DNA Microarrays and Related Genomics Techniques: Design, Analysis, and Interpretation of Experiments

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Overview

Considered highly exotic tools as recently as the late 1990s, microarrays are now ubiquitous in biological research. Traditional statistical approaches to design and analysis were not developed to handle the high-dimensional, small sample problems posed by microarrays. In just a few short years the number of statistical papers providing approaches to analyzing microarray data has gone from almost none to hundreds if not thousands. This overwhelming deluge is quite daunting to either the applied investigator looking for methodologies or the methodologist trying to keep up with the field. DNA Microarrays and Related Genomics Techniques: Design, Analysis, and Interpretation of Experiments consolidates discussions of methodological advances into a single volume.

The book’s structure parallels the steps an investigator or an analyst takes when conducting and analyzing a microarray experiment from conception to interpretation. It begins with foundational issues such as ensuring the quality and integrity of the data and assessing the validity of the statistical models employed, then moves on to cover critical aspects of designing a microarray experiment. The book includes discussions of power and sample size, where only very recently have developments allowed such calculations in a high dimensional context, followed by several chapters covering the analysis of microarray data. The amount of space devoted to this topic reflects both the variety of topics and the effort investigators have devoted to developing new methodologies. In closing, the book explores the intellectual frontier – interpretation of microarray data. It discusses new methods for facilitating and affecting formalization of the interpretation process and the movement to make large high dimensional datasets public for further analysis, and methods for doing so.

There is no question that this field will continue to advance rapidly and some of the specific methodologies discussed in this book will be replaced by new advances. Nevertheless, the field is now at a point where a foundation of key categories of methods has been laid out and begun to settle. Although the details may change, the majority of the principles described in this book and the foundational categories it contains will stand the test of time, making the book a touchstone for researchers in this field.

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Product Details

Table of Contents

Microarray Platforms and Blood Samples, P.M. Gaffney, K.L. Moser, E.C. Baechler, and T.W. Behrens
Introduction
Microarray Technology
Autoantigen and Cytokine Microarrays
DNA and Oligonucleotide Microarrays
Tiling Arrays
Data Analysis
Future Directions
References
Normalization of Microarray Data, R.S. Parrish and R.R. Delongchamp
Objectives of Normalization
Statistical Basis of Normalization
Normalization Algorithms
Evaluating Normalization Methods
References
Microarray Quality Control and Assessment, D. Finkelstein, M. Janis, A. Williams, K. Steiger, and J. Retief
Introduction
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
Conclusions
References
Epistemological Foundations of Statistical Methods for High-Dimensional Biology, S.O. Zakharkin, T. Mehta, M. Tanik, and D.B. Allison
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?
Acknowledgments
References
The Role of Sample Size on Measures of Uncertainty and Power, G.L. Gadbury, Q. Xiang, J. Edwards, G.P. Page, and D.B. Allison
Introduction
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
Discussion
Acknowledgements
References
Pooling Biological Samples in Microarray Experiments, C.M. Kendziorski
Introduction
Derivation of the Analogous Formula
Assumptions Used to Derive the Formula 9
Utility of Pooling
Conclusion
Designing Microarrays for the Analysis of Gene Expressions, J.Y. Chang and J.C. Hsu
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
Summary
References
Overview of Standard Clustering Approaches for Gene
Microarray Data Analysis, E. Garrett-Mayer
Introduction
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, B.S. Gorman and K. Zhang
Cluster Stability
Defining Stability
A Brief Overview of Clustering
Choice Points that Influence Stability and Instability
A General Approach for Detecting Stable Cluster Solutions
References
Dimensionality Reduction and Discrimination, J. Kowalski and Z. Zhang
Introduction
Dimension Reduction
Discrimination
Conclusion
References
Modeling Affymetrix Data at the Probe Level, T.-M. Chu, S. Deng,and R.D. Wolfinger
Introduction
Models
The Primate Example
Simulation Study
Discussion
References Parametric Linear Models, C.S. Coffey and S.S. Cofield
Introduction
Existing Methods for Two-Group Comparisons
Existing Methods for Linear Models
A Comparison of the Methods
Summary
References
The Use of Nonparametric Procedures in the Statistical Analysis of Microarray Data, T.M. Beasley, J.P.L. Brand, and J.D. Long
Introduction
Motivating Example
Nonparametric Bootstrap
Permutation-Based Nonparametric Methods
Chebby Checker Methods
Discussion
Bayesian Analysis of Microarray Data, J.W. Edwards and P. Ghosh
Introduction
Probability of True Differential Expression
Estimating the Null Distribution
Estimating the Evidence
Estimating the Prior Probability of Nondifferential Expression
Hierarchical Models
References
False Discovery Rate and Multiple Comparison Procedures, C. Sabatti
Multiple Comparison in Microarrays
Multiple Testing
Simultaneous Inference — Beyond Testing
References
Using Standards to Facilitate Interoperation of Heterogeneous Microarray Databases and Analytic Tools, K.-H. Cheung
Introduction
Using Standards to Tackle the Heterogeneity Problem
Future directions
Acknowledgements
References
Postanalysis Interpretation: “What Do I Do With this
Gene List?” M.V. Osier
Introduction
Overview of Current Methods
Knowledgebase Approaches
Supplementary Data Approaches
Tentative Function Assignment Approaches
Future Directions
Conclusions
Acknowledgements
References
Combining High Dimensional Biological Data to Study Complex Diseases and Quantitative Traits, G.P. Page and D.M. Ruden
Introduction
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
Conclusions about Combined HDB Studies
References

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