Analyzing microarray gene expression data:
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hoboken, NJ
Wiley-Interscience
2004
|
| Series: | Wiley series in probability and statistics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XX, 320 S. Ill., graph. Darst. |
| ISBN: | 0471226165 |
Staff View
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| 245 | 1 | 0 | |a Analyzing microarray gene expression data |c Geoffrey J. McLachlan ; Kim-Anh Do ; Christophe Ambroise |
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Record in the Search Index
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| adam_text | Contents
Preface xv
1 Microarrays in Gene Expression Studies 1
1.1 Introduction 1
1.2 Background Biology 2
1.2.1 Genome, Genotype, and Gene Expression 2
1.2.2 Of Wild Types and Other Alleles 3
1.2.3 Aspects of Underlying Biology and Physiochemistry 4
1.3 Polymerase Chain Reaction 5
1.4 cDNA 6
1.4.1 Expressed Sequence Tag 6
1.5 Microarray Technology and Application 7
1.5.1 History of Microarray Development 8
1.5.2 Tools of Microarray Technology 10
1.5.3 Limitations of Microarray Technology 18
1.5.4 Oligonucleotides versus cDNA Arrays 20
1.5.5 SAGE: Another Method for Detecting and Measuring
Gene Expression Levels 23
1.5.6 Emerging Technologies 24
1.6 Sampling of Relevant Research Entities and Public
Resources 24
vii
viii CONTENTS
2 Cleaning and Normalization 31
2.1 Introduction 31
2.2 Cleaning Procedures 32
2.2.1 Image Processing to Extract Information 32
2.2.2 Missing Value Estimation 36
2.2.3 Sources of Nonlinearity 38
2.3 Normalization and Plotting Procedures for Oligonucleotide
Arrays 38
2.3.1 Global Approaches for Oligonucleotide Array Data 38
2.3.2 Spiked Standard Approaches 39
2.3.3 Geometric Mean and Linear Regression Normalization
for Multiple Arrays 41
2.3.4 Nonlinear Normalization for Multiple Arrays Using
Smooth Curves 42
2.4 Normalization Methods for cDNA Microarray Data 44
2.4.1 Single Array Normalization 46
2.4.2 Multiple Slides Normalization 48
2.4.3 ANOVA and Related Methods for Normalization 49
2.4.4 Mixed Model Method for Normalization 50
2.4.5 SNOMAD 51
2.5 Transformations and Replication 52
2.5.1 Importance of Replication 52
2.5.2 Transformations 53
2.6 Analysis of the Alon Data Set 56
2.7 Comparison of Normalization Strategies and Discussion 56
3 Some Cluster Analysis Methods 61
3.1 Introduction 61
3.2 Reduction in the Dimension of the Feature Space 62
3.3 Cluster Analysis 63
3.4 Some Hierarchical Agglomerative Techniques 64
3.5 /, Means Clustering 68
3.6 Cluster Analysis with No A Priori Metric 69
3.7 Clustering via Finite Mixture Models 69
3.7.1 Definition 69
3.7.2 Advantages of Model Based Clustering 71
3.8 Fitting Mixture Models Via the EM Algorithm 72
3.8.1 E Step 73
3.8.2 M Step 74
CONTENTS IX
3.8.3 Choice of Starting Values for the EM Algorithm 75
3.9 Clustering Via Normal Mixtures 75
3.9.1 Heteroscedastic Components 75
3.9.2 Homoscedastic Components 76
3.9.3 Spherical Components 76
3.9.4 Choice of Root 77
3.9.5 Available Software 77
3.10 Mixtures of t Distributions 78
3.11 Mixtures of Factor Analyzers 78
3.12 Choice of Clustering Solution 80
3.13 Classification ML Approach 81
3.14 Mixture Models for Clinical and Microarray Data 82
