Transcription of Lecture 10: Multiple Testing - UW Genome Sciences
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Lecture 10: Multiple Testing - UW Genome Sciences
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?Why Multiple Testing MattersP(Making an error) = P(Not making an error) = 1 - P(Not making an error in m tests) = (1 - )mP(Making at least 1 error in m tests) = 1 - (1 - )mProbability of At Least 1 False PositiveCounting Errors Assume we are Testing H1, H2.
Why Multiple Testing Matters Genomics = Lots of Data = Lots of Hypothesis Tests A typical microarray experiment might result in performing 10000 separate hypothesis …
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