Product Description
Technology today allows the collection of biological information at an unprecedented level of detail and in increasingly vast quantities. To reap real knowledge from the mountains of data produced, however, requires interdisciplinary skills-a background not only in biology but also in computer science and the tools and techniques of data analysis.To help meet the challenges of DNA research, Data Analysis Tools for DNA Microarrays builds the foundation in the statistics and data analysis tools needed by biologists and provides the overview of microarrays needed by computer scientists. It first presents the basics of microarray technology and more importantly, the specific problems the technology poses from the data analysis perspective. It then introduces the fundamentals of statistics and the details of the techniques most commonly used to analyze microarray data. The final chapter focuses on commercial applications with sections exploring various software packages from BioDiscovery, Insightful, SAS, and Spotfire. The book is richly illustrated with more than 230 figures in full color and comes with a CD-ROM containing full-feature trial versions of software for image analysis (ImaGene, BioDiscovery Inc.) and data analysis (GeneSight, BioDiscovery Inc. and S-Plus Array Analyzer, Insightful Inc.).Written in simple language and illustrated in full color, Data Analysis Tools for DNA Microarrays lowers the communication barrier between life scientists and analytical scientists. It prepares those charged with analyzing microarray data to make informed choices about the techniques to use in a given situation and contribute to further advances in the field.

CUSTOMER REVIEWS (Average Customer Rating: 4.5 based on 12 reviews)

Very good book! by D. Rodrigues

5 Stars
July 02, 2009
I had to buy this book for a class, but I kept because it has very interesting information about basics of DNA microarrays but also data analysis. It is definitely very useful, I recommend.

comprehensive but not practical enough by Yossef Ben harosh (Israel)

4 Stars
May 09, 2009
Comprehensive on the theoretic sides of handling micro-array experiments (subjects like statistics, experiment design, normalization etc.), but not practical as a guide for using software to analyze data from micro-array experiments. This book is recommended as an introductory to the field.

good book to begin with ... by Ena Xiao

3 Stars
February 22, 2009
this book covers lots of topic, which is good, the only problem is that for lots of topic, the author only slidly mention about the the reasoning. Therefore, I will feel somewhat unclear about applying the theory after reading. It is good to start with this book...

Get a solid foundation for microarray data analysis. by T. Saldivar (El Paso TX)

5 Stars
February 17, 2007
I'm more than 2/3 through the book and I've never encountered a topic that I feel could have been better presented. My definition of a Great book is that I can understand and follow it, and this definitely is a Great book! Thanks to the author for writing such readable text. This text has not made it to my bookshelf at work, it stays on my desk.

a great book to read about microarray data analysis by Adi Laurentiu Tarca (Windsor, ON, Canada)

5 Stars
August 07, 2006
I have entered the area of microarray data analysis three years ago, having an engineering/machine learning background which includes good knowledge of statistics. After reading many journal papers about particular algorithms for microarray data analysis, I felt the need to read a book so that I could get the big picture of the field. At the beginning I was skeptical about reading Draghici's book because it was recommended to me as "excellent" by a biologist. I was pretty sure that given my background I will get bored of it quickly. My intuition failed me in this case because after reading it, I found it too as being far from ordinary, and answering my needs as well. The book is an easy-to-follow introduction to the area of microarray data analysis covering areas from image analysis and preprocessing, to differential expression, clustering, and high level analysis such as ontological analysis. The book is particularly useful in underlying common pitfalls with microarray data. Examples include failing to correct for multiple testing in microarray experiments and the misuse or overuse of the clustering algorithms. Abounding examples and clear illustration are given to support every single aspect treated in the text. In my opinion, graduate level students in biology, bioinformatics and statistics can greatly benefit from the lecture of this book. Another positive aspect is the fact that, with the exception of one chapter about the available commercial software, this book was written by just one author. This gives a continuity of ideas and a consistency of notations and terms throughout the entire book. This is usually not found in many other books on this topic as they are sometimes just edited collections of chapters written independently by different authors (see for instance the text by Berrar et. al which has about 40 contributors). A great incentive for me in writing this review was reading an overzealous critique to this book, written by Eric Wu in this webpage. I found some of his comments to be particularly misleading and out of context. For instance he says "the book only deals with the bare minimum of data analysis". Compared with other books in the field, the topics about data analysis covered in the book are not only more numerous but much more thoroughly explained. This book does not expedite the reader to some references but cares about explaining the things. If this book is the "bare minimum" at 500 pages, how is Mr. Wu going to characterize the other well known books in the field such as Knudsen, Simon, Speed, Baldi, etc. which have at most half as many as this book has. Knudsen, for instance, takes the reader from absolute measurements to and including ANOVA in 17 pages. Draghici covers the same topics in 7 chapters or about 250 pages, and that would be without counting the chapters on the basic statistics or image analysis. Another example of biased assessment is when Mr. Wu says "Exploratory data visualizing and data mining algorithms are not covered thoroughly in this book. For example, principal component analysis (PCA) is presented as a subsection of a chapter." The PCA description in the book is more than just fine to me. The book is not supposed to be an encyclopedia of statistics. What the reader needs to know is how PCA can help with the visualization of these multidimensional data sets and not necessarily give all the details about PCA. A last example I give of superficial judgment in Mr. Wu's view is the so called "inflation of Type I error rate". Mr. Wu says: "... if the probability of making a correct conclusion excludes the probability of making Type II errors, 1 - p should be stated as the probability of not making Type I errors".. In general, this statement would be true. However, the paragraph from the book to which Mr. Wu is referring to actually starts by saying: "When the t statistic for a gene is more extreme than the threshold..." etc. If the observed statistic is more extreme than the threshold, the statistical reasoning requires us to reject the null hypothesis. In this case type II errors (false negatives) CANNOT occur. Hence, in this case, the probability of drawing the correct conclusion is indeed 1-p, exactly as stated in the book. Overall, I find that the value you get per dollar spent when buying this book is high, and thereby I would strongly recommend it. Dr. Adi L. Tarca, Windsor (CANADA)

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