1st Edition
Statistical Analysis of Gene Expression Microarray Data
Edited By Terry Speed
Copyright 2003
236 Pages
48 Color & 99 B/W Illustrations
by
Chapman & Hall
240 Pages
by
Chapman & Hall
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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::
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.
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
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
"The 10 authors are among the world's authorities on the statistical analysis of this new class of biotechnology… . What I like best about this stimulating book is that it allows a simplified logical view of large complex multivariate data sets. … I highly recommend this book for library purchase, and for individuals in the field… ."
- Journal of the Royal Statistical Society, Series A, Vol. 157 (3)
"Analysis for gene expression data is the latest hot new topic in statistical data analysis...[this book] deals with microarray experiments: design and analysis for a comparative study, classification methods for data analysis, and clustering for data analysis. Scientists whose work concerns this type of data will want to get a copy of the book."
-Technometrics, 2003
"…This book is a milestone, documenting major significant advances in the statistical methodology. The four chapters, though independent, share common foci with issues of design, robustness, and the freely available associated software. The statistical ideas are introduced succinctly. The book is especially valuable for research scientists in the field seeking an understanding of the related statistical developments."
- Short Book Reviews of the ISI
