Advanced analysis of gene expression microarray data [1 ed.] 9812566457, 9789812566454

Table of contents :
Contents......Page 10
Preface......Page 8
1.1 The Microarray: Key to Functional Genomics and Systems Biology......Page 17
1.2 Applications of Microarray......Page 18
1.2.3 Gene Expression Patterns in Model Systems......Page 19
1.2.4 Differential Gene Expression Patterns in Diseases......Page 20
1.2.5 Gene Expression Patterns in Pathogens......Page 21
1.2.6 Gene Expression in Response to Drug Treatments......Page 22
1.2.8 Mutation Screening of Disease Genes......Page 23
1.3 Framework of Microarray Data Analysis......Page 24
1.4 Summary......Page 27
2.2 Cells......Page 29
2.3 Proteins......Page 31
2.4.1 DNA......Page 35
2.5 Central Dogma of Molecular Biology......Page 38
2.5.1 Genes and the Genetic Code......Page 39
2.5.2 Transcription and Gene Expression......Page 41
2.5.3 Translation and Protein Synthesis......Page 42
2.6 Genotype and Phenotype......Page 43
2.7 Summary......Page 46
3.1 Introduction......Page 47
3.2 Microarray Chip Manufacture......Page 48
3.2.1 Deposition-Based Manufacture......Page 49
3.2.2 In Situ Manufacture......Page 50
3.2.2.1 The Affymetrix GeneChip......Page 51
3.3.1 Sample Preparation and Labeling......Page 52
3.3.3 Image Scanning......Page 55
3.4 Image Processing......Page 56
3.5.1 Data Transformation......Page 58
3.5.2 Missing Value Estimation......Page 59
3.6 Data Normalization......Page 61
3.6.1.2 Iterative linear regression......Page 62
3.6.2.1 LOWESS: Locally weighted linear regression......Page 63
3.7 Summary......Page 65
4.1 Introduction......Page 67
4.2.1 Statistical Inference......Page 69
4.2.2 Hypothesis Test......Page 70
4.3.1 k-fold Change......Page 72
4.3.2 Unusual Ratios......Page 73
4.3.3 Model-Based Methods......Page 76
4.4.1 Paired t-Test......Page 78
4.4.2 Unpaired t-Test......Page 79
4.4.3 Variants of t-Test......Page 80
4.5.1 Classical Non-Parametric Statistics......Page 81
4.5.2 Other Non-Parametric Statistics......Page 82
4.5.3 Bootstrap Analysis......Page 83
4.6 Multiple Testing......Page 85
4.6.1.1 Sidak correction and Bonferroni correction......Page 86
4.6.2 False Discovery Rate......Page 87
4.6.3 Permutation Correction......Page 88
4.6.4 SAM: Significance Analysis of Microarrays......Page 89
4.7 ANOVA: Analysis of Variance......Page 93
4.7.1 One-Way ANOVA......Page 95
4.7.2 Two-Way ANOVA......Page 96
4.8 Summary......Page 98
5.1 Introduction......Page 99
5.2.1 Euclidean Distance......Page 101
5.2.2.1 Pearson's correlation coefficient......Page 102
5.2.3 Kullback-Leibler Divergence......Page 104
5.3.1 K-means and its Variations......Page 106
5.3.2 SOM and its Extensions......Page 108
5.3.3.1 HCS and CLICK......Page 110
5.3.3.2 CAST: Cluster affinity search technique......Page 112
5.3.4 Model-Based Clustering......Page 114
5.4.1 Agglomerative Algorithms......Page 115
5.4.2.1 DAA: Deterministic annealing algorithm......Page 118
5.4.2.2 SPC: Super-paramagnetic clustering......Page 119
5.5 Density-Based Approaches......Page 120
5.5.1 DBSCAN......Page 121
5.5.2 OPTICS......Page 122
5.5.3 DENCLUE......Page 123
5.6 GPX: Gene Pattern eXplorer......Page 126
5.6.1.1 The distance measure......Page 131
5.6.1.2 The density definition......Page 132
5.6.1.3 The attraction tree......Page 134
5.6.1.4 An example of attraction tree......Page 136
5.6.2 Interactive Exploration of Coherent Patterns......Page 138
5.6.2.1 Generating the index list......Page 139
5.6.2.2 The coherent pattern index and its graph......Page 141
5.6.2.3 Drilling down to subgroups......Page 142
5.6.3 Experimental Results......Page 144
5.6.3.2 Comparison with other algorithms......Page 145
5.6.4 Efficiency and Scalability......Page 150
5.7 Cluster Validation......Page 151
5.7.1 Homogeneity and Separation......Page 152
5.7.2 Agreement with Reference Partition......Page 153
5.7.3.1 P-value of a cluster......Page 154
5.8 Summary......Page 155
