Empirical Bayes analysis of a microarray experiment

Abstract

Microarrays are a new technology that enables 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 that 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. The empirical Bayes inferences are closely related to the frequentist False Discovery Rate criterion.

Publication
Stanford Statistics Technical Report
Date
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