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Multiple Testing Procedures With Applications To Genomics
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Book Synopsis Multiple Testing Procedures with Applications to Genomics by : Sandrine Dudoit
Download or read book Multiple Testing Procedures with Applications to Genomics written by Sandrine Dudoit and published by Springer. This book was released on 2008-11-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. These are applied to a range of problems in biomedical and genomic research, including identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments; tests of association between gene expression measures and biological annotation metadata; sequence analysis; and genetic mapping of complex traits using single nucleotide polymorphisms. The procedures are based on a test statistics joint null distribution and provide Type I error control in testing problems involving general data generating distributions, null hypotheses, and test statistics.
Book Synopsis Multiple Testing Procedures with Applications to Genomics by : Sandrine Dudoit
Download or read book Multiple Testing Procedures with Applications to Genomics written by Sandrine Dudoit and published by Springer Science & Business Media. This book was released on 2007-12-18 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. These are applied to a range of problems in biomedical and genomic research, including identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments; tests of association between gene expression measures and biological annotation metadata; sequence analysis; and genetic mapping of complex traits using single nucleotide polymorphisms. The procedures are based on a test statistics joint null distribution and provide Type I error control in testing problems involving general data generating distributions, null hypotheses, and test statistics.
Book Synopsis Multiple Hypothesis Testing by : Houston Nash Gilbert
Download or read book Multiple Hypothesis Testing written by Houston Nash Gilbert and published by . This book was released on 2009 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multiple Testing Procedures Controlling False Discovery Rate with Applications to Genomic Data by : Iris Mirales Gauran
Download or read book Multiple Testing Procedures Controlling False Discovery Rate with Applications to Genomic Data written by Iris Mirales Gauran and published by . This book was released on 2018 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent mutation studies, analyses based on protein domain positions are gaining popularity over traditional gene-centric approaches since the latter have limitations in considering the functional context that the position of the mutation provides. This presents a large-scale simultaneous inference problem, with hundreds of hypothesis tests to consider at the same time. The overarching objective of this thesis is to propose different multiple testing procedures which can address the problems posed by discrete genomic data. Specifically, we are interested in identifying significant mutation counts while controlling a given level of Type I error via False Discovery Rate (FDR) procedures. One main assumption is that the mutation counts follow a zero-inflated model in order to account for the true zeros in the count model and the excess zeros. The class of models considered is the Zero-inflated Generalized Poisson (ZIGP) distribution.
Book Synopsis Resampling-Based Multiple Testing by : Peter H. Westfall
Download or read book Resampling-Based Multiple Testing written by Peter H. Westfall and published by John Wiley & Sons. This book was released on 1993-01-12 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combines recent developments in resampling technology (including the bootstrap) with new methods for multiple testing that are easy to use, convenient to report and widely applicable. Software from SAS Institute is available to execute many of the methods and programming is straightforward for other applications. Explains how to summarize results using adjusted p-values which do not necessitate cumbersome table look-ups. Demonstrates how to incorporate logical constraints among hypotheses, further improving power.
Book Synopsis Large-scale Multiple Hypothesis Testing with Complex Data Structure by : Xiaoyu Dai
Download or read book Large-scale Multiple Hypothesis Testing with Complex Data Structure written by Xiaoyu Dai and published by . This book was released on 2018 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade, motivated by a variety of applications in medicine, bioinformatics, genomics, brain imaging, etc., a growing amount of statistical research has been devoted to large-scale multiple testing, where thousands or even greater numbers of tests are conducted simultaneously. However, due to the complexity of real data sets, the assumptions of many existing multiple testing procedures, e.g. that tests are independent and have continuous null distributions of p-values, may not hold. This poses limitations in their performances such as low detection power and inflated false discovery rate (FDR). In this dissertation, we study how to better proceed the multiple testing problems under complex data structures. In Chapter 2, we study the multiple testing with discrete test statistics. In Chapter 3, we study the discrete multiple testing with prior ordering information incorporated. In Chapter 4, we study the multiple testing under complex dependency structure. We propose novel procedures under each scenario, based on the marginal critical functions (MCFs) of randomized tests, the conditional random field (CRF) or the deep neural network (DNN). The theoretical properties of our procedures are carefully studied, and their performances are evaluated through various simulations and real applications with the analysis of genetic data from next-generation sequencing (NGS) experiments.
