Batch effects and noise in microroarray experimentals : sources and solution

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.

Author Notes

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

Foreword

Preface

1 Variation, Variability, Batches and Bias in Microarray Experiments: An IntroductionAndreas Scherer

2 Microarray Platforms and Aspects of Experimental VariationJohn Coller

2.1 Introduction

2.2 Microarray Platforms

2.3 Experimental Considerations

2.4 Conclusions

3 Experimental DesignPeter 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 StudiesNaomi 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 BiasMartin Schumacher and Frank Staedtler and Wendell D Jones and Andreas Scherer

5.1 Introduction

5.2 Observational Studies

5.3 Conclusion

6 Bioinformatic Strategies for cDNA-Microarray Data ProcessingJessica Fahl´en and Mattias Landfors and Eva Freyhult and Max Bylesjũo and Johan Trygg and 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 VarianceNysia 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 SetMichael J Boedigheimer and Jeff W Chou and J Christopher Corton and Jennifer Fostel and Raegan O'Lone and P Scott Pine and John Quackenbush and 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 ConditionsWalter Liggett and Jean Lozach and Anne Bergstrom Lucas and Ron L Peterson and Marc L Salit and Danielle Thierry-Mieg and 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 ConclusionsDisclaimer

10 Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes MethodsW 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 AnalysisWynn 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 DataJianying Li and Pierre Bushel and 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 ScoringTzu-Ming Chu and Wenjun Bao and 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 NormalizationXuxin Liu and Joel Parker and Cheng Fan and Charles M Perou and J Steve Marron

14.1 Introduction

14.2 Analysis of the NCIData

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 AnalysisLev 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 StudiesHuixiao Hong and Leming Shi and James C Fuscoe and Federico Goodsaid and Donna Mendrick and Weida Tong

16.1 Introduction

16.2 Batch Effects

17 Standard Operating Procedures in Clinical Gene Expression Biomarker Panel DevelopmentKhurram Shahzad and Anshu Sinha and 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 StandardizationGabriella Rustici and 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

Available:*

On Order

Summary

Summary

Author Notes

Table of Contents