Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R
Order-Restricted Analysis of Microarray Data
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Beschreibung
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.
Part II is the core of the book. Methodological topics discussed include:
· Multiplicity adjustment
· Test statistics and testing procedures for the analysis of dose-response microarray data
· Resampling-based inference and use of the SAM method at the presence of 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
· Hierarchical Bayesian models and non-linear models for dose-response microarray data
· Multiple contrast tests
All methodological issues in the book are illustrated using four “real-world” examples of dose-response microarray datasets from early drug development experiments.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 rateAll methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.
This book focuses on the analysis of microarray data in the dose-response setting in early drug development experiments in the pharmaceutical industry Part I discusses the dose-response setting and the problem of estimation of normal means under order restrictions Part II demonstrates the use of the IsoGene R library and in particular its graphical capacity
Autor*in
Dan Lin
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Dose-response microarray data Estimation and inference under order restrictions Gene expression R
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From the book reviews:
“This edited volume is designed for the analysis of dose-response microarray data in a pharmaceutical environment. … The book includes many useful topics and procedures for graduate students, practitioners, and researchers … in the arena of bioinformatics and statistical bioinformatics. The contributions are written to be accessible to readers with moderate to strong knowledge of statistics, computer science, and biology, since this is a genuine multidisciplinary area.” (S. E. Ahmed, Technometrics, Vol. 55 (3), August, 2013)
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