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Statistical Applications in Genetics and Molecular Biology

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Volume 4, Issue 1 (Nov 2005)

Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes

Xing Qiu
  • Department of Biostatistics and Computational Biology, University of Rochester
/ Lev Klebanov
  • Department of Probability and Statistics, Charles University, Institute of Informatics and Control of the National Academy of Sciences of the Czech Republic
/ Andrei Yakovlev
  • Department of Biostatistics and Computational Biology, University of Rochester
Published Online: 2005-11-22 | DOI: https://doi.org/10.2202/1544-6115.1157

Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test statistics across genes. The empirical Bayes methodology in the nonparametric and parametric formulations, as well as closely related methods employing a two-component mixture model, represent typical examples. It is frequently assumed that dependence between gene expressions (or associated test statistics) is sufficiently weak to justify the application of such methods for selecting differentially expressed genes. By applying resampling techniques to simulated and real biological data sets, we have studied a potential impact of the correlation between gene expression levels on the statistical inference based on the empirical Bayes methodology. We report evidence from these analyses that this impact may be quite strong, leading to a high variance of the number of differentially expressed genes. This study also pinpoints specific components of the empirical Bayes method where the reported effect manifests itself.

This article offers supplementary material which is provided at the end of the article.

Keywords: microarray analysis; gene expression; two-sample tests; empirical Bayes method; correlated data; resampling techniques

About the article

Published Online: 2005-11-22

Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1157. Export Citation

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