Batch Effects and Noise in Microarray Experiments Sources and Solutions

by Scherer, Andreas

• ISBN13: 9780470741382
• ISBN10: 0470741384
• Edition: 1st
• Format: Hardcover
• Copyright: 2009-12-14
• Publisher: Wiley

Summary

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.

Author Biography

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.

Table of Contents

List of Contributors

Foreword

Preface

Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction

Microarray Platforms and Aspects of Experimental Variation

Introduction

Microarray Platforms

Experimental Considerations

Conclusions

Experimental Design

Introduction

Principles of Experimental Design

Measures to Increase Precision and Accuracy

Systematic Errors in Microarray Studies

Conclusion

Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies

Introduction

A Statistical Linear Mixed Effects Model for Microarray Experiments

Blocks and Batches

Reducing Batch Effects by Normalization and Statistical Adjustment

Sample Pooling and Sample Splitting

Pilot Experiments

Conclusions

Acknowledgements

Aspects of Technical Bias

Introduction

Observational Studies

Conclusion

Bioinformatic Strategies for cDNA-Microarray Data Processing

Introduction

Pre-processing

Downstream analysis

Conclusion

Batch Effect Estimation of Microarray Platforms with Analysis of Variance

Introduction

Variance Component Analysis across Microarray Platforms

Methodology

Application: The MAQC Project

Discussion and Conclusion

Acknowledgements

Variance due to Smooth Bias in Rat Liver and Kidney Baseline Gene Expression in a Large Multi-laboratory Data Set

Introduction

Methodology

Results

Discussion

Acknowledgements

Microarray Gene Expression: The Effects of Varying Certain Measurement Conditions

Introduction

Input Mass Effect on the Amount of Normalization Applied

Probe-by-Probe Modeling of the Input Mass Effect

Further Evidence of Batch Effects

Conclusions

Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods

Introduction

Existing Methods for Adjusting Batch Effect

Empirical Bayes Method for Adjusting Batch Effect

Data Examples, Results and Robustness of the Empirical Bayes Method

Discussion

Identical Reference Samples and Empirical Bayes Method for Cross-Batch Gene Expression Analysis

Introduction

Methodology

Application: Expression Profiling of Blood from Muscular Dystrophy Patients

Discussion and Conclusion

Principal Variance Components Analysis: Estimating Batch Effects in Microarray Gene Expression Data

Introduction

Methods

Experimental Data

Application of the PVCA Procedure to the Three Example Data Sets

Discussion

Batch Profile Estimation, Correction, and Scoring

Introduction

Mouse Lung Tumorigenicity Data Set with Batch Effects

Discussion

Acknowledgements

Visualization of Cross-Platform Microarray Normalization

Introduction

Analysis of the NCI

Improved Statistical Power

Gene-by-Gene versus Multivariate Views

Conclusion

Toward Integration of Biological Noise: Aggregation Effect in Microarray Data Analysis

Introduction

Aggregated Expression Intensities

Covariance between Log-Expressions

Conclusion

Acknowledgements

Potential Sources of Spurious Associations and Batch Effects in Genome-Wide Association Studies

Introduction

Batch Effects

Standard Operating Procedures in Clinical Gene Expression Biomarker Panel Development

Introduction

Theoretical Framework

Systems-Biological Concepts in Medicine

General Conceptual Challenges

Strategies for Gene Expression Biomarker Development

Conclusions

Data, Analysis, and Standardization

Introduction

Reporting Standards

Computational Standards: From Microarray to Omic Sciences

Experimental Standards: Developing Quality Metrics and a Consensus on Data Analysis Methods

Conclusions and Future Perspective

References

Index

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