263 pages, colour plates, b/w illustrations, tables
Microarray Bioinformatics 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. Microarray Bioinformatics 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. Microarray Bioinformatics 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
1. Microarrays: making them and using them
2. Sequence databases for microarrays
3. Computer design of oligonucleotide probes
4. Image processing
6. Measuring and quantifying microarray variability
7. Analysis of differentially expressed genes
8. Analysis of relationships between genes, tissues or treatments
9. Classification of tissues and samples
10. Experimental design
11. Data storage, standards and sharing
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