Microarray Bioinformatics (英語) ペーパーバック – 2003/9/8
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This book is a comprehensive guide to all of the mathematics, statistics and computing you will need to successfully operate DNA microarray experiments. It is written for researchers, clinicians, laboratory heads and managers, from both biology and bioinformatics backgrounds, who work with, or who intend to work with microarrays. The book covers all aspects of microarray bioinformatics, giving you the tools to design arrays and experiments, to analyze your data, and to share your results with your organisation or with the international community. There are chapters covering sequence databases, oligonucleotide design, experimental design, image processing, normalisation, identifying differentially expressed genes, clustering, classification and data standards. The book is based on the highly successful Microarray Bioinformatics course at Oxford University, and therefore is ideally suited for teaching the subject at postgraduate or professional level.
'The author does an excellent job of covering high-level analysis of microarray data … [the book] provides the statistically naive biologist with a gentle introduction to the data transformations and manipulations needed to deal with microarrays, and the worked examples with publicly available data are well described … excellent value for any budding arrayer …'. Nature Genetics
'… excellent and clearly written … a pleasure to read.' ASM News
'The book would be ideal for biologists who wish to gain a grasp of the different analysis techniques available to the microarray user.' Microbiology Today
There's a lot to like here. Stekel covers everything, starting with selecting the probes and printing the arrays. Next comes raw array analysis - scanning, image processing, and measuring the effects of the array itself on the results. That covers the first six chapters. The next three go over analysis of the result, one more chapter covers experimential design, and the last chapter discusses storing, labelling, and sharing the data. Some of those topics, like experiment design, address issues that most other authors neglect.
Still, I came away feeling that I had read only half of each chapter. Going back, it turned out that I hadn't missed anything that really was there. I missed a lot, though. For example, probe selection includes a discussion of self-hybridization - good stuff. It stopped short of giving me any clear idea how much self-complementarity is too much. It mentioned DNA melting points, but without enough information for me to understand what is really melting, or how or why to choose one melting point over another. Handling of raw array data discussed Loess regression as a way to cancel out process differences across a single array. Again, it's good stuff, but what exactly is a Loess regression? Expression analysis mentions Spearman correlation as an alternative to Pearson correlation - it give Pearson's formulas, but not Spearman's. Later, when the author does give a "formula" for selecting sample sizes, it turns out to be some macro reference for some stat package. Throughout the book, I felt the same lack: I learned the names of many things, but not what they really are.
Maybe this book is OK for a first introduction. If you've had that introduction and want to take the second steps, this book probably won't meet your needs.
What you have to keep in mind is this book is intended for those who want a brief overview of all aspects of microarrays. Its a "forest for the trees" book on microarrays. The writing is very good and easy to follow, and its a great introductory text and reasonably priced.
Regardless of ones formal training, (e.g. Biology, Statistics, Computer Science, ... , health science) I think it would make an excellent little basic reference on ones bookshelf or to just have around in the lab for undergraduates/beginning graduate students.
Bottomline: If you prefer to learn things by starting at the start and not at the end then consider this book; Indeed its a great starter book to get your feet a little wet before jumping in over your head to the more gnarly stuff.
Example: he starts using "spot" and "feature" without making effort to explain what they mean in the context of microarrays. At times it seems he treats them as synonyms which is confusing. I consulted the index hoping to find somewhere precise definition of these terms but to no avail. At the end, I had to go to Wikipedia and various other pages which did the job but then if you have to use internet to understand the book than why not just get everything from the web and save the money for the book?
Noting that the target audience are novices in the area of microarrays (experts won't find anything valuable here), the book does a poor job of serving them. To recap: "Nice try. Could do better"