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by: Rupert Davis


Rupert Davis
GPA 3.79

Joshua Akey

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About this Document

Joshua Akey
Class Notes
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This 28 page Class Notes was uploaded by Rupert Davis on Wednesday September 9, 2015. The Class Notes belongs to GENOME 560 at University of Washington taught by Joshua Akey in Fall. Since its upload, it has received 13 views. For similar materials see /class/192401/genome-560-university-of-washington in Genome Sciences at University of Washington.




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Date Created: 09/09/15
Why You Are Taking This Course It s required now shut up and sit down Because I m a sadist You re about to be confused made to feel stupid and bored for an entire quarter shut up and sit down Why You Are Taking This Course Data are interesting and they are interesting because they help us understand the world Genomics Massive Amounts of Data Data Statistics is fundamental in genomics because it is integral in the design analysis and interpretation of experiments The Roots of Modern Statistics Emerged From Genetics Sir Francis Galton Inventor of ngerprints study of heredity of quantitative traits Regression amp correlation Also ef cacy of prayer attractiveness as function of distance from London Karl Pearson Polymath Studied genetics Correlation coef cient 96 test Standard deviation Sir Ronald Fisher The Genetical Theory of Natural Selection Founder of population genetics Analysis of variance likelihood Pvalue randomized experiments multiple regression etc etc etc Why I Am The Rightful Heir of Statistical Genomics Central Dogma of Statistics Probability Descriptive Inferential Statistics Course Stuff Syllabus Date Topic April 1 Collecting Data and Experimental Design April 3 Descriptive Statistics and Visualizing Data April 8 Randomness and Probability April 10 Distributions April 15 Estimating Parameters April 17 Hypothesis testing 1 Inferences based on one or two samples April 22 Hypothesis testing 2 ANOVA April 24 Linear Regression April 29 Analysis of Categorical Data May 1 Assessing Significance in High Dimensional Space Grading 5 problem sets 20 each sorry really no other way Books and Resources No required text Good onIine resources httpwwwmathwmedutrossetJCourses35 lbookpdf httpwwwstatsoftcomtextbookstathomehtml httpwwwstatberkeleyedustarkSticiGuiTextJtochtm Some good books if you ever have some extra Probability and Statistics for Engineering and the Sciences 6th ed Jay L Devore 2004 Duxbury press ThompsonBrooksCole Statistical Inference Casella G and Berger R L I990 Wadsworth Belmont CA Collecting Data and Experimental Design Experimental design encompasses the myriad details that constitute the substance of the actual planning conduct and interpretation of a research study Ransohoff 2007 Journal of Clinical Epidemiology 60205 Typical Genomics Data L Uh V u7 m c mm FN mm Nor wk 3 gnmant 1mm mama madnl DNA avmk amp mumquot umw 5ND J Clvro39nosuniu 4 a Hnnmnnes we Esta mu 5 cnoTTcAcacA 55 Hazvuwpea quota re acAAcAuYAAvA 50 mm cccsAYcTarsAVAc39IEGTB as 1 7111 n r m 5747 5 f U A F 530 39 J A r c 7n cmgsum z a c n mza 3x a 250 n 500 1000 2 or A non Ralalive abundance a 55 69 25 4221 10 us m 53 m quotquot7quot 3 u 55 nasw 194451 nu w Izse 5 3 u a A NEWS mean IEI39ISY 400 m 600 700 we 90 x mm Hon 1200 v 300 L400 1500 mm mI Bad Things Can Happen With Bad Experimental Design Common genetic variants account for differences in gene expression among ethnic groups mtlmd s Sptelmnn39 Laurel A Emmi loshtm T Buntitk Mitlud Moilry wtmen J Ewcns amp Vivian c henng39v v c mpared gene expression levels between so CEU and 82 ASN Hszap individuals Tests of differential expression performed by parametric ttests and adjustment for multiple testing through Sidak corrections Estimate 46 of genes to be differentially expressed 0n the design and analysis of gene expression studies ons in human populall 78 of Genes Are Estimated To Be Differentially Expressed p values Population and Time of Processing Are Confounded l N I CEU ASN n 8 E gt g lt Ln o a Q 9 E 3 Z m Jan Jan Jan Jan Jan 2002 2003 2004 2005 2006 Date Batch Effects Can Completely Account For Differential Expression Between Between Between Populations Population Years Adjusting For Years 3 2 1 0 fl l l l l I I l I I l I I I I I 00 02 04 06 08 10 00 02 04 06 08 10 00 02 04 05 08 10 P Values 78 of genes estimated 96 of genes estimated 0 of genes estimated to to be differentially to be differentially be differentially Elements of Good Experimental Design Experimental design is a whole subdiscipline in statistics research For geneticsgenomics studies the two most important ideas are 1 Randomization 2 Control Randomization and control are essential for making valid statistical inferences and minimizing bias caused by confounding variables Some Jargon Units the basic objects on which the experiment is done Variable a measured characteristic of a unit Treatment any speci c experimental condition applied to the units A treatment can be a combination of specific values called levels of each experimental factor Bias consistent divergence between the value of a variable in a sample from the corresponding value in a population Why Randomize Breaks the association between potential confounding variables and the explanatory variables Confounding variables variables whose effects cannot be distinguished from one another Helps to avoid hidden sources of bias Association of shoe size S and literacy L in kids eo Efo Randomization Randomization can be implemented in multiple ways depending on the particular experiment Randomly selecting individuals from a population Randomly assigning treatments to units in an experiment Randomization in the technical aspects of how an experiment is performed Confounding variable Confounding Experimental units Treatments Without randomization the confounding variable differs among treatments Confounding variable Confounding Experimental units Treatments With randomization the confounding variable does not differ among treatments Other Aspects of Good Experimental Design Balanced experimental design all treatments have equal sample size 0 O O O O O B tt th C e er an O O C O O O O O Balanced Unbalanced Replication reduces and allows estimates of variation Technical versus Biological Eliminating Bias Controls A control group is a group of subjects left untreated for the treatment of interest but otherwise experiencing the same conditions as the treated subjects Example one group of patients is given an inert placebo Thought Question Using the principles we just discussed how would you design the gene expression study of Cheung et al discussed previously


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