• Provides the first comprehensive coverage by statisticians of the issues, features, and problems associated with the analysis of microarray data
• Presents contributions from the pre-eminent statisticians working in the field
• Offers a presentation accessible to biologists, geneticists, and computer scientists as well as statisticians
• Covers the most important topics needed for the analysis of microarray data - pre-processing issues, experiment design, classification, and clustering

Statistical Analysis of Gene Expression Microarray Data promises to become the definitive basic reference in the field. Under the editorship of Terry Speed, some of the world's most pre-eminent authorities have joined forces to present the tools, features, and problems associated with the analysis of genetic microarray data. These include::

• Model-based analysis of oligonucleotide arrays, including expression index computation, outlier detection, and standard error applications
• Design and analysis of comparative experiments involving microarrays, with focus on two-color cDNA or long oligonucleotide arrays on glass slides
• Classification issues, including the statistical foundations of classification and an overview of different classifiers
• Clustering, partitioning, and hierarchical methods of analysis, including techniques related to principal components and singular value decomposition

Although the technologies used in large-scale, high throughput assays will continue to evolve, statistical analysis will remain a cornerstone of their success and future development.Statistical Analysis of Gene Expression Microarray Data will help you meet the challenges of large, complex datasets and contribute to new methodological and computational advances.

Contents

1. MODEL-BASED ANALYSIS OF OLIGONUCLEOTIDE ARRAYS AND ISSUES IN cDNA MICROARRAY ANALYSIS
   • Model-Based Analysis of Oligonucleotide Arrays
   • Issues in cDNA Microarray Analysis

2. DESIGN AND ANALYSIS OF COMPARATIVE MICROARRAY EXPERIMENTS
   • Introduction
   • Experimental Design
   • Two-Sample Comparisons
   • Single-Factor Experiments with more than Two Levels
   • Factorial Experiments
   • Some Topics for Further Research

3. CLASSIFICATION IN MICROARRAY EXPERIMENTS
   • Introduction
   • Overview of Different Classifiers
   • General Issues in Classification
   • Performance Assessment
   • Aggregating Predictors
   • Datasets
   • Results
   • Discussion
   • Software and Datasets

4. CLUSTERING MICROARRAY DATA
   • An Example
   • Dissimilarity
   • Clustering Methods
   • Partitioning Methods
   • Hierarchical Methods
   • Two-Way Clustering
   • Principal Components, the SVD, and Gene Shaving
   • Other Approaches
   • Software

Index

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