Combining multiple microarray studies and modeling interstudy variation

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dc.contributor.authorJung Kyoon Choi-
dc.contributor.authorUng Sik Yu-
dc.contributor.authorSang Soo Kim-
dc.contributor.authorO J Yoo-
dc.date.accessioned2017-04-19T09:00:31Z-
dc.date.available2017-04-19T09:00:31Z-
dc.date.issued2003-
dc.identifier.issn1367-4803-
dc.identifier.uri10.1093/bioinformatics/btg1010ko
dc.identifier.urihttps://oak.kribb.re.kr/handle/201005/6312-
dc.description.abstractWe have established a method for systematic integration of multiple microarray datasets. The method was applied to two different sets of cancer profiling studies. The change of gene expression in cancer was expressed as 'effect size', a standardized index measuring the magnitude of a treatment or covariate effect. The effect sizes were combined to obtain the estimate of the overall mean. The statistical significance was determined by a permutation test extended to multiple datasets. It was shown that the data integration promotes the discovery of small but consistent expression changes with increased sensitivity and reliability. The effect size methods provided the efficient modeling framework for addressing interstudy variation as well. Based on the result of homogeneity tests, a fixed effects model was adopted for one set of datasets that had been created in controlled experimental conditions. By contrast, a random effects model was shown to be appropriate for the other set of datasets that had been published by independent groups. We also developed an alternative modeling procedure based on a Bayesian approach, which would offer flexibility and robustness compared to the classical procedure.-
dc.publisherOxford Univ Press-
dc.titleCombining multiple microarray studies and modeling interstudy variation-
dc.title.alternativeCombining multiple microarray studies and modeling interstudy variation-
dc.typeArticle-
dc.citation.titleBioinformatics-
dc.citation.numberS-
dc.citation.endPagei90-
dc.citation.startPagei84-
dc.citation.volume19-
dc.contributor.affiliatedAuthorJung Kyoon Choi-
dc.contributor.affiliatedAuthorUng Sik Yu-
dc.contributor.affiliatedAuthorSang Soo Kim-
dc.contributor.alternativeName최정균-
dc.contributor.alternativeName유웅식-
dc.contributor.alternativeName김상수-
dc.contributor.alternativeName유욱준-
dc.identifier.bibliographicCitationBioinformatics, vol. 19, no. S, pp. i84-i90-
dc.identifier.doi10.1093/bioinformatics/btg1010-
dc.subject.keywordBayesian meta-analysis-
dc.subject.keywordEffect size-
dc.subject.keywordMeta-analysis-
dc.subject.keywordMicroarray-
dc.subject.localBayesian meta-analysis-
dc.subject.localEffect size-
dc.subject.localmeta-analysis-
dc.subject.localMeta-analysis-
dc.subject.localmicroarray-
dc.subject.localmicroarry-
dc.subject.localMicroarray-
dc.subject.localmicroarrays-
dc.description.journalClassY-
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