MODELING DOSE-RESPONSE MICROARRAY DATA IN EARLY DRUG DEVELOPMENT EXPERIMENTS USING R by Danyu Lin (Editor), Ziv Shkedy (Editor), Daniel Yekutieli (Editor) ( 9783642240065 )

Item Details

Title:

MODELING DOSE-RESPONSE MICROARRAY DATA IN EARLY DRUG DEVELOPMENT EXPERIMENTS USING R
ORDER-RESTRICTED ANALYSIS OF MICROARRAY DATA

By:

Danyu Lin (Editor), Ziv Shkedy (Editor), Daniel Yekutieli (Editor)

Format:

Paperback

List price:	£42.99
We currently do not stock this item, please contact the publisher directly for further information.

ISBN 10:

3642240062

ISBN 13:

9783642240065

Publisher:

SPRINGER-VERLAG BERLIN AND HEIDELBERG GMBH & CO. KG

Pub. date:

26 August, 2012

Edition:

2012

Series:

Use R!

Pages:

282

Description:

This volume provides user-friendly software and a GUI package to assist with microarray data analysis in early drug development. Each methodological issue is illustrated using real-world examples of early drug development dose-response microarray experiments.

Synopsis:

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.

Illustrations:

92 black & white illustrations, 4 colour illustrations, 20 black & white

Publication:

Germany

Imprint:

Springer-Verlag Berlin and Heidelberg GmbH & Co. K

Returns:

Returnable

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