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 href="http://www.the-batch-effect-book.org/">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.
Autorentext
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.
Klappentext
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.
Inhalt
List of Contributors xiii
Foreword xvii
Preface xix
1 Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction 1
Andreas Scherer
2 Microarray Platforms and Aspects of Experimental Variation 5
John A Coller Jr
2.1 Introduction 5
2.2 Microarray Platforms 6
2.2.1 Affymetrix 6
2.2.2 Agilent 7
2.2.3 Illumina 7
2.2.4 Nimblegen 8
2.2.5 Spotted Microarrays 8
2.3 Experimental Considerations 9
2.3.1 Experimental Design 9
2.3.2 Sample and RNA Extraction 9
2.3.3 Amplification 12
2.3.4 Labeling 13
2.3.5 Hybridization 13
2.3.6 Washing 14
2.3.7 Scanning 15
2.3.8 Image Analysis and Data Extraction 16
2.3.9 Clinical Diagnosis 17
2.3.10 Interpretation of the Data 17
2.4 Conclusions 17
3 Experimental Design 19
Peter Grass
3.1 Introduction 19
3.2 Principles of Experimental Design 20
3.2.1 Definitions 20
3.2.2 Technical Variation 21
3.2.3 Biological Variation 21
3.2.4 Systematic Variation 22
3.2.5 Population, Random Sample, Experimental and Observational Units 22
3.2.6 Experimental Factors 22
3.2.7 Statistical Errors 23
3.3 Measures to Increase Precision and Accuracy 24
3.3.1 Randomization 25
3.3.2 Blocking 25
3.3.3 Replication 25
3.3.4 Further Measures to Optimize Study Design 26
3.4 Systematic Errors in Microarray Studies 28
3.4.1 Selection Bias 28
3.4.2 Observational Bias 28
3.4.3 Bias at Specimen/Tissue Collection 29
3.4.4 Bias at mRNA Extraction and Hybridization 30
3.5 Conclusion 30
4 Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies 33
Naomi Altman
4.1 Introduction 33
4.1.1 Batch Effects 35
4.2 A Statistical Linear Mixed Effects Model for Microarray Experiments 35
4.2.1 Using the Linear Model for Design 37
4.2.2 Examples of Design Guided by the Linear Model 37
4.3 Blocks and Batches 39
4.3.1 Complete Block Designs 39
4.3.2 Incomplete Block Designs 39
4.3.3 Multiple Batch Effects 40
4.4 Reducing Batch Effects by Normalization and Statistical Adjustment 41
4.4.1 Between and Within Batch Normalization with Multi-array Methods 43
4.4.2 Statistical Adjustment 46
4.5 Sample Pooling and Sample Splitting 47
4.5.1 Sample Pooling 47
4.5.2 Sample Splitting: Technical Replicates 48
4.6 Pilot Experiments 49
4.7 Conclusions 49
Acknowledgements 50
5 Aspects of Technical Bias 51
Martin Schumacher, Frank Staedtler, Wendell D Jones, and Andreas Scherer
5.1 Introduction 51
5.2 Observational Studies 52
5.2.1 Same Protocol, Different Times of Processing 52
5.2.2 Same Protocol, Different Sites (Study 1) 53
5.2.3 Same Protocol, Different Sites (Study 2) 55
5.2.4 Batch Effect Characteristics at the Probe Level 57
5.3 Conclusion 60
6 Bioinformatic Strategies for cDNA-Microarray Data Processing 61
Jessica Fahlén, Mattias Landfors, Eva Freyhult, Max Bylesjö, Johan Trygg, Torgeir R Hvidsten, and Patrik Rydén
6.1 Introduction 61
6.1.1 Spike-in Experiments 62
6.1.2 Key Measures Sensitivity and Bias 63
6.1.3 The IC Curve and MA Plot 63
6.2 Pre-processing 64
6.2.1 Scanning Procedures 65
6.2.2 Background Correction 65
6.2.3 Saturation 67
6.2.4 Normalization 68
6.2.5 Filtering 70
6.3 Downstream Analysis 71
6.3...