Exploration and Analysis of DNA Microarray and Protein Array Data

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. 3.2 A Very Simple Hypothetical Microarray Experiment. 3.3 A Typical Microarray Experiment. 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. 4.2 Quality Assessment. 4.3 Adjusting for Background. 4.4 Expression Level Calculation for Two-Channel cDNA Microarrays. 4.5 Expression Level Calculation for Oligonucleotide Arrays. 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. 5.6 Judging the Success of a Normalization. 5.7 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. 7.9 The False Discovery Rate. 7.10 Small Variance-Adjusted t Tests and SAM. 7.11 Conditional t. 7.12 Borrowing Strength across Genes. 7.13 Two-Channel Experiments. 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. 8.6 Miscellaneous Issues. Supplementary Reading. Exercises. 9 Pattern Discovery. 9.1 Initial Considerations. 9.2 Cluster Analysis. 9.3 Seeking Patterns Visually. 9.4 Two-Way Clustering. Software Notes. Supplementary Reading. Exercises. 10 Class Prediction. 10.1 Initial Considerations. 10.2 Linear Discriminant Analysis. 10.3 Extensions of Fisher's LDA. 10.4 Nearest Neighbors. 10.5 Recursive Partitioning. 10.6 Neural Networks. 10.7 Support Vector Machines. 10.8 Integration of Genomic Information. 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.