Publisher Description

Batch Effects and Noise in Microarray Experiments: Sources and Solutions looks at the issue of technical noise and batch effects in microarray studies and illustrates how to alleviate such factors whilst interpreting the relevant biological information. Each chapter focuses on sources of noise and batch effects before starting an experiment, with examples of statistical methods for detecting, measuring, and managing batch effects within and across datasets provided online. Throughout the book the importance of standardization and the value of standard operating procedures in the development of genomics biomarkers is emphasized. Key Features: A thorough introduction to Batch Effects and Noise in Microrarray Experiments.A unique compilation of review and research articles on handling of batch effects and technical and biological noise in microarray data.An extensive overview of current standardization initiatives.All datasets and methods used in the chapters, as well as colour images, are available on www.the-batch-effect-book.org, so that the data can be reproduced. An exciting compilation of state-of-the-art review chapters and latest research results, which will benefit all those involved in the planning, execution, and analysis of gene expression studies.

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Batch Effects and Noise in Microarray Experiments: Sources and Solutions looks at the issue of technical noise and batch effects in microarray studies and illustrates how to alleviate such factors whilst interpreting the relevant biological information. Each chapter focuses on sources of noise and batch effects before starting an experiment, with examples of statistical methods for detecting, measuring, and managing batch effects within and across datasets provided online. Throughout the book the importance of standardization and the value of standard operating procedures in the development of genomics biomarkers is emphasized. Key Features: A thorough introduction to Batch Effects and Noise in Microrarray Experiments. A unique compilation of review and research articles on handling of batch effects and technical and biological noise in microarray data. An extensive overview of current standardization initiatives. All datasets and methods used in the chapters, as well as colour images, are available on (www.the-batch-effect-book.org), so that the data can be reproduced. An exciting compilation of state-of-the-art review chapters and latest research results, which will benefit all those involved in the planning, execution, and analysis of gene expression studies.

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Batch Effects and Noise in Microarray Experiments: Sources and Solutions looks at the issue of technical noise and batch effects in microarray studies and illustrates how to alleviate such factors whilst interpreting the relevant biological information. Each chapter focuses on sources of noise and batch effects before starting an experiment, with examples of statistical methods for detecting, measuring, and managing batch effects within and across datasets provided online. Throughout the book the importance of standardization and the value of standard operating procedures in the development of genomics biomarkers is emphasized. Key Features: A thorough introduction to Batch Effects and Noise in Microrarray Experiments. A unique compilation of review and research articles on handling of batch effects and technical and biological noise in microarray data. An extensive overview of current standardization initiatives. All datasets and methods used in the chapters, as well as colour images, are available on (www.the-batch-effect-book.org), so that the data can be reproduced. An exciting compilation of state-of-the-art review chapters and latest research results, which will benefit all those involved in the planning, execution, and analysis of gene expression studies.

Author Biography

Andreas Scherer studied biology in Cologne, Germany, and Freiburg, Germany, and received his Ph.D. for his studies in the fields of genetics, developmental biology, and microbiology. Following a postdoctoral position at UT Southwestern Medical Center in Dallas, TX, he worked for many years in pharmaceutical industry in various positions in the field of experimental and statistical genomics biomarker discovery. In 2007, Andreas Scherer founded Spheromics, a company specialized in analytical and consultancy services in gene expression technologies and biomarker development.

