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Analysis of Microarray Gene Expression Data : Trends in Logic - Ting Lee Mei-Ling

Analysis of Microarray Gene Expression Data

Trends in Logic

Hardcover Published: 30th April 2004
ISBN: 9780792370871
Number Of Pages: 377

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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.

From the reviews:

"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)

List of Figuresp. xiii
List of Tablesp. xvi
Prefacep. xvii
Genome Probing Using Microarrays
Introductionp. 3
DNA, RNA, Proteins, and Gene Expressionp. 7
The Molecules of Lifep. 7
Genesp. 8
DNAp. 9
RNAp. 12
The Genetic Codep. 13
Proteinsp. 14
Gene Expression and Microarraysp. 15
Complementary DNA (cDNA)p. 16
Nucleic Acid Hybridizationp. 16
Microarray Technologyp. 19
Transcriptional Profilingp. 20
Sequencing-based Transcriptional Profilingp. 20
Hybridization-based Transcriptional Profilingp. 22
Microarray Technological Platformsp. 23
Probe Selection and Synthesisp. 24
Array Manufacturingp. 30
Target Labelingp. 31
Hybridizationp. 34
Scanning and Image Analysisp. 35
Microarray Datap. 36
Spotted Array Datap. 36
In-situ Oligonucleotide Array Datap. 37
So I Have My Microarray Data - What's Next?p. 39
Confirming Microarray Resultsp. 39
Northern Blot Analysisp. 40
Reverse-transcription PCR and Quantitative Real-time RT-PCRp. 40
Inherent Variability in Array Datap. 45
Genetic Populationsp. 45
Variability in Gene Expression Levelsp. 47
Variability Due to Specimen Samplingp. 47
Variability Due to Cell Cycle Regulationp. 48
Experimental Variabilityp. 48
Test the Variability by Replicationp. 50
Duplicated Spotsp. 50
Multiple Arrays and Biological Replicationsp. 51
Background Noisep. 53
Pixel-by-pixel Analysis of Individual Spotsp. 53
General Models for Background Noisep. 56
Additive Background Noisep. 57
Correction for Background Noisep. 58
Example: Replication Test Data Setp. 59
Noise Models for GeneChip Arraysp. 62
Elusive Nature of Background Noisep. 63
Transformation and Normalizationp. 67
Data Transformationsp. 67
Logarithmic Transformationp. 67
Square Root Transformationp. 68
Box-Cox Transformation Familyp. 69
Affine Transformationp. 69
The Generalized-log Transformationp. 71
Data Normalizationp. 72
Normalization Across G Genesp. 74
Example: Mouse Juvenile Cystic Kidney Data Setp. 75
Normalization Across G Genes and N Samplesp. 77
Color Effects and MA Plotsp. 78
Normalization Based on LOWESS Functionp. 80
Normalization Based on Rank-invariant Genesp. 82
Normalization Based on a Sample Poolp. 82
Global Normalization Using ANOVA Modelsp. 82
Other Normalization Issuesp. 83
Missing Values in Array Datap. 85
Missing Values in Array Datap. 85
Sources of Problemp. 85
Statistical Classification of Missing Datap. 86
Missing Values in Replicated Designsp. 88
Imputation of Missing Valuesp. 89
Saturated Intensity Readingsp. 93
Saturated Intensity Readingsp. 93
Multiple Power-levels for Spotted Arraysp. 93
Imputing Saturated Intensity Readingsp. 95
High Intensities in Oligonucleotide Arraysp. 97
Statistical Models and Analysis
Experimental Designp. 103
Factors Involved in Experimentsp. 103
Types of Design Structuresp. 106
Common Practice in Microarray Studiesp. 112
Reference Designp. 112
Time-course Experimentp. 114
Color Reversalp. 115
Loop Designp. 116
Example: Time-course Loop Designp. 117
ANOVA Models for Microarray Datap. 121
A Basic Log-linear Modelp. 121
ANOVA With Multiple Factorsp. 123
Main Effectsp. 123
Interaction Effectsp. 123
A Generic Fixed-Effects ANOVA Modelp. 124
Estimation for Interaction Effectsp. 126
Two-stage Estimation Proceduresp. 126
Examplep. 128
Identifying Differentially Expressed Genesp. 130
Standard MSE-based Approachp. 130
Other Approachesp. 132
Modified MSE-based Approachp. 132
Mixed-effects Modelsp. 135
ANOVA for Split-plot Designp. 136
Log Intensity Versus Log Ratiop. 138
Multiple Testing in Microarray Studiesp. 143
Hypothesis Testing for Any Individual Genep. 143
Multiple Testing for the Entire Gene Setp. 144
Framework for Multiple Testingp. 144
Test Statistic for Each Genep. 145
Two Error Control Criteria in Multiple Testingp. 146
Implementation Algorithmsp. 147
Example of Multiple Testing Algorithmsp. 152
Concluding Remarksp. 153
Permutation Tests in Microarray Datap. 157
Basic Conceptsp. 157
Permutation Tests in Microarray Studiesp. 160
Exchangeability in Microarray Designsp. 160
Limitation of Having Few Permutationsp. 162
Pooling Test Results Across Genesp. 162
Lipopolysaccharide-E. coli Data Setp. 163
Statistical Modelp. 164
Permutation Testing and Resultsp. 166
Bayesian Methods for Microarray Datap. 171
Mixture Model for Gene Expressionp. 171
Variations on the Mixture Modelp. 173
Example of Gamma Modelsp. 175
Mixture Model for Differential Expressionp. 176
Mixture Model for Color Ratio Datap. 176
Relation of Mixture Model to ANOVA Modelp. 180
Bayes Interpretation of Mixture Modelp. 182
