Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R Order-Restricted Analysis of Microarray Data
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Sprache:Englisch
53,49 €
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Produktdetails
Format
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
27.08.2012
Herausgeber
Dan Lin + weitereVerlag
SpringerSeitenzahl
282 (Printausgabe)
Dateigröße
10883 KB
Sprache
Englisch
EAN
9783642240072
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.
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