Book Synopsis Some New Developments on Multiple Testing Procedures by : Lilun Du
Download or read book Some New Developments on Multiple Testing Procedures written by Lilun Du and published by . This book was released on 2015 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the context of large-scale multiple testing, hypotheses are often accompanied with certain prior information. In chapter 2, we present a single-index modulated multiple testing procedure, which maintains control of the false discovery rate while incorporating prior information, by assuming the availability of a bivariate p-value for each hypothesis. To find the optimal rejection region for the bivariate p-value, we propose a criteria based on the ratio of probability density functions of the bivariate p-value under the true null and non-null. This criteria in the bivariate normal setting further motivates us to project the bivariate p-value to a single index p-value, for a wide range of directions. The true null distribution of the single index p-value is estimated via parametric and nonparametric approaches, leading to two procedures for estimating and controlling the false discovery rate. To derive the optimal projection direction, we propose a new approach based on power comparison, which is further shown to be consistent under some mild conditions. Multiple testing based on chi-squared test statistics is commonly used in many scientific fields such as genomics research and brain imaging studies. However, the challenges associated with designing a formal testing procedure when there exists a general dependence structure across the chi-squared test statistics have not been well addressed. In chapter 3, we propose a Factor Connected procedure to fill in this gap. We first adopt a latent factor structure to construct a testing framework for approximating the false discovery proportion (FDP) for a large number of highly correlated chi-squared test statistics with finite degrees of freedom k. The testing framework is then connected to simultaneously testing k linear constraints in a large dimensional linear factor model involved with some observable and unobservable common factors, resulting in a consistent estimator of FDP based on the associated unadjusted p-values.
Book Synopsis Modelling and Resampling Based Multiple Testing with Applications to Genetics by : Yifan Huang
Download or read book Modelling and Resampling Based Multiple Testing with Applications to Genetics written by Yifan Huang and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Multiple hypotheses testing is a common problem in practice. For instance, in microarray experiments, whether the goal is to select maintenance genes for normalization or to identify differentially expressed genes between samples, multiple genes are under consideration. Multiplicity inflates the type I error rate of the hypothesis testing, so we need to adjust the testing procedure to control the overly error rate. My research focuses on the strong control of Familywise Error Rate (FWER). There are mainly two different types of approaches to multiple testing. One is modelling based approach and the other non-modelling based. Modelling based approaches fit models to the data so that the joint distribution of the test statistics is tractable. Non-modelling based approaches consist of inequality based methods and resampling based methods. They require less or no information about the joint distribution of the test statistics. I have shown in Chapter 1 that frequently used Hochberg's step-up method is a special case of partition testing based on Simes' test. This is a new result. Hochberg's step-up method is an inequity based non-modelling partition testing. Modelling based partition testing is applicable whether the joint distribution of the test statistics is known or not. By applying modelling based partition testing when the joint distribution of test statistics is known, I illustrate that modelling based approaches are often more powerful than inequality based non-modelling approaches. In Chapter 2, I construct counterexamples to the validity of permutation test, demonstrating that the resampling based methods are often invalid. My results suggest recommendation of modelling based approaches. When the joint distribution of the test statistics is untractable, modelling followed by bootstrap can be applied. I use modelling followed by bootstrap in Chapter 3 to select maintenance genes for normalizing the gene expression data.
Book Synopsis Estimation and Selection in High-Dimensional Genomic Studies by : Hisashi Noma
Download or read book Estimation and Selection in High-Dimensional Genomic Studies written by Hisashi Noma and published by Springer. This book was released on 2020-04-23 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of the statistical methods used in genome-wide screening of relevant genomic features or genes. Gene screening can facilitate deeper understanding of disease biology at the molecular level, possibly leading to discovery of new molecular targets for developing new treatments and developing diagnostic tests to predict patients’ prognosis or response to treatment. The most common approach to such gene screening studies is to apply multiple univariate analysis based on separate statistical tests for individual genes to test the null hypothesis of no association with clinical variables. This book first provides an overview of the state of the art of such multiple testing methodologies for gene screening, including frequentist multiple tests, empirical Bayes, and full-Bayes model-based methods for controlling the family-wise error rate or false discovery rate. Optimal discovery procedures and model-based variants are also discussed. Although great endeavor has been directed toward developing multiple testing methods, there are other, more relevant and effective analyses that should be given much attention in gene screening, including gene ranking, estimation of effect sizes, and classification accuracy based on selected genes. The core contents of this book provide a framework for integrated gene screening analysis based on hierarchical mixture modeling and empirical Bayes. Within this framework effective tools for multiple testing, ranking, estimation of effect size, and classification accuracy are derived. Methods for sample size determination for gene screening studies are also provided. With this content, the book is certain to expand the existing framework of statistical analysis based on multiple testing for gene screening to one based on estimation and selection.
