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.
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.
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
A Coller Jr). 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 Fahlen, Mattias
Landfors, Eva Freyhult, Max Bylesjo, Johan Trygg, Torgeir R Hvidsten, and
Patrik Ryden). 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. 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 R 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 S Marron). 14.1 Introduction. 14.2 Analysis of the NCI 60 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 Potential Sources of Spurious
Associations. 16.3 Batch Effects. 16.4 Conclusion. Disclaimer. 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.
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.
