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

by Allison, David B.; Page, Grier P.; Beasley, T. Mark; Edwards, Jode W.

• ISBN13: 9781420028799
• ISBN10: 1420028790
• Copyright: 2005-11-14
• Publisher: Taylor & Francis

Summary

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 Experimentsconsolidates 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.

Table of Contents

Microarray Platforms and Blood Samples

Introduction

Microarray Technology

Autoantigen and Cytokine Microarrays

DNA and Oligonucleotide Microarrays

Tiling Arrays

Data Analysis

Future Directions

References

Normalization of Microarray Data

Objectives of Normalization

Statistical Basis of Normalization

Normalization Algorithms

Evaluating Normalization Methods

References

Microarray Quality Control and Assessment

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

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

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

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

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

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

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

Introduction

Dimension Reduction

Discrimination

Conclusion

References

Modeling Affymetrix Data at the Probe Level

Introduction

Models

The Primate Example

Simulation Study

Discussion

References

Parametric Linear Models

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

Introduction

Motivating Example

Nonparametric Bootstrap

Permutation-Based Nonparametric Methods

Chebby Checker Methods

Discussion

Bayesian Analysis of Microarray Data

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

Multiple Comparison in Microarrays

Multiple Testing

Simultaneous Inference - Beyond Testing

References

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

Introduction

Using Standards to Tackle the Heterogeneity Problem

Future directions

Acknowledgements

References

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

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

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

Table of Contents provided by Publisher. All Rights Reserved.