DNA Microarrays, Part B: Databases and Statistics (eBook)

Cover Page 1
Table of Contents 6
Contributors to Volume 411 10
Volumes in Series 14
Chapter 1: RNA Extraction for Arrays 38
Introduction 38
Blood as a Biological Specimen 39
Overview of the LeukoLOCK Procedure for Isolation of RNA from Whole Blood Samples 44
Use of Solid Tissues for Gene Expression Analysis 45
RNA Quality Measurements for Microarray Analysis 48
Conclusion 49
Acknowledgments 49
References 49
Chapter 2: Analyzing Micro-RNA Expression Using Microarrays 51
Introduction 51
Micro-RNAs Are Important Factors in Human Cancer 52
Focus of This Chapter 53
Microarray Platforms 54
miRNA Preparation for Analysis on Microarrays 55
Analysis of miRNA Microarray Results 56
Methods of Normalization for miRNA Microarray Experiments 59
Case Study: miRNA Microarray Expression Analysis of Human Lung and Placental Tissues 60
Scanning and Data Extraction 61
Sample Size Calculation 61
Calculation of Array-Specific Thresholds 64
Global Normalization 64
Statistical Differential Analysis 64
Hierarchical Clustering 65
Conclusion 66
Acknowledgments 67
References 67
Chapter 3: Troubleshooting Microarray Hybridizations 71
Introduction 71
General Considerations 72
Printing 75
Sample Preparation and Labeling 76
Background Fluorescence 79
Hybridization Quality Assessment 81
Concluding Remarks 85
Acknowledgments 85
Internet Resources 85
References 86
Chapter 4: Use of External Controls in Microarray Experiments 87
Introduction 87
Description and Availability of External Controls 88
Assesment of Array Performance Using External RNA Controls 92
Methods for Synthesis and Utilization of External RNA Controls 95
Evaluation of Data Analysis Methodology Using Spike-In Data Sets 96
Concluding Remarks 98
Acknowledgments 98
References 98
Chapter 5: Standards in Gene Expression Microarray Experiments 100
Introduction 100
Variability 101
A Digression: Traceability, Validation, and Uncertainty 103
Standards in Traceability, Validation, and Uncertainty for DNA Microarray Gene Expression Profiles 105
Data Exchange Standards 108
Standards in the Gene Expression Process Model 109
The Future 112
References 112
Chapter 6: Scanning Microarrays: Current Methods and Future Directions 116
Introduction 116
Overview of the Scanning Process 118
User-Controlled Parameters 119
Instrumentation/Hardware Effects 126
Alternative Scanning Technologies Provide Advantages 130
Specific Considerations for Multiple Slide, Multiple Scanner, and/or Multiple Laboratory Experiments 132
Conclusions 133
Acknowledgments 133
References 133
Chapter 7: An Introduction to BioArray Software Environment 136
Introduction 136
Getting Started 137
The Basics of BASE 139
Working with BASE 143
References 155
Chapter 8: Bioconductor: An Open Source Framework for Bioinformatics and Computational Biology 156
Introduction: Bioconductor in Brief 157
Technical Details 157
Array Preprocessing 164
Addressing Multiple Comparisons 168
Conclusions: Data Analysis for High-Throughput Biology and Bioconductor 169
Acknowledgment 170
References 170
Chapter 9: TM4 Microarray Software Suite 171
Introduction 171
MADAM 174
Spotfinder 182
Spotfinder Protocol Description 192
MIDAS 197
MeV 204
Sample Analysis Walk-Through 221
References 227
Chapter 10: Clustering Microarray Data 231
Introduction 231
Distance Metrics 232
Distance Metrics 234
Agglomerative Hierarchical Clustering 235
Data Partitioning 239
Computational Considerations 241
Is There a Best Method and/or Best Metric? 242
Freely Available Clustering/Analysis and Visualization Software 242
Other Analysis Packages 248
Conclusions 249
References 249
Chapter 11: Analysis of Variance of Microarray Data 251
Introduction 251
Linear Modeling and Analysis of Variance 252
Fixed versus Random Effects 252
Types of Microarrays 255
Biological versus Technical Replication 257
Data Extraction and Normalization 259
Gene-Specific ANOVA 263
Significance Thresholds 265
Software 267
References 268
Chapter 12: Microarray Quality Control 270
Introduction 270
Pixel Statistics 272
Pixel Statistics Methods 276
Smooth Patterns and Block Effects 277
Models of Array Patterns 279
Conceptual Models for Probe Signals 280
Population Models for Probe Signals 281
Signal Processing Including Control Probes 282
Matrix Model of Cross-Hybridization Noise (SIAM) 283
SIAM: General Method and Simplified Equations 283
Multichannel Methods 284
Model Fitting of Curvilinear Patterns 287
