Batch Effects and Noise in Microarray Experiments
Sources and Solutions
Wiley Series in Probability and Statistics
1. Auflage Oktober 2009
272 Seiten, Hardcover
Wiley & Sons Ltd
Kurzbeschreibung
Batch effects and experimental shift are major sources for noise in a microarray dataset. Their effect on gene expression profiling has been largely ignored until now. This book provides a valuable insight into the nature of batch effects, providing guidance on possible ways of dealing with it and illustrating ways of keeping it to a minimum. Guidance in the design of balanced experiments is provided by leading experts in the field and examples are drawn from real-life examples.
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.
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
