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An Empirical Bayes Adjustment to Increase the Sensitivity of Detecting Differentially Expressed Genes in Microarray Experiments


Susmita Datta¹, Glen A. Satten², Dale J. Benos³, Jiazeng Xia³, Martin J. Heslin⁴, Somnath Datta^{* 5}

1 Department of Mathematics and Statistics, Georgia State University, Atlanta,  GA 30303.
2 Centers for Disease Control and Prevention, Atlanta, GA 30333.
3 Department of Physiology and Biophysics, University of Alabama at Birmingham,  Birmingham, AL 35294.
4 Department of Surgery, University of Alabama at Birmingham,  Birmingham, AL 35294.
5 Department of Statistics, University of Georgia, Athens, GA 30602.

* Corresponding author: somnath.datta@louisville.edu

Bioinformatics,  20, 235-242 (2004)

Motivation: Detection of differentially expressed genes is one of the major goals of microarray experiments. Pairwise comparison for each gene is not appropriate without controlling the overall (experimentwise) type 1 error rate.  Dudoit et al. have advocated use of permutation-based step-down P-value adjustments to correct the observed significance levels for the individual (i.e., for each gene) two sample t-tests.

Results: In this paper, we consider an ANOVA formulation of the gene expression levels corresponding to multiple tissue types. We provide resampling-based step-down adjustments to correct the observed significance levels for the individual ANOVA t-tests for each gene and for each pair of tissue type comparisons.  More importantly, we introduce a novel empirical Bayes adjustment to the t-test statistics that can be incorporated into the step-down procedure.  Using simulated data, we show that the empirical Bayes adjustment improved the sensitivity of detecting differentially expressed genes up to 16%, while maintaining a high level of specificity. This adjustment also reduces the FNR (false non-discovery rate) to some degree at the cost of a modest increase in the FDR (false discovery rate). We illustrate our approach using a human colon cancer data set consisting of oligonucleotide arrays of normal, adenoma and carcinoma cells. The number of genes with differential expression level declared statistically significant was about fifty when comparing normal to adenoma cells and about five when comparing adenoma to carcinoma cells. This list includes genes previously known to be associated with colon cancer as well as some novel genes.


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Data set (colon cancer) download

For further details on the data set contact Dr. Dale Benos (benos@physiology.uab.edu)

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R-code used to calculate the P-values of the empirical Bayes adjusted t-tests for the colon cancer data download

This is being distributed without warrantee; use at your own risk!
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Mean (log) expression level  versus  standard deviation of the residuals for the colon cancer data:

For easy visualization,  values for the top 2000 (most differentially expressed) genes are plotted.

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