Jump to ContentJump to Main Navigation
Show Summary Details

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

IMPACT FACTOR increased in 2015: 1.265
5-year IMPACT FACTOR: 1.423
Rank 42 out of 123 in category Statistics & Probability in the 2015 Thomson Reuters Journal Citation Report/Science Edition

SCImago Journal Rank (SJR) 2015: 0.954
Source Normalized Impact per Paper (SNIP) 2015: 0.554
Impact per Publication (IPP) 2015: 1.061

Mathematical Citation Quotient (MCQ) 2015: 0.06

See all formats and pricing


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

Xing Qiu1 / Lev Klebanov2 / Andrei Yakovlev3

1Department of Biostatistics and Computational Biology, University of Rochester

2Department of Probability and Statistics, Charles University, Institute of Informatics and Control of the National Academy of Sciences of the Czech Republic

3Department of Biostatistics and Computational Biology, University of Rochester

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 4, Issue 1, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1157, November 2005

Publication History

Published Online:

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

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.

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

Supplementary Article Materials

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Jelle J. Goeman and Aldo Solari
Statistics in Medicine, 2014, Volume 33, Number 11, Page 1946
G. Yaari, C. R. Bolen, J. Thakar, and S. H. Kleinstein
Nucleic Acids Research, 2013, Volume 41, Number 18, Page e170
Jian Zhang and Faming Liang
Biometrics, 2010, Volume 66, Number 4, Page 1078
Feng Li, Françoise Seillier-Moiseiwitsch, and Valeriy R. Korostyshevskiy
Computational Statistics & Data Analysis, 2011, Volume 55, Number 11, Page 3059

Comments (0)

Please log in or register to comment.