Batch Effects and Noise in Microarray Experiments - Sources and Solutions

Informationen zum Autor 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. Klappentext 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. Zusammenfassung 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. Inhaltsverzeichnis 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

List of contents

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

Product details

• Authors: a Scherer, Andreas Scherer, Andreas (EDT) Scherer, Andreas (Spheromics Scherer, Scherer Andreas
• Assisted by: Andreas Scherer (Editor)
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 28.10.2009
• EAN: 9780470741382
• ISBN: 978-0-470-74138-2
• No. of pages: 288
• Series: Wiley Series in Probability and Statistics; Wiley Series in Probability an; Wiley Series in Probability and Statistics
• Subjects: Natural sciences, medicine, IT, technology > Mathematics; Statistik, Statistics, Klinische Studien, VERSUCHSPLANUNG, Statistische Versuchsplanung, Experimental Design, Clinical Trials, Statistische Genetik / Microarray-Analyse, Statistical Genetics / Microarray Analysis