Department of Statistics Unitmark
Dietrich College of Humanities and Social Sciences

Correlation-sharing for Detection of Differential Gene Expression

Publication Date

August, 2006

Publication Type

Tech Report

Author(s)

Robert Tibshirani and Larry Wasserman

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.