Batch effects and noise in microarray experiments : sources and solutions /
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| Imprint: | Chichester, U.K. : J. Wiley, 2009. |
|---|---|
| Description: | xx, 252 p. : ill. ; 26 cm. |
| Language: | English |
| Series: | Wiley series in probability and statistics Wiley series in probability and statistics. |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/7932620 |
Table of Contents:
- List of Contributors
- Foreword
- Preface
- 1. Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction
- 2. Microarray Platforms and Aspects of Experimental Variation
- 2.1. Introduction
- 2.2. Microarray Platforms
- 2.3. Experimental Considerations
- 2.4. Conclusions
- 3. Experimental Design
- 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
- 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
- 5.1. Introduction
- 5.2. Observational Studies
- 5.3. Conclusion
- 6. Bioinformatic Strategies for cDNA-Microarray Data Processing
- 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
- 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
- 8.1. Introduction
- 8.2. Methodology
- 8.3. Results
- 8.4. Discussion
- Acknowledgements
- 9. Microarray Gene Expression: The Effects of Varying Certain Measurement Conditions
- 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
- 10. Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods
- 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
- 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 Data
- 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 Scoring
- 13.1. Introduction
- 13.2. Mouse Lung Tumorigenicity Data Set with Batch Effects
- 13.3. Discussion
- Acknowledgements
- 14. Visualization of Cross-Platform Microarray Normalization
- 14.1. Introduction
- 14.2. Analysis of the NCI
- 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 Analysis
- 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 Studies
- 16.1. Introduction
- 16.2. Batch Effects
- 17. Standard Operating Procedures in Clinical Gene Expression Biomarker Panel Development
- 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 Standardization
- 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