3.14.1 Unconditional Approach 83
3.14.2 Conditional Approach 84
3.15 Choice of the Number of Components in a Mixture Model 84
3.15.1 Order of a Mixture Model 84
3.15.2 Approaches for Assessing Mixture Order 84
3.15.3 Bayesian Information Criterion 85
3.15.4 Integrated Classification Likelihood Criterion 85
3.16 Resampling Approach 86
3.17 Other Resampling Approaches for Number of Clusters 87
3.17.1 The Gap Statistic 87
3.17.2 The Clest Method for the Number of Clusters 88
3.18 Simulation Results for Two Resampling Approaches 88
3.19 Principal Component Analysis 91
3.19.1 Introduction 91
3.19.2 Singular Value Decomposition 93
3.19.3 Some Other Multivariate Exploratory Methods 94
3.20 Canonical Variate Analysis 94
3.20.1 Linear Projections with Group Structure 94
3.20.2 Canonical Variates 95
3.21 Partial Least Squares 97
4 Clustering of Tissue Samples 99
4.1 Introduction 99
4.2 Notation 100
4.3 Two Clustering Problems 101
4.4 Principal Component Analysis 102
X CONTENTS
4.5 The EMMIX GENE Clustering Procedure 103
4.6 Step 1: Screening of Genes 104
4.7 Step 2: Clustering of Genes: Formation of Metagenes 105
4.8 Step 3: Clustering of Tissues 107
4.9 EMMIX GENE Software 108
4.10 Example: Clustering of Alon Data 108
4.10.1 Clustering on Basis of 446 Genes 108
4.10.2 Clustering on Basis of Gene Groups 109
4.10.3 Clustering on Basis of Metagenes 112
4.11 Example: Clustering of van t Veer Data 112
4.11.1 Screening and Clustering of Genes 113
4.11.2 Usefulness of the Selected Genes 115
4.11.3 Clustering of Tissues 121
4.11.4 Use of Underlying Signatures with Clinical Data 123
4.12 Choosing the Number of Clusters in Microarray Data 124
4.12.1 Some Previous Attempts 124
4.13 Likelihood Ratio Test Applied to Microarray Data 125
4.13.1 GolubData 125
4.13.2 AlizadehData 126
4.13.3 BittnerData 127
4.13.4 van t Veer Data 127
4.14 Effect of Selection Bias on the Number of Clusters 128
4.15 Clustering on Microarray and Clinical Data 128
4.16 Discussion 130
5 Screening and Clustering of Genes 133
5.1 Detection of Differentially Expressed Genes 133
5.1.1 Introduction 133
5.1.2 Fold Change 134
5.1.3 Multiplicity Problem 134
5.1.4 Overview of Literature 135
5.2 Test of a Single Hypothesis 137
5.3 Gene Statistics 138
5.3.1 Calculation of Interactions via ANOVA Models 138
5.3.2 Two Sample ^ Statistics 139
5.4 Multiple Hypothesis Testing 139
5.4.1 Outcomes with Multiple Hypotheses 140
5.4.2 Controlling the FWER 140
CONTENTS xi
5.4.3 False Discovery Rate (FDR) 141
5.4.4 Benjamini Hochberg Procedure 142
5.4.5 False Nondiscovery Rate (FNR) 143
5.4.6 Positive FDR 143
5.4.7 Positive FNR 143
5.4.8 Linking False Rates with Posterior Probabilities 143
5.5 Null Distribution of Test Statistic 144
5.5.1 Permutation Method 144
5.5.2 Null Replications of the Test Statistic 145
5.5.3 The SAM Method 146
5.5.4 Application of SAM Method to Alon Data 146
5.6 Recent Approaches for Strong Control of the FDR 148
5.6.1 The q Value 148
5.6.2 Technical Definition of q Value 149
5.6.3 Controlling FDR Strongly 150
5.6.4 Selecting Genes via the q Value 151
5.6.5 Application to Hedenfalk Data 152
5.7 Two Component Mixture Model Framework 154
5.7.1 Definition of Model 154
5.7.2 Bayes Rule 155
5.7.3 Estimated FDR 155
