Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health) [Hardcover]

Robert Gentleman (Editor), Vincent Carey (Editor), Wolfgang Huber (Editor), Rafael Irizarry (Editor), Sandrine Dudoit (Editor)

Book Description

ISBN-10: 0387251464 | ISBN-13: 978-0387251462 | Publication Date: August 31, 2005 | Edition: 1

Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

Editorial Reviews

Review

From the reviews: "The book has several nice touches that readers will appreciate. First, the liberal use of color shows the full capabilities of Bioconductor pakages and brings the material to life. Second, color figures are dispersed throughout the text rather than being relegated to a central section of color plates. Third, the index indicates whether a term references a package, function or class. This book is an excellent resource... In summary, this book is a must have for any Bioconductor user." (J. Wade Davis, Journal of the American Statistical Association, Vol. 102, No. 477, 2007) "This book is solid evidence of the influence that quantitative researchers can have on biological investigations. Organized into separate chapters of shared authorship, the book provides a valuable overview of the impact that the authors and their colleagues have had on the analysis of genomic data." (R.W. Doerge, Biostatistics, December 2006) "This book provides an in-depth demonstration of the potential of the Bioconductor project, through a varied mixture of descriptions, figures and examples. … The book … is an exciting opportunity for researchers to learn directly from the software developers themselves. The range of material covered by the book is diverse and well structured. An abundance of fully worked case studies illustrate the methods in practice. … it should be a must for any researcher considering getting started with the software … ." (Rebecca Walls, Journal of Applied Statistics, Vol. 34 (3), 2007) "The book provides an extensive overview over the most important tasks in analyzing genomic data with Bioconductor. … The book is well written and communicates hands-on experience of the developers of the respective Bioconductor packages themselves. … The book is targeted to a broad range of researchers interested in genomic data analysis, including biologists, bioinformaticians, and statisticians. … It is a very valuable resource for modern genomic data analysis. There is no comparable book on the market." (Jörg Rahnenführer, Statistical Papers, Vol. 50, 2009)

From the Back Cover

Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R. This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms curation and delivery of biological metadata for use in statistical modeling and interpretation statistical analysis of high-throughput data, including machine learning and visualization, modeling and visualization of graphs and networks. The developers of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data, and a major section of the book is devoted to exposition of fully worked case studies. This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers. Robert Gentleman is Head of the Program in Computational Biology at the Fred Hutchinson Cancer Research Center in Seattle. He is one of the two authors of the original R system and a leading member of the R core team. Vincent Carey is Associate Professor of Medicine (Biostatistics), Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School. Gentleman and Carey are co-founders of the Bioconductor project. Wolfgang Huber is Group Leader in the European Molecular Biology Laboratory at the European Bioinformatics Institute in Cambridge. He has made influential contributions to the error modeling of microarray data. Rafael Irizarry is Associate Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health in Baltimore. He is co-developer of RMA and GCRMA, two of the most popular methodologies for preprocessing high-density oligonucleotide arrays. Sandrine Dudoit is Assistant Professor in the Department of Biostatistics at the University of California, Berkeley. She has made seminal discoveries in the fields of multiple testing and generalized cross-validation and spearheaded the deployment of these findings in applied genomic science.

Product Details

• Hardcover: 492 pages
• Publisher: Springer; 1 edition (August 31, 2005)
• Language: English
• ISBN-10: 0387251464
• ISBN-13: 978-0387251462
• Product Dimensions: 9.3 x 6.3 x 1.2 inches
• Shipping Weight: 2 pounds (View shipping rates and policies)
• Average Customer Review: 2.4 out of 5 stars  See all reviews (5 customer reviews)
• Amazon Best Sellers Rank: #587,632 in Books (See Top 100 in Books)
  • #81 in Books > Professional & Technical > Professional Science > Biological Sciences > Bioinformatics

Customer Reviews

Most Helpful Customer Reviews

24 of 25 people found the following review helpful:

4.0 out of 5 stars Book contains many chapters to help get you started, June 29, 2006

By 

A. Smith (Mad town, WI) - See all my reviews
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This review is from: Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health) (Hardcover)

I purchased this book to learn specific details and look at applications for the functions present in bioconductor. I have had trouble applying some of the chapters to custom data because they are written for specific microarray/data formats. Overall, this book is a good value because it contains examples of how bioconductor can be used to aid in hypothesis testing, but I struggle to apply what I have read to the different types of data I have. The section on Statistical analysis for genomic experiments and the section on gaphs and networks should be the reason you purchase this book. They are very helpful and interesting. The case studies were not very helpful in my opinion.

15 of 16 people found the following review helpful:

2.0 out of 5 stars technically accurate but pedagogically flawed, February 8, 2007

By 

M. Driscoll (San Francisco, CA) - See all my reviews
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This review is from: Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health) (Hardcover)

If you're like me, you came upon this book because you decided to use R for analysis of microarray data, but you're mired in its gory and frustrating details.

Yes, you need a reference book. But not this one, and certainly not this edition. Better documentation can be found elsewhere (dare I say online?).

The code examples given are technically accurate and run as advertised, but they are of the "monkey see, monkey do" variety. They provide little intuition for how to use R for oneself, outside the covers of this text. For example, Chapter 23 discusses linear models for microarray data (using the "limma" package), and several code examples contain the parameter 'adjust = "fdr"'. The reader is never enlightened that this refers to a "false discovery rate" adjustment.

In other cases, example code is simply missing. Chapter 21 covers the Rgraphviz graphing library, with a figure showing the three common graphical layouts -- but no example code for producing these graphs is given (I had to find it outside the book).

For those trying to use R for computational biology, I recommend getting an overview of the R programming language first (Venables and Ripley's book "Modern Applied Statistics with S" is a great text), and only then wading into references such as this one, if at all.

1 of 1 people found the following review helpful:

4.0 out of 5 stars extremely helpful, but suffers from multiple author problem, February 10, 2009

By 

Lisa Jones (CA United States) - See all my reviews

This review is from: Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health) (Hardcover)

This book is great for helping you get started analyzing all types of microarrays in R. However, the chapters are written by several different authors which causes the book to be a little disorganized. This is probably the case with many books that have contributed chapters. In the end, the technical information is there, sometimes you just have to visit a couple of different chapters.