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Lecture 10: Multiple Testing

Lecture 10: Multiple TestingGoals Correcting for Multiple Testing in R Methods for addressing Multiple Testing (FWERand FDR) Define the Multiple Testing problem and relatedconceptsType I and II ErrorsCorrect Decision1 - Correct Decision1 - Incorrect Decision Incorrect Decision H0 TrueH0 FalseDo NotReject H0 Rejct H0 Actual Situation Truth DecisionType II ErrorType I Error)()(ErrorIITypePErrorITypeP==!"Why Multiple Testing MattersGenomics = Lots of Data = Lots of Hypothesis TestsA typical microarray experiment might result in performing10000 separate hypothesis tests. If we use a standard p-valuecut-off of , we d expect 500 genes to be deemed significant by chance. In general, if we perform m hypothesis tests, what is theprobability of at least 1 false positive?

False Discovery Rate m 0 m-m 0 m V S R Called Significant U T m - R Not Called Significant True True Total Null Alternative V = # Type I errors [false positives] •False discovery rate (FDR) is designed to control the proportion of false positives among the set of rejected hypotheses (R)

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Lecture 10: Multiple Testing - UW Genome Sciences

www.gs.washington.edu

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