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Correlation-sharing for detection of differential gene expression

Robert Tibshirani·Larry Wasserman
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Abstract

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.

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Robert Tibshirani, Larry Wasserman. 2006-08-02. Correlation-sharing for detection of differential gene expression. https://arxiv.org/abs/math/0608061

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