Lin, D., Shkedy, Z., Yekutieli, D., Amaratunga, D., Bijnens, L. (Eds.)
2012, XV, 282 p. 96 illus., 4 illus. in color.
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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
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.
Content Level »Graduate
Keywords »Dose-response microarray data - Estimation and inference under order restrictions - Gene expression - R
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.