Features
- Cover Type: Hard Cover with 473 pages
- Published by: Springer
- Edition: 1st Edition August 31, 2005
- Written in: English
- ISBN 10 Number: 0387251464
- ISBN 13 Number: 978-0387251462
-
Book Dimensions:
9.4 x 6.1 x 1.2 inches
- Weighs: 2 pounds
Product 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 great 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)
Product Description
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.
Reader ReviewsI 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.
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