Resampling Based Multiple Testing With Applications To Microarray Data Analysis
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Resampling Based Multiple Testing With Applications To Microarray Data Analysis
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Author : Dongmei Li
language : en
Publisher:
Release Date : 2009
Resampling Based Multiple Testing With Applications To Microarray Data Analysis written by Dongmei Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with DNA microarrays categories.
Abstract: In microarray data analysis, resampling methods are widely used to discover significantly differentially expressed genes under different biological conditions when the distributions of test statistics are unknown. When sample size is small, however, simultaneous testing of thousands, or even millions, of null hypotheses in microarray data analysis brings challenges to the multiple hypothesis testing field. We study small sample behavior of three commonly used resampling methods, including permutation tests, post-pivot resampling methods, and pre-pivot resampling methods in multiple hypothesis testing. We show the model-based pre-pivot resampling methods have the largest maximum number of unique resampled test statistic values, which tend to produce more reliable P-values than the other two resampling methods. To avoid problems with the application of the three resampling methods in practice, we propose new conditions, based on the Partitioning Principle, to control the multiple testing error rates in fixed-effects general linear models. Meanwhile, from both theoretical results and simulation studies, we show the discrepancies between the true expected values of order statistics and the expected values of order statistics estimated by permutation in the Significant Analysis of Microarrays (SAM) procedure. Moreover, we show the conditions for SAM to control the expected number of false rejections in the permutation-based SAM procedure. We also propose a more powerful adaptive two-step procedure to control the expected number of false rejections with larger critical values than the Bonferroni procedure.
Multiple Testing Procedures With Applications To Genomics
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Author : Sandrine Dudoit
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-12-18
Multiple Testing Procedures With Applications To Genomics written by Sandrine Dudoit and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-18 with Computers categories.
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.
Dna Microarrays And Related Genomics Techniques
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Author : David B. Allison
language : en
Publisher: CRC Press
Release Date : 2005-11-14
Dna Microarrays And Related Genomics Techniques written by David B. Allison and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-11-14 with Mathematics categories.
Considered highly exotic tools as recently as the late 1990s, microarrays are now ubiquitous in biological research. Traditional statistical approaches to design and analysis were not developed to handle the high-dimensional, small sample problems posed by microarrays. In just a few short years the number of statistical papers providing approaches
Modeling Dose Response Microarray Data In Early Drug Development Experiments Using R
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Author : Dan Lin
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-08-27
Modeling Dose Response Microarray Data In Early Drug Development Experiments Using R written by Dan Lin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-27 with Mathematics categories.
This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to analyze this data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students. Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as inference under order restrictions and non-linear parametric models, which are used in the second part of the book. Part II is the core of the book, in which we focus on the analysis of dose-response microarray data. Methodological topics discussed include: • Multiplicity adjustment • Test statistics and procedures for the analysis of dose-response microarray data • Resampling-based inference and use of the SAM method for small-variance genes in the data • Identification and classification of dose-response curve shapes • Clustering of order-restricted (but not necessarily monotone) dose-response profiles • Gene set analysis to facilitate the interpretation of microarray results • Hierarchical Bayesian models and Bayesian variable selection • Non-linear models for dose-response microarray data • Multiple contrast tests • Multiple confidence intervals for selected parameters adjusted for the false coverage-statement rate All methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.
Resampling Based Multiple Testing
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Author : Peter H. Westfall
language : en
Publisher: John Wiley & Sons
Release Date : 1993-01-12
Resampling Based Multiple Testing written by Peter H. Westfall and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-01-12 with Mathematics categories.
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.
Methods Of Microarray Data Analysis Ii
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Author : Simon M. Lin
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-08
Methods Of Microarray Data Analysis Ii written by Simon M. Lin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-08 with Science categories.
