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Fully Bayesian mixture model for differential gene expression: simulations and model checks.

Alex Lewin1, Natalia Bochkina, Sylvia Richardson

  • 1Imperial, London. a.m.lewin@imperial.ac.uk

Statistical Applications in Genetics and Molecular Biology
|January 4, 2008
PubMed
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This study introduces a Bayesian model to identify over-expressed and under-expressed genes. The new method accurately estimates gene expression proportions and improves false discovery rate estimation for biological research.

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Differential gene expression analysis is crucial for understanding biological processes.
  • Existing methods often rely on fixed proportions of differentially expressed genes.
  • Accurate estimation of gene expression variability is essential.

Purpose of the Study:

  • To develop a flexible Bayesian hierarchical model for detecting differentially expressed genes.
  • To estimate the proportion of differentially expressed genes and mixture parameters in a fully Bayesian manner.
  • To guide the selection of appropriate mixture priors using predictive model checks.

Main Methods:

  • A 3-component mixture prior is formulated to classify genes.
  • Gene variances are modeled as exchangeable to account for inter-gene variability.

Related Experiment Videos

  • Bayesian estimation is employed for model parameters and proportions.

    • Main Results:

    • The model successfully estimates the proportion of differentially expressed genes and mixture parameters.
    • Accurate estimation of false discovery rates is achieved.
    • Predictive model checks favored a mixture model with extra variability around zero over a point mass null.

    Conclusions:

    • The proposed Bayesian hierarchical model offers an improved approach to differential gene expression analysis.
    • The method provides a data-driven way to select appropriate mixture priors.
    • The developed R software package facilitates the application of this model in biological research.