After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis. The contribution of this book is to provide readers with an integrated presentation of various topics on analyzing microarray data.
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. 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. Part III: Unsupervised Exploratory Analysis. 15. Cluster Analysis. 16. Principal Components and Singular Value Decomposition. 17. Self-organizing Maps. Part IV: Supervised Learning Methods. 18. Discrimination and Classification. 19. Artificial Neural Networks. 20. Support Vector Machines.
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)
After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis. The contribution of this book is to provide readers with an integrated presentation of various topics on analyzing microarray data.
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. 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. Part III: Unsupervised Exploratory Analysis. 15. Cluster Analysis. 16. Principal Components and Singular Value Decomposition. 17. Self-organizing Maps. Part IV: Supervised Learning Methods. 18. Discrimination and Classification. 19. Artificial Neural Networks. 20. Support Vector Machines.
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)
Inhaltsverzeichnis
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. 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. Part III: Unsupervised Exploratory Analysis. 15. Cluster Analysis. 16. Principal Components and Singular Value Decomposition. 17. Self-organizing Maps. Part IV: Supervised Learning Methods. 18. Discrimination and Classification. 19. Artificial Neural Networks. 20. Support Vector Machines.
Klappentext
After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis. The contribution of this book is to provide readers with an integrated presentation of various topics on analyzing microarray data.
