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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.
List of contents
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.
About the author
Dhannika Amaratunga, PhD, is a Senior Research Fellow in the Nonclinical Biostatistics Department at Johnson & Johnson Pharmaceutical Research & Development, LLC. He has a doctorate in statistics from Princeton University and has been working in the pharmaceutical industry for over fifteen years. His research interests include analysis of large multivariate data sets, particularly those generated by functional genomics research, robust and resistant statistical methods, linear and nonlinear modeling, and biostatistics.
Summary
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.
Additional text
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)
Report
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)