Book Synopsis Handbook of Multiple Comparisons by : Xinping Cui
Download or read book Handbook of Multiple Comparisons written by Xinping Cui and published by CRC Press. This book was released on 2021-11-18 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by experts that include originators of some key ideas, chapters in the Handbook of Multiple Testing cover multiple comparison problems big and small, with guidance toward error rate control and insights on how principles developed earlier can be applied to current and emerging problems. Some highlights of the coverages are as follows. Error rate control is useful for controlling the incorrect decision rate. Chapter 1 introduces Tukey's original multiple comparison error rates and point to how they have been applied and adapted to modern multiple comparison problems as discussed in the later chapters. Principles endure. While the closed testing principle is more familiar, Chapter 4 shows the partitioning principle can derive confidence sets for multiple tests, which may become important as the profession goes beyond making decisions based on p-values. Multiple comparisons of treatment efficacy often involve multiple doses and endpoints. Chapter 12 on multiple endpoints explains how different choices of endpoint types lead to different multiplicity adjustment strategies, while Chapter 11 on the MCP-Mod approach is particularly useful for dose-finding. To assess efficacy in clinical trials with multiple doses and multiple endpoints, the reader can see the traditional approach in Chapter 2, the Graphical approach in Chapter 5, and the multivariate approach in Chapter 3. Personalized/precision medicine based on targeted therapies, already a reality, naturally leads to analysis of efficacy in subgroups. Chapter 13 draws attention to subtle logical issues in inferences on subgroups and their mixtures, with a principled solution that resolves these issues. This chapter has implication toward meeting the ICHE9R1 Estimands requirement. Besides the mere multiple testing methodology itself, the handbook also covers related topics like the statistical task of model selection in Chapter 7 or the estimation of the proportion of true null hypotheses (or, in other words, the signal prevalence) in Chapter 8. It also contains decision-theoretic considerations regarding the admissibility of multiple tests in Chapter 6. The issue of selected inference is addressed in Chapter 9. Comparison of responses can involve millions of voxels in medical imaging or SNPs in genome-wide association studies (GWAS). Chapter 14 and Chapter 15 provide state of the art methods for large scale simultaneous inference in these settings.
Book Synopsis Applications and Power Analysis of New Multiple Testing Procedures Based on the Covering Principle by : Yu Zhang
Download or read book Applications and Power Analysis of New Multiple Testing Procedures Based on the Covering Principle written by Yu Zhang and published by . This book was released on 2021 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple comparison tests are quite prevalent in clinical trials to increase efficiency and the chance of finding treatment or drug effects. However, making multiple comparisons will lead to the potential inflation of the Type I error rate. How well one test procedure controls the rate of false-positive conclusions becomes the main concern nowadays. A novel method named Covering Principle has been proposed recently. It provides a new approach to designing multiple testing procedures from the angle of rejection regions in the sample space, rather than in the parameter space, which uses in the methods based on the closed testing and partitioning principle. This paper presented several cases studies and a simulation study using Holm's, Fixed and Fallback based on the Covering Principle. Meanwhile, the power comparison results between the Covering Principle and the graphical method are exhibited and analyzed in this paper.
