Statistical Analysis of Gene Expression Microarray Data: (Chapman & Hall/CRC Interdisciplinary Statistics)

Although less than a decade old, the field of microarray data analysis is now thriving and growing at a remarkable pace. Biologists, geneticists, and computer scientists as well as statisticians all need an accessible, systematic treatment of the techniques used for analyzing the vast amounts of data generated by large-scale gene expression studies. And there is arguably no group better qualified to do so than the authors of this book. 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.

Table of Contents:
MODEL-BASED ANALYSIS OF OLIGONUCLEOTIDE ARRAYS AND ISSUES IN cDNA MICROARRAY ANALYSIS, Cheng Li, George C. Tseng, and Wing Hung Wong Model-Based Analysis of Oligonucleotide Arrays Issues in cDNA Microarray Analysis Acknowledgments DESIGN AND ANALYSIS OF COMPARATIVE MICROARRAY EXPERIMENTS, Yee Hwa Yang and Terry Speed Introduction Experimental Design Two-Sample Comparisons Single-Factor Experiments with more than Two Levels Factorial Experiments Some Topics for Further Research CLASSIFICATION IN MICROARRAY EXPERIMENTS, \ Sandrine Dudoit and Jane Fridlyand Introduction Overview of Different Classifiers General Issues in Classification Performance Assessment Aggregating Predictors Datasets Results Discussion Software and Datasets Acknowledgments CLUSTERING MICROARRAY DATA, Hugh Chipman, Trevor J. Hastie, and Robert Tibshirani An Example Dissimilarity Clustering Methods Partitioning Methods Hierarchical Methods Two-Way Clustering Principal Components, the SVD, and Gene Shaving Other Approaches Software REFERENCES INDEX