Analysis of microarray gene expression data / Mei-Ling Ting Lee.

Format:

Book

Author/Creator:

Lee, Mei-Ling Ting.

Language:

English

Subjects (All):

DNA microarrays--Statistical methods.

DNA microarrays.

Gene expression--Statistical methods.

Gene expression.

Oligonucleotide Array Sequence Analysis--methods.

Gene Expression--methods.

Statistics.

Medical Subjects:

Oligonucleotide Array Sequence Analysis--methods.

Gene Expression--methods.

Physical Description:

xvi, 371 pages : illustrations (some color) ; 25 cm

Place of Publication:

Boston : Kluwer Academic, [2004]

Summary:

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.

Contents:

Part I Genome Probing Using Microarrays

2. DNA, RNA, Proteins, and Gene Expression 7

2.1 The Molecules of Life 7

2.2 Genes 8

2.3 DNA 9

2.4 RNA 12

2.5 The Genetic Code 13

2.6 Proteins 14

2.7 Gene Expression and Microarrays 15

2.8 Complementary DNA (cDNA) 16

2.9 Nucleic Acid Hybridization 16

3. Microarray Technology 19

3.1 Transcriptional Profiling 20

3.1.1 Sequencing-based Transcriptional Profiling 20

3.1.2 Hybridization-based Transcriptional Profiling 22

3.2 Microarray Technological Platforms 23

3.3 Probe Selection and Synthesis 24

3.4 Array Manufacturing 30

3.5 Target Labeling 31

3.6 Hybridization 34

3.7 Scanning and Image Analysis 35

3.8 Microarray Data 36

3.8.1 Spotted Array Data 36

3.8.2 In-situ Oligonucleotide Array Data 37

3.9 So I Have My Microarray Data - What's Next? 39

3.9.1 Confirming Microarray Results 39

3.9.2 Northern Blot Analysis 40

3.9.3 Reverse-transcription PCR and Quantitative Real-time RT-PCR 40

4. Inherent Variability in Array Data 45

4.1 Genetic Populations 45

4.2 Variability in Gene Expression Levels 47

4.2.1 Variability Due to Specimen Sampling 47

4.2.2 Variability Due to Cell Cycle Regulation 48

4.2.3 Experimental Variability 48

4.3 Test the Variability by Replication 50

4.3.1 Duplicated Spots 50

4.3.2 Multiple Arrays and Biological Replications 51

5. Background Noise 53

5.1 Pixel-by-pixel Analysis of Individual Spots 53

5.2 General Models for Background Noise 56

5.2.1 Additive Background Noise 57

5.2.2 Correction for Background Noise 58

5.2.3 Example: Replication Test Data Set 59

5.2.4 Noise Models for GeneChip Arrays 62

5.2.5 Elusive Nature of Background Noise 63

6. Transformation and Normalization 67

6.1 Data Transformations 67

6.1.1 Logarithmic Transformation 67

6.1.2 Square Root Transformation 68

6.1.3 Box-Cox Transformation Family 69

6.1.4 Affine Transformation 69

6.1.5 The Generalized-log Transformation 71

6.2 Data Normalization 72

6.2.1 Normalization Across G Genes 74

6.2.2 Example: Mouse Juvenile Cystic Kidney Data Set 75

6.2.3 Normalization Across G Genes and N Samples 77

6.2.4 Color Effects and MA Plots 78

6.2.5 Normalization Based on LOWESS Function 80

6.2.6 Normalization Based on Rank-invariant Genes 82

6.2.7 Normalization Based on a Sample Pool 82

6.2.8 Global Normalization Using ANOVA Models 82

6.2.9 Other Normalization Issues 83

7. Missing Values in Array Data 85

7.1 Missing Values in Array Data 85

7.1.1 Sources of Problem 85

7.2 Statistical Classification of Missing Data 86

7.3 Missing Values in Replicated Designs 88

7.4 Imputation of Missing Values 89

8. Saturated Intensity Readings 93

8.1 Saturated Intensity Readings 93

8.2 Multiple Power-levels for Spotted Arrays 93

8.2.1 Imputing Saturated Intensity Readings 95

8.3 High Intensities in Oligonucleotide Arrays 97

Part II Statistical Models and Analysis

9. Experimental Design 103

9.1 Factors Involved in Experiments 103

