{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T19:09:00Z","timestamp":1781118540327,"version":"3.54.1"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":2377,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,5,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: A major challenge in utilizing microarray technologies to measure nucleic acid abundances is \u2018normalization\u2019, the goal of which is to separate biologically meaningful signal from other confounding sources of signal, often due to unavoidable technical factors. It is intuitively clear that true biological signal and confounding factors need to be simultaneously considered when performing normalization. However, the most popular normalization approaches do not utilize what is known about the study, both in terms of the biological variables of interest and the known technical factors in the study, such as batch or array processing date.<\/jats:p><jats:p>Results: We show here that failing to include all study-specific biological and technical variables when performing normalization leads to biased downstream analyses. We propose a general normalization framework that fits a study-specific model employing every known variable that is relevant to the expression study. The proposed method is generally applicable to the full range of existing probe designs, as well as to both single-channel and dual-channel arrays. We show through real and simulated examples that the method has favorable operating characteristics in comparison to some of the most highly used normalization methods.<\/jats:p><jats:p>Availability: An R package called snm implementing the methodology will be made available from Bioconductor (http:\/\/bioconductor.org).<\/jats:p><jats:p>Contact: \u00a0jstorey@princeton.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btq118","type":"journal-article","created":{"date-parts":[[2010,4,2]],"date-time":"2010-04-02T00:13:12Z","timestamp":1270167192000},"page":"1308-1315","source":"Crossref","is-referenced-by-count":114,"title":["Supervised normalization of microarrays"],"prefix":"10.1093","volume":"26","author":[{"given":"Brigham H.","family":"Mecham","sequence":"first","affiliation":[{"name":"1 Department of Genome Sciences, University of Washington, Seattle, WA 98195, 2 Divisions of Human Biology and Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA 98109 and 3 Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter S.","family":"Nelson","sequence":"additional","affiliation":[{"name":"1 Department of Genome Sciences, University of Washington, Seattle, WA 98195, 2 Divisions of Human Biology and Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA 98109 and 3 Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA"},{"name":"1 Department of Genome Sciences, University of Washington, Seattle, WA 98195, 2 Divisions of Human Biology and Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA 98109 and 3 Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John D.","family":"Storey","sequence":"additional","affiliation":[{"name":"1 Department of Genome Sciences, University of Washington, Seattle, WA 98195, 2 Divisions of Human Biology and Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA 98109 and 3 Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2010,3,31]]},"reference":[{"key":"2023012507515668500_B1","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1093\/bioinformatics\/bth384","article-title":"Normalization of microarray data using a spatial mixed model analysis which includes splines","volume":"20","author":"Baird","year":"2004","journal-title":"Bioinformatics"},{"key":"2023012507515668500_B2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1093\/bioinformatics\/19.2.185","article-title":"A comparison of normalization methods for high density oligonucleotide array data based on variance and bias","volume":"19","author":"Bolstad","year":"2003","journal-title":"Bioinformatics"},{"key":"2023012507515668500_B3","doi-asserted-by":"crossref","first-page":"R44","DOI":"10.1186\/gb-2007-8-3-r44","article-title":"Normalization of two-channel microarrays accounting for experimental design and intensity-dependent relationships","volume":"8","author":"Dabney","year":"2007","journal-title":"Genome Biol."},{"key":"2023012507515668500_B4","first-page":"111","article-title":"Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments","volume":"12","author":"Dudoit","year":"2002","journal-title":"Stat. 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