Data Analysis Tools for DNA Microarrays

Rating:
Summary: Data Analysis Tools for DNA Microarrays
Review: A much needed book for the biologist interested in using DNA/protein microarrays. Examples are specific for microarrays. The material starts from ground zero and begins
with image analysis. All major methods for analysis are discussed.
Well worth the cost, quality graphics, includes software (have not used as yet).
A must read before discussing experimetnal design with your stats person.

Rating:
Summary: Data Analysis Tools for DNA Microarrays
Review: A much needed book for the biologist interested in using DNA/protein microarrays. Examples are specific for microarrays. The material starts from ground zero and begins
with image analysis. All major methods for analysis are discussed.
Well worth the cost, quality graphics, includes software (have not used as yet).
A must read before discussing experimetnal design with your stats person.

Rating:
Summary: Detailed and understandable
Review: Draghici managed to write a manual on applying microarray (data) with a great feeling for explanation of hard issues. The book is relatively easy to read, very complete and covers most, if not all, analysis techniques that are currently around for microarrays.

Highly recommendable!

Rating:
Summary: Good Overview of Microarray Technology
Review: I have had the book for about a month now and I consult it quite frequently. Great coverage of Microarray Data Anlysis. It manages to be thourough without being dry or using excessive jargon. It's very readable and useful for both novices and experienced readers.

It's main strength lies in the use of excellent examples that show the main pitfalls encountered in analyzing microarray data. It has great coverage of statistics and their potential misuse and misunderstanding when they are applied to gene expression data sets. The experimental design section is especially helpful for researchers that are designing a project.

The graphics are excellent and the book is printed on good quality paper.

The book includes two CD's with demo versions of several commercial software packages.

Overall a great buy.

Rating:
Summary: Introduction to Statistical Data Analysis of Microarrays
Review: The targeted audience of this book is biologists who are eager to get an understanding of the analysis tools they use for microarrays. The book does an excellent job addressing this tier of audience.

The book has plenty of examples. Almost all the examples, whether fake or real, are microarray-related. Whenever needed, figures or charts are provided to illustrate ideas. A few chapters that introduce basic statistical concepts provide solved problems and exercises. All these efforts are worthwhile making difficult statistical concepts easy to understand in the context of microarrays and making the book especially valuable for biologists who do not have strong background in statistics.

This book has an emphasis on major statistical aspects of microarray data analysis. There are 17 chapters in this book. About 8 of them are directly related to statistics. Especially, there is one whole chapter devoted to multiple hypothesis testing, one chapter for ANOVA, and one chapter for experimental design. The above subjects are presented in a thorough, yet easy-to-follow style. Statistical issues are often not well addressed in published papers using microarrays. This book on microarray data analysis does an excellent job emphasizing this aspect.

The title of the book indicates "data analysis". However, since this is not a clearly defined term, you should be aware that the book only deals with "the bare minimum" of data analysis. That is routines, such as normalization, transformation, statistical testing, and clustering, that have to be carried out each and every time. 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. It does not provide explanations on concepts such as loading factors nor scree test. Series data (e.g. time series) are on two pages only and there is no mention of Fourier transformation. Support vector machine (SVM), which is widely used today as a supervised classification method, is not presented at all.

As I mentioned at the beginning, the targeted audience is biologists. If you are a statistician or a bioinformatician who wants to mathematically explore data analysis algorithms, you should look somewhere else. You may be disappointed that many concepts are not rigorously or accurately defined in this book. For example, the book uses capital letters to denote random variables. But the concept of random variables is not rigorously defined in the book. One of the consequences is the weak definition of mathematical expectation. Another example is the inflation of Type I error rate. On page 220, the author claims that the probability of "drawing the correct conclusion" is 1 - p, where p is the calculated probability of a statistic versus a parameter. However, 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 summary, this is a good book on microarray analysis tools for biologists using microarrays. However, people who are seeking in-depth descriptions of these algorithms should look somewhere else.

Rating:
Summary: Far from superficial...
Review: When entering the minefields of microarray data analysis, one has to understand and keep up with state-of-the-art technologies and interdisciplinary literatures. A background in molecular biology is clearly not enough to evaluate the pro and cons of the various statistical methods for selecting truly modulated candidate genes in a given experimental biological system. Choosing between the available analysis software's is not an easy task either. Draghici presents a complete visit of the microarray underworld by initiating the reader to all the facettes of this domain. From the fundamentals of slide production and target hybridization to image processing, statistical analysis, experimental design, data management and biological interpretation, all aspects treated herein are described with pertinent details. Draghici slowly, but successfully, tames the reticent molecular biologist to the arid world of statistics and even entertains the reader with anecdotes and humoristic citations.
Clearly written, with appropriate mathematical examples for each topic, this book even includes exercises at the end of some chapters, for the zealous student sleeping in all of us. It constitutes a very good didactic tool and the included CD's allow a good peek in some of the available image/data analysis software's on the market.
As a core facility manager and eternal student, I strongly recommend Draghici's book to life scientists and students who are struggling with statistical analysis and data mining techniques.

Brigitte Malette, Ph. D.
Project Leader, Microarray Platform
Centre for Structural and Functional Genomics
Concordia University
Montreal

Rating:
Summary: Far from superficial...
Review: When entering the minefields of microarray data analysis, one has to understand and keep up with state-of-the-art technologies and interdisciplinary literatures. A background in molecular biology is clearly not enough to evaluate the pro and cons of the various statistical methods for selecting truly modulated candidate genes in a given experimental biological system. Choosing between the available analysis software's is not an easy task either. Draghici presents a complete visit of the microarray underworld by initiating the reader to all the facettes of this domain. From the fundamentals of slide production and target hybridization to image processing, statistical analysis, experimental design, data management and biological interpretation, all aspects treated herein are described with pertinent details. Draghici slowly, but successfully, tames the reticent molecular biologist to the arid world of statistics and even entertains the reader with anecdotes and humoristic citations.
Clearly written, with appropriate mathematical examples for each topic, this book even includes exercises at the end of some chapters, for the zealous student sleeping in all of us. It constitutes a very good didactic tool and the included CD's allow a good peek in some of the available image/data analysis software's on the market.
As a core facility manager and eternal student, I strongly recommend Draghici's book to life scientists and students who are struggling with statistical analysis and data mining techniques.

Brigitte Malette, Ph. D.
Project Leader, Microarray Platform
Centre for Structural and Functional Genomics
Concordia University
Montreal