Analysis of Microarray Gene Expression Data

By: Mei-Ling Ting Lee

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Hardcover | 25 June 2015

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400 Pages

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23.5 x 15.88 x 1.91

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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.

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"This book aims to be a comprehensive work on statistical techniques for analysis of microarray data. ... the book contains an elaborate discussion on several variants of useful ANOVA models. ... Standard multiple testing and permutation methods are well illustrated. ... In conclusion, the book is a successful attempt to be a complete reference work for microarray data analysis. It is certainly a rich source of references." (M. A. van de Wiel, Kwantitatieve Methoden, Issue 3, 2006)

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List of Figures	p. xiii
List of Tables	p. xvi
Preface	p. xvii
Genome Probing Using Microarrays
Introduction	p. 3
DNA, RNA, Proteins, and Gene Expression	p. 7
The Molecules of Life	p. 7
Genes	p. 8
DNA	p. 9
RNA	p. 12
The Genetic Code	p. 13
Proteins	p. 14
Gene Expression and Microarrays	p. 15
Complementary DNA (cDNA)	p. 16
Nucleic Acid Hybridization	p. 16
Microarray Technology	p. 19
Transcriptional Profiling	p. 20
Sequencing-based Transcriptional Profiling	p. 20
Hybridization-based Transcriptional Profiling	p. 22
Microarray Technological Platforms	p. 23
Probe Selection and Synthesis	p. 24
Array Manufacturing	p. 30
Target Labeling	p. 31
Hybridization	p. 34
Scanning and Image Analysis	p. 35
Microarray Data	p. 36
Spotted Array Data	p. 36
In-situ Oligonucleotide Array Data	p. 37
So I Have My Microarray Data - What's Next?	p. 39
Confirming Microarray Results	p. 39
Northern Blot Analysis	p. 40
Reverse-transcription PCR and Quantitative Real-time RT-PCR	p. 40
Inherent Variability in Array Data	p. 45
Genetic Populations	p. 45
Variability in Gene Expression Levels	p. 47
Variability Due to Specimen Sampling	p. 47
Variability Due to Cell Cycle Regulation	p. 48
Experimental Variability	p. 48
Test the Variability by Replication	p. 50
Duplicated Spots	p. 50
Multiple Arrays and Biological Replications	p. 51
Background Noise	p. 53
Pixel-by-pixel Analysis of Individual Spots	p. 53
General Models for Background Noise	p. 56
Additive Background Noise	p. 57
Correction for Background Noise	p. 58
Example: Replication Test Data Set	p. 59
Noise Models for GeneChip Arrays	p. 62
Elusive Nature of Background Noise	p. 63
Transformation and Normalization	p. 67
Data Transformations	p. 67
Logarithmic Transformation	p. 67
Square Root Transformation	p. 68
Box-Cox Transformation Family	p. 69
Affine Transformation	p. 69
The Generalized-log Transformation	p. 71
Data Normalization	p. 72
Normalization Across G Genes	p. 74
Example: Mouse Juvenile Cystic Kidney Data Set	p. 75
Normalization Across G Genes and N Samples	p. 77
Color Effects and MA Plots	p. 78
Normalization Based on LOWESS Function	p. 80
Normalization Based on Rank-invariant Genes	p. 82
Normalization Based on a Sample Pool	p. 82
Global Normalization Using ANOVA Models	p. 82
Other Normalization Issues	p. 83
Missing Values in Array Data	p. 85
Missing Values in Array Data	p. 85
Sources of Problem	p. 85
Statistical Classification of Missing Data	p. 86
Missing Values in Replicated Designs	p. 88
Imputation of Missing Values	p. 89
Saturated Intensity Readings	p. 93
