{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T03:56:21Z","timestamp":1760586981201,"version":"3.37.3"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T00:00:00Z","timestamp":1566777600000},"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\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["P30CA014089","P01CA196569"],"award-info":[{"award-number":["P30CA014089","P01CA196569"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Gloria Borges WunderGlo Foundation-the Wunder Project"},{"DOI":"10.13039\/100006508","name":"Dhont Family Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006508","id-type":"DOI","asserted-by":"publisher"}]},{"name":"San Pedro Peninsula Cancer Guild"},{"name":"Daniel Butler Research Fund and Call to Cure Fund"},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Large amounts of information generated by genomic technologies are accompanied by statistical and computational challenges due to redundancy, badly behaved data and noise. Dimensionality reduction (DR) methods have been developed to mitigate these challenges. However, many approaches are not scalable to large dimensions or result in excessive information loss.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The proposed approach partitions data into subsets of related features and summarizes each into one and only one new feature, thus defining a surjective mapping. A constraint on information loss determines the size of the reduced dataset. Simulation studies demonstrate that when multiple related features are associated with a response, this approach can substantially increase the number of true associations detected as compared to principal components analysis, non-negative matrix factorization or no DR. This increase in true discoveries is explained both by a reduced multiple-testing challenge and a reduction in extraneous noise. In an application to real data collected from metastatic colorectal cancer tumors, more associations between gene expression features and progression free survival and response to treatment were detected in the reduced than in the full untransformed dataset.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Freely available R package from CRAN, https:\/\/cran.r-project.org\/package=partition.<\/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\/btz661","type":"journal-article","created":{"date-parts":[[2019,8,21]],"date-time":"2019-08-21T07:32:30Z","timestamp":1566372750000},"page":"676-681","source":"Crossref","is-referenced-by-count":9,"title":["Partition: a surjective mapping approach for dimensionality reduction"],"prefix":"10.1093","volume":"36","author":[{"given":"Joshua","family":"Millstein","sequence":"first","affiliation":[{"name":"Department of Preventive Medicine , CA 90033, USA"}]},{"given":"Francesca","family":"Battaglin","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Medical Oncology , Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA"},{"name":"Clinical and Experimental Oncology Department, Medical Oncology Unit 1, Veneto Institute of Oncology IOV-IRCCS , Padua 35128, Italy"}]},{"given":"Malcolm","family":"Barrett","sequence":"additional","affiliation":[{"name":"Department of Preventive Medicine , CA 90033, USA"}]},{"given":"Shu","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Preventive Medicine , CA 90033, USA"}]},{"given":"Wu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Medical Oncology , Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA"}]},{"given":"Sebastian","family":"Stintzing","sequence":"additional","affiliation":[{"name":"Medical Department, Division of Oncology and Hematology, Charit\u00e9 Universitaetsmedizin Berlin , Berlin 10117, Germany"}]},{"given":"Volker","family":"Heinemann","sequence":"additional","affiliation":[{"name":"Department of Medicine III, University Hospital Munich , Munich 80336, Germany"}]},{"given":"Heinz-Josef","family":"Lenz","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Medical Oncology , Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,8,26]]},"reference":[{"key":"2023013110093618100_btz661-B1","doi-asserted-by":"crossref","first-page":"R106.","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol"},{"key":"2023013110093618100_btz661-B2","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/nature16965","article-title":"Genomic analyses identify molecular subtypes of pancreatic cancer","volume":"531","author":"Bailey","year":"2016","journal-title":"Nature"},{"key":"2023013110093618100_btz661-B3","doi-asserted-by":"crossref","first-page":"244.","DOI":"10.1186\/1471-2105-9-244","article-title":"Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis","volume":"9","author":"Biswas","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"2023013110093618100_btz661-B4","first-page":"289","article-title":"Expression, function and clinical relevance of MIA (melanoma inhibitory activity)","volume":"17","author":"Bosserhoff","year":"2002","journal-title":"Histol. 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