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Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition (Chapman & Hall/CRC Mathematical and Computational Bi
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Richly 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.
- Sales Rank: #544671 in Books
- Brand: Brand: Chapman and Hall/CRC
- Published on: 2011-12-06
- Original language: English
- Number of items: 1
- Dimensions: 1.90" h x 6.20" w x 9.30" l, 4.25 pounds
- Binding: Hardcover
- 1036 pages
- Used Book in Good Condition
Review
Praise for the First Edition
The book by Draghici is an excellent choice to be used as a textbook for a graduate-level bioinformatics course. This well-written book with two accompanying CD-ROMs will create much-needed enthusiasm among statisticians.
―Journal of Statistical Computation and Simulation, Vol. 74
I really like Draghici's book. As the author explains in the Preface, the book is intended to serve both the statistician who knows very little about DNA microarrays and the biologist who has no expertise in data analysis. The author lays out a study plan for the statistician that excludes 5 of the 17 chapters (4-8). These chapters present the basics of statistical distributions, estimation, hypothesis testing, ANOVA, and experimental design. What that leaves for the statistician is the three-chapter primer on microarrays and image processing, plus all of the data analysis tools specific to the microarray situation. … it includes two CDs with trial versions of several specialised software packages. Anyone who uses microarray data should certainly own a copy.
―Technometrics, Vol. 47, No. 1, February 2005
About the Author
Sorin Drăghici holds the Robert J. Sokol MD Endowed Chair in Systems Biology in the Department of Obstetrics and Gynecology, and is a professor in the Department of Clinical and Translational Science and the Department of Computer Science, as well as the head of the Intelligent Systems and Bioinformatics Laboratory at Wayne State University. He is also the chief of the Bioinformatics and Data Analysis Section in the Perinatology Research Branch of the National Institute for Child Health and Development. A senior member of IEEE, Dr. Drăghici is an editor of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Journal of Biomedicine and Biotechnology, and International Journal of Functional Informatics and Personalized Medicine. He earned a Ph.D. in computer science from the University of St. Andrews, UK.
Most helpful customer reviews
9 of 9 people found the following review helpful.
thorough, rich, and readable
By Rebecca L. Tagett
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? Thanks to the extremely thorough preceding chapter on ANOVA, the chapter on experiment design is naturally set up for a straightforward and clear presentation of the rules, guidelines and concepts of experiment design. It presents the classic design models and several examples used by professionals in the field. The reader will feel confident designing basic experiments without having to refer the big hard core texts on the subject.
Throughout the book, very essential snippets of R code are revealed. You won't need much more or less than what you see here, once you have a little R under your belt. Dr Draghici's specialty is pathway analysis, and he is the coauthor of the R package "SPIA", available on bioconductor. The pros and cons of the different pathway analysis methods are discussed in detail.
2 of 2 people found the following review helpful.
Great Book!
By DV Klopfenstein
What a great book. The author is an excellent teacher who is skilled in transferring his in-depth knowledge to people unfamiliar with the material. The book is engaging and highly readable. In his short, but densely-packed chapter on GO-Terms, I learned more from him in a couple hours than I did from TAs in a 3-hour class augmented by many additional hours of my own independent searching on the web to "fill in the gaps". Fantastic book! Draghici highlights the gotchas and pitfalls researchers inadvertently run into and don't know it. I constantly read and re-read sections of his book which are relevant to corners of my current research where I wonder if there was something that I might have missed. It is amazing how many "golden nuggets" of non-obvious information packs into a readable and concise format. I feel like I have a second adviser in my Doctoral research.
3 of 3 people found the following review helpful.
A unique and insightful R / statistics / microarray book
By George G. Roberts
Readers of all backgrounds will find much to be pleased about with this book. Whether you need to learn basic biology or just need a refresher, this book will serve you well. Starting with basic biology, Draghici moves on to illuminate the basic issues confronting both Affymetrix and cDNA microarrays. The section on basic R programming is simple enough for a non-computer scientist to understand and yet moves rapidly enough to satisfy experienced programmers who will appreciate the commentary which helps to dispel some of the initial "weirdness" of R. I used this section to brush up on my R after being away for a while. Next the book covers statistics in a way that I found to be very readable. Many books will teach you statistics, but what sets this book apart from others is the way that it helps the reader to develop intuition about the statistics without sacrificing rigor. Later chapters focus on a variety of topics including machine learning / gene ontology / pathway analysis etc.
The color illustrations were an unexpected surprise in such a technical book and very useful in illuminating the material. In summary, this is a great book and a great price. You will want to keep it as a reference.
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