{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:51:46Z","timestamp":1772844706051,"version":"3.50.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"French government under management of Agence Nationale de la Recherche as part of the \u2018Investissements d\u2019Avenir\u2019 program","award":["ANR-19-P3IA-0001"],"award-info":[{"award-number":["ANR-19-P3IA-0001"]}]},{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"publisher","award":["075-15-2021-634"],"award-info":[{"award-number":["075-15-2021-634"]}],"id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Association Sciences et Technologie\u2014Groupe de Recherche Servier and the doctoral school Fronti\u00e8res de l\u2019Innovation en Recherche et Education-Programme Bettencourt"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Single-cell RNA-seq (scRNAseq) datasets are characterized by large ambient dimensionality, and their analyses can be affected by various manifestations of the dimensionality curse. One of these manifestations is the hubness phenomenon, i.e. existence of data points with surprisingly large incoming connectivity degree in the datapoint neighbourhood graph. Conventional approach to dampen the unwanted effects of high dimension consists in applying drastic dimensionality reduction. It remains unexplored if this step can be avoided thus retaining more information than contained in the low-dimensional projections, by correcting directly hubness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We investigated hubness in scRNAseq data. We show that hub cells do not represent any visible technical or biological bias. The effect of various hubness reduction methods is investigated with respect to the clustering, trajectory inference and visualization tasks in scRNAseq datasets. We show that hubness reduction generates neighbourhood graphs with properties more suitable for applying machine learning methods; and that it outperforms other state-of-the-art methods for improving neighbourhood graphs. As a consequence, clustering, trajectory inference and visualization perform better, especially for datasets characterized by large intrinsic dimensionality. Hubness is an important phenomenon characterizing data point neighbourhood graphs computed for various types of sequencing datasets. Reducing hubness can be beneficial for the analysis of scRNAseq data with large intrinsic dimensionality in which case it can be an alternative to drastic dimensionality reduction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and Implementation<\/jats:title>\n                    <jats:p>The code used to analyze the datasets and produce the figures of this article is available from https:\/\/github.com\/sysbio-curie\/schubness.<\/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\/btab795","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T15:21:04Z","timestamp":1637162464000},"page":"1045-1051","source":"Crossref","is-referenced-by-count":4,"title":["Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1491-6947","authenticated-orcid":false,"given":"Elise","family":"Amblard","sequence":"first","affiliation":[{"name":"Universit\u00e9 de Paris, INSERM, HIPI , F-75010 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Bac","sequence":"additional","affiliation":[{"name":"Institut Curie, PSL Research University , F-75005 Paris, France"},{"name":"INSERM, U900 , F-75005 Paris, France"},{"name":"CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University , 75006 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Chervov","sequence":"additional","affiliation":[{"name":"Institut Curie, PSL Research University , F-75005 Paris, France"},{"name":"INSERM, U900 , F-75005 Paris, France"},{"name":"CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University , 75006 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vassili","family":"Soumelis","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Paris, INSERM, HIPI , F-75010 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9517-7284","authenticated-orcid":false,"given":"Andrei","family":"Zinovyev","sequence":"additional","affiliation":[{"name":"Institut Curie, PSL Research University , F-75005 Paris, France"},{"name":"INSERM, U900 , F-75005 Paris, France"},{"name":"CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University , 75006 Paris, France"},{"name":"Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky University , 603000 Nizhny Novgorod, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"2023020108535260500_btab795-B1","doi-asserted-by":"crossref","first-page":"194","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":"2023020108535260500_btab795-B2","first-page":"1","author":"Albergante","year":"2019"},{"key":"2023020108535260500_btab795-B3","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1016\/j.celrep.2020.01.049","article-title":"Transcriptional programs define intratumoral heterogeneity of Ewing sarcoma at single-cell resolution","volume":"30","author":"Aynaud","year":"2020","journal-title":"Cell Rep"},{"key":"2023020108535260500_btab795-B4","doi-asserted-by":"crossref","first-page":"110","DOI":"10.3389\/fnbot.2019.00110","article-title":"Lizard brain: tackling locally low-dimensional yet globally complex organization of multi-dimensional datasets","volume":"13","author":"Bac","year":"2019","journal-title":"Front. 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