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.

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.