The Analysis of Gene Expression Data

1. Front Matter

   Pages i-xix

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2. The Analysis of Gene Expression Data: An Overview of Methods and Software

   • Giovanni Parmigiani, Elizabeth S. Garrett, Rafael A. Irizarry, Scott L. Zeger

   Pages 1-45

3. Visualization and Annotation of Genomic Experiments

   • Robert Gentleman, Vincent Carey

   Pages 46-72

4. Bioconductor R Packages for Exploratory Analysis and Normalization of cDNA Microarray Data

   • Sandrine Dudoit, Jean Yee Hwa Yang

   Pages 73-101

5. An R Package for Analyses of Affymetrix Oligonucleotide Arrays

   • Rafael A. Irizarry, Laurent Gautier, Leslie M. Cope

   Pages 102-119

6. DNA-Chip Analyzer (dChip)

   • Cheng Li, Wing Hung Wong

   Pages 120-141

7. Expression Profiler

   • Jaak Vilo, Misha Kapushesky, Patrick Kemmeren, Ugis Sarkans, Alvis Brazma

   Pages 142-162

8. An S-PLUS Library for the Analysis and Visualization of Differential Expression

   • Jae K. Lee, Michael O’Connell

   Pages 163-184

9. Dragon and Dragon View: Methods for the Annotation, Analysis, and Visualization of Large-Scale Gene Expression Data

   • Christopher M. L. S. Bouton, George Henry, Carlo Colantuoni, Jonathan Pevsner

   Pages 185-209

10. Snomad: Biologist-Friendly Web Tools for the Standardization and NOrmalization of Microarray Data

    • Carlo Colantuoni, George Henry, Christopher M. L. S. Bouton, Scott L. Zeger, Jonathan Pevsner

    Pages 210-228

11. Microarray Analysis Using the MicroArray Explorer

    • Peter F. Lemkin, Gregory C. Thornwall, Jai Evans

    Pages 229-253

12. Parametric Empirical Bayes Methods for Microarrays

    • Michael A. Newton, Christina Kendziorski

    Pages 254-271

13. SAM Thresholding and False Discovery Rates for Detecting Differential Gene Expression in DNA Microarrays

    • John D. Storey, Robert Tibshirani

    Pages 272-290

14. Adaptive Gene Picking with Microarray Data: Detecting Important Low Abundance Signals

    • Yi Lin, Samuel T. Nadler, Hong Lan, Alan D. Attie, Brian S. Yandell

    Pages 291-312

15. MAANOVA: A Software Package for the Analysis of Spotted cDNA Microarray Experiments

    • Hao Wu, M. Kathleen Kerr, Xiangqin Cui, Gary A. Churchill

    Pages 313-341

16. GeneClust

    • Kim-Anh Do, Bradley Broom, Sijin Wen

    Pages 342-361

17. POE: Statistical Methods for Qualitative Analysis of Gene Expression

    • Elizabeth S. Garrett, Giovanni Parmigiani

    Pages 362-387

18. Bayesian Decomposition

    • Michael F. Ochs

    Pages 388-408

19. Bayesian Clustering of Gene Expression Dynamics

    • Paola Sebastiani, Marco Ramoni, Isaac S. Kohane

    Pages 409-427

20. Relevance Networks: A First Step Toward Finding Genetic Regulatory Networks Within Microarray Data

    • Atul J. Butte, Isaac S. Kohane

    Pages 428-446

Thedevelopmentoftechnologiesforhigh–throughputmeasurementofgene expression in biological system is providing powerful new tools for inv- tigating the transcriptome on a genomic scale, and across diverse biol- ical systems and experimental designs. This technological transformation is generating an increasing demand for data analysis in biological inv- tigations of gene expression. This book focuses on data analysis of gene expression microarrays. The goal is to provide guidance to practitioners in deciding which statistical approaches and packages may be indicated for their projects, in choosing among the various options provided by those packages, and in correctly interpreting the results. The book is a collection of chapters written by authors of statistical so- ware for microarray data analysis. Each chapter describes the conceptual and methodological underpinning of data analysis tools as well as their software implementation, and will enable readers to both understand and implement an analysis approach. Methods touch on all aspects of statis- cal analysis of microarrays, from annotation and ?ltering to clustering and classi?cation. All software packages described are free to academic users. The materials presented cover a range of software tools designed for varied audiences. Some chapters describe simple menu-driven software in a user-friendly fashion and are designed to be accessible to microarray data analystswithoutformalquantitativetraining.Mostchaptersaredirectedat microarray data analysts with master’s-level training in computer science, biostatistics, or bioinformatics. A minority of more advanced chapters are intended for doctoral students and researchers.