Table of Contents

List of Contributors xiii Foreword xvii Preface xix 1 Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction 1
Andreas Scherer 2 Microarray Platforms and Aspects of Experimental Variation 5
John A Coller Jr 2.1 Introduction 5 2.2 Microarray Platforms 6 2.2.1 Affymetrix 6 2.2.2 Agilent 7 2.2.3 Illumina 7 2.2.4 Nimblegen 8 2.2.5 Spotted Microarrays 8 2.3 Experimental Considerations 9 2.3.1 Experimental Design 9 2.3.2 Sample and RNA Extraction 9 2.3.3 Amplification 12 2.3.4 Labeling 13 2.3.5 Hybridization 13 2.3.6 Washing 14 2.3.7 Scanning 15 2.3.8 Image Analysis and Data Extraction 16 2.3.9 Clinical Diagnosis 17 2.3.10 Interpretation of the Data 17 2.4 Conclusions 17 3 Experimental Design 19
Peter Grass 3.1 Introduction 19 3.2 Principles of Experimental Design 20 3.2.1 Definitions 20 3.2.2 Technical Variation 21 3.2.3 Biological Variation 21 3.2.4 Systematic Variation 22 3.2.5 Population, Random Sample, Experimental and Observational Units 22 3.2.6 Experimental Factors 22 3.2.7 Statistical Errors 23 3.3 Measures to Increase Precision and Accuracy 24 3.3.1 Randomization 25 3.3.2 Blocking 25 3.3.3 Replication 25 3.3.4 Further Measures to Optimize Study Design 26 3.4 Systematic Errors in Microarray Studies 28 3.4.1 Selection Bias 28 3.4.2 Observational Bias 28 3.4.3 Bias at Specimen/Tissue Collection 29 3.4.4 Bias at mRNA Extraction and Hybridization 30 3.5 Conclusion 30 4 Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies 33
Naomi Altman 4.1 Introduction 33 4.1.1 Batch Effects 35 4.2 A Statistical Linear Mixed Effects Model for Microarray Experiments 35 4.2.1 Using the Linear Model for Design 37 4.2.2 Examples of Design Guided by the Linear Model 37 4.3 Blocks and Batches 39 4.3.1 Complete Block Designs 39 4.3.2 Incomplete Block Designs 39 4.3.3 Multiple Batch Effects 40 4.4 Reducing Batch Effects by Normalization and Statistical Adjustment 41 4.4.1 Between and Within Batch Normalization with Multi-array Methods 43 4.4.2 Statistical Adjustment 46 4.5 Sample Pooling and Sample Splitting 47 4.5.1 Sample Pooling 47 4.5.2 Sample Splitting: Technical Replicates 48 4.6 Pilot Experiments 49 4.7 Conclusions 49 Acknowledgements 50 5 Aspects of Technical Bias 51
Martin Schumacher, Frank Staedtler, Wendell D Jones, and Andreas Scherer 5.1 Introduction 51 5.2 Observational Studies 52 5.2.1 Same Protocol, Different Times of Processing 52 5.2.2 Same Protocol, Different Sites (Study 1) 53 5.2.3 Same Protocol, Different Sites (Study 2) 55 5.2.4 Batch Effect Characteristics at the Probe Level 57 5.3 Conclusion 60 6 Bioinformatic Strategies for cDNA-Microarray Data Processing 61
Jessica Fahlén, Mattias Landfors, Eva Freyhult, Max Bylesjö, Johan Trygg, Torgeir R Hvidsten, and Patrik Rydén 6.1 Introduction 61 6.1.1 Spike-in Experiments 62 6.1.2 Key Measures – Sensitivity and Bias 63 6.1.3 The IC Curve and MA Plot 63 6.2 Pre-processing 64 6.2.1 Scanning Procedures 65 6.2.2 Background Correction 65 6.2.3 Saturation 67 6.2.4 Normalization 68 6.2.5 Filtering 70 6.3 Downstream Analysis 71 6.3.1 Gene Selection 71 6.3.2 Cluster Analysis 71 6.4 Conclusion 73 7 Batch Effect Estimation of Microarray Platforms with Analysis of Variance 75
Nysia I George and James J Chen 7.1 Introduction 75 7.1.1 Microarray Gene Expression Data 76 7.1.2 Analysis of Variance in Gene Expression Data 77 7.2 Variance Component Analysis across Microarray Platforms 78 7.3 Methodology 78 7.3.1 Data Description 78 7.3.2 Normalization 79 7.3.3 Gene-Specific ANOVA Model 81 7.4 Application: The MAQC Project 81 7.5 Discussion and Conclusion 85 Acknowledgements 85 8 Variance due to Smooth Bias in Rat Liver and Kidney Baseline Gene Expression in a Large Multi-laboratory Data Set 87
