Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data (Wiley Series on Methods and Applications in Data Mining)

DATA MINING FOR GENOMICS AND PROTEOMICS......Page 4
CONTENTS......Page 10
PREFACE......Page 16
ACKNOWLEDGMENTS......Page 20
1 INTRODUCTION......Page 22
1.1.1 The Central Dogma of Molecular Biology......Page 23
1.1.2 Genome......Page 24
1.1.3 Proteome......Page 25
1.1.4 DNA (Deoxyribonucleic Acid)......Page 26
1.1.5 RNA (Ribonucleic Acid)......Page 27
1.1.7 Genetic Code......Page 28
1.1.8 Gene......Page 30
1.1.9 Gene Expression and the Gene Expression Level......Page 33
1.1.10 Protein......Page 34
1.2.4 Transcriptomics and Other -omics . . .......Page 35
1.2.5 Data Mining......Page 36
2.1 Introduction......Page 38
2.2 Microarray Technology......Page 39
2.2.1 Spotted Microarrays......Page 40
2.2.2 Affymetrix GeneChip(®) Microarrays......Page 41
2.2.3 Bead-Based Microarrays......Page 45
2.3 Low-Level Preprocessing of Affymetrix Microarrays......Page 46
2.3.1 MAS5......Page 48
2.3.2 RMA......Page 52
2.3.3 GCRMA......Page 54
2.4.1 Microarray Gene Expression Data Society (MGED) Standards......Page 55
2.4.2.1 Gene Expression Omnibus (GEO)......Page 58
2.5 Gene Expression Matrix......Page 59
2.5.1 Elements of Gene Expression Microarray Data Analysis......Page 63
2.6 Additional Preprocessing, Quality Assessment, and Filtering......Page 64
2.6.1 Quality Assessment......Page 66
2.6.2 Filtering......Page 71
2.7 Basic Exploratory Data Analysis......Page 73
2.7.1 t Test......Page 75
2.7.1.2 t Test for Unequal Variances......Page 76
2.7.2 ANOVA F Test......Page 77
2.7.3 SAM t Statistic......Page 78
2.7.5 Adjustment for Multiple Comparisons......Page 80
2.7.5.3 Step-Down Holm Procedure......Page 82
2.7.5.4 Step-Up Benjamini and Hochberg Procedure......Page 83
2.7.5.5 Permutation Based Multiplicity Adjustment......Page 84
2.8 Unsupervised Learning (Taxonomy-Related Analysis)......Page 85
2.8.1 Cluster Analysis......Page 86
2.8.1.1 Measures of Similarity or Distance......Page 88
2.8.1.2 K-Means Clustering......Page 91
2.8.1.3 Hierarchical Clustering......Page 92
2.8.1.4 Two-Way Clustering and Related Methods......Page 99
2.8.2 Principal Component Analysis......Page 101
2.8.3 Self-Organizing Maps......Page 106
Exercises......Page 111
3.1 Overview......Page 116
3.1.1 Gene Expression Matrix . . . Again......Page 119
3.1.2 Biomarker Discovery......Page 121
3.1.3 Classification Systems......Page 126
3.1.3.2 Terms Associated with Common Assumptions Underlying Parametric Learning Algorithms......Page 127
3.1.3.3 Visualization of Classification Results......Page 131
3.1.4.2 Leave-One-Out and K-Fold Cross-Validation......Page 132
3.1.4.3 External and Internal Cross-Validation......Page 133
3.1.4.5 Ensemble-Based Validation (Using Out-of-Bag Samples)......Page 134
3.1.5 Reporting Validation Results......Page 135
3.1.5.1 Binary Classifiers......Page 136
3.1.5.2 Multiclass Classifiers......Page 138
3.2.1 Introduction......Page 140
3.2.2 Univariate Versus Multivariate Approaches......Page 142
3.2.3 Supervised Versus Unsupervised Methods......Page 144
3.2.4.1 Filters, Wrappers, Hybrid, and Embedded Models......Page 147
3.2.4.2 Strategy: Exhaustive, Complete, Sequential, Random, and Hybrid Searches......Page 152
3.2.5 Feature Selection for Multiclass Discrimination......Page 154
3.2.6 Regularization and Feature Selection......Page 155
3.2.7 Stability of Biomarkers......Page 156
3.3.1 Introduction......Page 157
3.3.2 Learning Algorithm......Page 160
3.3.3 A Stepwise Hybrid Feature Selection with T(2)......Page 168
3.4 Support Vector Machines......Page 170
3.4.1 Hard-Margin Support Vector Machines......Page 171
