Synopses & Reviews
This book focuses on the analysis of microarray data in the dose-response setting in early drug development experiments in the pharmaceutical industry, the goal being to cover this important topic in early drug development and to provide user-friendly software - R library IsoGene - and a GUI package that can be used to analyze dose-response microarray data. It is intended for biostatisticians in the pharmaceutical industry, biologists who conduct dose-response microarray experiments, and biostatistics/bioinformatics graduate students. All methodological issues in the book are illustrated using four "real-world" examples of early drug development dose-response microarray experiments. In Part I we discuss the dose-response setting and the problem of estimation of normal means under order restrictions. In particular we discuss the pooled-adjacent-violator (PAV) algorithm, isotonic regression, as well as the likelihood ratio test, which will be used in the second part of the book. The core part of the book is Part II. We start with a brief overview of the specific setting of dose-response microarray experiments together with a discussion about the setting, data structure, and the case studies that will be analyzed in later chapters. We demonstrate the use of the IsoGene R library and in particular its graphical capacity. We then focus on the multiplicity problem, which arises when thousands of genes are tested simultaneously. Although multiple testing is discussed in many books and publications related to microarray data analysis, our goal here is to discuss the main concepts and to illustrate how each method can be implemented in practice. In the next step we shift gears and discuss four test statistics that can be used to test for gene significance.
Synopsis
This volume provides user-friendly software and a GUI package to assist with microarray data analysis in early drug development. Each methodological issue is illustrated using real-world examples of early drug development dose-response microarray experiments.
Synopsis
This book focuses on the analysis of dose-response microarray data in pharmaceutical setting, the goal being to cover this important topic for early drug development and to provide user-friendly R packages that can be used to analyze dose-response microarray 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 the likelihood ratio test and non-linear parametric models, which are used in the second part of the book.
About the Author
Dan Lin holds a Ph.D. degree in Biostatistics from Hasselt University, Belgium. She currently works as a researcher at Hasselt University in the area of functional genomics and statistical bioinformatics. Ziv Shkedy is an associate professor for biostatistics and bioinformatics at Hasselt University, Belgium.
Table of Contents
Introduction.- Part I: Dose-response Modeling: An Introduction.- Estimation Under Order Restrictions.- The Likelihood Ratio Test.- Part II: Dose-response Microarray Experiments.- Functional Genomic Dose-response Experiments.- Adjustment for Multiplicity.- Test for Trend.- Order Restricted Bisclusters.- Classification of Trends in Dose-response Microarray Experiments Using Information Theory Selection Methods.- Multiple Contrast Test.- Confidence Intervals for the Selected Parameters.- Case Study Using GUI in R: Gene Expression Analysis After Acute Treatment With Antipsychotics.