Michael J Boedigheimer, Jeff W Chou, J Christopher Corton, Jennifer Fostel, Raegan O'Lone, P Scott Pine, John Quackenbush, Karol L Thompson, and Russell D Wolfinger 8.1 Introduction 87 8.2 Methodology 89 8.3 Results 89 8.3.1 Assessment of Smooth Bias in Baseline Expression Data Sets 89 8.3.2 Relationship between Smooth Bias and Signal Detection 91 8.3.3 Effect of Smooth Bias Correction on Principal Components Analysis 92 8.3.4 Effect of Smooth Bias Correction on Estimates of Attributable Variability 94 8.3.5 Effect of Smooth Bias Correction on Detection of Genes Differentially Expressed by Fasting 95 8.3.6 Effect of Smooth Bias Correction on the Detection of Strain-Selective Gene Expression 96 8.4 Discussion 97 Acknowledgements 99 9 Microarray Gene Expression: The Effects of Varying Certain Measurement Conditions 101
Walter Liggett, Jean Lozach, Anne Bergstrom Lucas, Ron L Peterson, Marc L Salit, Danielle Thierry-Mieg, Jean Thierry-Mieg, and Russell D Wolfinger 9.1 Introduction 101 9.2 Input Mass Effect on the Amount of Normalization Applied 103 9.3 Probe-by-Probe Modeling of the Input Mass Effect 103 9.4 Further Evidence of Batch Effects 108 9.5 Conclusions 110 10 Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods 113
W Evan Johnson and Cheng li 10.1 Introduction 113 10.1.1 Bayesian and Empirical Bayes Applications in Microarrays 114 10.2 Existing Methods for Adjusting Batch Effect 115 10.2.1 Microarray Data Normalization 115 10.2.2 Batch Effect Adjustment Methods for Large Sample Size 115 10.2.3 Model-Based Location and Scale Adjustments 116 10.3 Empirical Bayes Method for Adjusting Batch Effect 117 10.3.1 Parametric Shrinkage Adjustment 117 10.3.2 Empirical Bayes Batch Effect Parameter Estimates using Nonparametric Empirical Priors 120 10.4 Data Examples, Results and Robustness of the Empirical Bayes Method 121 10.4.1 Microarray Data with Batch Effects 121 10.4.2 Results for Data Set 1 124 10.4.3 Results for Data Set 2 124 10.4.4 Robustness of the Empirical Bayes Method 126 10.4.5 Software Implementation 127 10.5 Discussion 128 11 Identical Reference Samples and Empirical Bayes Method for Cross-Batch Gene Expression Analysis 131
Wynn L Walker and Frank R Sharp 11.1 Introduction 131 11.2 Methodology 133 11.2.1 Data Description 133 11.2.2 Empirical Bayes Method for Batch Adjustment 134 11.2.3 Naïve t-test Batch Adjustment 135 11.3 Application: Expression Profiling of Blood from Muscular Dystrophy Patients 135 11.3.1 Removal of Cross-Experimental Batch Effects 135 11.3.2 Removal of Within-Experimental Batch Effects 136 11.3.3 Removal of Batch Effects: Empirical Bayes Method versus t-Test Filter 137 11.4 Discussion and Conclusion 138 11.4.1 Methods for Batch Adjustment Within and Across Experiments 138 11.4.2 Bayesian Approach is Well Suited for Modeling Cross-Experimental Batch Effects 139 11.4.3 Implications of Cross-Experimental Batch Corrections for Clinical Studies 139 12 Principal Variance Components Analysis: Estimating Batch Effects in Microarray Gene Expression Data 141
Jianying Li, Pierre R Bushel, Tzu-Ming Chu, and Russell D Wolfinger 12.1 Introduction 141 12.2 Methods 143 12.2.1 Principal Components Analysis 143 12.2.2 Variance Components Analysis and Mixed Models 145 12.2.3 Principal Variance Components Analysis 145 12.3 Experimental Data 146 12.3.1 A Transcription Inhibition Study 146 12.3.2 A Lung Cancer Toxicity Study 147 12.3.3 A Hepato-toxicant Toxicity Study 147 12.4 Application of the PVCA Procedure to the Three Example Data Sets 148 12.4.1 PVCA Provides Detailed Estimates of Batch Effects 148 12.4.2 Visualizing the Sources of Batch Effects 149 12.4.3 Selecting the Principal Components in the Modeling 150 12.5 Discussion 153 13 Batch Profile Estimation, Correction, and Scoring 155
Tzu-Ming Chu, Wenjun Bao, Russell S Thomas, and Russell D Wolfinger 13.1 Introduction 155 13.2 Mouse Lung Tumorigenicity Data Set with Batch Effects 157 13.2.1 Batch Profile Estimation 159 13.2.2 Batch Profile Correction 160 13.2.3 Batch Profile Scoring 161 13.2.4 Cross-Validation Results 162 13.3 Discussion 164 Acknowledgements 165 14 Visualization of Cross-Platform Microarray Normalization 167