6.1 Introduction......Page 157
6.2 Selection of Informative Genes......Page 160
6.2.1.1 Differentially expressed genes......Page 161
6.2.1.2 Gene pairs......Page 162
6.2.1.3 Virtual genes......Page 164
6.2.1.4 Genetic algorithms......Page 166
6.2.2.1 PCA: Principal component analysis......Page 168
6.2.2.2 Gene shaving......Page 170
6.3.1 Linear Discriminant Analysis......Page 171
6.3.2.1 KNN: k-Nearest Neighbor......Page 174
6.3.2.2 Weighted voting......Page 175
6.3.3 Decision Trees......Page 176
6.3.4 Support Vector Machines......Page 178
6.4 Class Discovery......Page 179
6.4.2 CLIFF: CLustering via Iterative Feature Filtering......Page 181
6.4.2.1 The sample-partition process......Page 182
6.4.2.2 The gene-filtering process......Page 183
6.4.3.1 Measurements for phenotype structure detection......Page 184
6.4.3.2 Algorithms......Page 189
6.4.3.3 Experimental results......Page 200
6.5.1 Prediction Accuracy......Page 206
6.5.2 Prediction Reliability......Page 207
6.6 Summary......Page 208
7.1 Introduction......Page 211
7.2 Mining Association Rules......Page 213
7.2.1 Concepts of Association-Rule Mining......Page 214
7.2.2 The Apriori Algorithm......Page 216
7.2.3 The FP-Growth Algorithm......Page 217
7.2.4 The CARPENTER Algorithm......Page 218
7.2.5 Generating Association Rules in Microarray Data......Page 220
7.2.5.1 Rule filtering......Page 221
7.2.5.2 Rule grouping......Page 222
7.3 Mining Pattern-Based Clusters in Microarray Data......Page 223
7.3.1.1 Coupled two-way clustering(CTWC)......Page 224
7.3.1.2 Plaid model......Page 225
7.3.1.3 Biclustering and o-Clusters......Page 226
7.3.2.1 o-pCluster......Page 227
7.3.2.2 OP-Cluster......Page 229
7.4.1 Three-dimensional Microarray Data......Page 230
7.4.2 Coherent Gene Clusters......Page 231
7.4.2.1 Problem description......Page 233
7.4.2.2 Maximal coherent sample sets......Page 235
7.4.2.3 The mining algorithms......Page 238
7.4.2.4 Experimental results......Page 243
7.4.3.1 The tri-cluster model......Page 248
7.4.3.2 Properties of tri-clusters......Page 250
7.4.3.3 Mining tri-clusters......Page 251
7.5 Summary......Page 254
8.1 Introduction......Page 255
8.2 Single-Array Visualization......Page 257
8.2.1 Box Plot......Page 258
8.2.2 Histogram......Page 259
8.2.3 Scatter Plot......Page 260
8.2.4 Gene Pies......Page 262
8.3.1 Global Visualizations......Page 263
8.3.2 Optimal Visualizations......Page 265
8.3.3 Projection Visualization......Page 266
8.4 VizStruct......Page 267
8.4.1.1 Discrete-time signal paradigm......Page 269
8.4.1.2 The Fourier harmonic projection algorithm......Page 270
8.4.2.1 Basic properties......Page 273
8.4.2.2 Advanced properties......Page 274
8.4.2.3 Harmonic equivalency......Page 276
8.4.2.4 Effects of harmonic twiddle power index......Page 277
8.4.3 Enhancements of Fourier Harmonic Projections......Page 279
8.4.4.3 Classifier construction and evaluation......Page 281
8.4.4.4 Dimension arrangement......Page 283
8.4.4.5 Visualization of various data sets......Page 286
8.4.4.6 Comparison of FFHP to Sammon's mapping......Page 291
8.4.5.1 Data sets for visualization......Page 293
8.4.5.3 Rat kidney data set......Page 294
8.4.5.4 Yeast-A data set......Page 295
8.5 Summary......Page 298
9.2 Meta-Analysis of Microarray Data......Page 301
9.2.1 Meta-Analysis of Differential Genes......Page 302
9.2.2 Meta-Analysis of Co-Expressed Genes......Page 303
9.3 Semi-Supervised Clustering......Page 304
9.3.1 General Semi-Supervised Clustering Algorithms......Page 305
9.3.2.1 Seed-generation methods......Page 307
9.3.2.2 Pattern-selection rules......Page 308
9.3.2.3 The framework for the seed-generation approach......Page 311
9.4 Integration of Gene Expression Data with Other Data......Page 312
9.4.1 A Probabilistic Model for Joint Mining......Page 315
9.4.2 A Graph-Based Model for Joint Mining......Page 316
9.5 Summary......Page 320
10. Conclusion......Page 321
Bibliography......Page 323
Index......Page 347