Saturated Intensity Readings	p. 93
Multiple Power-levels for Spotted Arrays	p. 93
Imputing Saturated Intensity Readings	p. 95
High Intensities in Oligonucleotide Arrays	p. 97
Statistical Models and Analysis
Experimental Design	p. 103
Factors Involved in Experiments	p. 103
Types of Design Structures	p. 106
Common Practice in Microarray Studies	p. 112
Reference Design	p. 112
Time-course Experiment	p. 114
Color Reversal	p. 115
Loop Design	p. 116
Example: Time-course Loop Design	p. 117
ANOVA Models for Microarray Data	p. 121
A Basic Log-linear Model	p. 121
ANOVA With Multiple Factors	p. 123
Main Effects	p. 123
Interaction Effects	p. 123
A Generic Fixed-Effects ANOVA Model	p. 124
Estimation for Interaction Effects	p. 126
Two-stage Estimation Procedures	p. 126
Example	p. 128
Identifying Differentially Expressed Genes	p. 130
Standard MSE-based Approach	p. 130
Other Approaches	p. 132
Modified MSE-based Approach	p. 132
Mixed-effects Models	p. 135
ANOVA for Split-plot Design	p. 136
Log Intensity Versus Log Ratio	p. 138
Multiple Testing in Microarray Studies	p. 143
Hypothesis Testing for Any Individual Gene	p. 143
Multiple Testing for the Entire Gene Set	p. 144
Framework for Multiple Testing	p. 144
Test Statistic for Each Gene	p. 145
Two Error Control Criteria in Multiple Testing	p. 146
Implementation Algorithms	p. 147
Example of Multiple Testing Algorithms	p. 152
Concluding Remarks	p. 153
Permutation Tests in Microarray Data	p. 157
Basic Concepts	p. 157
Permutation Tests in Microarray Studies	p. 160
Exchangeability in Microarray Designs	p. 160
Limitation of Having Few Permutations	p. 162
Pooling Test Results Across Genes	p. 162
Lipopolysaccharide-E. coli Data Set	p. 163
Statistical Model	p. 164
Permutation Testing and Results	p. 166
Bayesian Methods for Microarray Data	p. 171
Mixture Model for Gene Expression	p. 171
Variations on the Mixture Model	p. 173
Example of Gamma Models	p. 175
Mixture Model for Differential Expression	p. 176
Mixture Model for Color Ratio Data	p. 176
Relation of Mixture Model to ANOVA Model	p. 180
Bayes Interpretation of Mixture Model	p. 182
Empirical Bayes Methods	p. 183
Example of Empirical Bayes Fitting	p. 184
Hierarchical Bayes Models	p. 187
Example of Hierarchical Modeling	p. 189
Power and Sample Size Considerations	p. 193
Test Hypotheses in Microarray Studies	p. 194
Distributions of Estimated Differential Expression	p. 196
Summary Measures of Estimated Differential Expression	p. 196
Multiple Testing Framework	p. 197
Dependencies of Estimation Errors	p. 199
Familywise Type I Error Control	p. 200
Type I Error Control: the Sidak Approach	p. 201
Type I Error Control: the Bonferroni Approach	p. 203
Familywise Type II Error Control	p. 204
Type II Error Control: the Sidak Approach	p. 206
Type II Error Control: the Bonferroni Approach	p. 206
Contrast of Planning and Implementation in Multiple Testing	p. 207
Power Calculations for Different Summary Measures	p. 208
Designs with Linear Summary Measure	p. 208
Numerical Example for Linear Summary	p. 210
Designs with Quadratic Summary Measure	p. 211
Numerical Example for Quadratic Summary	p. 213
A Bayesian Perspective on Power and Sample Size	p. 214
Connection to Local Discovery Rates	p. 215
Representative Local True Discovery Rate	p. 215
Numerical Example for TDR and FDR	p. 216
Applications to Standard Designs	p. 216
Treatment-control Designs	p. 217
Sample Size for a Treatment-control Design	p. 218
Multiple-treatment Designs	p. 221
Power Table for a Multiple-treatment Design	p. 224
