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Praise for the First Edition extremely well written a comprehensive and up-to-date overview of this important field. Journal of Environmental Quality Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition provides comprehensive coverage of recent advancements in microarray data analysis. A cutting-edge guide, the Second Edition demonstrates various methodologies for analyzing data in biomedical research and offers an overview of the modern techniques used in microarray technology to study patterns of gene activity. The new edition answers the need for an efficient outline of all phases of this revolutionary analytical technique, from preprocessing to the analysis stage. Utilizing research and experience from highly-qualified authors in fields of data analysis, Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition features: * A new chapter on the interpretation of findings that includes a discussion of signatures and material on gene set analysis, including network analysis * New topics of coverage including ABC clustering, biclustering, partial least squares, penalized methods, ensemble methods, and enriched ensemble methods * Updated exercises to deepen knowledge of the presented material and provide readers with resources for further study The book is an ideal reference for scientists in biomedical and genomics research fields who analyze DNA microarrays and protein array data, as well as statisticians and bioinformatics practitioners. Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition is also a useful text for graduate-level courses on statistics, computational biology, and bioinformatics.
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:
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.
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