{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:14:42Z","timestamp":1787008482188,"version":"build-2736575974"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2018,6,21]],"date-time":"2018-06-21T00:00:00Z","timestamp":1529539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"A-STAR\/SIgN"},{"name":"A-STAR\/SIgN immunomonitoring platform"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Recent flow and mass cytometers generate datasets of dimensions 20 to 40 and a million single cells. From these, many tools facilitate the discovery of new cell populations associated with diseases or physiology. These new cell populations require the identification of new gating strategies, but gating strategies become exponentially more difficult to optimize when dimensionality increases. To facilitate this step, we developed Hypergate, an algorithm which given a cell population of interest identifies a gating strategy optimized for high yield and purity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Hypergate achieves higher yield and purity than human experts, Support Vector Machines and Random-Forests on public datasets. We use it to revisit some established gating strategies for the identification of innate lymphoid cells, which identifies concise and efficient strategies that allow gating these cells with fewer parameters but higher yield and purity than the current standards. For phenotypic description, Hypergate\u2019s outputs are consistent with fields\u2019 knowledge and sparser than those from a competing method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Hypergate is implemented in R and available on CRAN. The source code is published at http:\/\/github.com\/ebecht\/hypergate under an Open Source Initiative-compliant licence.<\/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\/bty491","type":"journal-article","created":{"date-parts":[[2018,6,18]],"date-time":"2018-06-18T07:48:28Z","timestamp":1529308108000},"page":"301-308","source":"Crossref","is-referenced-by-count":37,"title":["Reverse-engineering flow-cytometry gating strategies for phenotypic labelling and high-performance cell sorting"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1859-9202","authenticated-orcid":false,"given":"Etienne","family":"Becht","sequence":"first","affiliation":[{"name":"Singapore Immunology Network, Agency for Science Technology and Research, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yannick","family":"Simoni","sequence":"additional","affiliation":[{"name":"Singapore Immunology Network, Agency for Science Technology and Research, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elaine","family":"Coustan-Smith","sequence":"additional","affiliation":[{"name":"Department of Paediatrics, National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilien","family":"Evrard","sequence":"additional","affiliation":[{"name":"Singapore Immunology Network, Agency for Science Technology and Research, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Cheng","sequence":"additional","affiliation":[{"name":"Singapore Immunology Network, Agency for Science Technology and Research, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lai Guan","family":"Ng","sequence":"additional","affiliation":[{"name":"Singapore Immunology Network, Agency for Science Technology and Research, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dario","family":"Campana","sequence":"additional","affiliation":[{"name":"Department of Paediatrics, National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evan W","family":"Newell","sequence":"additional","affiliation":[{"name":"Singapore Immunology Network, Agency for Science Technology and Research, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,6,21]]},"reference":[{"key":"2023013107231362000_bty491-B1","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1038\/nmeth.2365","article-title":"Critical assessment of automated flow cytometry data analysis techniques","volume":"10","author":"Aghaeepour","year":"2013","journal-title":"Nat. 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