Xuxin Liu, Joel Parker, Cheng Fan, Charles M Perou, and J S Marron 14.1 Introduction 167 14.2 Analysis of the NCI 60 Data 169 14.3 Improved Statistical Power 174 14.4 Gene-by-Gene versus Multivariate Views 178 14.5 Conclusion 181 15 Toward Integration of Biological Noise: Aggregation Effect in Microarray Data Analysis 183
Lev Klebanov and Andreas Scherer 15.1 Introduction 183 15.2 Aggregated Expression Intensities 185 15.3 Covariance between Log-Expressions 186 15.4 Conclusion 189 Acknowledgements 190 16 Potential Sources of Spurious Associations and Batch Effects in Genome-Wide Association Studies 191
Huixiao Hong, Leming Shi, James C Fuscoe, Federico Goodsaid, Donna Mendrick, and Weida Tong 16.1 Introduction 191 16.2 Potential Sources of Spurious Associations 192 16.2.1 Spurious Associations Related to Study Design 194 16.2.2 Spurious Associations Caused in Genotyping Experiments 195 16.2.3 Spurious Associations Caused by Genotype Calling Errors 195 16.3 Batch Effects 196 16.3.1 Batch Effect in Genotyping Experiment 196 16.3.2 Batch Effect in Genotype Calling 197 16.4 Conclusion 201 Disclaimer 201 17 Standard Operating Procedures in Clinical Gene Expression Biomarker Panel Development 203
Khurram Shahzad, Anshu Sinha, Farhana Latif, and Mario C Deng 17.1 Introduction 203 17.2 Theoretical Framework 204 17.3 Systems-Biological Concepts in Medicine 204 17.4 General Conceptual Challenges 205 17.5 Strategies for Gene Expression Biomarker Development 205 17.5.1 Phase 1: Clinical Phenotype Consensus Definition 206 17.5.2 Phase 2: Gene Discovery 207 17.5.3 Phase 3: Internal Differential Gene List Confirmation 209 17.5.4 Phase 4: Diagnostic Classifier Development 209 17.5.5 Phase 5: External Clinical Validation 210 17.5.6 Phase 6: Clinical Implementation 211 17.5.7 Phase 7: Post-Clinical Implementation Studies 212 17.6 Conclusions 213 18 Data, Analysis, and Standardization 215
Gabriella Rustici, Andreas Scherer, and John Quackenbush 18.1 Introduction 215 18.2 Reporting Standards 216 18.3 Computational Standards: From Microarray to Omic Sciences 219 18.3.1 The Microarray Gene Expression Data Society 219 18.3.2 The Proteomics Standards Initiative 220 18.3.3 The Metabolomics Standards Initiative 220 18.3.4 The Genomic Standards Consortium 220 18.3.5 Systems Biology Initiatives 221 18.3.6 Data Standards in Biopharmaceutical and Clinical Research 221 18.3.7 Standards Integration Initiatives 222 18.3.8 The MIBBI project 223 18.3.9 OBO Foundry 223 18.3.10 FuGE and ISA-TAB 223 18.4 Experimental Standards: Developing Quality Metrics and a Consensus on Data Analysis Methods 226 18.5 Conclusions and Future Perspective 228 References 231 Index 245

Long Description

Batch Effects and Noise in Microarray Experiments: Sources and Solutions looks at the issue of technical noise and batch effects in microarray studies and illustrates how to alleviate such factors whilst interpreting the relevant biological information. Each chapter focuses on sources of noise and batch effects before starting an experiment, with examples of statistical methods for detecting, measuring, and managing batch effects within and across datasets provided online. Throughout the book the importance of standardization and the value of standard operating procedures in the development of genomics biomarkers is emphasized. Key Features: A thorough introduction to Batch Effects and Noise in Microrarray Experiments. A unique compilation of review and research articles on handling of batch effects and technical and biological noise in microarray data. An extensive overview of current standardization initiatives. All datasets and methods used in the chapters, as well as colour images, are available on (www.the-batch-effect-book.org), so that the data can be reproduced. An exciting compilation of state-of-the-art review chapters and latest research results, which will benefit all those involved in the planning, execution, and analysis of gene expression studies.

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