Exploration and analysis of DNA microarray and protein array data /
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| Author / Creator: | Amaratunga, Dhammika, 1956- |
|---|---|
| Imprint: | Hoboken, N.J. : Wiley-Interscience, c2004. |
| Description: | xiv, 246 p. : ill. ; 25 cm. |
| Language: | English |
| Series: | Wiley series in probability and statistics |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/5002827 |
Table of Contents:
- Preface
- 1. A Brief Introduction
- 1.1. A Note on Exploratory Data Analysis
- 1.2. Computing Considerations and Software
- 1.3. A Brief Outline of the Book
- 2. Genomics Basics
- 2.1. Genes
- 2.2. DNA
- 2.3. Gene Expression
- 2.4. Hybridization Assays and Other Laboratory Techniques
- 2.5. The Human Genome
- 2.6. Genome Variations and Their Consequences
- 2.7. Genomics
- 2.8. The Role of Genomics in Pharmaceutical Research
- 2.9. Proteins
- 2.10. Bioinformatics
- Supplementary Reading
- Exercises
- 3. Microarrays
- 3.1. Types of Microarray Experiments
- 3.1.1. Experiment Type 1: Tissue-Specific Gene Expression
- 3.1.2. Experiment Type 2: Development Genetics
- 3.1.3. Experiment Type 3: Genetic Diseases
- 3.1.4. Experiment Type 4: Complex Diseases
- 3.1.5. Experiment Type 5: Pharmacological Agents
- 3.1.6. Experiment Type 6: Plant Breeding
- 3.1.7. Experiment Type 7: Environmental Monitoring
- 3.2. A Very Simple Hypothetical Microarray Experiment
- 3.3. A Typical Microarray Experiment
- 3.3.1. Microarray Preparation
- 3.3.2. Sample Preparation
- 3.3.3. The Hybridization Step
- 3.3.4. Scanning the Microarray
- 3.3.5. Interpreting the Scanned Image
- 3.4. Multichannel cDNA Microarrays
- 3.5. Oligonucleotide Arrays
- 3.6. Bead-Based Arrays
- 3.7. Confirmation of Microarray Results
- Supplementary Reading and Electronic References
- Exercises
- 4. Processing the Scanned Image
- 4.1. Converting the Scanned Image to the Spotted Image
- 4.1.1. Gridding
- 4.1.2. Segmentation
- 4.1.3. Quantification
- 4.2. Quality Assessment
- 4.2.1. Visualizing the Spotted Image
- 4.2.2. Numerical Evaluation of Array Quality
- 4.2.3. Spatial Problems
- 4.2.4. Spatial Randomness
- 4.2.5. Quality Control of Arrays
- 4.2.6. Assessment of Spot Quality
- 4.3. Adjusting for Background
- 4.3.1. Estimating the Background
- 4.3.2. Adjusting for the Estimated Background
- 4.4. Expression Level Calculation for Two-Channel cDNA Microarrays
- 4.5. Expression Level Calculation for Oligonucleotide Arrays
- 4.5.1. The Average Difference
- 4.5.2. A Weighted Average Difference
- 4.5.3. Perfect Matches Only
- 4.5.4. Background Adjustment Approach
- 4.5.5. Model-Based Approach
- 4.5.6. Absent-Present Calls
- Supplementary Reading
- Exercises
- 5. Preprocessing Microarray Data
- 5.1. Logarithmic Transformation
- 5.2. Variance Stabilizing Transformations
- 5.3. Sources of Bias
- 5.4. Normalization
- 5.5. Intensity-Dependent Normalization
- 5.5.1. Smooth Function Normalization
- 5.5.2. Quantile Normalization
- 5.5.3. Normalization of Oligonucleotide Arrays
- 5.5.4. Normalization of Two-Channel Arrays
- 5.5.5. Spatial Normalization
- 5.5.6. Stagewise Normalization
- 5.6. Judging the Success of a Normalization
- 5.7. Outlier Identification
- 5.7.1. Nonresistant Rules for Outlier Identification
- 5.7.2. Resistant Rules for Outlier Identification
- 5.8. Assessing Replicate Array Quality
- Exercises
- 6. Summarization
