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Abstract:
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In studies that use DNA arrays to assess changes in gene expression, our goal is to evaluate the statistical significance of treatments on expressions for predefined sets of genes, e.g., sets of genes grouped by gene ontology (GO) terms. Computer simulations demonstrated that ignoring the correlations among genes overstates the significance assigned to GO terms. We propose statistical tests which are based on meta-analysis methods for combining p-values. With the tests, reliable corrections for the effect of correlations among genes on the significance level of a GO term can be constructed for an alternative hypothesis where all transcripts in the GO term increase (decrease) in response to treatment. For general alternatives, which allow some transcripts to increase and others to decrease, the bias of naïve significance calculations can be greatly decreased although not eliminated.
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