Analysis of microarray gene expression data:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer Science + Business Media
2004
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| Ausgabe: | 1. ed. |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 371 S. Ill., graph. Darst. |
| ISBN: | 0792370872 9780792370871 |
Internformat
MARC
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| 245 | 1 | 0 | |a Analysis of microarray gene expression data |c by Mei-Ling Lee |
| 250 | |a 1. ed. | ||
| 264 | 1 | |a New York, NY |b Springer Science + Business Media |c 2004 | |
| 300 | |a XVI, 371 S. |b Ill., graph. Darst. | ||
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Datensatz im Suchindex
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| adam_text | ANALYSIS OF MICROARRAY
GENE EXPRESSION DATA
MEI-LING TING LEE
Charming Laboratory
Department of Medicine
Brigham and Women s Hospital
and
Harvard Medical School
Boston, Massachusetts, USA
Kluwer Academic Publishers
Boston/Dordrecht/London
Contents
List of Figures xiii
List of Tables xvi
Preface • xvii
Part I GENOME PROBING USING MICROARRAYS
1 INTRODUCTION 3
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
311 Sequencing-based Transcriptional Profiling 20
312 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
vi ANALYSIS OF MICROARRA Y GENE EXPRESSION DATA
3 8 Microarray Data 36
381 Spotted Array Data 36
382 In-situ Oligonucleotide Array Data 37
3 9 So I Have My Microarray Data - What s Next? 39
391 Confirming Microarray Results 39
392 Northern Blot Analysis 40
393 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
421 Variability Due to Specimen Sampling 47
422 Variability Due to Cell Cycle Regulation 48
423 Experimental Variability 48
4 3 Test the Variability by Replication 50
431 Duplicated Spots 50
432 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
521 Additive Background Noise 57
522 Correction for Background Noise 58
523 Example: Replication Test Data Set 59
524 Noise Models for GeneChip Arrays 62
525 Elusive Nature of Background Noise 63
6 TRANSFORMATION AND NORMALIZATION 67
6 1 Data Transformations 67
611 Logarithmic Transformation 67
612 • Square Root Transformation 68
613 Box-Cox Transformation Family 69
614 Affiiie Transformation 69
615 The Generalized-log Transformation 71
6 2 Data Normalization 72
621 Normalization Across G Genes 74
622 Example: Mouse Juvenile Cystic Kidney
Data Set 75
623 Normalization Across G Genes and N Samples 77
624 Color Effects and MA Plots 78
625 Normalization Based on LOWESS Function 80
626 Normalization Based on Rank-invariant
Genes 82
627 Normalization Based on a Sample Pool 82
Contents
% vii
628 Global Normalization Using ANOVA Models 82
629 Other Normalization Issues 83
7 MISSING VALUES IN ARRAY DATA 85
7 1 Missing Values in Array Data 85
711 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
821 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
931 Reference Design 112
932 Time-course Experiment 114
933 Color Reversal 115
934 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 21- 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 4 1 Example 128
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
viii ANALYSIS OF MICROARRA Y GENE EXPRESSION DATA
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
11 2 6 Concluding Remarks 153
12 PERMUTATION TESTS IN MICROARRAY DATA 157
12 1 Basic Concepts 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
14 1 Test Hypotheses in Microarray Studies
14 2 Distributions of Estimated Differential Expression
14 3 Summary Measures of Estimated Differential Expression
14 4 Multiple Testing Framework
14 5 Dependencies of Estimation Errors
14 6 Familywise Type I Error Control
14 6 1 Type I Error Control: the Sidak Approach
Contents ix
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
x ANALYSIS OF MICROARRAY GENE EXPRESSION DATA
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 /C-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 SQM 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
Contents , xi
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 fe-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 3 4 Discussion 298
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 Examples 311
20 6 1 Functional Classification of Genes 311
20 6 2 SVM and One-versus-All Classification
Scheme 313
Appendices 316
Sample Size Table for Treatment-control Designs 317
xii ANALYSIS OF MICROARRAY GENE EXPRESSION DATA
Power Table for Multiple-treatment Designs 327
Glossary of Notation 349
Author Index 367
Topic Index 373
|
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| spelling | Lee, Mei-Ling Ting Verfasser aut Analysis of microarray gene expression data by Mei-Ling Lee 1. ed. New York, NY Springer Science + Business Media 2004 XVI, 371 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022124068&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lee, Mei-Ling Ting Analysis of microarray gene expression data |
| title | Analysis of microarray gene expression data |
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| title_short | Analysis of microarray gene expression data |
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