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

Author

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  • Qiu Xing

    (Department of Biostatistics and Computational Biology, University of Rochester)

  • Klebanov Lev

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

  • Yakovlev Andrei

    (Department of Biostatistics and Computational Biology, University of Rochester)

Abstract

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.

Suggested Citation

  • Qiu Xing & Klebanov Lev & Yakovlev Andrei, 2005. "Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-32, November.
    • Handle: RePEc:bpj:sagmbi:v:4:y:2005:i:1:n:34
    DOI: 10.2202/1544-6115.1157
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    Cited by:

    1. Li, Feng & Seillier-Moiseiwitsch, Françoise & Korostyshevskiy, Valeriy R., 2011. "Region-based statistical analysis of 2D PAGE images," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 3059-3072, November.
    2. Jian Zhang, 2010. "A Bayesian model for biclustering with applications," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(4), pages 635-656, August.
    3. Sairam Rayaprolu & Zhiyi Chi, 2021. "False Discovery Variance Reduction in Large Scale Simultaneous Hypothesis Tests," Methodology and Computing in Applied Probability, Springer, vol. 23(3), pages 711-733, September.
    4. Wenguang Sun & T. Tony Cai, 2009. "Large‐scale multiple testing under dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 393-424, April.
    5. T. Tony Cai & Weidong Liu, 2016. "Large-Scale Multiple Testing of Correlations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 229-240, March.
    6. Bickel David R., 2012. "Empirical Bayes Interval Estimates that are Conditionally Equal to Unadjusted Confidence Intervals or to Default Prior Credibility Intervals," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-34, February.
    7. David R. Bickel, 2013. "Minimax-Optimal Strength of Statistical Evidence for a Composite Alternative Hypothesis," International Statistical Review, International Statistical Institute, vol. 81(2), pages 188-206, August.
    8. Frank Emmert-Streib & Galina V Glazko, 2011. "Pathway Analysis of Expression Data: Deciphering Functional Building Blocks of Complex Diseases," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-6, May.
    9. Gordon, Alexander & Chen, Linlin & Glazko, Galina & Yakovlev, Andrei, 2009. "Balancing type one and two errors in multiple testing for differential expression of genes," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1622-1629, March.
    10. Lim Johan & Kim Jayeon & Kim Byung Soo, 2010. "An Alternative Model of Type A Dependence in a Gene Set of Correlated Genes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-12, January.

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