Reference Pattern Corrections 287
Two-Channel Error Propagation 287
Metrics from Array Patterns and Reference Channel 288
Appendix: A General Fast Method of Structure Analysis for High-Dimensional Data 289
Acknowledgments 291
References 291
Chapter 13: Analysis of a Multifactor Microarray Study Using Partek Genomics Solution 293
Statistical Analysis of Microarray Data 293
Description of the Experiment 293
Importing and Normalizing GeneChip Data 294
Exploratory Data Analysis 294
Interpreting the PCA Plot from Fig. 2 294
Multidimensional Scaling (MDS) 296
Identifying Outliers Using PCA and MDS 297
Hierarchical Clustering 297
Finding Differentially Expressed Genes Using Analysis of Variance (ANOVA) 298
Random vs Fixed Effects: Mixed Model ANOVA 300
Hierarchical Designs and Nested/Nesting Relationships 300
Creating Gene Lists of Interest Using ANOVA and Linear Contrasts 301
Examining the Results 301
Multiple Test Correction 301
Examining Results for a Single Gene 303
Poststatistical Analysis 303
Visualizing Locations of Significant Genes on the Genome 304
Summary 306
References 307
Chapter 14: Statistics for ChIP-chip and DNase Hypersensitivity Experiments on NimbleGen Arrays 307
Introduction 308
ChIP-chip: An Overview 309
DNase-chip: An Overview 310
Properties of ChIP-chip and DNase-chip Data 310
Previously Developed Methods for Analysis of ChIP-chip Data 311
ACME 312
Optimizing Window Size and Threshold 315
Optimizing Probe Resolution 315
Recommendations for Assessing Data Quality 316
Additional Features of ACME 317
Summary 318
References 318
Chapter 15: Extrapolating Traditional DNA Microarray Statistics to Tiling and Protein Microarray Technologies 319
Introduction 320
Definitions 321
Statistical Preliminaries 321
Microarray Data 325
Microarray Normalization 329
Scoring for Significance 337
Summary 346
Acknowledgment 346
References 346
Chapter 16: Random Data Set Generation to Support Microarray Analysis 349
Introduction 349
Random Permutations 349
Tests of Genetic Association 351
Gene Clustering 353
Supervised Classification 355
Conclusions 361
References 361
Chapter 17: Using Ontologies to Annotate Microarray Experiments 362
Introduction 363
What Is an Ontology? 363
Gene Ontology 364
What Is the MO Used For? 365
How Was the MO Built? 365
Where Can I Get the MO? 366
MO in Detail 367
Who Uses the MO? 368
How Is the MO Presented to Users? 372
Releases and Management of the MO 373
Future of the MO 375
Conclusion 375
References 375
Chapter 18: Interpreting Experimental Results Using Gene Ontologies 377
Introduction 377
Find Statistically Overrepresented GO Terms within a Group of Genes 380
GOstat: A Tool to Find Statistically Overrepresented Gene Ontologies 382
Visualization and Further Analysis 386
Discussion 386
Acknowledgments 387
References 387
Chapter 19: Gene Expression Omnibus: Microarray Data Storage, Submission, Retrieval, and Analysis 389
Purpose and Scope of the Gene Expression Omnibus (GEO) 390
Structure 391
Interpreting GEO Profiles Charts 393
Submission 396
Navigating GEO and Finding What You Need 398
Conclusion 404
Acknowledgments 405
References 405
Chapter 20: Data Storage and Analysis in ArrayExpress 407
Introduction 407
How to Query and Retrieve Data from the ArrayExpress Repository 411
How to Query Data in the ArrayExpress Data Warehouse 412
Data Analysis with Expression Profiler 414
How to Submit Data to ArrayExpress 420
Future 421
Acknowledgments 421
References 422
Chapter 21: Clustering Methods for Analyzing Large Data Sets: Gonad Development, A Study Case 424
Introduction 424
Selection of Data Sets 425
Detection of Statistically Significant Variations by the Rank Difference Analysis of Microarray Method (RDAM) 426
Reproducibility of Replicates 427
Relationships between Experimental Points 429
Evolution of Total Variation across Ordered Comparisons in Each Experiment 432
Similarity of Comparison Results 433
Combinatorial Clustering 435
Boolean Clustering 436
Gene Clustering of Transcriptional Networks 438
Conclusion 443
Acknowledgments 443
References 443
Chapter 22: Visualizing Networks 445
Introduction 445
Summary 456
Acknowledgments 457
References 457
Chapter 23: Random Forests for Microarrays 459
Introduction 459
Classification 460
Random Forests for Classification 461
Unsupervised Learning and Clustering 465
Case Study: Prostate Cancer Data Set 466
Conclusion 468
References 468
Author Index 470
Subject Index 498