Exploration and analysis of DNA microarray and protein array data / Dhammika Amaratunga, Javier Cabrera.

Format:

Book

Author/Creator:

Amaratunga, Dhammika, 1956-

Contributor:

Cabrera, Javier.

Series:

Wiley series in probability and statistics

Language:

English

Subjects (All):

DNA microarrays--Statistical methods.

DNA microarrays.

Protein microarrays--Statistical methods.

Protein microarrays.

Oligonucleotide Array Sequence Analysis--statistics & numerical data.

Protein Array Analysis--statistics & numerical data.

Statistics.

Medical Subjects:

Oligonucleotide Array Sequence Analysis--statistics & numerical data.

Protein Array Analysis--statistics & numerical data.

Physical Description:

xiv, 246 pages : illustrations ; 25 cm.

Place of Publication:

Hoboken, NJ : Wiley-Interscience, [2004]

Summary:

The emergence of genomics, the study of genes, is one of the major scientific revolutions of this century. Microarrays, a method used to analyze numerous DNA samples rapidly, enables scientists to make sense of this mountain of data using statistical analysis. They are being used in such areas of biomedical research as studying patterns for gene activity that cause cancers to spread. This book presents a comprehensive methodology for analyzing DNA microarray and protein array data. The most comprehensive treatment of this important emerging field, Exploration and Analysis of DNA Microarray and Protein Array Data includes: A review of basic molecular biology and a chapter introducing microarrays and their preparation Chapters on processing scanned images, preprocessing microarray data, group comparative experiments, and other designs Discussions of clustering, protein arrays, and applications for diagnostic tools Ample exercises assist absorbtion

Contents:

1.1 A Note on Exploratory Data Analysis 3

1.2 Computing Considerations and Software 4

2 Genomics Basics 8

2.1 Genes 8

2.2 DNA 9

2.3 Gene Expression 10

2.4 Hybridization Assays and Other Laboratory Techniques 12

2.5 The Human Genome 14

2.6 Genome Variations and Their Consequences 15

2.8 The Role of Genomics in Pharmaceutical Research 18

2.9 Proteins 20

2.10 Bioinformatics 21

3 Microarrays 23

3.1 Types of Microarray Experiments 24

3.1.1 Experiment Type 1: Tissue-Specific Gene Expression 24

3.1.2 Experiment Type 2: Development Genetics 24

3.1.3 Experiment Type 3: Genetic Diseases 25

3.1.4 Experiment Type 4: Complex Diseases 26

3.1.5 Experiment Type 5: Pharmacological Agents 26

3.1.6 Experiment Type 6: Plant Breeding 27

3.1.7 Experiment Type 7: Environmental Monitoring 27

3.2 A Very Simple Hypothetical Microarray Experiment 28

3.3 A Typical Microarray Experiment 30

3.3.1 Microarray Preparation 30

3.3.2 Sample Preparation 32

3.3.3 The Hybridization Step 32

3.3.4 Scanning the Microarray 33

3.3.5 Interpreting the Scanned Image 33

3.4 Multichannel cDNA Microarrays 34

3.5 Oligonucleotide Arrays 35

3.6 Bead-Based Arrays 36

3.7 Confirmation of Microarray Results 37

4 Processing the Scanned Image 39

4.1 Converting the Scanned Image to the Spotted Image 39

4.1.1 Gridding 40

4.1.2 Segmentation 40

4.1.3 Quantification 41

4.2 Quality Assessment 42

4.2.1 Visualizing the Spotted Image 43

4.2.2 Numerical Evaluation of Array Quality 44

4.2.3 Spatial Problems 45

4.2.4 Spatial Randomness 46

4.2.5 Quality Control of Arrays 47

4.2.6 Assessment of Spot Quality 48

4.3 Adjusting for Background 49

4.3.2 Adjusting for the Estimated Background 53

4.4 Expression Level Calculation for Two-Channel cDNA Microarrays 53

4.5 Expression Level Calculation for Oligonucleotide Arrays 54

4.5.1 The Average Difference 54

4.5.2 A Weighted Average Difference 54

4.5.3 Perfect Matches Only 55

4.5.4 Background Adjustment Approach 56

4.5.5 Model-Based Approach 56

4.5.6 Absent-Present Calls 56

5 Preprocessing Microarray Data 60

5.1 Logarithmic Transformation 60

5.2 Variance Stabilizing Transformations 62

5.3 Sources of Bias 63

5.4 Normalization 63

5.5 Intensity-Dependent Normalization 65

5.5.1 Smooth Function Normalization 68

5.5.2 Quantile Normalization 68

5.5.3 Normalization of Oligonucleotide Arrays 70

5.5.4 Normalization of Two-Channel Arrays 70

5.5.5 Spatial Normalization 71

5.5.6 Stagewise Normalization 72

5.6 Judging the Success of a Normalization 73

5.7 Outlier Identification 77

5.7.1 Nonresistant Rules for Outlier Identification 77

5.7.2 Resistant Rules for Outlier Identification 78

5.8 Assessing Replicate Array Quality 79

6 Summarization 82

6.1 Replication 82

6.2 Technical Replicates 83

6.3 Biological Replicates 86

6.4 Experiments with Both Technical and Biological Replicates 87

6.5 Multiple Oligonucleotide Arrays 90

6.6 Estimating Fold Change in Two-Channel Experiments 92

6.7 Bayes Estimation of Fold Change 93

7 Two-Group Comparative Experiments 95

7.1 Basics of Statistical Hypothesis Testing 96

7.2 Fold Changes 99

7.3 The Two-Sample t Test 100

7.4 Diagnostic Checks 103

7.5 Robust t Tests 104

7.6 Randomization Tests 105

7.7 The Mann-Whitney-Wilcoxon Rank Sum Test 108

7.8 Multiplicity 109

7.8.1 A Pragmatic Approach to the Issue of Multiplicity 109

7.8.2 Simple Multiplicity Adjustments 110

7.8.3 Sequential Multiplicity Adjustments 111

7.9 The False Discovery Rate 113

7.9.1 The Positive False Discovery Rate 114

7.10 Small Variance-Adjusted t Tests and SAM 115

7.10.1 Modifying the t Statistic 117

7.10.2 Assesing Significance with the SAM t Statistic 117

7.10.3 Strategies for Using SAM 120

7.10.4 An Empirical Bayes Framework 120

7.10.5 Understanding the SAM Adjustment 121

7.11 Conditional t 123

7.12 Borrowing Strength across Genes 126

7.12.1 Simple Methods 127

7.12.2 A Bayesian Model 129

7.13 Two-Channel Experiments 130

7.13.1 The Paired Sample t Test and SAM 131

7.13.2 Borrowing Strength via Hierarchical Modeling 131

8 Model-Based Inference and Experimental Design Considerations 135

8.1 The F Test 136

8.2 The Basic Linear Model 138

8.3 Fitting the Model in Two Stages 140

8.4 Multichannel Experiments 141

8.5 Experimental Design Considerations 141

8.5.1 Comparing Two Varieties with Two-Channel Microarrays 141

8.5.2 Comparing Multiple Varieties with Two-Channel Microarrays 143

8.5.3 Single-Channel Microarray Experiments 145

8.6 Miscellaneous Issues 146

9 Pattern Discovery 149

9.1 Initial Considerations 149

9.2 Cluster Analysis 151

9.2.1 Dissimilarity Measures and Similarity Measures 152

9.2.2 Guilt by Association 155

9.2.3 Hierarchical Clustering 155

9.2.4 Partitioning Methods 160

9.2.5 Model-Based Clustering 166

9.2.6 Chinese Restaurant Clustering 167

9.3 Seeking Patterns Visually 168

9.3.1 Principal Components Analysis 169

9.3.2 Factor Analysis 174

9.3.3 Biplots 176

9.3.4 Spectral Map Analysis 177

9.3.5 Multidimensional Scaling 179

9.3.6 Projection Pursuit 179

9.3.7 Data Visualization with the Grand Tour and Projection Pursuit 181

9.4 Two-Way Clustering 182

9.4.1 Block Clustering 182

9.4.2 Gene Shaving 182

9.4.3 The Plaid Model 183

10 Class Prediction 186

10.1 Initial Considerations 187

10.1.1 Misclassification Rates 188

10.1.2 Reducing the Number of Classifiers 189

10.2 Linear Discriminant Analysis 193

10.3 Extensions of Fisher's LDA 197

10.4 Nearest Neighbors 200

10.5 Recursive Partitioning 201

10.5.1 Classification Trees 201

10.5.2 Activity Region Finding 206

10.6 Neural Networks 206

10.7 Support Vector Machines 208

10.8 Integration of Genomic Information 210

10.8.1 Integration of Gene Expression Data and Molecular Structure Data 210

10.8.2 Pathway Inference 211

11 Protein Arrays 214

11.2 Protein Array Experiments 215

11.3 Special Issues with Protein Arrays 216

11.4 Analysis 217

11.5 Using Antibody Antigen Arrays to Measure Protein Concentrations 218.

Notes:

Includes bibliographical references (pages 222-236) and indexes.

ISBN:

0471273988

OCLC:

52086239

Online:

Publisher description