Correlation-sharing for detection of differential gene expression
Statistics Theory
2007-06-13 v1 Molecular Networks
Statistics Theory
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.
Cite
@article{arxiv.math/0608061,
title = {Correlation-sharing for detection of differential gene expression},
author = {Robert Tibshirani and Larry Wasserman},
journal= {arXiv preprint arXiv:math/0608061},
year = {2007}
}