Multiple-testing strategy for analyzing cDNA array data on gene expression

Biometrics
Robert R DelongchampRalph L Kodell

Abstract

An objective of many functional genomics studies is to estimate treatment-induced changes in gene expression. cDNA arrays interrogate each tissue sample for the levels of mRNA for hundreds to tens of thousands of genes, and the use of this technology leads to a multitude of treatment contrasts. By-gene hypotheses tests evaluate the evidence supporting no effect, but selecting a significance level requires dealing with the multitude of comparisons. The p-values from these tests order the genes such that a p-value cutoff divides the genes into two sets. Ideally one set would contain the affected genes and the other would contain the unaffected genes. However, the set of genes selected as affected will have false positives, i.e., genes that are not affected by treatment. Likewise, the other set of genes, selected as unaffected, will contain false negatives, i.e., genes that are affected. A plot of the observed p-values (1 - p) versus their expectation under a uniform [0, 1] distribution allows one to estimate the number of true null hypotheses. With this estimate, the false positive rates and false negative rates associated with any p-value cutoff can be estimated. When computed for a range of cutoffs, these rates summarize the ab...Continue Reading

References

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Citations

Aug 31, 2006·The Pharmacogenomics Journal·J J ChenC-J Lin
Dec 7, 2006·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·B Moerkerke, E Goetghebeur
Nov 27, 2004·Bioinformatics·Chen-An TsaiJames J Chen
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Jan 22, 2013·Journal of Biopharmaceutical Statistics·Ralph L Kodell, James J Chen
Dec 17, 2009·Cancer Control : Journal of the Moffitt Cancer Center·James J ChenMichael J Schell

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