Correlation-sharing for detection of differential gene expression

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dc.creatorTibshirani, Robert
dc.creatorWasserman, Larry
dc.date2006-08-02
dc.date.accessioned2026-07-07T08:08:05Z
dc.date.available2026-07-07T08:08:05Z
dc.descriptionWe propose a method for detecting differential gene expression that exploits the correlation between genes. Our proposal averages the univariate scores of each feature with the scores in correlation neighborhoods. In a number of real and simulated examples, the new method often exhibits lower false discovery rates than simple t-statistic thresholding. We also provide some analysis of the asymptotic behavior of our proposal. The general idea of correlation-sharing can be applied to other prediction problems involving a large number of correlated features. We give an example in protein mass spectrometry.
dc.identifierhttps://arxiv.org/abs/math/0608061
dc.identifierhttp://arxiv.org/abs/math/0608061
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131140
dc.subjectStatistics Theory
dc.subjectMolecular Networks
dc.subject62H15; 62P10
dc.titleCorrelation-sharing for detection of differential gene expression
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