DNA Microarrays and Related Genomics Techinques parallels the steps an investigator or an analyst takes when conducting and analyzing a microarray experiment from conception to interpretation.

Features:

• Assesses the validity of statistical methods and how to ensure the quality and integrity of data
• Examines critical aspects of designing a microarray experiment, including power and sample size
• Presents a general overview of microarray platforms currently in use with an emphasis on high-density DNA arrays
• Explores bioinformatics and array design issues that may affect data quality
• Offers a meta-methodology and framework in which to evaluate the epistemological foundations of proposed statistical methods
• Discusses issues in the analysis of microarray data and new methods for interpreting microarray data

Contents

Microarray Platforms and Blood Samples

• Microarray Technology
• Autoantigen and Cytokine Microarrays
• DNA and Oligonucleotide Microarrays
• Tiling Arrays
• Data Analysis

Normalization of Microarray Data

• Objectives of Normalization
• Statistical Basis of Normalization
• Normalization Algorithms
• Evaluating Normalization Methods

Microarray Quality Control and Assessment

• Array Quality and Qesign
• Bioinformatic Quality
• Manufacturing Quality
• Experimental Design Quality
• Experimenatal Execution
• Quality Control Metrics
• Data Analysis Quality
• Quality of Interpretation
• Quality of Validation
• Making Decisions Based on Quality

Epistemological Foundations of Statistical Methods for High-Dimensional Biology

• The Challenge We Face
• Our Vantage Point: From Samples to Populations
• What is Validity?
• Comparison of Different Methods
• Data Sets of Unknown Nature: Circular Reasoning
• The Search for Proof: Deduction
• The Proof of the Pudding is in the Eating: Induction
• Combined Modes
• Where to from Here

The Role of Sample Size on Measures of Uncertainty and Power

• TP, TN, and EDR in Microarray Experiments
• Sample Size and Sources of Uncertainty in Microarray Studies
• On the Distribution of p-Values
• A Mixture Model for the Distribution of p-Values
• Planning Future Experiments: The Role of Sample Size on TP, TN, and EDR
• Sample Size and Threshold Selection: Illustrating the Procedure

Pooling Biological Samples in Microarray Experiments

• Derivation of the Analogous Formula
• Assumptions Used to Derive the Formula 9
• Utility of Pooling

Designing Microarrays for the Analysis of Gene Expressions

• Two Approaches to Gene Expressions Analysis
• Designing 2-Channel Microarrays
• Modeling 2-Channel Microarray Gene Expression Data
• Estimation When the Microarray design is not Orthogonal

Overview of Standard Clustering Approaches for Gene Microarray Data Analysis

• Distance and Similarity Measures
• Hierarchical Clustering
• K-means and K-medoids
• Self-Organizing Maps
• Cluster Affinity Search Technique
• Other Related Methods
• Assessing Cluster Fit and Choosing K
• Choosing Genes and Samples for Clustering

Cluster Stability

• Cluster Stability
• Defining Stability
• A Brief Overview of Clustering
• Choice Points that Influence Stability and Instability
• A General Approach for Detecting Stable Cluster Solutions

Dimensionality Reduction and Discrimination

• Dimension Reduction
• Discrimination

Modeling Affymetrix Data at the Probe Level

• Models
• The Primate Example
• Simulation Study

Parametric Linear Models

• Existing Methods for Two-Group Comparisons
• Existing Methods for Linear Models
• A Comparison of the Methods

The Use of Nonparametric Procedures in the Statistical Analysis of Microarray Data

• Motivating Example
• Nonparametric Bootstrap
• Permutation-Based Nonparametric Methods
• Chebby Checker Methods

Bayesian Analysis of Microarray Data

• Probability of True Differential Expression
• Estimating the Null Distribution
• Estimating the Evidence
• Estimating the Prior Probability of Nondifferential Expression
• Hierarchical Models

False Discovery Rate and Multiple Comparison Procedures

• Multiple Comparison in Microarrays
• Multiple Testing
• Simultaneous Inference - Beyond Testing

Using Standards to Facilitate Interoperation of Heterogeneous Microarray Databases and Analytic Tools

• Using Standards to Tackle the Heterogeneity Problem

Postanalysis Interpretation: "What Do I Do With this Gene List?"

• Overview of Current Methods
• Knowledgebase Approaches
• Supplementary Data Approaches
• Tentative Function Assignment Approaches

Combining High Dimensional Biological Data to Study Complex Diseases and Quantitative Traits

• Heritable Changes in Gene Expression
• Combined HDB Techniques to Identify Candidate or Causal Genes for Complex Diseases and Quantitative Traits
• Theoretical Papers
• Software and Bioinformatics Tools
• Issues With Combined High Dimensional Biological Projects

Index

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