Items related to Analysis of Microarray Gene Expression Data
Synopsis
Table of Contents B Part I: Genome Probing Using Microarrays 1. Introduction 2. DNA, RNA, Proteins, and Gene Expression 3. Microarray Technology 4. Inherent Variability in Array Data 5. Background Noise 6. Transformation and Normalization 7. Missing Values in Array Data 8. Saturated Intensity Readings B Part II: Statistical Models and Analysis 9. Experimental Design 10. ANOVA Models for Microarray Data 11. Multiple Testing in Microarray Studies 12. Permutation Tests in Microarray Data 13. Bayesian Methods for Microarray Data 14. Power and Sample Size Considerations B Part III. Unsupervised Exploratory Analysis 15. Cluster Analysis 16. Principal Components and Singular Value Decomposition 17. Self-organizing Maps B Part IV. Supervised Learning Methods 18. Discrimination and Classification 19. Artificial Neural Networks 20. Support Vector MachinesB
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Review
From the reviews:
"This book aims to be a comprehensive work on statistical techniques for analysis of microarray data. ... the book contains an elaborate discussion on several variants of useful ANOVA models. ... Standard multiple testing and permutation methods are well illustrated. ... In conclusion, the book is a successful attempt to be a complete reference work for microarray data analysis. It is certainly a rich source of references." (M. A. van de Wiel, Kwantitatieve Methoden, Issue 3, 2006)
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- PublisherSpringer-Verlag New York Inc.
- Publication date2004
- ISBN 10 1402077890
- ISBN 13 9781402077890
- BindingPaperback
- LanguageEnglish
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