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Statistical Analysis of Gene Expression Microarray Data

Edition:
1st
Author(s):
Terry Speed
ISBN:
9781584883272
Format:
Hardback
Publication Date:
March 26, 2003
Content Details:
240 pages | 48 illustrations
Language:
English

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List Price:   $135.00

  
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  • About the Book

    Book Summary

    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.
  • Features

    • 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

    Reviews

      "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
  • 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