A biologist's guide to analysis of DNA microarray data
| Autor principal: | |
|---|---|
| Formato: | Libro |
| Idioma: | Inglés |
| Publicado: |
New York :
Wiley-Interscience
c2002.
|
| Materias: | |
| Acceso en línea: | Descripción del editor Índice Información biográfica |
| Ver en Universidad de Navarra: | https://unika.unav.edu/discovery/fulldisplay?docid=alma991008298219708016&context=L&vid=34UNAV_INST:VU1&search_scope=34UNAV_TODO&tab=34UNAV_TODO&lang=es |
Tabla de Contenidos:
- Machine generated contents note: Preface xi
- Acknowledgments xiii
- 1 Introduction I
- 1.1 Hybridization 1
- 1.2 Affymetrix GeneChip Technology 3
- 1.3 Spotted Arrays 6
- 1.4 Serial Analysis of Gene Expression (SAGE) 8
- 1.5 Example: Affymetrix vs. Spotted Arrays 9
- 1.6 Summary 11
- 1.7 Further Reading 13
- 2 Overview of Data Analysis 15
- 3 Basic Data Analysis 17
- 3.1 Absolute Measurements 17
- 3.2 Scaling 18
- 3.2.1 Example: Linear and Nonlinear Scaling 20
- 3.3 Detection of Outliers 20
- 3.4 Fold Change 21
- 3.5 Significance 22
- 3.5.1 Nonparametric Tests 24
- 3.5.2 Correction for Multiple Testing 24
- 3.5.3 Example I: t-Test and ANOVA 25
- 3.5.4 Example II: Number of Replicates 26
- 3.6 Summary 28
- 3.7 Further Reading 29
- 4 Visualization by Reduction of Dimensionality 33
- 4.1 Principal Component Analysis 33
- 4.2 Example 1: PCA on Small Data Matrix 35
- 4.3 Example 2: PCA on Real Data 37
- 4.4 Summary 37
- 4.5 Further Reading 39
- 5 Cluster Analysis 41
- 5.1 Hierarchical Clustering 41
- 5.2 K-means Clustering 43
- 5.3 Self-Organizing Maps 44
- 5.4 Distance Measures 45
- 5.4.1 Example: Comparison of Distance Measures 47
- 5.5 Normalization 49
- 5.6 Visualization of Clusters 50
- 5.6.1 Example: Visualization of Gene Clusters in
- Bladder Cancer 50
- 5.7 Summary 50
- 5.8 Further Reading 52
- 6 Beyond Cluster Analysis 55
- 6.1 Function Prediction 55
- 6.2 Discovery of Regulatory Elements in Promoter
- Regions 56
- 6.2.1 Example 1: Discovery of Proteasomal Element 57
- 6.2.2 Example 2: Rediscovery of Mlu Cell Cycle
- Box (MCB) 57
- 6.3 Integration of data 58
- 6.4 Summary 59
- 6.5 Further Reading 59
- 7 Reverse Engineering of Regulatory Networks 63
- 7.1 The Time-Series Approach 63
- 7.2 The Steady-State Approach 64
- 7.3 Limitations of Network Modeling 65
- 7.4 Example 1: Steady-State Model 65
- 7.5 Example 2: Steady-State Model on Real Data 66
- 7.6 Example 3: Steady-State Model on Real Data 68
- 7.7 Example 4: Linear Time-Series Model 68
- 7.8 Further Reading 71
- 8 Molecular Classifiers 75
- 8.1 Classification Schemes 76
- 8.1.1 Nearest Neighbor 76
- 8.1.2 Neural Networks 76
- 8.1.3 Support Vector Machine 76
- 8.2 Example I: Classification of Cancer Subtypes 77
- 8.3 Example II: Classification of Cancer Subtypes 78
- 8.4 Summary 79
- 8.5 Further Reading 79
- 9 Selection of Genes for Spotting on Arrays 81
- 9.1 Gene Finding 82
- 9.2 Selection of Regions Within Genes 82
- 9.3 Selection of Primers for PCR 83
- 9.4 Selection of Unique Oligomer Probes 83
- 9.4.1 Example: Finding PCR Primers for Gene
- AF105374 83
- 9.5 Experimental Design 84
- 9.6 Further Reading 84
- 10 Limitations of Expression Analysis 87
- 10.1 Relative VersusAbsoluteRNA Quantification 88
- 10.2 Further Reading 88
- 11 Genotyping Chips 91
- 11.1 Example: NeuralNetworksfor GeneChipprediction 91
- 11.2 Further Reading 93
- 12 Software Issues and Data Formats 95
- 12.1 Standardization Efforts 96
- 12.2 Standard File Format 97
- 12.2.1 Example: Small Scripts in Awk 97
- 12.3 Software for Clustering 98
- 12.3.1 Example: Clustering with ClustArray 99
- 12.4 Software for Statistical Analysis 99
- 12.4.1 Example: StatisticalAnalysis with R 99
- 12.4.2 The affyR Software Package 103
- 12.4.3 Commercial Statistics Packages 103
- 12.5 Summary 103
- 12.6 Further Reading 104
- 13 Commercial Software Packages 105
- 14 Bibliography 109
- Index 123.