- 6.1. Replication
- 6.2. Technical Replicates
- 6.3. Biological Replicates
- 6.4. Experiments with Both Technical and Biological Replicates
- 6.5. Multiple Oligonucleotide Arrays
- 6.6. Estimating Fold Change in Two-Channel Experiments
- 6.7. Bayes Estimation of Fold Change
- Exercises
- 7. Two-Group Comparative Experiments
- 7.1. Basics of Statistical Hypothesis Testing
- 7.2. Fold Changes
- 7.3. The Two-Sample t Test
- 7.4. Diagnostic Checks
- 7.5. Robust t Tests
- 7.6. Randomization Tests
- 7.7. The Mann-Whitney-Wilcoxon Rank Sum Test
- 7.8. Multiplicity
- 7.8.1. A Pragmatic Approach to the Issue of Multiplicity
- 7.8.2. Simple Multiplicity Adjustments
- 7.8.3. Sequential Multiplicity Adjustments
- 7.9. The False Discovery Rate
- 7.9.1. The Positive False Discovery Rate
- 7.10. Small Variance-Adjusted t Tests and SAM
- 7.10.1. Modifying the t Statistic
- 7.10.2. Assesing Significance with the SAM t Statistic
- 7.10.3. Strategies for Using SAM
- 7.10.4. An Empirical Bayes Framework
- 7.10.5. Understanding the SAM Adjustment
- 7.11. Conditional t
- 7.12. Borrowing Strength across Genes
- 7.12.1. Simple Methods
- 7.12.2. A Bayesian Model
- 7.13. Two-Channel Experiments
- 7.13.1. The Paired Sample t Test and SAM
- 7.13.2. Borrowing Strength via Hierarchical Modeling
- Supplementary Reading
- Exercises
- 8. Model-Based Inference and Experimental Design Considerations
- 8.1. The F Test
- 8.2. The Basic Linear Model
- 8.3. Fitting the Model in Two Stages
- 8.4. Multichannel Experiments
- 8.5. Experimental Design Considerations
- 8.5.1. Comparing Two Varieties with Two-Channel Microarrays
- 8.5.2. Comparing Multiple Varieties with Two-Channel Microarrays
- 8.5.3. Single-Channel Microarray Experiments
- 8.6. Miscellaneous Issues
- Supplementary Reading
- Exercises
- 9. Pattern Discovery
- 9.1. Initial Considerations
- 9.2. Cluster Analysis
- 9.2.1. Dissimilarity Measures and Similarity Measures
- 9.2.2. Guilt by Association
- 9.2.3. Hierarchical Clustering
- 9.2.4. Partitioning Methods
- 9.2.5. Model-Based Clustering
- 9.2.6. Chinese Restaurant Clustering
- 9.2.7. Discussion
- 9.3. Seeking Patterns Visually
- 9.3.1. Principal Components Analysis
- 9.3.2. Factor Analysis
- 9.3.3. Biplots
- 9.3.4. Spectral Map Analysis
- 9.3.5. Multidimensional Scaling
- 9.3.6. Projection Pursuit
- 9.3.7. Data Visualization with the Grand Tour and Projection Pursuit
- 9.4. Two-Way Clustering
- 9.4.1. Block Clustering
- 9.4.2. Gene Shaving
- 9.4.3. The Plaid Model
- Software Notes
- Supplementary Reading
- Exercises
- 10. Class Prediction
- 10.1. Initial Considerations
- 10.1.1. Misclassification Rates
- 10.1.2. Reducing the Number of Classifiers
- 10.2. Linear Discriminant Analysis
- 10.3. Extensions of Fisher's LDA
- 10.4. Nearest Neighbors
- 10.5. Recursive Partitioning
- 10.5.1. Classification Trees
- 10.5.2. Activity Region Finding
- 10.6. Neural Networks
- 10.7. Support Vector Machines
- 10.8. Integration of Genomic Information
- 10.8.1. Integration of Gene Expression Data and Molecular Structure Data
- 10.8.2. Pathway Inference
- Software Notes
- Supplementary Reading
- Exercises
- 11. Protein Arrays
- 11.1. Introduction
- 11.2. Protein Array Experiments
- 11.3. Special Issues with Protein Arrays
- 11.4. Analysis
- 11.5. Using Antibody Antigen Arrays to Measure Protein Concentrations
- Exercises
- References
- Author Index
- Subject Index
