Batch Effects and Noise in Microarray Experiments

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.

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