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Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach

Author

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  • Opgen-Rhein Rainer

    (Department of Statistics, University of Munich)

  • Strimmer Korbinian

    (Department of Statistics, University of Munich)

Abstract

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.

Suggested Citation

  • Opgen-Rhein Rainer & Strimmer Korbinian, 2007. "Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-20, February.
    • Handle: RePEc:bpj:sagmbi:v:6:y:2007:i:1:n:9
    DOI: 10.2202/1544-6115.1252
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    Citations

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    Cited by:

    1. Mr. Jorge A Chan-Lau, 2017. "Variance Decomposition Networks: Potential Pitfalls and a Simple Solution," IMF Working Papers 2017/107, International Monetary Fund.
    2. Zhang, Xinyu & Chen, Ti & Wan, Alan T.K. & Zou, Guohua, 2009. "Robustness of Stein-type estimators under a non-scalar error covariance structure," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2376-2388, November.
    3. Marot, Guillemette & Foulley, Jean-Louis & Jaffrzic, Florence, 2009. "A structural mixed model to shrink covariance matrices for time-course differential gene expression studies," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1630-1638, March.
    4. Vera Djordjilović & Monica Chiogna & Chiara Romualdi, 2020. "Simulating gene silencing through intervention analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 887-907, August.
    5. Xiaoquan Wen, 2017. "Robust Bayesian FDR Control Using Bayes Factors, with Applications to Multi-tissue eQTL Discovery," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 28-49, June.
    6. Sophie Depiereux & Florence Le Gac & Bertrand De Meulder & Michael Pierre & Raphaël Helaers & Yann Guiguen & Patrick Kestemont & Eric Depiereux, 2015. "Meta-Analysis of Microarray Data of Rainbow Trout Fry Gonad Differentiation Modulated by Ethynylestradiol," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-27, September.
    7. Lim Johan & Kim Jayoun & Kim Sang-cheol & Yu Donghyeon & Kim Kyunga & Kim Byung Soo, 2012. "Detection of Differentially Expressed Gene Sets in a Partially Paired Microarray Data Set," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-30, February.
    8. Tong, Tiejun & Jang, Homin & Wang, Yuedong, 2012. "James–Stein type estimators of variances," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 232-243.
    9. Korbinian Strimmer, 2008. "Comments on: Augmenting the bootstrap to analyze high dimensional genomic data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(1), pages 25-27, May.
    10. Xiao Min & Chen Ting & Huang Kunpeng & Ming Ruixing, 2020. "Optimal Estimation for Power of Variance with Application to Gene-Set Testing," Journal of Systems Science and Information, De Gruyter, vol. 8(6), pages 549-564, December.
    11. Martin Bod’a, 2017. "Stochastic sensitivity analysis of concentration measures," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 441-471, June.
    12. Peter Hall & D. M. Titterington & Jing‐Hao Xue, 2009. "Tilting methods for assessing the influence of components in a classifier," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 783-803, September.

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