Batch Effects and Noise in Microarray Experiments: Sources and Solutions
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.
- 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.
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).
2.2 Microarray Platforms.
2.3 Experimental Considerations.
3 Experimental Design (Peter Grass).
3.2 Principles of Experimental Design.
3.3 Measures to Increase Precision and Accuracy.
3.4 Systematic Errors in Microarray Studies.
4 Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies (Naomi Altman).
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.
5 Aspects of Technical Bias (Martin Schumacher, Frank Staedtler, Wendell D Jones, and Andreas Scherer).
5.2 Observational Studies.
6 Bioinformatic Strategies for cDNA-Microarray Data Processing (Jessica Fahlén, Mattias Landfors, Eva Freyhult, Max Bylesjö, Johan Trygg, Torgeir R Hvidsten, and Patrik Rydén).
6.3 Downstream Analysis.
7 Batch Effect Estimation of Microarray Platforms with Analysis of Variance (Nysia I George and James J Chen).
7.2 Variance Component Analysis across Microarray Platforms.
7.4 Application: The MAQC Project.
7.5 Discussion and Conclusion.
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).
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).
9.2 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.
10 Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods (W Evan Johnson and Cheng Li).
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.
11 Identical Reference Samples and Empirical Bayes Method for Cross-Batch Gene Expression Analysis (Wynn L Walker and Frank R Sharp).
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).
12.3 Experimental Data.
12.4 Application of the PVCA Procedure to the Three Example Data Sets.
13 Batch Profile Estimation, Correction, and Scoring (Tzu-Ming Chu, Wenjun Bao, Russell S Thomas, and Russell D Wolfinger).
13.2 Mouse Lung Tumorigenicity Data Set with Batch Effects.
14 Visualization of Cross-Platform Microarray Normalization (Xuxin Liu, Joel Parker, Cheng Fan, Charles M Perou, and J S Marron).
14.2 Analysis of the NCI 60 Data.
14.3 Improved Statistical Power.
14.4 Gene-by-Gene versus Multivariate Views.
15 Toward Integration of Biological Noise: Aggregation Effect in Microarray Data Analysis (Lev Klebanov and Andreas Scherer).
15.2 Aggregated Expression Intensities.
15.3 Covariance between Log-Expressions.
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).
16.2 Potential Sources of Spurious Associations.
16.3 Batch Effects.
17 Standard Operating Procedures in Clinical Gene Expression Biomarker Panel Development (Khurram Shahzad, Anshu Sinha, Farhana Latif, and Mario C Deng).
17.2 Theoretical Framework.
17.3 Systems-Biological Concepts in Medicine.
17.4 General Conceptual Challenges.
17.5 Strategies for Gene Expression Biomarker Development.
18 Data, Analysis, and Standardization (Gabriella Rustici, Andreas Scherer, and John Quackenbush).
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.