Clinical Chemistry 50: 2222, 2004;
10.1373/clinchem.2004.031898
(Clinical Chemistry. 2004;50:2222.)
© 2004 American Association for Clinical Chemistry, Inc.
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Book, Software, and Web Site Reviews |
Exploration and Analysis of DNA Microarray and Protein Array Data. Dhammika Amaratunga and Javier Cabrera. Hoboken, NJ: Wiley-Interscience-John Wiley and Sons, Inc., 2004, 260 pp., $84.95, hardcover. ISBN 0-471-27398-8.
Pascale F. Macgregor
UHN Microarray Centre, Clinical Genomics Centre, University Health Network, Toronto, ON M5G 2C4, Canada
The field of genomics has exploded in the past five years, largely as a result of the development of microarray technologies. Scientists in biomedical research who use global expression profiling are now facing the daunting task of making sense of microarray data. This book aims at answering that need by providing the reader with an extensive overview of different computational, visualization, and statistical methods currently used for microarray data analysis. Both authors were trained as statisticians at Princeton University.
This thorough book covers most of the strategies currently used in the field of expression array data analysis. Sometimes it does not offer a sufficiently critical view of the different strategies, for example, in the class prediction area, where much controversy exists. The use of a companion web site and the "supplementary reading" and "exercises" sections at the end of each chapter are all excellent initiatives that will be helpful to the reader.
Because statisticians who collaborate with biomedical researchers on microarray projects may not have an up-to-date basic knowledge of genomics, the book offers a general introductory chapter on genomics basics. The book then closely follows the different steps involved in microarray experimentation: description and choice of microarray platform, processing of the scanned image, preprocessing of the data, and summarization of the data. Chapters 7 and 8 offer a description of different statistical strategies used in microarray data analysis, first in simple experiments (two-group comparison), then in more complex experimental designs. Finally, chapters 9 and 10 discuss multivariate methods, focusing on pattern discovery (unsupervised analysis) and pattern prediction (supervised analysis). Chapter 11 offers a succinct description of protein arrays and a very brief overview of protein array analysis. In that respect the comprehensive nature of this book’s title is somewhat misleading as only 11 pages of 260 are devoted to protein arrays. This book also lacks coverage of the application of microarrays to comparative genomic hybridization and of the specific data analysis challenges associated with using DNA microarrays to measure DNA copy number as opposed to expression, as well as with the integration of both sets of data.
This book is well written, but it will not be easily accessible to a general audience, particularly to laboratorians in a medical laboratory setting or to clinicians working with basic scientists on microarray projects; it does not answer the need for a simple textbook usable by researchers with only basic statistical and mathematics knowledge. It will be a valuable reference for those with a good statistical background who are interested in a more thorough understanding of the statistical theories behind the various algorithms used in microarray analysis. In this respect, it does not compare well to other microarray textbooks, such as DNA Microarrays: A Molecular Cloning Manual, recently published by Cold Spring Harbor Laboratory Press. In summary, this book offers an extensive overview of current microarray data analysis methods but will be of interest only to researchers already very familiar with the field.
