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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…mehr
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 href="http://www.the-batch-effect-book.org/">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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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.
Inhaltsangabe
List of Contributors Foreword Preface 1 Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction Andreas Scherer 2 Microarray Platforms and Aspects of Experimental Variation John Coller 2.1 Introduction 2.2 Microarray Platforms 2.3 Experimental Considerations 2.4 Conclusions 3 Experimental Design Peter Grass 3.1 Introduction 3.2 Principles of Experimental Design 3.3 Measures to Increase Precision and Accuracy 3.4 Systematic Errors in Microarray Studies 3.5 Conclusion 4 Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies Naomi Altman 4.1 Introduction 4.2 A Statistical Linear Mixed Effects Model for Microarray Experiments 4.3 Blocks and Batches 4.4 Reducing Batch Effects by Normalization and Statistical Adjustment 4.5 Sample Pooling and Sample Splitting 4.6 Pilot Experiments 4.7 Conclusions Acknowledgements 5 Aspects of Technical Bias Martin Schumacher, Frank Staedtler, Wendell D Jones, and Andreas Scherer 5.1 Introduction 5.2 Observational Studies 5.3 Conclusion 6 Bioinformatic Strategies for cDNA-Microarray Data Processing Jessica Fahl en, Mattias Landfors, Eva Freyhult, Max Bylesj o, Johan Trygg, Torgeir R Hvidsten, and Patrik Ryd en 6.1 Introduction 6.2 Pre-processing 6.3 Downstream analysis 6.4 Conclusion 7 Batch Effect Estimation of Microarray Platforms with Analysis of Variance Nysia I George and James J Chen 7.1 Introduction 7.2 Variance Component Analysis across Microarray Platforms 7.3 Methodology 7.4 Application: The MAQC Project 7.5 Discussion and Conclusion Acknowledgements 8 Variance due to Smooth Bias in Rat Liver and Kidney Baseline Gene Expression in a Large Multi-laboratory Data Set 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 8.2 Methodology 8.3 Results 8.4 Discussion Acknowledgements 9 Microarray Gene Expression: The Effects of Varying Certain Measurement Conditions 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 9.2 Input Mass Effect on the Amount of Normalization Applied 9.3 Probe-by-Probe Modeling of the Input Mass Effect 9.4 Further Evidence of Batch Effects 9.5 Conclusions Disclaimer 10 Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods W Evan Johnson and Cheng Li 10.1 Introduction 10.2 Existing Methods for Adjusting Batch Effect 10.3 Empirical Bayes Method for Adjusting Batch Effect 10.4 Data Examples, Results and Robustness of the Empirical Bayes Method 10.5 Discussion 11 Identical Reference Samples and Empirical Bayes Method for Cross-Batch Gene Expression Analysis Wynn L Walker and Frank R Sharp 11.1 Introduction 11.2 Methodology 11.3 Application: Expression Profiling of Blood from Muscular Dystrophy Patients 11.4 Discussion and Conclusion 12 Principal Variance Components Analysis: Estimating Batch Effects in Microarray Gene Expression Data Jianying Li, Pierre Bushel, Tzu-Ming Chu, and Russell D Wolfinger 12.1 Introduction 12.2 Methods 12.3 Experimental Data 12.4 Application of the PVCA Procedure to the Three Example Data Sets 12.5 Discussion 13 Batch Profile Estimation, Correction, and Scoring Tzu-Ming Chu, Wenjun Bao, Russell S Thomas, and Russell D Wolfinger 13.1 Introduction 13.2 Mouse Lung Tumorigenicity Data Set with Batch Effects 13.3 Discussion Acknowledgements 14 Visualization of Cross-Platform Microarray Normalization Xuxin Liu, Joel Parker, Cheng Fan, Charles M Perou, and J Steve Marron 14.1 Introduction 14.2 Analysis of the NCI Data 14.3 Improved Statistical Power 14.4 Gene-by-Gene versus Multivariate Views 14.5 Conclusion 15 Toward Integration of Biological Noise: Aggregation Effect in Microarray Data Analysis Lev Klebanov and Andreas Scherer 15.1 Introduction 15.2 Aggregated Expression Intensities 15.3 Covariance between Log-Expressions 15.4 Conclusion Acknowledgements 16 Potential Sources of Spurious Associations and Batch Effects in Genome-Wide Association Studies Huixiao Hong, Leming Shi, James C Fuscoe, Federico Goodsaid, Donna Mendrick, and Weida Tong 16.1 Introduction 16.2 Batch Effects 17 Standard Operating Procedures in Clinical Gene Expression Biomarker Panel Development Khurram Shahzad, Anshu Sinha, Farhana Latif, and Mario C Deng 17.1 Introduction 17.2 Theoretical Framework 17.3 Systems-Biological Concepts in Medicine 17.4 General Conceptual Challenges 17.5 Strategies for Gene Expression Biomarker Development 17.6 Conclusions 18 Data, Analysis, and Standardization Gabriella Rustici, Andreas Scherer, and John Quackenbush 18.1 Introduction 18.2 Reporting Standards 18.3 Computational Standards: From Microarray to Omic Sciences 18.4 Experimental Standards: Developing Quality Metrics and a Consensus on Data Analysis Methods 18.5 Conclusions and Future Perspective References Index
