Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
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Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach
1Department of Statistics, University of Munich
2Department of Statistics, University of Munich
Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 6, Issue 1, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: 10.2202/1544-6115.1252, February 2007
- Published Online:
High-dimensional case-control analysis is encountered in many different settings in genomics. In order to rank genes accordingly, many different scores have been proposed, ranging from ad hoc modifications of the ordinary t statistic to complicated hierarchical Bayesian models.Here, we introduce the shrinkage t statistic that is based on a novel and model-free shrinkage estimate of the variance vector across genes. This is derived in a quasi-empirical Bayes setting. The new rank score is fully automatic and requires no specification of parameters or distributions. It is computationally inexpensive and can be written analytically in closed form.Using a series of synthetic and three real expression data we studied the quality of gene rankings produced by the shrinkage t statistic. The new score consistently leads to highly accurate rankings for the complete range of investigated data sets and all considered scenarios for across-gene variance structures.
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