Product Description
Interest in microarrays has increased considerably in the last ten years. This increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. Statistics for Microarrays: Design, Analysis and Inference is the first book that presents a coherent and systematic overview of statistical methods in all stages in the process of analysing microarray data – from getting good data to obtaining meaningful results.

Provides an overview of statistics for microarrays, including experimental design, data preparation, image analysis, normalization, quality control, and statistical inference.
Features many examples throughout using real data from microarray experiments.
Computational techniques are integrated into the text.
Takes a very practical approach, suitable for statistically-minded biologists.
Supported by a Website featuring colour images, software, and data sets.

Primarily aimed at statistically-minded biologists, bioinformaticians, biostatisticians, and computer scientists working with microarray data, the book is also suitable for postgraduate students of bioinformatics.

CUSTOMER REVIEWS (Average Customer Rating: 4.0 based on 1 review)

Very good, on the whole

This is the best introduction I know for anyone trying to learn the bioinformatics of microarrays. It starts with a brief description of the DNA microarrays, their chemistry, and the sources of uncertainty in their measurements, just enough for a non-biologist to get the general ideas. It skips the steps of scanning and spot recognition, mostly, and jumps right into analysis of the array of spot readings.

That is where the text comes into its own. One happy surprise is the book's emphasis on quality control and error management. Quality issues are addressed first by themselves, then as they affect the design and analysis of an experiment's biological meaning. This covers a wide variety of issues, including dye swaps, array background correction, and inference in the presence of low-quality data. There are soft spots in the discussion, especially in handling of missing data. That fits the general tone of the book, though, by stressing understanding over rigor.

This book comes with a macro package for the R environment, an open-source system somewhat like Matlab or Mathematica. That is both the strength and the weakness of this book. The strength of course, is the working code. It lets you see a real implementation of the algorithms that the authors describe. The weakness is that the implementations don't explain how the algorithms were developed, why they work, or how to recognize when they've been pushed past their breaking points. If you need more than rote recitation of the authors' implementation, you may find this frustrating. Also, the book uses five data sets for concrete discussion, but the software kit seems to include only one.

Microarray data sets (a few individual with thousands of measurements each) are very different from standard statistical data sets (lots of individuals with few measurements each). Despite the dramatic improvements of the last few years, the processing of the arrays themselves still varis widely under even the tightest control. Microarrays really do need different kinds of analysis and experimental design. This is a very readable explanation of why and how those procedures are used. I just wish the procedures themselves were presented in a little more depth.

//wiredweird
August 02, 2004

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