Abstract
We propose model-based inference for differential gene expression, using a non-parametric Bayesian probability model for the distribution of gene intensities under various conditions. The probability model is a mixture of normal distributions. The resulting inference is similar to a popular empirical Bayes approach that is used for the same inference problem. The use of fully model-based inference mitigates some of the necessary limitations of the empirical Bayes method. We argue that inference is no more difficult than posterior simulation in traditional nonparametric mixture-of-normal models. The approach proposed is motivated by a microarray experiment that was carried out to identify genes that are differentially expressed between normal tissue and colon cancer tissue samples. Additionally, we carried out a small simulation study to verify the methods proposed. In the motivating case-studies we show how the nonparametric Bayes approach facilitates the evaluation of posterior expected false discovery rates. We also show how inference can proceed even in the absence of a null sample of known non-differentially expressed scores. This highlights the difference from alternative empirical Bayes approaches that are based on plug-in estimates.
Original language | English (US) |
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Pages (from-to) | 627-644 |
Number of pages | 18 |
Journal | Journal of the Royal Statistical Society. Series C: Applied Statistics |
Volume | 54 |
Issue number | 3 |
DOIs | |
State | Published - 2005 |
Keywords
- Density estimation
- Dirichlet process
- Gene expression
- Microarrays
- Mixture models
- Nonparametric Bayes method
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty