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Bayesian models based on test statistics for multiple hypothesis testing problems.

Yuan Ji1, Yiling Lu, Gordon B Mills

  • 1Department of Bioinformatics and Computational Biology, The University of Texas, M. D. Anderson Cancer Center, Houston, TX 77030, USA. yuanji@mdanderson.org

Bioinformatics (Oxford, England)
|February 5, 2008
PubMed
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This study introduces a novel Bayesian method for multiple hypothesis testing in bioinformatics, simplifying complex analyses like differential gene expression. The approach models test statistics directly, offering a robust Bayesian False Discovery Rate (FDR) control.

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Multiple hypothesis testing is common in bioinformatics, particularly for differential gene expression analysis.
  • Existing methods can be computationally complex, especially in defining posterior model probabilities.

Purpose of the Study:

  • To develop a Bayesian method for multiple hypothesis testing in bioinformatics.
  • To simplify the modeling process by focusing on test statistics rather than full data.
  • To introduce a graphical tool for assessing model validity in bioinformatics experiments.

Main Methods:

  • A Bayesian approach modeling distributions of test statistics under null and alternative hypotheses.
  • Direct modeling of test statistics to reduce computational complexity.

Related Experiment Videos

  • Application of a Bayesian False Discovery Rate (FDR) control method.
  • Development of a graphical model-assessment tool.

    • Main Results:

    • Extensive simulations demonstrate the performance of the proposed Bayesian models.
    • The utility of the model-assessment tool is validated.
    • The methodology is successfully applied to siRNA screening and gene expression experiments.

    Conclusions:

    • The proposed Bayesian method offers an efficient and robust approach to multiple hypothesis testing in bioinformatics.
    • The model-assessment tool aids in validating assumptions for diverse bioinformatics applications.
    • This methodology enhances the analysis of high-throughput biological data.