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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?

• 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

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Lecture 10: Multiple Testing - UW Genome Sciences

www.gs.washington.edu

Why Multiple Testing Matters Genomics = Lots of Data = Lots of Hypothesis Tests A typical microarray experiment might result in performing 10000 separate hypothesis tests.

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DNA Isolation from Strawberries

www.gs.washington.edu

source for extracting DNA because they are easy to pulverize and contain enzymes called pectinases and cellulases that help to break down cell walls. And most important, strawberries have eight copies of each chromosome (they are octoploid), so there is a lot of DNA to isolate. The purpose of each ingredient in the procedure is as follows:

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Lecture 7: Hypothesis Testing and ANOVA

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•Calculate a test statistic in the sample data that is ... candidate gene. We then divide these N individuals into ... Sum of MS F Squares Source of df Variation! SST G k"1! SST E N"k! SST G k"1 SST E N"k. Non-Parametric Alternative • Kruskal-Wallis …

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•Before data is collected, we regard observations as random variables (X 1,X 2,…,X n) •This implies that until data is collected, any function (statistic) of the observations (mean, sd, etc.) is also a random variable •Thus, any statistic, because it is a random variable, has a probability distribution - referred to as a sampling ...

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I-9 Form: Instructions for Nonresident on H-1B or TN visa Instructions for both New Hires and Updating & Reverification For more detailed information about completing Form I-9, employers and employees should refer to the Handbook for Employers: Instructions for Completing Form I-9 (M-274). _____

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Lecture 5: Estimation - University of Washington

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Bayesian Estimation: ÒSimpleÓ Example ¥I want to estimate the recombination fraction between locus A and B from 5 heterozygous (AaBb) parents. I examine 30 gametes for each and observe 4, 3, 5, 6, and 7 recombinant gametes in the Þve parents. What is the mle of the recombination fraction? ¥Tedious to show Bayesian analysis. LetÕs simplify ...

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Lecture 9: Linear Regression - University of Washington

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Lecture 9: Linear Regression. Goals • Linear regression in R •Estimating parameters and hypothesis testing ... •Previous coding would result in colinearity •Solution is to set up a series of dummy variable. In general for k levels you need k-1 dummy variables x 1 …

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Plasmid replication requires host DNA replication machinery. 2. Most wild plasmids carry genes needed for transfer and copy number ... Large or small region of homologous DNA cloned that will integrate into the chromosomal target. 5. Need a counter selection method to kill the donor cells 6. Screen for what you think is correct.

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Lecture 2: Descriptive Statistics and Exploratory Data ...

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Multivariate Data •Organize units into clusters •Descriptive, not inferential •Many approaches •“Clusters” always produced Clustering Data Reduction Approaches (PCA) •Reduce n-dimensional dataset into much smaller number •Finds a new (smaller) set of variables that retains most of the information in the total sample

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