Skip to Content

Statistical Analysis of Gene Expression Microarray Data

Edited by Terry Speed

Contributors: T.J. Hastie, R.J. Tibshirani, Wing Hung Wong, Sandrine Dudoit, Jane Fridlyand, Cheng Li, Yee Hwa Yang, Hugh Chipman, George C. Tseng

Chapman and Hall/CRC – 2004 – 240 pages

Series: Chapman & Hall/CRC Interdisciplinary Statistics

Purchasing Options:

  • Add to CartHardback: $115.95
    978-1-58488-327-2
    March 25th 2003

Description

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.

  • 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

    Related Subjects

    1. Bioinformatics

    Name: Statistical Analysis of Gene Expression Microarray Data (Hardback)Chapman and Hall/CRC 
    Description: Edited by Terry SpeedContributors: T.J. Hastie, R.J. Tibshirani, Wing Hung Wong, Sandrine Dudoit, Jane Fridlyand, Cheng Li, Yee Hwa Yang, Hugh Chipman, George C. Tseng. 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...
    Categories: Bioinformatics