Lecture 10: Multiple Testing

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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 H0Rejct H0Actual 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?

• Positive false discovery rate (pFDR): the rate that discoveries are false pFDR = E(V/R | R > 0) Digression: p-values •Implicit in all multiple testing procedures is the assumption that the distribution of p-values is

  Discoveries