List of Contributors Foreword Preface 1 Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction Andreas Scherer 2 Microarray Platforms and Aspects of Experimental Variation John Coller 2.1 Introduction 2.2 Microarray Platforms 2.3 Experimental Considerations 2.4 Conclusions 3 Experimental Design Peter Grass 3.1 Introduction 3.2 Principles of Experimental Design 3.3 Measures to Increase Precision and Accuracy 3.4 Systematic Errors in Microarray Studies 3.5 Conclusion 4 Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies Naomi Altman 4.1 Introduction 4.2 A Statistical Linear Mixed Effects Model for Microarray Experiments 4.3 Blocks and Batches 4.4 Reducing Batch Effects by Normalization and Statistical Adjustment 4.5 Sample Pooling and Sample Splitting 4.6 Pilot Experiments 4.7 Conclusions Acknowledgements 5 Aspects of Technical Bias Martin Schumacher, Frank Staedtler, Wendell D Jones, and Andreas Scherer 5.1 Introduction 5.2 Observational Studies 5.3 Conclusion 6 Bioinformatic Strategies for cDNA-Microarray Data Processing Jessica Fahl en, Mattias Landfors, Eva Freyhult, Max Bylesj o, Johan Trygg, Torgeir R Hvidsten, and Patrik Ryd en 6.1 Introduction 6.2 Pre-processing 6.3 Downstream analysis 6.4 Conclusion 7 Batch Effect Estimation of Microarray Platforms with Analysis of Variance Nysia I George and James J Chen 7.1 Introduction 7.2 Variance Component Analysis across Microarray Platforms 7.3 Methodology 7.4 Application: The MAQC Project 7.5 Discussion and Conclusion Acknowledgements 8 Variance due to Smooth Bias in Rat Liver and Kidney Baseline Gene Expression in a Large Multi-laboratory Data Set 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 8.2 Methodology 8.3 Results 8.4 Discussion Acknowledgements 9 Microarray Gene Expression: The Effects of Varying Certain Measurement Conditions 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 9.2 Input Mass Effect on the Amount of Normalization Applied 9.3 Probe-by-Probe Modeling of the Input Mass Effect 9.4 Further Evidence of Batch Effects 9.5 Conclusions Disclaimer 10 Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods W Evan Johnson and Cheng Li 10.1 Introduction 10.2 Existing Methods for Adjusting Batch Effect 10.3 Empirical Bayes Method for Adjusting Batch Effect 10.4 Data Examples, Results and Robustness of the Empirical Bayes Method 10.5 Discussion 11 Identical Reference Samples and Empirical Bayes Method for Cross-Batch Gene Expression Analysis Wynn L Walker and Frank R Sharp 11.1 Introduction 11.2 Methodology 11.3 Application: Expression Profiling of Blood from Muscular Dystrophy Patients 11.4 Discussion and Conclusion 12 Principal Variance Components Analysis: Estimating Batch Effects in Microarray Gene Expression Data Jianying Li, Pierre Bushel, Tzu-Ming Chu, and Russell D Wolfinger 12.1 Introduction 12.2 Methods 12.3 Experimental Data 12.4 Application of the PVCA Procedure to the Three Example Data Sets 12.5 Discussion 13 Batch Profile Estimation, Correction, and Scoring Tzu-Ming Chu, Wenjun Bao, Russell S Thomas, and Russell D Wolfinger 13.1 Introduction 13.2 Mouse Lung Tumorigenicity Data Set with Batch Effects 13.3 Discussion Acknowledgements 14 Visualization of Cross-Platform Microarray Normalization Xuxin Liu, Joel Parker, Cheng Fan, Charles M Perou, and J Steve Marron 14.1 Introduction 14.2 Analysis of the NCI Data 14.3 Improved Statistical Power 14.4 Gene-by-Gene versus Multivariate Views 14.5 Conclusion 15 Toward Integration of Biological Noise: Aggregation Effect in Microarray Data Analysis Lev Klebanov and Andreas Scherer 15.1 Introduction 15.2 Aggregated Expression Intensities 15.3 Covariance between Log-Expressions 15.4 Conclusion Acknowledgements 16 Potential Sources of Spurious Associations and Batch Effects in Genome-Wide Association Studies Huixiao Hong, Leming Shi, James C Fuscoe, Federico Goodsaid, Donna Mendrick, and Weida Tong 16.1 Introduction 16.2 Batch Effects 17 Standard Operating Procedures in Clinical Gene Expression Biomarker Panel Development Khurram Shahzad, Anshu Sinha, Farhana Latif, and Mario C Deng 17.1 Introduction 17.2 Theoretical Framework 17.3 Systems-Biological Concepts in Medicine 17.4 General Conceptual Challenges 17.5 Strategies for Gene Expression Biomarker Development 17.6 Conclusions 18 Data, Analysis, and Standardization Gabriella Rustici, Andreas Scherer, and John Quackenbush 18.1 Introduction 18.2 Reporting Standards 18.3 Computational Standards: From Microarray to Omic Sciences 18.4 Experimental Standards: Developing Quality Metrics and a Consensus on Data Analysis Methods 18.5 Conclusions and Future Perspective References Index
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