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Activity Number:
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394
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Type:
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Invited
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Date/Time:
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Thursday, August 12, 2004 : 8:30 AM to 10:20 AM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #300058 |
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Title:
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A Multiple-testing Strategy for Analyzing cDNA Array Data on Gene Expression
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Author(s):
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Ralph L. Kodell*+ and Robert R. Delongchamp and John F. Bowyer and James J. Chen
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Companies:
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U.S. Food and Drug Administration and U.S. Food and Drug Administration and U.S. Food and Drug Administration and National Center for Toxicological Research
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Address:
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NCTR, Division of Biometry and Risk Assessment, Jefferson, AR, 72079,
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Keywords:
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subset selection ; p value plot ; false discovery rate ; false nondiscovery rate ; ROC curve ; optimal cutoff
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Abstract:
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CDNA arrays interrogate tissue samples for the levels of mRNA for hundreds to tens of thousands of genes to measure treatment-induced changes in gene expression. Selecting a significance level for by-gene hypothesis tests requires dealing with the multitude of treatment contrasts. The p values from these tests order the genes such that a p value cutoff divides the genes into two sets. The set of genes selected as affected will have false positives while the set selected as unaffected will contain false negatives. A p value plot allows one to estimate the number of true null hypotheses (truly unaffected genes). With this estimate, the false-positive and false-negative rates associated with any p value cutoff can be estimated. An optimal cutoff depends upon the relative cost of falsely classifying a gene as affected versus the cost of falsely classifying a gene as unaffected. Here, a method analogous to methods developed for ROC curves is proposed for selecting the cutoff. The false discovery rate (FDR) and false nondiscovery rate (FNR) associated with the cutoff are then estimated. Two functional genomics studies are used for illustration.
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