9.2 Types of Design Structures 106

9.3 Common Practice in Microarray Studies 112

9.3.1 Reference Design 112

9.3.2 Time-course Experiment 114

9.3.3 Color Reversal 115

9.3.4 Loop Design 116

9.3.5 Example: Time-course Loop Design 117

10. ANOVA Models for Microarray Data 121

10.1 A Basic Log-linear Model 121

10.2 ANOVA With Multiple Factors 123

10.2.1 Main Effects 123

10.2.2 Interaction Effects 123

10.3 A Generic Fixed-Effects ANOVA Model 124

10.3.1 Estimation for Interaction Effects 126

10.4 Two-stage Estimation Procedures 126

10.5 Identifying Differentially Expressed Genes 130

10.5.1 Standard MSE-based Approach 130

10.5.2 Other Approaches 132

10.5.3 Modified MSE-based Approach 132

10.6 Mixed-effects Models 135

10.7 ANOVA for Split-plot Design 136

10.8 Log Intensity Versus Log Ratio 138

11. Multiple Testing in Microarray Studies 143

11.1 Hypothesis Testing for Any Individual Gene 143

11.2 Multiple Testing for the Entire Gene Set 144

11.2.1 Framework for Multiple Testing 144

11.2.2 Test Statistic for Each Gene 145

11.2.3 Two Error Control Criteria in Multiple Testing 146

11.2.4 Implementation Algorithms 147

11.2.5 Example of Multiple Testing Algorithms 152

12. Permutation Tests in Microarray Data 157

12.2 Permutation Tests in Microarray Studies 160

12.2.1 Exchangeability in Microarray Designs 160

12.2.2 Limitation of Having Few Permutations 162

12.2.3 Pooling Test Results Across Genes 162

12.3 Lipopolysaccharide-E. coli Data Set 163

12.3.1 Statistical Model 164

12.3.2 Permutation Testing and Results 166

13. Bayesian Methods for Microarray Data 171

13.1 Mixture Model for Gene Expression 171

13.1.1 Variations on the Mixture Model 173

13.1.2 Example of Gamma Models 175

13.2 Mixture Model for Differential Expression 176

13.2.1 Mixture Model for Color Ratio Data 176

13.2.2 Relation of Mixture Model to ANOVA Model 180

13.2.3 Bayes Interpretation of Mixture Model 182

13.3 Empirical Bayes Methods 183

13.3.1 Example of Empirical Bayes Fitting 184

13.4 Hierarchical Bayes Models 187

13.4.1 Example of Hierarchical Modeling 189

14. Power and Sample Size Considerations 193

14.1 Test Hypotheses in Microarray Studies 194

14.2 Distributions of Estimated Differential Expression 196

14.3 Summary Measures of Estimated Differential Expression 196

14.4 Multiple Testing Framework 197

14.5 Dependencies of Estimation Errors 199

14.6 Familywise Type I Error Control 200

14.6.1 Type I Error Control: the Sidak Approach 201

14.6.2 Type I Error Control: the Bonferroni Approach 203

14.7 Familywise Type II Error Control 204

14.7.1 Type II Error Control: the Sidak Approach 206

14.7.2 Type II Error Control: the Bonferroni Approach 206

14.8 Contrast of Planning and Implementation in Multiple Testing 207

14.9 Power Calculations for Different Summary Measures 208

14.9.1 Designs with Linear Summary Measure 208

14.9.2 Numerical Example for Linear Summary 210

14.9.3 Designs with Quadratic Summary Measure 211

14.9.4 Numerical Example for Quadratic Summary 213

14.10 A Bayesian Perspective on Power and Sample Size 214

14.10.1 Connection to Local Discovery Rates 215

14.10.2 Representative Local True Discovery Rate 215

14.10.3 Numerical Example for TDR and FDR 216

14.11 Applications to Standard Designs 216

14.11.1 Treatment-control Designs 217

14.11.2 Sample Size for a Treatment-control Design 218

14.11.3 Multiple-treatment Designs 221

14.11.4 Power Table for a Multiple-treatment Design 224

14.11.5 Time-course and Similar Multiple-treatment Designs 227

14.12 Relation Between Power, Replication and Design 228