Microarray technology is a major experimental tool for functional genomic explorations, and will continue to be a major tool throughout this decade and beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray data analysis is an emerging field, very few analytical models currently exist. Methods of Microarray Data Analysis II is the second book in this pioneering series dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods, ranging from data normalization, feature selection, and discriminative analysis to machine learning techniques. Currently, there are no standard procedures for the design and analysis of microarray experiments. Methods of Microarray Data Analysis II focuses on a single data set, using a different method of analysis in each chapter. Real examples expose the strengths and weaknesses of each method for a given situation, aimed at helping readers choose appropriate protocols and utilize them for their own data set. In addition, web links are provided to the programs and tools discussed in several chapters. This book is an excellent reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses in undergraduate and graduate programs.
Biological Knowledge Discovery Handbook
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Author : Mourad Elloumi
language : en
Publisher: John Wiley & Sons
Release Date : 2015-02-04
Biological Knowledge Discovery Handbook written by Mourad Elloumi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-04 with Computers categories.
The first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD) providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing also known as data mining and data postprocessing) and analyzes both verification systems and discovery systems. BIOLOGICAL DATA PREPROCESSING Part A: Biological Data Management Part B: Biological Data Modeling Part C: Biological Feature Extraction Part D Biological Feature Selection BIOLOGICAL DATA MINING Part E: Regression Analysis of Biological Data Part F Biological Data Clustering Part G: Biological Data Classification Part H: Association Rules Learning from Biological Data Part I: Text Mining and Application to Biological Data Part J: High-Performance Computing for Biological Data Mining Combining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.
Bioinformatics Research And Applications
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Author : Ion Mandoiu
language : en
Publisher: Springer
Release Date : 2008-04-30
Bioinformatics Research And Applications written by Ion Mandoiu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-30 with Science categories.
This book constitutes the refereed proceedings of the Fourth International Symposium on Bioinformatics Research and Applications, ISBRA 2008, held in Atlanta, GA, USA in May 2008. The 35 revised full papers presented together with 6 workshop papers and 6 invited papers were carefully reviewed and selected from a total of 94 submissions. The papers cover a wide range of topics, including clustering and classification, gene expression analysis, gene networks, genome analysis, motif finding, pathways, protein structure prediction, protein domain interactions, phylogenetics, and software tools.
Handbook Of Statistics In Clinical Oncology
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Author : John Crowley
language : en
Publisher: CRC Press
Release Date : 2005-12-01
Handbook Of Statistics In Clinical Oncology written by John Crowley and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-01 with Mathematics categories.
A compendium of cutting-edge statistical approaches to solving problems in clinical oncology, Handbook of Statistics in Clinical Oncology, Second Edition focuses on clinical trials in phases I, II, and III, proteomic and genomic studies, complementary outcomes and exploratory methods. Cancer Forum called the first edition a
Advanced Analysis Of Gene Expression Microarray Data
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Author : Aidong Zhang
language : en
Publisher: World Scientific Publishing Company
Release Date : 2006-06-27
Advanced Analysis Of Gene Expression Microarray Data written by Aidong Zhang and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-27 with Science categories.
This book focuses on the development and application of the latest advanced data mining, machine learning, and visualization techniques for the identification of interesting, significant, and novel patterns in gene expression microarray data.Biomedical researchers will find this book invaluable for learning the cutting-edge methods for analyzing gene expression microarray data. Specifically, the coverage includes the following state-of-the-art methods:• Gene-based analysis: the latest novel clustering algorithms to identify co-expressed genes and coherent patterns in gene expression microarray data sets• Sample-based analysis: supervised and unsupervised methods for the reduction of the gene dimensionality to select significant genes. A series of approaches to disease classification and discovery are also described• Pattern-based analysis: methods for ascertaining the relationship between (subsets of) genes and (subsets of) samples. Various novel pattern-based clustering algorithms to find the coherent patterns embedded in the sub-attribute spaces are discussed• Visualization tools: various methods for gene expression data visualization. The visualization process is intended to transform the gene expression data set from high-dimensional space into a more easily understood two- or three-dimensional space.