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Bioinformatics and Computational Biology Solutions Using R and Bioconductor Hardcover – Sep 13 2005


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Hardcover, Sep 13 2005
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Amazon.com: 5 reviews
25 of 26 people found the following review helpful
Book contains many chapters to help get you started June 29 2006
By A. Smith - Published on Amazon.com
Format: 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.
17 of 18 people found the following review helpful
technically accurate but pedagogically flawed Feb. 8 2007
By M. Driscoll - Published on Amazon.com
Format: 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
extremely helpful, but suffers from multiple author problem Feb. 10 2009
By Lisa Jones - Published on Amazon.com
Format: 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.
2 of 3 people found the following review helpful
it's not well organized Feb. 28 2008
By Stephanie - Published on Amazon.com
Format: Hardcover Verified Purchase
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.
1 of 2 people found the following review helpful
Horribly written April 9 2011
By Ray - Published on Amazon.com
Format: Hardcover
Lots of mistakes. Lots of codes unable to run. Lack of consistency because of multiple authors. This is definitely the worst textbook I've ever seen. I literally want to kill myself when I was running the code. Amazon really should give us an option for a zero rating.


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