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.