5.7.4 Bayes Risk in terms of Estimated FDR and FNR 156
5.8 Nonparametric Empirical Bayes Approach 158
5.8.1 Method ofEfronetal. (2001) 158
5.8.2 Mixture Model Method (MMM) 158
5.8.3 Nonparametric Bayesian Approach 159
5.8.4 Application of Empirical Bayes Methods to Alon
Data 159
5.9 Parametric Mixture Models for Differential Gene
Expression 160
5.9.1 Parametric Empirical Bayes Methods 160
5.9.2 Finding Clusters of Differentially Expressed Genes 164
5.9.3 Example: Fitting Normal Mixtures to ^ Statistic
Values 165
5.10 Use of the P Value as a Summary Statistic 166
5.10.1 Beta Mixture for Distribution of P Values 168
5.10.2 Example: Fitting Beta Mixtures to P Values 169
5.11 Clustering of Genes 171
5.12 Finding Correlated Genes 173
xii CONTENTS
5.13 Clustering of Genes via Full Expression Profiles 173
5.14 Clustering of Genes via PCA of Expression Profiles 174
5.15 Clustering of Genes with Repeated Measurements 175
5.15.1 A Mixture Model for Technical Replicates 175
5.15.2 Application of EM Algorithm 176
5.15.3 M Step 176
5.16 Gene Shaving 177
5.16.1 Introduction 177
5.16.2 Methodology and implementation 177
5.16.3 Optimal cluster size via the Gap statistic 178
5.16.4 Supervised Gene Shaving 179
5.16.5 Real Data Example 179
5.16.6 Computer Software 180
6 Discriminant Analysis 185
6.1 Introduction 185
6.2 Basic Notation 185
6.3 Error Rates 187
6.4 Decision Theoretic Approach 187
6.5 Training Data 189
6.6 Different Types of Error Rates 190
6.7 Sample Based Discriminant Rules 191
6.8 Parametric Discriminant Rules 192
6.9 Discrimination via Normal Models 193
6.9.1 Heteroscedastic Normal Model 193
6.9.2 Plug in Sample NQDR 194
6.9.3 Homoscedastic Normal Model 195
6.9.4 Optimal Error Rates 197
6.9.5 Plug in Sample NLDR 197
6.9.6 Normal Mixture Model 198
6.10 Fisher s Linear Discriminant Function 199
6.10.1 Separation Approach 199
6.10.2 Regression Approach 199
6.11 Logistic Discrimination 201
6.12 Nearest Centroid Rule 202
6.13 Support Vector Machines 203
6.13.1 Two Classes 203
6.13.2 Selection of Feature Variables 204
CONTENTS Xiii
6.13.3 Multiple Classes 205
6.13.4 Computer Software 206
6.14 Variants of Support Vector Machines 207
6.15 Neural Networks 207
6.16 Nearest Neighbor Rules 208
6.16.1 Introduction 208
6.16.2 Definition of a fc NN Rule 209
6.17 Classification Trees 210
6.18 Error Rate Estimation 211
6.18.1 Apparent Error Rate 211
6.18.2 Bias Correction of the Apparent Error Rate 213
6.19 Cross Validation 213
6.19.1 Leave One Out(LOO) Estimator 213
6.19.2 rFold Cross Validation 214
6.20 Error Rate Estimation via the Bootstrap 214
6.20.1 The 0.632 Estimator 214
6.20.2 Mean Squared Error of the Estimated Error Rate 215
6.21 Selection of Feature Variables 216
6.22 Error Rate Estimation with Selection Bias 218
6.22.1 Selection Bias 218
6.22.2 External Cross Validation 218
6.22.3 The 0.632+Estimator 219
7 Supervised Classification of Tissue Samples 221
7.1 Introduction 221
7.2 Reducing the Dimension of the Feature Space of Genes 222
7.2.1 Principal Components 223
7.2.2 Partial Least Squares 223
7.2.3 Ranking of Genes 223
7.2.4 Grouping of Genes 224
7.3 SVM with Recursive Feature Elimination (RFE) 224
7.4 Selection Bias: SVM with RFE 226
7.5 Selection Bias: Fisher s Rule with Forward Selection 228
