Download Multiple testing procedures with applications to genomics by Sandrine Dudoit PDF

By Sandrine Dudoit

The normal method of a number of checking out or simultaneous inference was once to take a small variety of correlated or uncorrelated exams and estimate a family-wise variety I blunders cost that minimizes the the chance of only one kind I mistakes out of the entire set whan all of the null hypotheses carry. Bounds like Bonferroni or Sidak have been occasionally used to as strategy for constraining the typeI errors as they represented higher bounds. different techniques have been to take advantage of multivariate equipment for exams information comparable to Tukey's least major distinction, Scheffe's technique and Dunnett's attempt. extra lately stepdown approaches became well known in medical trials yet there the multiplicity is mostly five or much less. With the creation of the bootstrap and advances in laptop velocity that allowed permutation ways to achieve a better prominence additionally Westfall and younger got here up with a prescription for utilizing resampling to regulate person p-values for the a number of trying out and this was once applied within the SAS strategy MULTTEST and documented either within the SAS handbook and the e-book through Westfall and younger within the mid Nineteen Nineties. The authors of this article are looking to expand a number of checking out to microarrays the place actually millions of speculation are being verified on a unmarried array. Dudoit and van der Laan expand the idea to allow bootstrapping to paintings in a wider context the place many standards except familywise blunders cost (FWER)are thought of together with fake discovery fee (FDR). they are saying that for difficulties concerning very excessive dimensional info an assumption they name subset pivotality doesn't observe. This assumption is basically what's wanted within the Westfall and younger concept and consists of using what the authors name a knowledge producing null distribution. To create a style that works for microarray and different excessive dimensional info the authors base their procedrues onthe joint null distribution of the try data instead of the knowledge producing null distributions that every one different equipment count on.

The ebook presents a really common thought that generalizes the tips of resampling dependent the right way to a brand new framework. The authors intend the e-book for either statisticians and utilized scientists who come across high-dimensional info of their topic sector. The booklet presents a really exact and hugely theoretical account of a number of checking out and will now not be compatible for a few utilized statisticians and scientists. however the rules are very important to all specially within the zone of genomics. The authors declare that chapters 4-7 are theoretical chapters that will not be compatible for everybody yet they insist that the introductory chapters 1-3 and the purposes chapters 8-13 are meant for individuals with an outstanding organic history yet no longer inevitably a truly robust statistical heritage. i don't proportion their view approximately chapters 1-3 which i feel will be tough for someone lack a graduate point facts history yet I do agree that the functions chapters 8-13 are palatable for the meant viewers and is specific fascinating for people with wisdom of and curiosity within the organic sciences.

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Com). 3 Overview of applications to biomedical and genomic research The novel multiple testing procedures introduced above have been applied to a number of testing problems in biomedical and genomic research. Many of these applications concern microarray experiments, which are popular highthroughput assays for measuring the abundance of deoxyribonucleic acids (DNA) and ribonucleic acids (RNA) in different types of cell samples for thousands of sequences simultaneously (Phimister and Cohen, 1999; Packer, 2002; Packer and Axton, 2005; Speed, 2003).

Tau. edu/~genovese), and John Storey (faculty. edu/~jstorey). , 2001). Although such methods constitute an important alternative to frequentist approaches, their thorough treatment is beyond the scope of this book. 2 and discusses its software implementation and application to a variety of testing problems in biomedical and genomic research. The present chapter introduces a general statistical framework for multiple hypothesis testing and motivates the methods developed in Chapters 2–7. These methodological chapters provide specific multiple testing procedures for controlling a range of Type I error rates that are broadly defined as parameters Θ(FVn ,Rn ) of the joint distribution FVn ,Rn of the numbers of Type I errors Vn and rejected hypotheses Rn .

One could also consider the M = J(J −1)/2 submodels M(j, j ) = {P ∈ M : X(j) ⊥ X(j )}, j, j = 1, . . , J, j < j , corresponding to pairwise independence of the elements of X. , functions Ψ (P ) = ψ = (ψ(m) : m = 1, . . , M ) ∈ IRM of the data generating distribution P , and each null hypothesis H0 (m) refers to a single parameter, ψ(m) = Ψ (P )(m) ∈ IR. One distinguishes between two types of testing problems for such parametric hypotheses, one-sided and two-sided tests. 2 Multiple hypothesis testing framework One-sided tests H0 (m) = I (ψ(m) ≤ ψ0 (m)) vs.

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