Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R

Order-Restricted Analysis of Microarray Data

Nonfiction, Science & Nature, Mathematics, Statistics, Computers, Application Software
Cover of the book Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R by , Springer Berlin Heidelberg
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Author: ISBN: 9783642240072
Publisher: Springer Berlin Heidelberg Publication: August 27, 2012
Imprint: Springer Language: English
Author:
ISBN: 9783642240072
Publisher: Springer Berlin Heidelberg
Publication: August 27, 2012
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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