Z702544
Guide to Analysis of DNA Microarray Data, 2nd ed.
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About This Item
ISBN-10:
0-47165604-6
ISBN-13:
978-0-47165604-3
UNSPSC Code:
55101509
Pricing and availability is not currently available.
publication info
S. Knudsen, John Wiley & Sons, 2004, 168 pp., soft cover
General description
Written for those who don′t have any special training in data analysis and statistics, this book begins where DNA array equipment leaves off: the image produced by the microarray. The text deals with the questions that arise starting at this point, providing an introduction to microarray technology, then moving on to image analysis, data analysis, cluster analysis, and beyond. This book offers practitioners reliable information using concrete examples and a clear style. This edition features entirely new chapters on Image analysis; Experiment design; Automated analysis, integrated analysis, and systems biology; Interpretation of results .
Table of Contents
1. Introduction to DNA Microarray Technology.
1.1 Hybridization.
1.2 Gold Rush?
1.3 The Technology behind DNA Microarrays.
1.4 Parallel Sequencing on Microbead Arrays.
1.5 Example: Affymetrix vs. Spotted Arrays.
1.6 Summary.
1.7 Further Reading.
2. Overview of Data Analysis.
3. Image Analysis.
3.1 Gridding.
3.2 Segmentation.
3.3 Intensity Extraction.
3.4 Background Correction.
3.5 Software.
3.6 Summary.
3.7 Further Reading.
4. Basic Data Analysis.
4.1 Normalization.
4.2 Dye Bias, Spatial Bias, Print Tip Bias.
4.3 Expression Indices.
4.4 Detection of Outliers.
4.5 Fold Change.
4.6 Significance.
4.7 Mixed Cell Populations.
4.8 Summary.
4.9 Further Reading.
5. Visualization by Reduction of Dimensionality.
5.1 Principal Component Analysis.
5.2 Example 1: PCA on Small Data Matrix.
5.3 Example 2: PCA on Real Data.
5.4 Summary.
5.5 Further Reading.
6. Cluster Analysis.
6.1 Hierarchical Clustering.
6.2 K-means Clustering.
6.3 Self-Organizing Maps.
6.4 Distance Measures.
6.4.1 Example: Comparison of Distance Measures.
6.5 Gene Normalization.
6.6 Visualization of Clusters.
6.7 Summary.
6.8 Further Reading.
7. Beyond Cluster Analysis.
7.1 Function Prediction.
7.2 Discovery of Regulatory Elements in Promoter Regions.
7.3 Summary.
7.4 Further Reading.
8. Automated Analysis, Integrated Analysis and Systems Biology.
8.1 Integrated Analysis.
8.2 Systems Biology.
8.3 Further Reading.
9. Reverse Engineering of Regulatory Networks.
9.1 The Time-Series Approach.
9.2 The Steady-State Approach.
9.3 Limitations of Network Modeling.
9.4 Example 1: Steady-State Model.
9.5 Example 2: Steady-State Model on Bacillus Data.
9.6 Example 3: Linear Time-Series Model.
9.7 Further Reading.
10. Molecular Classifiers.
10.1 Feature Selection.
10.2 Validation.
10.3. Classificatino Schemes .
10.4 Performance Evaluation.
10.5 Example I: Classification of Bladder Cancer Subtypes.
10.6 Example II: Classification of SRBCT Cancer Subtypes.
10.7 Summary.
10.8 Further Reading.
11. The Design of Probes for Arrays.
11.1 Selection of Genes for an Array.
11.2 Gene Finding.
11.3 Selection of Regions Within Genes.
11.4 Selection of Primers for PCR.
11.5 Selection of Unique Oligomer Probes.
11.6 Remapping of Probes.
11.7 Further Reading.
12. Genotyping and Resequencing Chips.
12.1 Example: Neural Networks for GeneChip Prediction.
12.2 Further Reading.
13. Experiment Design and Interpretation of Results.
13.1 Factorial Designs.
13.2 Designs for Two-Channel Arrays.
13.3 Hypothesis Driven Experiments.
13.4 Independent Verification.
13.5 Interpretation of Results.
13.6 Limitations of Expression Analysis.
13.7 Further Reading.
14. Software Issues and Data Formats.
14.1 Standardization Efforts.
14.2 Databases.
14.3 Standard File Format.
14.4 Software for Clustering.
14.5 Software for Statistical Analysis.
14.6 Summary.
14.7 Further Reading.
Appendix A: Web Resources: Commercial Software Packages.