Empirical Bayes Methodsp. 183
Example of Empirical Bayes Fittingp. 184
Hierarchical Bayes Modelsp. 187
Example of Hierarchical Modelingp. 189
Power and Sample Size Considerationsp. 193
Test Hypotheses in Microarray Studiesp. 194
Distributions of Estimated Differential Expressionp. 196
Summary Measures of Estimated Differential Expressionp. 196
Multiple Testing Frameworkp. 197
Dependencies of Estimation Errorsp. 199
Familywise Type I Error Controlp. 200
Type I Error Control: the Sidak Approachp. 201
Type I Error Control: the Bonferroni Approachp. 203
Familywise Type II Error Controlp. 204
Type II Error Control: the Sidak Approachp. 206
Type II Error Control: the Bonferroni Approachp. 206
Contrast of Planning and Implementation in Multiple Testingp. 207
Power Calculations for Different Summary Measuresp. 208
Designs with Linear Summary Measurep. 208
Numerical Example for Linear Summaryp. 210
Designs with Quadratic Summary Measurep. 211
Numerical Example for Quadratic Summaryp. 213
A Bayesian Perspective on Power and Sample Sizep. 214
Connection to Local Discovery Ratesp. 215
Representative Local True Discovery Ratep. 215
Numerical Example for TDR and FDRp. 216
Applications to Standard Designsp. 216
Treatment-control Designsp. 217
Sample Size for a Treatment-control Designp. 218
Multiple-treatment Designsp. 221
Power Table for a Multiple-treatment Designp. 224
Time-course and Similar Multiple-treatment Designsp. 227
Relation Between Power, Replication and Designp. 228
Effects of Replicationp. 228
Controlling Sources of Variabilityp. 229
Assessing Power from Microarray Pilot Studiesp. 230
Example 1: Juvenile Cystic Kidney Diseasep. 230
Example 2: Opioid Dependencep. 231
Unsupervised Exploratory Analysis
Cluster Analysisp. 237
Distance and Similarity Measuresp. 238
Distance Measuresp. 239
Properties of Distance Measuresp. 239
Minkowski Distance Measuresp. 240
Mahalanobis Distancep. 241
Similarity Measuresp. 241
Inner Productp. 241
Pearson Correlation Coefficientp. 242
Spearman Rank Correlation Coefficientp. 243
Inter-cluster Distancep. 243
Mahalanobis Inter-cluster Distancep. 244
Neighbor-based Inter-cluster Distancep. 244
Hierarchical Clusteringp. 244
Single Linkage Methodp. 245
Complete Linkage Methodp. 245
Average Linkage Clusteringp. 245
Centroid Linkage Methodp. 246
Median Linkage Clusteringp. 246
Ward's Clustering Methodp. 246
Applicationsp. 246
Comparisons of Clustering Algorithmsp. 247
K-means Clusteringp. 247
Bayesian Cluster Analysisp. 248
Two-way Clustering Methodsp. 248
Reliability of Clustering Patterns for Microarray Datap. 249
Principal Components and Singular Value Decompositionp. 251
Principal Component Analysisp. 251
Applications of Dominant Principal Componentsp. 253
Singular-value Decompositionp. 254
Computational Procedures for SVDp. 255
Eigengenes and Eigenarraysp. 256
Fraction of Eigenexpressionp. 256
Generalized Singular Value Decompositionp. 257
Robust Singular Value Decompositionp. 257
Self-Organizing Mapsp. 261
The Basic Logic of a SOMp. 261
The SOM Updating Algorithmp. 265
Program GENECLUSTERp. 267
Supervised SOMp. 268
Applicationsp. 268
Using SOM to Cluster Genesp. 268
Using SOM to Cluster Tumorsp. 269
Multiclass Cancer Diagnosisp. 270
Supervised Learning Methods
Discrimination and Classificationp. 277
Fisher's Linear Discriminant Analysisp. 278
Maximum Likelihood Discriminant Rulesp. 279
Bayesian Classificationp. 280
k-Nearest Neighbor Classifierp. 281
Neighborhood Analysisp. 282
A Gene-casting Weighted Voting Schemep. 283
Example: Classification of Leukemia Samplesp. 284
Artificial Neural Networksp. 287
Single-layer Neural Networkp. 288
Separating Hyperplanesp. 288
Class Labelsp. 289
Decision Rulesp. 290
Risk Functionsp. 290
Gradient Descent Proceduresp. 290
Rosenblatt's Perceptron Methodp. 291
General Structure of Multilayer Neural Networksp. 292
Training a Multilayer Neural Networkp. 294
Sigmoid Functionsp. 294
Mathematical Formulationp. 295
Training Algorithmp. 296
Discussionp. 298
Cancer Classification Using Neural Networksp. 298
Support Vector Machinesp. 301
Geometric Margins for Linearly Separable Groupsp. 301
Convex Optimization in the Dual Spacep. 305
Support Vectorsp. 306
Linearly Nonseparable Groupsp. 307
Nonlinear Separating Boundaryp. 308
Kernel Functionsp. 309
Kernels Defined by Symmetric Functionsp. 309
Use of SVM for Classifying Genesp. 310
Examplesp. 311
Functional Classification of Genesp. 311
SVM and One-versus-All Classification Schemep. 313
Appendicesp. 316
Sample Size Table for Treatment-control Designsp. 317
Power Table for Multiple-treatment Designsp. 327
Glossary of Notationp. 349
Author Indexp. 367
Topic Indexp. 373
Table of Contents provided by Rittenhouse. All Rights Reserved.

ISBN: 9780792370871
ISBN-10: 0792370872
Series: Trends in Logic
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 377
Published: 30th April 2004
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 23.5 x 15.5  x 1.91
Weight (kg): 0.83