  We 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.
