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.