Abstract

AbstractMicroarrays are a novel technology that facilitates the simultaneous measurement of thousands of gene expression levels. A typical microarray experiment can produce millions of data points, raising serious problems of data reduction, and simultaneous inference. We consider one such experiment in which oligonucleotide arrays were employed to assess the genetic effects of ionizing radiation on seven thousand human genes. A simple nonparametric empirical Bayes model is introduced, which is used to guide the efficient reduction of the data to a single summary statistic per gene, and also to make simultaneous inferences concerning which genes were affected by the radiation. Although our focus is on one specific experiment, the proposed methods can be applied quite generally. The empirical Bayes inferences are closely related to the frequentist false discovery rate (FDR) criterion.

Keywords

Frequentist inferenceBayes' theoremFalse discovery rateInferenceComputer scienceNonparametric statisticsBayes factorStatisticData miningBayesian probabilityStatisticsGene chip analysisDNA microarrayBayesian inferenceMathematicsArtificial intelligenceBiologyGeneticsGeneGene expression

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Publication Info

Year
2001
Type
article
Volume
96
Issue
456
Pages
1151-1160
Citations
1755
Access
Closed

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Cite This

Bradley Efron, Robert Tibshirani, John D. Storey et al. (2001). Empirical Bayes Analysis of a Microarray Experiment. Journal of the American Statistical Association , 96 (456) , 1151-1160. https://doi.org/10.1198/016214501753382129

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DOI
10.1198/016214501753382129