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Description

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.

Reviews

“I would highly recommend this book to someone who already has a considerable amount of experience with microarrays… .The strengths of the book include a vast number of recent and historic references for each topic; different authors for each chapter, producing different perspectives for many of the ideas, reinforcing ideas, and creating a reference for which individual chapters are meaningful… .I found the book full of relevant and timely issues related to microarray analysis. Many of the chapters clarified topics I had previously misunderstood or generated ideas to further my own research. Most of the chapters were well written and could be read through easily. I would recommend this book to someone who has had some experience in with microarrays and is looking to expand their knowledge about the topic (or to change the direction of their analytic research).”

—Journal of Biopharmaceutical Statistics

“… is written as an overview of topics in the microarray literature: ensuring quality data, designing studies, analyzing data, and interpretation of results. The book is purported for both biologists (and others) conducting microarray experiments and statisticians (and others) analyzing data; the authors succeed at making the material both interesting and relevant to both parties, … Overall, I found the book full of relevant and timely issues related to microarray analysis. … the chapters were well written and could be read through easily. I would recommend this book to someone who has had some experience in with microarrays and is looking to expand their knowledge about the topic …”

—Johana Dardin, Department of Mathematics Pomona College, California, USA, in Journal of Biopharmaceutical Statistics, Vol. 17, 2007

"Overall, this book does provide a broad coverage of the disparate steps involved in a microarray-powered study. For the reader who is just about to enter the dynamic world of microarray data analysis, it provides a timely and comprehensive starting point, with all the main points and most relevant topics being presented, in only one volume. Moreover, important issues (such as multiple testing), are discussed throughout the book."

– Giovanni Montana, Imperial College, in Journal of Applied Statistics, January 2008, Vol. 35, No. 1

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

Related Subjects

  1. Bioinformatics
  2. Genetics

Name: DNA Microarrays and Related Genomics Techniques: Design, Analysis, and Interpretation of Experiments (Hardback)Chapman and Hall/CRC 
Description: Edited by David B. Allison, Grier P. Page, T. Mark Beasley, Jode W. EdwardsSeries Editor: Shein-Chung ChowContributors: Jane Chang, Kei Cheung, Tzu-Ming Chu, Christopher Coffey, Robert Delongchamp, David Finkelstein, Gary L. Gadbury, Patrick M. Gaffney, Elizabeth Garrett, Bernard S. Gorman, Christina Kendziorski, Michael Osier, Chiara Sabatti, Zhen Zhang, Stacey Cofield, Pulak Ghosh, Jason Hsu, Jeanne Kowalski, Rudolph S. Parrish, Qinfang Xiang, Kui Zhang, Kathy L. Moser, Emily C. Baechler, Timothy W. Behrens, Michael Janis, Alan Williams, Kathryn Steiger, Jacques Retief, Stanislav O. Zakharkin, Tapan Mehta, Murat Tanik, Shibing Deng, Russ Wolfinger, Jacob P.L. Brand, Jeff Long, Douglas Ruden. 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...
Categories: Bioinformatics, Genetics