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Hierarchical Bayes variable selection and microarray experiments

Author

Listed:
  • Nott, David J.
  • Yu, Zeming
  • Chan, Eva
  • Cotsapas, Chris
  • Cowley, Mark J.
  • Pulvers, Jeremy
  • Williams, Rohan
  • Little, Peter

Abstract

Hierarchical and empirical Bayes approaches to inference are attractive for data arising from microarray gene expression studies because of their ability to borrow strength across genes in making inferences. Here we focus on the simplest case where we have data from replicated two colour arrays which compare two samples and where we wish to decide which genes are differentially expressed and obtain estimates of operating characteristics such as false discovery rates. The purpose of this paper is to examine the frequentist performance of Bayesian variable selection approaches to this problem for different prior specifications and to examine the effect on inference of commonly used empirical Bayes approximations to hierarchical Bayes procedures. The paper makes three main contributions. First, we describe how the log odds of differential expression can usually be computed analytically in the case where a double tailed exponential prior is used for gene effects rather than a normal prior, which gives an alternative to the commonly used B-statistic for ranking genes in simple comparative experiments. The second contribution of the paper is to compare empirical Bayes procedures for detecting differential expression with hierarchical Bayes methods which account for uncertainty in prior hyperparameters to examine how much is lost in using the commonly employed empirical Bayes approximations. Third, we describe an efficient MCMC scheme for carrying out the computations required for the hierarchical Bayes procedures. Comparisons are made via simulation studies where the simulated data are obtained by fitting models to some real microarray data sets. The results have implications for analysis of microarray data using parametric hierarchical and empirical Bayes methods for more complex experimental designs: generally we find that the empirical Bayes methods work well, which supports their use in the analysis of more complex experiments when a full hierarchical Bayes analysis would impose heavy computational demands.

Suggested Citation

  • Nott, David J. & Yu, Zeming & Chan, Eva & Cotsapas, Chris & Cowley, Mark J. & Pulvers, Jeremy & Williams, Rohan & Little, Peter, 2007. "Hierarchical Bayes variable selection and microarray experiments," Journal of Multivariate Analysis, Elsevier, vol. 98(4), pages 852-872, April.
  • Handle: RePEc:eee:jmvana:v:98:y:2007:i:4:p:852-872
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    References listed on IDEAS

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    1. Ibrahim J. G. & Chen M-H. & Gray R. J., 2002. "Bayesian Models for Gene Expression With DNA Microarray Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 88-99, March.
    2. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    3. Huang, Jian & Wang, Deli & Zhang, Cun-Hui, 2005. "A Two-Way Semilinear Model for Normalization and Analysis of cDNA Microarray Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 814-829, September.
    4. Fan, Jianqing & Peng, Heng & Huang, Tao, 2005. "Semilinear High-Dimensional Model for Normalization of Microarray Data: A Theoretical Analysis and Partial Consistency," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 781-796, September.
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