(7 reviews) Author: ISBN : 9781439809754 New from $75.29 Format: PDFRichly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems.
New to the Second Edition
Completely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, Gene Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying CD-ROM.
With all the necessary prerequisites included, this best-selling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data.
- Series: Chapman & Hall/CRC Mathematical & Computational Biology (Book 4)
- Hardcover: 1036 pages
- Publisher: Chapman and Hall/CRC; 2 edition (December 6, 2011)
- Language: English
- ISBN-10: 1439809755
- ISBN-13: 978-1439809754
- Product Dimensions: 1.9 x 6.5 x 9.8 inches
- Shipping Weight: 4.4 pounds
Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition PDF
This book is extremely rich yet readable. It covers a lot of different topics, and chapters can be read in order or referred to as needed.
However, if you take the time to read it through carefully, you will enjoy yourself, because the author is a master of pedagogy, and often humorous.
While expression microarrays are the platforms covered by this book, most of the material has much broader application beyond that. Cell biology, R, statistics, experiment design, molecular pathways, and machine learning are all covered. Even if you don't work with microarrays, there is something in this book for you, anyway you should know the basics of microarray analysis.
Computer scientists or other non biologists may want to start at the beginning, which is an introduction to the cell and its basic mechanisms. It may seem like a lot of memorization and daunting to those who have never learned biology, but this chapter is well written with plenty of color images. It gives all of the fundamental biology needed to understand microarrays.
The author knows which topics need to be covered in more depth than other authors are generally willing to do. I especially appreciated the section on degrees of freedom. If you never really had a grasp on this topic (but used it anyway) you will be grateful for section 8.4!
Another chapter that I especially appreciated was "Experiment Design", which is so critical to any scientist of molecular biology and bioinformatics. How many of us in bioinformatics have been asked to analyze data from poorly designed experiments?
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