From the reviews: "This book aims to be a comprehensive work on statistical techniques for analysis of microarray data. ... the book contains an elaborate discussion on several variants of useful ANOVA models. ... Standard multiple testing and permutation methods are well illustrated. ... In conclusion, the book is a successful attempt to be a complete reference work for microarray data analysis. It is certainly a rich source of references." (M. A. van de Wiel, Kwantitatieve Methoden, Issue 3, 2006)

• DNA, RNA, Protein, and Gene Expression.- Microarray Technology.- Inherent Variability in Microarray Data.- Background Noise.- Transformation and Normalization.- Missing Values in Microarray Data.- Saturated Intensity Readings in Microarray Data.- Experimental Design.- Anova Models for Michrorray Data.- Multiple Testing in Microarray Studies.- Permutation Tests in Microarray Data.- Bayesian Methods for Microarray Data.- Power and Sample Size Considerations at the Planning Stage.- Cluster Analysis.- Principal Components and Singular Value Decomposition.- Self-organizing Maps.- Discrimination and Classification.- Artificial Neural Networks.- Support Vector Machines.