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Buy Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health) by Robert Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit available and for sale on Brightsurf
| View Larger Image | Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)
by Robert Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit
| | List Price: | $89.95 | | Price: | $71.96 | | You Save: | $17.99 (20%) |  | | Available: | Usually ships in 24 hours | | | | | Sales Rank: | 107178 | | Studio: | Springer | | | Binding: | Hardcover | | Number Of Pages: | 473 | | Publication Date: | August 31, 2005 | | Publisher: | Springer |
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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. |
CUSTOMER REVIEWS (Average Customer Rating: 2.5 based on 3 reviews)
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it's not well organized 
I find this book is not so good for people without any gene or microarray experiment background. It didn't even give clear definition of the basic concepts.
Another problem is that it's not well organized because every chapter is written by different authors who have different interest and preference and use slightly different terms for the same thing.
February 28, 2008 | |
technically accurate but pedagogically flawed 
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.
February 08, 2007 | |
Book contains many chapters to help get you started 
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.
June 29, 2006 | |
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