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Illustrating the lack of robustness, in a striking spike-in experiment all existing normalization methods fail because of an imbalance between up- and down-regulated genes. This means it is still important to develop a normalization method that is robust against violation of the standard assumptions<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We develop a new algorithm based on identification of the least-variant set (LVS) of genes across the arrays. The array-to-array variation is evaluated in the robust linear model fit of pre-normalized probe-level data. The genes are then used as a reference set for a non-linear normalization. The method is applicable to any existing expression summaries, such as MAS5 or RMA.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>We show that LVS normalization outperforms other normalization methods when the standard assumptions are not satisfied. In the complex spike-in study, LVS performs similarly to the ideal (in practice unknown) housekeeping-gene normalization. An R package called lvs is available in <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.meb.ki.se\/~yudpaw\" ext-link-type=\"uri\">http:\/\/www.meb.ki.se\/~yudpaw<\/jats:ext-link>.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-9-140","type":"journal-article","created":{"date-parts":[[2008,3,5]],"date-time":"2008-03-05T07:13:25Z","timestamp":1204701205000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Normalization of oligonucleotide arrays based on the least-variant set of genes"],"prefix":"10.1186","volume":"9","author":[{"given":"Stefano","family":"Calza","sequence":"first","affiliation":[]},{"given":"Davide","family":"Valentini","sequence":"additional","affiliation":[]},{"given":"Yudi","family":"Pawitan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2008,3,5]]},"reference":[{"key":"2125_CR1","volume-title":"IN SPIE Bios","author":"A Hartemink","year":"2001","unstructured":"Hartemink A, Gifford D, Jaakkola T, Young R: Maximum likelihood estimation of optimal scaling factors for expression array normalizations. 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