References.
Index.
1.1 Hybridization.
1.2 Gold Rush?
1.3 The Technology behind DNA Microarrays.
1.4 Parallel Sequencing on Microbead Arrays.
1.5 Example: Affymetrix vs. Spotted Arrays.
1.6 Summary.
1.7 Further Reading.
2. Overview of Data Analysis.
3. Image Analysis.
3.1 Gridding.
3.2 Segmentation.
3.3 Intensity Extraction.
3.4 Background Correction.
3.5 Software.
3.6 Summary.
3.7 Further Reading.
4. Basic Data Analysis.
4.1 Normalization.
4.2 Dye Bias, Spatial Bias, Print Tip Bias.
4.3 Expression Indices.
4.4 Detection of Outliers.
4.5 Fold Change.
4.6 Significance.
4.7 Mixed Cell Populations.
4.8 Summary.
4.9 Further Reading.
5. Visualization by Reduction of Dimensionality.
5.1 Principal Component Analysis.
5.2 Example 1: PCA on Small Data Matrix.
5.3 Example 2: PCA on Real Data.
5.4 Summary.
5.5 Further Reading.
6. Cluster Analysis.
6.1 Hierarchical Clustering.
6.2 K-means Clustering.
6.3 Self-Organizing Maps.
6.4 Distance Measures.
6.4.1 Example: Comparison of Distance Measures.
6.5 Gene Normalization.
6.6 Visualization of Clusters.
6.7 Summary.
6.8 Further Reading.
7. Beyond Cluster Analysis.
7.1 Function Prediction.
7.2 Discovery of Regulatory Elements in Promoter Regions.
7.3 Summary.
7.4 Further Reading.
8. Automated Analysis, Integrated Analysis and Systems Biology.
8.1 Integrated Analysis.
8.2 Systems Biology.
8.3 Further Reading.
9. Reverse Engineering of Regulatory Networks.
9.1 The Time-Series Approach.
9.2 The Steady-State Approach.
9.3 Limitations of Network Modeling.
9.4 Example 1: Steady-State Model.
9.5 Example 2: Steady-State Model on Bacillus Data.
9.6 Example 3: Linear Time-Series Model.
9.7 Further Reading.
10. Molecular Classifiers.
10.1 Feature Selection.
10.2 Validation.
10.3. Classificatino Schemes .
10.4 Performance Evaluation.
10.5 Example I: Classification of Bladder Cancer Subtypes.
10.6 Example II: Classification of SRBCT Cancer Subtypes.
10.7 Summary.
10.8 Further Reading.
11. The Design of Probes for Arrays.
11.1 Selection of Genes for an Array.
11.2 Gene Finding.
11.3 Selection of Regions Within Genes.
11.4 Selection of Primers for PCR.
11.5 Selection of Unique Oligomer Probes.
11.6 Remapping of Probes.
11.7 Further Reading.
12. Genotyping and Resequencing Chips.
12.1 Example: Neural Networks for GeneChip Prediction.
12.2 Further Reading.
13. Experiment Design and Interpretation of Results.
13.1 Factorial Designs.
13.2 Designs for Two-Channel Arrays.
13.3 Hypothesis Driven Experiments.
13.4 Independent Verification.
13.5 Interpretation of Results.
13.6 Limitations of Expression Analysis.
13.7 Further Reading.
14. Software Issues and Data Formats.
14.1 Standardization Efforts.
14.2 Databases.
14.3 Standard File Format.
14.4 Software for Clustering.
14.5 Software for Statistical Analysis.
14.6 Summary.
14.7 Further Reading.
Appendix A: Web Resources: Commercial Software Packages.
References.
Index.
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