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Analyzing Microarray Gene Expression Data

Analyzing Microarray Gene Expression Data

Geoffrey McLachlan , Kim-Anh Do , Christophe Ambroise

ISBN: 978-0-471-22616-1

Aug 2004

368 pages

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A multi-discipline, hands-on guide to microarray analysis of biological processes

Analyzing Microarray Gene Expression Data provides a comprehensive review of available methodologies for the analysis of data derived from the latest DNA microarray technologies. Designed for biostatisticians entering the field of microarray analysis as well as biologists seeking to more effectively analyze their own experimental data, the text features a unique interdisciplinary approach and a combined academic and practical perspective that offers readers the most complete and applied coverage of the subject matter to date.

Following a basic overview of the biological and technical principles behind microarray experimentation, the text provides a look at some of the most effective tools and procedures for achieving optimum reliability and reproducibility of research results, including:

  • An in-depth account of the detection of genes that are differentially expressed across a number of classes of tissues
  • Extensive coverage of both cluster analysis and discriminant analysis of microarray data and the growing applications of both methodologies
  • A model-based approach to cluster analysis, with emphasis on the use of the EMMIX-GENE procedure for the clustering of tissue samples
  • The latest data cleaning and normalization procedures
  • The uses of microarray expression data for providing important prognostic information on the outcome of disease

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Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data  (Hardcover £83.50)

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A Primer on Statistical Distributions (Hardcover £131.00)

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The Collected Works of Geoffrey J. McLachlan

This item: Analyzing Microarray Gene Expression Data

Discriminant Analysis and Statistical Pattern Recognition

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Analyzing Microarray Gene Expression Data

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The EM Algorithm and Extensions, 2nd Edition

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Finite Mixture Models

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1. Microarrays in Gene Expression Studies.

2. Cleaning and Normalization.

3. Some Cluster Analysis Methods.

4. Clustering of Tissue Samples.

5. Screening and Clustering of Genes.

6. Discriminant Analysis.

7. Supervised Classification of Tissue Samples.

8. Linking Microarray Data with Survival Analysis.


Author Index.

Subject Index.

"…well written, logically organized…useful to those unfamiliar with the microarray literature…" (Journal of the American Statistical Association, December 2005)

“….would serve as a very good resource for a graduate topics seminar in advanced applied statistics.” (Short Book Reviews, Vol.25, No.1, April 2005)

"For the intended audience of biostatisticians and biologists…this book will be an asset." (E-STREAMS, February 2005)

"I liked this book…expands on both the tools and applications of microarray data analysis…" (Technometrics, February 2005)