Statistics and Data Analysis for Microarrays Using R and Bioconductor

Abstract

Introduction Bioinformatics - An Emerging Discipline The Cell and Its Basic Mechanisms The Cell The Building Blocks of Genomic Information Expression of Genetic Information The Need for High-Throughput Methods Microarrays Microarrays - Tools for Gene Expression Analysis Fabrication of Microarrays Applications of Microarrays Challenges in Using Microarrays in Gene Expression Studies Sources of Variability Reliability and Reproducibility Issues in DNA Microarray Measurements Introduction What Is Expected from Microarrays? Basic Considerations of Microarray Measurements Sensitivity Accuracy Reproducibility Cross Platform Consistency Sources of Inaccuracy and Inconsistencies in Microarray Measurements The MicroArray Quality Control (MAQC) Project Image Processing Introduction Basic Elements of Digital Imaging Microarray Image Processing Image Processing of cDNA Microarrays Image Processing of Affymetrix Arrays Introduction to R Introduction to R The Basic Concepts Data Structures and Functions Other Capabilities The R Environment Installing Bioconductor Graphics Control Structures in R Programming in R vs C/C++/Java Bioconductor: Principles and Illustrations Overview The Portal Some Explorations and Analyses Elements of Statistics Introduction Some Basic Concepts Elementary Statistics Degrees of Freedom Probabilities Bayes' Theorem Testing for (or Predicting) a Disease Probability Distributions Probability Distributions Central Limit Theorem Are Replicates Useful? Basic Statistics in R Introduction Descriptive Statistics in R Probabilities and Distributions in R Central Limit Theorem Statistical Hypothesis Testing Introduction The Framework Hypothesis Testing and Significance I Do Not Believe God Does Not Exist An Algorithm for Hypothesis Testing Errors in Hypothesis Testing Classical Approaches to Data Analysis Introduction Tests Involving a Single Sample Tests Involving Two Samples Analysis of Variance (ANOVA) Introduction One-Way ANOVA Two-Way ANOVA Quality Control Linear Models in R Introduction and Model Formulation Fitting Linear Models in R Extracting Information from a Fitted Model: Testing Hypotheses and Making Predictions Some Limitations of the Linear Models Dealing with Multiple Predictors and Interactions in the Linear Models, and Interpreting Model Coefficients Experiment Design The Concept of Experiment Design Comparing Varieties Improving the Production Process Principles of Experimental Design Guidelines for Experimental Design A Short Synthesis of Statistical Experiment Designs Some Microarray Specific Experiment Designs Multiple Comparisons Introduction The Problem of Multiple Comparisons A More Precise Argument Corrections for Multiple Comparisons Corrections for Multiple Comparisons in R Analysis and Visualization Tools Introduction Box Plots Gene Pies Scatter Plots Volcano Plots Histograms Time Series Time Series Plots in R Principal Component Analysis (PCA) Independent Component Analysis (ICA) Cluster Analysis Introduction Distance Metric Clustering Algorithms Partitioning around Medoids (PAM) Biclustering Clustering in R Quality Control Introduction Quality Control for Affymetrix Data Quality Control of Illumina Data Data Pre-Processing and Normalization Introduction General Pre-Processing Techniques Normalization Issues Specific to cDNA Data Normalization Issues Specific to Affymetrix Data Other Approaches to the Normalization of Affymetrix Data Useful Pre-Processing and Normalization Sequences Normalization Procedures in R Batch Pre-Processing Normalization Functions and Procedures for Illumina Data Methods for Selecting Differentially Regulated Genes Introduction Criteria Fold Change Unusual Ratio Hypothesis Testing, Corrections for Multiple Comparisons, and Resampling ANOVA Noise Sampling Model-Based Maximum Likelihood Estimation Methods Affymetrix Comparison Calls Significance Analysis of Microarrays (SAM) A Moderated t-Statistic Other Methods Reproducibility Selecting Differentially Expressed (DE) Genes in R The Gene Ontology (GO) Introduction The Need for an Ontology What Is the Gene Ontology (GO)? What Does GO Contain? Access to GO Other Related Resources Functional Analysis and Biological Interpretation of Microarray Data Over-Representation Analysis (ORA) Onto-Express Functional Class Scoring The Gene Set Enrichment Analysis (GSEA) Uses, Misuses, and Abuses in GO Profiling Introduction Known Unknowns Which Way Is Up? Negative Annotations Common Mistakes in Functional Profiling Using a Custom Level of Abstraction through the GO Hierarchy Correlation between GO Terms GO Slims and Subsets A Comparison of Several Tools for Ontological Analysis Introduction Existing tools for Ontological Analysis Comparison of Existing Functional Profiling Tools Drawbacks and Limitations of the Current Approach Focused Microarrays - Comparison and Selection Introduction Criteria for Array Selection Onto-Compare Some Comparisons ID Mapping Issues Introduction Name Space Issues in Annotation Databases A Comparison of Some ID Mapping Tools Pathway Analysis Terms and Problem Definition Over-Representation and Functional Class Scoring Approaches in Pathway Analysis An Approach for the Analysis of Metabolic Pathways An Impact Analysis of Signaling Pathways Variations on the Impact Analysis Theme Pathway Guide Kinetic models vs. Impact Analysis Conclusions Data Sets and Software Availability Machine Learning Techniques Introduction Main Concepts and Definitions Supervised Learning Practicalities Using R The Road Ahead What Next? References A Summary appears at the end of each chapter.