Time-course and Similar Multiple-treatment Designs	p. 227
Relation Between Power, Replication and Design	p. 228
Effects of Replication	p. 228
Controlling Sources of Variability	p. 229
Assessing Power from Microarray Pilot Studies	p. 230
Example 1: Juvenile Cystic Kidney Disease	p. 230
Example 2: Opioid Dependence	p. 231
Unsupervised Exploratory Analysis
Cluster Analysis	p. 237
Distance and Similarity Measures	p. 238
Distance Measures	p. 239
Properties of Distance Measures	p. 239
Minkowski Distance Measures	p. 240
Mahalanobis Distance	p. 241
Similarity Measures	p. 241
Inner Product	p. 241
Pearson Correlation Coefficient	p. 242
Spearman Rank Correlation Coefficient	p. 243
Inter-cluster Distance	p. 243
Mahalanobis Inter-cluster Distance	p. 244
Neighbor-based Inter-cluster Distance	p. 244
Hierarchical Clustering	p. 244
Single Linkage Method	p. 245
Complete Linkage Method	p. 245
Average Linkage Clustering	p. 245
Centroid Linkage Method	p. 246
Median Linkage Clustering	p. 246
Ward's Clustering Method	p. 246
Applications	p. 246
Comparisons of Clustering Algorithms	p. 247
K-means Clustering	p. 247
Bayesian Cluster Analysis	p. 248
Two-way Clustering Methods	p. 248
Reliability of Clustering Patterns for Microarray Data	p. 249
Principal Components and Singular Value Decomposition	p. 251
Principal Component Analysis	p. 251
Applications of Dominant Principal Components	p. 253
Singular-value Decomposition	p. 254
Computational Procedures for SVD	p. 255
Eigengenes and Eigenarrays	p. 256
Fraction of Eigenexpression	p. 256
Generalized Singular Value Decomposition	p. 257
Robust Singular Value Decomposition	p. 257
Self-Organizing Maps	p. 261
The Basic Logic of a SOM	p. 261
The SOM Updating Algorithm	p. 265
Program GENECLUSTER	p. 267
Supervised SOM	p. 268
Applications	p. 268
Using SOM to Cluster Genes	p. 268
Using SOM to Cluster Tumors	p. 269
Multiclass Cancer Diagnosis	p. 270
Supervised Learning Methods
Discrimination and Classification	p. 277
Fisher's Linear Discriminant Analysis	p. 278
Maximum Likelihood Discriminant Rules	p. 279
Bayesian Classification	p. 280
k-Nearest Neighbor Classifier	p. 281
Neighborhood Analysis	p. 282
A Gene-casting Weighted Voting Scheme	p. 283
Example: Classification of Leukemia Samples	p. 284
Artificial Neural Networks	p. 287
Single-layer Neural Network	p. 288
Separating Hyperplanes	p. 288
Class Labels	p. 289
Decision Rules	p. 290
Risk Functions	p. 290
Gradient Descent Procedures	p. 290
Rosenblatt's Perceptron Method	p. 291
General Structure of Multilayer Neural Networks	p. 292
Training a Multilayer Neural Network	p. 294
Sigmoid Functions	p. 294
Mathematical Formulation	p. 295
Training Algorithm	p. 296
Discussion	p. 298
Cancer Classification Using Neural Networks	p. 298
Support Vector Machines	p. 301
Geometric Margins for Linearly Separable Groups	p. 301
Convex Optimization in the Dual Space	p. 305
Support Vectors	p. 306
Linearly Nonseparable Groups	p. 307
Nonlinear Separating Boundary	p. 308
Kernel Functions	p. 309
Kernels Defined by Symmetric Functions	p. 309
Use of SVM for Classifying Genes	p. 310
Examples	p. 311
Functional Classification of Genes	p. 311
SVM and One-versus-All Classification Scheme	p. 313
Appendices	p. 316
Sample Size Table for Treatment-control Designs	p. 317
Power Table for Multiple-treatment Designs	p. 327
Glossary of Notation	p. 349
Author Index	p. 367
Topic Index	p. 373
Table of Contents provided by Rittenhouse. All Rights Reserved.

Analysis of Microarray Gene Expression Data

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