3.4.2 Soft-Margin Support Vector Machines......Page 178
3.4.3 Kernels......Page 181
3.4.4.2 Pairwise Approach......Page 186
3.4.5 SVMs and Feature Selection: Recursive Feature Elimination......Page 187
3.4.6 Summary......Page 188
3.5.1 Introduction......Page 189
3.5.2 Random Forests Learning Algorithm......Page 193
3.5.3 Random Forests and Feature Selection......Page 195
3.5.4 Summary......Page 197
3.6.1.3 Ensemble Classifiers and Biomarker Discovery......Page 198
3.6.2 Bootstrap Methods......Page 199
3.6.3 Bootstrap and Linear Discriminant Analysis......Page 200
3.6.4 The Modified Bagging Schema......Page 201
3.7 Other Learning Algorithms......Page 203
3.7.1 k-Nearest Neighbor Classifiers......Page 204
3.7.2 Artificial Neural Networks......Page 206
3.7.2.1 Perceptron......Page 207
3.7.2.2 Multilayer Feedforward Neural Networks......Page 208
3.7.2.3 Training the Network (Supervised Learning)......Page 213
3.8 Eight Commandments of Gene Expression Analysis (for Biomarker Discovery)......Page 218
Exercises......Page 219
4.1 Introduction......Page 222
4.3 The Method......Page 223
4.3.1 Identification of the Informative Set of Genes......Page 224
4.3.2 Primary Expression Patterns of the Informative Set of Genes......Page 229
4.4 Using the Informative Set of Genes to Identify Robust Multivariate Biomarkers......Page 232
4.5 Summary......Page 233
Exercises......Page 236
5.1 Introduction......Page 240
5.2 Protein Chip Technology......Page 243
5.2.1 Antibody Microarrays......Page 244
5.2.3 Protein Microarrays......Page 246
5.3 Two-Dimensional Gel Electrophoresis......Page 247
5.4 MALDI-TOF and SELDI-TOF Mass Spectrometry......Page 249
5.4.1 MALDI-TOF Mass Spectrometry......Page 250
5.4.2 SELDI-TOF Mass Spectrometry......Page 251
5.5.1 Introduction......Page 253
5.5.2.1 Quality Assessment......Page 255
5.5.2.5 Peak Detection......Page 256
5.5.2.6 Intensity Normalization......Page 257
5.6 Analysis of Protein Expression Data......Page 258
5.6.2 Basic Exploratory Data Analysis......Page 260
5.6.3 Unsupervised Learning......Page 261
5.6.4 Supervised Learning—Feature Selection and Biomarker Discovery......Page 263
5.6.5 Supervised Learning—Classification Systems......Page 264
5.7.1 Introduction......Page 265
5.7.2 The Universal Protein Resource (UniProt)......Page 267
5.7.3 Search Programs......Page 268
5.7.4 Tandem Mass Spectrometry......Page 270
5.8 Summary......Page 272
6.1 Introduction......Page 274
6.2.1 Data Set Selection, Downloading, and Consolidation......Page 275
6.2.2 Filtering Probe Sets......Page 277
6.2.3 Designing a Multistage Classification Schema......Page 278
6.3 Identifying the Informative Set of Genes (Exercises 4.2–4.6)......Page 286
6.3.1 The Informative Set of Genes......Page 287
6.3.2 Primary Expression Patterns of the Informative Set......Page 288
6.3.3 The Most Frequently Used Genes of the Primary Expression Patterns......Page 291
6.4 Using the Informative Set of Genes to Identify Robust Multivariate Markers (Exercise 4.8)......Page 292
6.5 Validating Biomarkers on an Independent Test Data Set (Exercise 4.8)......Page 293
6.6 Using a Training Set that Combines More than One Data Set (Exercises 3.5 and 4.1–4.8)......Page 295
6.6.1 Combining the Two Data Sets into a Single Training Set......Page 296
6.6.4 Identifying the Informative Set of Genes......Page 297
6.6.5 Primary Expression Patterns of the Informative Set of Genes......Page 301
6.6.6 The Most Frequently Used Genes of the Primary Expression Patterns......Page 302
6.6.7 Using the Informative Set of Genes to Identify Robust Multivariate Markers......Page 306
6.6.8 Validating Biomarkers on an Independent Test Data Set......Page 308
REFERENCES......Page 310
INDEX......Page 328