A Biologist's Guide to Analysis of DNA Microarray Data

Description

A great introductory book that details reliable approaches to problems met in standard microarray data analyses. It provides examples of established approaches such as cluster analysis, function prediction, and principle component analysis. Discover real examples to illustrate the key concepts of data analysis. Written for those without any advanced background in math, statistics, or computer sciences, this book is essential for anyone interested in harnessing the immense potential of microarrays in biology and medicine.

A Biologist's Guide to ANALYSIS OF DNA MICROARRAY DATA 2
- Contents 10
- Preface 14
- Acknowledgments 16
- 1 Introduction 17
  - 1.1 Hybridization 17
  - 1.2 Affymetrix GeneChip Technology 19
  - 1.3 Spotted Arrays 22
  - 1.4 Serial Analysis of Gene Expression (SAGE) 24
  - 1.5 Example: Atymetrix vs. Spotted Arrays 25
  - 1.6 Summary 27
  - 1.7 Further Reading 29
- 2 Overview of Data Analysis 31
- 3 Basic Data Analysis 33
  - 3.1 Absolute Measurements 33

A Biologist's Guide to ANALYSIS OF DNA MICROARRAY DATA 2
- Contents 10
- Preface 14
- Acknowledgments 16
- 1 Introduction 17
  - 1.1 Hybridization 17
  - 1.2 Affymetrix GeneChip Technology 19
  - 1.3 Spotted Arrays 22
  - 1.4 Serial Analysis of Gene Expression (SAGE) 24
  - 1.5 Example: Atymetrix vs. Spotted Arrays 25
  - 1.6 Summary 27
  - 1.7 Further Reading 29
- 2 Overview of Data Analysis 31
- 3 Basic Data Analysis 33
  - 3.1 Absolute Measurements 33
  - 3.2 Scaling 34
    - 3.2.1 Example: Linear and Nonlinear Scaling 36
  - 3.3 Detection of Outliers 36
  - 3.4 Fold Change 37
  - 3.5 Significance 38
    - 3.5.2 Correction for Multiple Testing 40
    - 3.5.1 Nonparametric Tests 40
    - 3.5.3 Example I: t-Test and ANOVA 41
    - 3.5.4 Example II: Number of Replicates 42
  - 3.6 Summary 44
  - 3.7 Further Reading 45
- 4 Visualization by Reduction of Dimensionality 49
  - 4.1 Principal Component Analysis 49
  - 4.2 Example 1: PCA on Small Data Matrix 51
  - 4.3 Example 2: PCA on Real Data 53
  - 4.4 Summary 53
  - 4.5 Further Reading 55
- 5 Cluster Analysis 57
  - 5.1 Hierarchical Clusterinig 57
  - 5.2 K-means Clustering 59
  - 5.3 Self-Organizing Maps 60
  - 5.4 Distance Measures 61
    - 5.4.1 Example: Comparison of Distance Measures 63
  - 5.5 Normalization 65
  - 5.7 Summary 66
  - 5.6 Visualization of Clusters 66
    - 5.6.1 Example: Visualization of Gene Clusters in Bladder Cancer 66
  - 5.8 Further Reading 68
- 6 Beyond Cluster Analysis 71
  - 6.1 Function Prediction 71
  - 6.2 Discovery of Regulatory Elements in Promoter Regions 72
    - 6.2.1 Example 1: Discovery of Proteasomal Element 73
    - 6.2.2 Example 2: Rediscovery of Mlu Cell Cycle Box (MCB) 73
  - 6.3 Integration of data 74
  - 6.5 Further Reading 83
  - 6.4 Summary 83
- 7 Reverse Engineering of Regulatory Networks 87
  - 7.1 The Time-Series Approach 87
  - 7.2 The Steady-State Approach 88
  - 7.3 Limitations of Network Modeling 89
  - 7.4 Example 1: Steady-State Model 89
  - 7.5 Example 2: Steady-State Model on Real Data 90
  - 7.7 Example 4: Linear Time-Series Model 92
  - 7.6 Example 3: Steady-State Model on Real Data 92
  - 7.8 Further Reading 95
- 8 Molecular Classifiers 99
  - 8.1 Classification Schemes 100
    - 8.1.3 Support Vector Machine 100
    - 8.1.1 Nearest Neighbor 100
    - 8.1.2 Neural Networks 100
  - 8.2 Example I: Classification of Cancer Subtypes 101
  - 8.3 Example II: Classification of Cancer Subtypes 102
  - 8.4 Summary 103
  - 8.5 Further Reading 103
- 9 Selection of Genes for Spotting on Arrays 105
  - 9.1 Gene Finding 106
  - 9.2 Selection of Regions Within Genes 106
  - 9.3 Selection of Primers for PCR 107
  - 9.4 Selection of Unique Oligomer Probes 107
    - 9.4.1 Example: Finding PCR Primers for Gene AF105374 107
  - 9.5 Experimental Design 108
  - 9.6 Further Reading 108
- 10 Limitations of Expression Analysis 111
  - 10.1 Relative Versus Absolute RNA Quantification 112
  - 10.2 Further Reading 112
- 11 Genotyping Chips 115
  - 11.1 Example: Neural Networks for GeneChip prediction 115
  - 11.2 Further Reading 117
- 12 Software Issues and Data Formats 119
  - 12.1 Standardization Efforts 120
  - 12.2 Standard File Format 121
    - 12.2.1 Example: Small Scripts in Awk 121
  - 12.3 Software for Clustering 122
    - 12.3.1 Example: Clustering with ClustArray 123
  - 12.4 Software for Statistical Analysis 123
    - 12.4.1 Example: Statistical Analysis with R 123
    - 12.4.2 The affyR Software Package 127
    - 12.4.3 Commercial Statistics Packages 127
  - 12.5 Summary 127
  - 12.6 Further Reading 128
- 13 Commercial Software Packages 129
- 14 Bibliography 133
- Index 147

Author's affiliation

Steen Knudsen: Center for Biological Sequence Analysis, BioC

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