A Practical Approach to Microarray Data Analysis

1. Front Matter

   Pages i-xv

   PDF

2. Introduction to Microarray Data Analysis

   • Werner Dubitzky, Martin Granzow, C. Stephen Downes, Daniel Berrar

   Pages 1-46

3. Data Pre-Processing Issues in Microarray Analysis

   • Nicholas A. Tinker, Laurian S. Robert, Gail Butler, Linda J. Harris

   Pages 47-64

4. Missing Value Estimation

   • Olga G. Troyanskaya, David Botstein, Russ B. Altman

   Pages 65-75

5. Normalization

   • Norman Morrison, David C. Hoyle

   Pages 76-90

6. Singular Value Decomposition and Principal Component Analysis

   • Michael E. Wall, Andreas Rechtsteiner, Luis M. Rocha

   Pages 91-109

7. Feature Selection in Microarray Analysis

   • Eric P. Xing

   Pages 110-131

8. Introduction to Classification in Microarray Experiments

   • Sandrine Dudoit, Jane Fridly

   Pages 132-149

9. Bayesian Network Classifiers for Gene Expression Analysis

   • Byoung-Tak Zhang, Kyu-Baek Hwang

   Pages 150-165

10. Classifying Microarray Data Using Support Vector Machines

    • Sayan Mukherjee

    Pages 166-185

11. Weighted Flexible Compound Covariate Method for Classifying Microarray Data

    • Yu Shyr, KyungMann Kim

    Pages 186-200

12. Classification of Expression Patterns Using Artificial Neural Networks

    • Markus Ringnér, Patrik Edén, Peter Johansson

    Pages 201-215

13. Gene Selection and Sample Classification Using a Genetic Algorithm and k-Nearest Neighbor Method

    • Leping Li, Clarice R. Weinberg

    Pages 216-229

14. Clustering Genomic Expression Data: Design and Evaluation Principles

    • Francisco Azuaje, Nadia Bolshakova

    Pages 230-245

15. Clustering or Automatic Class Discovery: Hierarchical Methods

    • Derek C. Stanford, Douglas B. Clarkson, Antje Hoering

    Pages 246-260

16. Discovering Genomic Expression Patterns with Self-Organizing Neural Networks

    • Francisco Azuaje

    Pages 261-273

17. Clustering or Automatic Class Discovery: Non-Hierarchical, non-SOM

    • Ka Yee Yeung

    Pages 274-288

18. Correlation and Association Analysis

    • Simon M. Lin, Kimberly F. Johnson

    Pages 289-305

19. Global Functional Profiling of Gene Expression Data

    • Sorin Draghici, Stephen A. Krawetz

    Pages 306-325

20. Microarray Software Review

    • Yuk Fai Leung, Dennis Shun Chiu Lam, Chi Pui Pang1

    Pages 326-344

In the past several years, DNA microarray technology has attracted tremendous interest in both the scientific community and in industry. With its ability to simultaneously measure the activity and interactions of thousands of genes, this modern technology promises unprecedented new insights into mechanisms of living systems. Currently, the primary applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery (pharmacogenomics), and toxicological research (toxicogenomics). Typical scientific tasks addressed by microarray experiments include the identification of coexpressed genes, discovery of sample or gene groups with similar expression patterns, identification of genes whose expression patterns are highly differentiating with respect to a set of discerned biological entities (e.g., tumor types), and the study of gene activity patterns under various stress conditions (e.g., chemical treatment). More recently, the discovery, modeling, and simulation of regulatory gene networks, and the mapping of expression data to metabolic pathways and chromosome locations have been added to the list of scientific tasks that are being tackled by microarray technology. Each scientific task corresponds to one or more so-called data analysis tasks. Different types of scientific questions require different sets of data analytical techniques. Broadly speaking, there are two classes of elementary data analysis tasks, predictive modeling and pattern-detection. Predictive modeling tasks are concerned with learning a classification or estimation function, whereas pattern-detection methods screen the available data for interesting, previously unknown regularities or relationships.

• Bayesian network
• Evaluation
• algorithms
• bioinformatics
• classification
• computer science
• data mining
• gene expression
• genes
• genetic algorithms

• School of Biomedical Sciences, University of Ulster at Coleraine, Northern Ireland

  Daniel P. Berrar

• Faculty of Life and Health Science and Faculty of Informatics, University of Ulster at Coleraine, Northern Ireland

  Werner Dubitzky

• 4T2consulting, Weingarten, Germany

  Martin Granzow

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