{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T11:15:26Z","timestamp":1783077326766,"version":"3.54.6"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2018,4,9]],"date-time":"2018-04-09T00:00:00Z","timestamp":1523232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100004836","name":"Danish Council for Independent Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004836","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001732","name":"Danish National Research Foundation","doi-asserted-by":"publisher","award":["DNRF82"],"award-info":[{"award-number":["DNRF82"]}],"id":[{"id":"10.13039\/501100001732","id-type":"DOI","asserted-by":"publisher"}]},{"name":"EU ELIXIR"},{"name":"Danish Council for Research"},{"name":"Natural Sciences"},{"name":"FNU"},{"name":"VILLUM Center for Bioanalytical Science"},{"DOI":"10.13039\/100008398","name":"VILLUM Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100008398","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Data clustering is indispensable for identifying biologically relevant molecular features in large-scale omics experiments with thousands of measurements at multiple conditions. Optimal clustering results yield groups of functionally related features that may include genes, proteins and metabolites in biological processes and molecular networks. Omics experiments typically include replicated measurements of each feature within a given condition to statistically assess feature-specific variation. Current clustering approaches ignore this variation by averaging, which often leads to incorrect cluster assignments.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present VSClust that accounts for feature-specific variance. Based on an algorithm derived from fuzzy clustering, VSClust unifies statistical testing with pattern recognition to cluster the data into feature groups that more accurately reflect the underlying molecular and functional behavior. We apply VSClust to artificial and experimental datasets comprising hundreds to &amp;gt;80\u00a0000 features across 6\u201320 different conditions including genomics, transcriptomics, proteomics and metabolomics experiments. VSClust avoids arbitrary averaging methods, outperforms standard fuzzy c-means clustering and simplifies the data analysis workflow in large-scale omics studies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Download VSClust at https:\/\/bitbucket.org\/veitveit\/vsclust or access it through computproteomics.bmb.sdu.dk\/Apps\/VSClust.<\/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\/bty224","type":"journal-article","created":{"date-parts":[[2018,4,6]],"date-time":"2018-04-06T12:25:55Z","timestamp":1523017555000},"page":"2965-2972","source":"Crossref","is-referenced-by-count":37,"title":["VSClust: feature-based variance-sensitive clustering of omics data"],"prefix":"10.1093","volume":"34","author":[{"given":"Veit","family":"Schw\u00e4mmle","sequence":"first","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark"},{"name":"VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense M, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ole N","family":"Jensen","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark"},{"name":"VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense M, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,4,9]]},"reference":[{"key":"2023061313374604900_bty224-B1","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-0450-1","volume-title":"Pattern Recognition with Fuzzy Objective Function Algorithms","author":"Bezdek","year":"1981"},{"key":"2023061313374604900_bty224-B2","doi-asserted-by":"crossref","first-page":"2810","DOI":"10.1093\/bioinformatics\/btt487","article-title":"Rdavidwebservice: a versatile r interface to david","volume":"29","author":"Fresno","year":"2013","journal-title":"Bioinformatics"},{"key":"2023061313374604900_bty224-B3","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1142\/S0219720005001375","article-title":"Noise-robust soft clustering of gene expression time-course data","volume":"03","author":"Futschik","year":"2005","journal-title":"J. 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