Statistics, Ghent University , 9000 Ghent, Belgium"},{"name":"Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research , 9052 Ghent, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4022-715X","authenticated-orcid":false,"given":"Benjamin","family":"Rombaut","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Computer Science and Statistics, Ghent University , 9000 Ghent, Belgium"},{"name":"Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research , 9052 Ghent, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0415-1506","authenticated-orcid":false,"given":"Yvan","family":"Saeys","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Computer Science and Statistics, Ghent University , 9000 Ghent, Belgium"},{"name":"Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research , 9052 Ghent, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7119-5330","authenticated-orcid":false,"given":"Sofie","family":"Van Gassen","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Computer 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