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dc.contributor.authorIckstadt, Katjade
dc.contributor.authorKrause, Andreasde
dc.contributor.authorSchwender, Holgerde
dc.date.accessioned2004-12-06T18:41:54Z-
dc.date.available2004-12-06T18:41:54Z-
dc.date.issued2003de
dc.identifier.urihttp://hdl.handle.net/2003/5012-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15111-
dc.description.abstractMicroarrays enable to measure the expression levels of tens of thousands of genes simultaneously. One important statistical question in such experiments is which of the several thousand genes are differentially expressed. Answering this question requires methods that can deal with multiple testing problems. One such approach is the control of the False Discovery Rate (FDR). Two recently developed methods for the identification of differentially expressed genes and the estimation of the FDR are the SAM (Significance Analysis of Microarrays) procedure and an empirical Bayes approach. In the two group case, both methods are based on a modified version of the standard t-statistic. However, it is also possible to use the Wilcoxon rank sum statistic. While there already exists a version of the empirical Bayes approach based on this rank statistic, we introduce in this paper a new version of SAM based on Wilcoxon rank sums. We furthermore compare these four procedures by applying them to simulated and real gene expression data.en
dc.format.extent497614 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectidentification of differentially expressed genesen
dc.subjectgene expressionen
dc.subjectmultiple testingen
dc.subjectfalse discovery rateen
dc.subject.ddc310de
dc.titleComparison of the Empirical Bayes and the Significance Analysis of Microarraysen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

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