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.

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.