Modeling Dose Response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data 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. 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.