14.12.1 Effects of Replication 228

14.12.2 Controlling Sources of Variability 229

14.13 Assessing Power from Microarray Pilot Studies 230

14.13.1 Example 1: Juvenile Cystic Kidney Disease 230

14.13.2 Example 2: Opioid Dependence 231

Part III Unsupervised Exploratory Analysis

15. Cluster Analysis 237

15.1 Distance and Similarity Measures 238

15.2 Distance Measures 239

15.2.1 Properties of Distance Measures 239

15.2.2 Minkowski Distance Measures 240

15.2.3 Mahalanobis Distance 241

15.3 Similarity Measures 241

15.3.1 Inner Product 241

15.3.2 Pearson Correlation Coefficient 242

15.3.3 Spearman Rank Correlation Coefficient 243

15.4 Inter-cluster Distance 243

15.4.1 Mahalanobis Inter-cluster Distance 244

15.4.2 Neighbor-based Inter-cluster Distance 244

15.5 Hierarchical Clustering 244

15.5.1 Single Linkage Method 245

15.5.2 Complete Linkage Method 245

15.5.3 Average Linkage Clustering 245

15.5.4 Centroid Linkage Method 246

15.5.5 Median Linkage Clustering 246

15.5.6 Ward's Clustering Method 246

15.5.7 Applications 246

15.5.8 Comparisons of Clustering Algorithms 247

15.6 K-means Clustering 247

15.7 Bayesian Cluster Analysis 248

15.8 Two-way Clustering Methods 248

15.9 Reliability of Clustering Patterns for Microarray Data 249

16. Principal Components and Singular Value Decomposition 251

16.1 Principal Component Analysis 251

16.1.1 Applications of Dominant Principal Components 253

16.2 Singular-value Decomposition 254

16.3 Computational Procedures for SVD 255

16.4 Eigengenes and Eigenarrays 256

16.5 Fraction of Eigenexpression 256

16.6 Generalized Singular Value Decomposition 257

16.7 Robust Singular Value Decomposition 257

17. Self-Organizing Maps 261

17.1 The Basic Logic of a SOM 261

17.2 The SOM Updating Algorithm 265

17.3 Program GENECLUSTER 267

17.4 Supervised SOM 268

17.5 Applications 268

17.5.1 Using SOM to Cluster Genes 268

17.5.2 Using SOM to Cluster Tumors 269

17.5.3 Multiclass Cancer Diagnosis 270

Part IV Supervised Learning Methods

18. Discrimination and Classification 277

18.1 Fisher's Linear Discriminant Analysis 278

18.2 Maximum Likelihood Discriminant Rules 279

18.3 Bayesian Classification 280

18.4 k-Nearest Neighbor Classifier 281

18.5 Neighborhood Analysis 282

18.6 A Gene-casting Weighted Voting Scheme 283

18.7 Example: Classification of Leukemia Samples 284

19. Artificial Neural Networks 287

19.1 Single-layer Neural Network 288

19.1.1 Separating Hyperplanes 288

19.1.2 Class Labels 289

19.1.3 Decision Rules 290

19.1.4 Risk Functions 290

19.1.5 Gradient Descent Procedures 290

19.1.6 Rosenblatt's Perceptron Method 291

19.2 General Structure of Multilayer Neural Networks 292

19.3 Training a Multilayer Neural Network 294

19.3.1 Sigmoid Functions 294

19.3.2 Mathematical Formulation 295

19.3.3 Training Algorithm 296

19.4 Cancer Classification Using Neural Networks 298

20. Support Vector Machines 301

20.1 Geometric Margins for Linearly Separable Groups 301

20.2 Convex Optimization in the Dual Space 305

20.3 Support Vectors 306

20.4 Linearly Nonseparable Groups 307

20.5 Nonlinear Separating Boundary 308

20.5.1 Kernel Functions 309

20.5.2 Kernels Defined by Symmetric Functions 309

20.5.3 Use of SVM for Classifying Genes 310

20.6.1 Functional Classification of Genes 311

20.6.2 SVM and One-versus-All Classification Scheme 313

Sample Size Table for Treatment-control Designs 317

Power Table for Multiple-treatment Designs 327.

Notes:

Includes bibliographical references (pages [351]-365) and indexes.

ISBN:

0792370872

1402077890

1402077882

OCLC:

54081725