Book Synopsis Multiple Comparisons Using R by : Frank Bretz
Download or read book Multiple Comparisons Using R written by Frank Bretz and published by CRC Press. This book was released on 2016-04-19 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using R describes the common underlying theory of multiple comparison procedures through numerous examples. It also presents a detailed description of available software implementations in R. The R packages and source code for the analyses are available at http://CRAN.R-project.org After giving examples of multiplicity problems, the book covers general concepts and basic multiple comparisons procedures, including the Bonferroni method and Simes’ test. It then shows how to perform parametric multiple comparisons in standard linear models and general parametric models. It also introduces the multcomp package in R, which offers a convenient interface to perform multiple comparisons in a general context. Following this theoretical framework, the book explores applications involving the Dunnett test, Tukey’s all pairwise comparisons, and general multiple contrast tests for standard regression models, mixed-effects models, and parametric survival models. The last chapter reviews other multiple comparison procedures, such as resampling-based procedures, methods for group sequential or adaptive designs, and the combination of multiple comparison procedures with modeling techniques. Controlling multiplicity in experiments ensures better decision making and safeguards against false claims. A self-contained introduction to multiple comparison procedures, this book offers strategies for constructing the procedures and illustrates the framework for multiple hypotheses testing in general parametric models. It is suitable for readers with R experience but limited knowledge of multiple comparison procedures and vice versa. See Dr. Bretz discuss the book.
Book Synopsis Bioinformatics and Human Genomics Research by : Diego A. Forero
Download or read book Bioinformatics and Human Genomics Research written by Diego A. Forero and published by CRC Press. This book was released on 2021-12-22 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in high-throughput biological methods have led to the publication of a large number of genome-wide studies in human and animal models. In this context, recent tools from bioinformatics and computational biology have been fundamental for the analysis of these genomic studies. The book Bioinformatics and Human Genomics Research provides updated and comprehensive information about multiple approaches of the application of bioinformatic tools to research in human genomics. It covers strategies analysis of genome-wide association studies, genome-wide expression studies and genome-wide DNA methylation, among other topics. It provides interesting strategies for data mining in human genomics, network analysis, prediction of binding sites for miRNAs and transcription factors, among other themes. Experts from all around the world in bioinformatics and human genomics have contributed chapters in this book. Readers will find this book as quite useful for their in silico explorations, which would contribute to a better and deeper understanding of multiple biological processes and of pathophysiology of many human diseases.
Book Synopsis Statistical Evidence by : Richard Royall
Download or read book Statistical Evidence written by Richard Royall and published by Routledge. This book was released on 2017-11-22 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interpreting statistical data as evidence, Statistical Evidence: A Likelihood Paradigm focuses on the law of likelihood, fundamental to solving many of the problems associated with interpreting data in this way. Statistics has long neglected this principle, resulting in a seriously defective methodology. This book redresses the balance, explaining why science has clung to a defective methodology despite its well-known defects. After examining the strengths and weaknesses of the work of Neyman and Pearson and the Fisher paradigm, the author proposes an alternative paradigm which provides, in the law of likelihood, the explicit concept of evidence missing from the other paradigms. At the same time, this new paradigm retains the elements of objective measurement and control of the frequency of misleading results, features which made the old paradigms so important to science. The likelihood paradigm leads to statistical methods that have a compelling rationale and an elegant simplicity, no longer forcing the reader to choose between frequentist and Bayesian statistics.
Book Synopsis Bioinformatics in Human Health and Heredity by : Ranajit Chakraborty
Download or read book Bioinformatics in Human Health and Heredity written by Ranajit Chakraborty and published by Newnes. This book was released on 2012-10-03 with total page 614 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. A series of handbooks is the only way of presenting the various aspects of statistical methodology, applications and developments. This volume deals with bioinformatics.
Book Synopsis Simultaneous Statistical Inference by : Thorsten Dickhaus
Download or read book Simultaneous Statistical Inference written by Thorsten Dickhaus and published by Springer Science & Business Media. This book was released on 2014-01-23 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph will provide an in-depth mathematical treatment of modern multiple test procedures controlling the false discovery rate (FDR) and related error measures, particularly addressing applications to fields such as genetics, proteomics, neuroscience and general biology. The book will also include a detailed description how to implement these methods in practice. Moreover new developments focusing on non-standard assumptions are also included, especially multiple tests for discrete data. The book primarily addresses researchers and practitioners but will also be beneficial for graduate students.
Book Synopsis Multiple Testing in Microarrays by : Yongchao Ge
Download or read book Multiple Testing in Microarrays written by Yongchao Ge and published by . This book was released on 2003 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt:
