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Other titles in the Wiley Series in Probability and Statistics series:
Batch Effects and Noise in Microarray Experiments: Sources and Solutions (Wiley Series in Probability and Statistics)by Andreas Scherer
Synopses & Reviews
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.
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.
Book News Annotation:
Researchers, clinicians, laboratory personnel, managers, and others responsible for gene expression studies are the expected readers as like professionals in a wide range of fields explain bias in microarray data, describe sources of technical and biological variation in such experiments and genome-wide associated studies, and suggest how to reduce bias. Many of the statistical methods they provide for reducing bias and alleviating its effects are previously unpublished. Among their topics are microarray platforms and aspects of experimental variation, aspects of technical bias, bioinformatic strategies for cDNA-microarray data processing, adjusting batch effects in microarray experiments with small sample size using empirical Bayes methods, visualizing cross-platform microarray normalization, and standard operating procedures in clinical gene expression biomarker panel development. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com)
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.
About the Author
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.
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.
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