7.6 Selection Bias: Noninformative Data 230
7.7 Discussion of Selection Bias 232
7.8 Selection of Marker Genes with SVM 233
7.8.1 Description of van de Vijver Breast Cancer Data 233
7.8.2 Application of SVM with RFE 234
Xiv CONTENTS
7.9 Nearest Shrunken Centroids 236
7.9.1 Definition 236
7.10 Comparison of Nearest Shrunken Centroids with SVM 239
7.10.1 AlonData 239
7.10.2 van de Vijver Data 239
7.11 Selection Bias Working with the Top 70 Genes 245
7.11.1 Bias in Error Rates 245
7.11.2 Bias in Comparative Studies of Error Rates 246
7.11.3 Bias in Plots 248
7.12 Discriminant Rules Via Initial Grouping of Genes 249
7.12.1 Supervised Version of EMMIX GENE 249
7.12.2 Bayesian Tree Classification 249
7.12.3 Tree Harvesting 249
7.12.4 Block PCA 250
7.12.5 Grouping of Genes via Supervised Procedures 250
8 Linking Microarray Data with Survival Analysis 253
8.1 Introduction 253
8.2 Four Lung Cancer Data Sets 254
8.3 Statistical Analysis of Two Data Sets 255
8.4 Ontario Data set 256
8.4.1 Cluster Analysis 256
8.4.2 Survival Analysis 259
8.4.3 Discriminant Analysis 260
8.5 Stanford Data Set 261
8.5.1 Cluster Analysis of AC Tumors 262
8.5.2 Survival Analysis 263
8.5.3 Discriminant Analysis 266
8.6 Discussion 266
References 267
Author Index 297
Subject Index 313
|
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| spelling | McLachlan, Geoffrey J. 1946- Verfasser (DE-588)128823348 aut Analyzing microarray gene expression data Geoffrey J. McLachlan ; Kim-Anh Do ; Christophe Ambroise Hoboken, NJ Wiley-Interscience 2004 XX, 320 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Microarray - Genexpression Genexpression (DE-588)4020136-3 gnd rswk-swf Microarray (DE-588)4544227-7 gnd rswk-swf Genexpression (DE-588)4020136-3 s Microarray (DE-588)4544227-7 s b DE-604 Do, Kim-Anh Verfasser aut Ambroise, Christophe Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010554667&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | McLachlan, Geoffrey J. 1946- Do, Kim-Anh Ambroise, Christophe Analyzing microarray gene expression data Microarray - Genexpression Genexpression (DE-588)4020136-3 gnd Microarray (DE-588)4544227-7 gnd |
| subject_GND | (DE-588)4020136-3 (DE-588)4544227-7 |
| title | Analyzing microarray gene expression data |
| title_auth | Analyzing microarray gene expression data |
| title_exact_search | Analyzing microarray gene expression data |
| title_full | Analyzing microarray gene expression data Geoffrey J. McLachlan ; Kim-Anh Do ; Christophe Ambroise |
| title_fullStr | Analyzing microarray gene expression data Geoffrey J. McLachlan ; Kim-Anh Do ; Christophe Ambroise |
| title_full_unstemmed | Analyzing microarray gene expression data Geoffrey J. McLachlan ; Kim-Anh Do ; Christophe Ambroise |
| title_short | Analyzing microarray gene expression data |
| title_sort | analyzing microarray gene expression data |
| topic | Microarray - Genexpression Genexpression (DE-588)4020136-3 gnd Microarray (DE-588)4544227-7 gnd |
| topic_facet | Microarray - Genexpression Genexpression Microarray |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010554667&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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