• ANOVA
• Clustering
• Computer
• DNA
• DNA-Chip
• Master Patient Index
• Microarray
• Parametric statistics
• bioinformatics
• biostatistics
• cluster analysis
• data analysis
• genes
• modeling
• statistics

From the reviews:

"This useful book about methods and software for microarray data analysis is written by the developers. The goal of the book is to provide guidance to practitioners in the techniques of analysis and programs. The book combines the advantages of both a tutorial in methods and catalogue for software. Procedures are described and their implementations illustrated. All of the software described is freely available to academic users...The table of contents indicates the encyclopedic coverage, spanning a very broad spectrum of methods. The collection is introduced with an informative overview. While the authors acknowledge the formative stage of current work, they have provided a useful guide to the here and now." ISI Short Book Reviews, Vol. 23/3, August 2003

"The opening chapter contributed by the editors is an excellent overview of the field and the threads that make it up...There is a definite hands-on character to all the presentations. Generally, each contributor selected a different but interesting set of data to demonstrate the approach." Journal of Statsitical Software, April 2005

"The editors state that the goal of this book is ‘to provide guidance to practitioners in deciding which statistical approaches and packages may be indicated for their projects … . All chapters contain clear directions for installing and using the presented packages. … For the practitioner of microarray data analysis, this book contains a wealth of readily available tools. For the statistician who wants to get acquainted with this rapidly evolving area, the book offers a glimpse of current problems and methods … ." (Ricardo A. Maronna, Statistical Papers, Vol. 45 (2), 2004)

"This volume provides a summary of many of the issues and software that are available. … This is a welcome volume. The first chapter provides a wonderful introduction to the field. The later chapters are more specialised. These chaptersare good references to the individual programs and methods and can be used to find links to the major software for microarray analysis." (Peter A. Lachenbruch, ISCB Newsletter, Issue 38, December, 2004)

"This useful book about methods and software for microarray data analysis is written by the developers. The goal of the book is to provide guidance to practitioners in the techniques of analysis and programs. The book combines the advantages of both a tutorial in methods and catalogue for software. Procedures are described and their implementations illustrated. … While the authors acknowledge the formative stage of current work, they have provided a useful guide to the here and now." (D. F. Andrews, Short Book Reviews, Vol. 23 (2), 2003)

"This book is a collection of papers on statistical techniques for gene expression data. … The book is well written and I enjoyed reading the book. … The chapters that discuss the same statistical topics often have different approaches or focuses on the type of arrays. This makes it worthwhile to read the sections on a particular topic in all chapters. I recommend this book to anyone who is interested in or performing the analysis of microarray data." (dr E. R. van den Heuvel, Kwantitatieve Methoden, April, 2004)

"Gene expression microarray technology was introduced in 1995, but it is only in 1998 that the scientific community started using this tool … . Writing a book on such a novel subject matter … is not a simple task. … ‘The Analysis of Gene Expression Data: Methods and Software’ appears as a successful attempt. … this book succeeds in the not trivial task of providing an informative, accessible, overall picture of the contributions of statisticians to the analysis of microarray data." (Chiara Sabatti, Statistics in Medicine, Vol. 23, 2004)

• Departments of Oncology, Biostatistics,and Pathology, Johns Hopkins University, Baltimore, USA

  Giovanni Parmigiani

• Departments of Oncology and Biostatistics, Johns Hopkins University, Baltimore, USA

  Elizabeth S. Garrett

• Departments of Biostatistics, Johns Hopkins University, Baltimore, USA

  Rafael A. Irizarry

• Departments of Biostatistics and Epidemiology, Johns Hopkins University, Baltimore, USA

  Scott L. Zeger

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