Statistical Analysis of Gene Expression Microarray Data Review

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(More customer reviews)Microarray studies are becoming the preferred research tools in many areas, including cancer research, development studies, and studies in organisms' responses to their environments. Because of differences between organisms or between experiments, microarray data is always statistical in nature. The problem is that the data aren't well suited to traditional statistics. Instead of studying a few characteristics in large numbers of individuals, microarray studies typically yield thousands of data values for a few dozen samples.
That mismatch, between current statistical practice and microarray analysis requirements, seem to be driving many innovations in statistical analysis. This book is a brief survey of four of those areas of analysis: model-based analysis, experimental design, classification, and clustering.
The first section, on model-based analysis, is brief. Mostly, it seems to establish the language used in later sections. The next, on experimental design, deals with ways for getting the most information out of the fewest samples. The costs of arrays and processing are dropping, but still high. More analysis on less data makes good economic sense. The DNA samples analyzed also have costs - some can only be prepared in minute amounts, others must be extracted surgically from human patients. Either way, it's important to maximize the knowledge harvested from limited amounts of biologcal material.
The next section, on discrimination, is a bit longer. It briefly summarizes a wide variety of techniques for deciding which category best represents any one sample. This section gives a good review of analytic approaches: Fisher classifiers and their descendants, principal components, support vectors, and decision trees. Within trees, the authors note that the number of missing values in typical microarray data may interfere with standard analysis, and that surrogate variables may be needed in many cases. AI and data mining techniques aren't broadly represented, but this chapter is still very informative.
The final section, on clustering, was shorter. It was reasonably informative, and I gleaned a few new facts from it. Mostly, though, it seemed to present techniques that are already well known.
This book is a survey, so it emphasizes breadth over depth. Many algorithms described only briefly, and some are just mentioned by name. The developer will need to chase references to find an implementable level of detail. Still, the book has value as an index to references and as a comparison of techniques.
//wiredweird

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Although less than a decade old, the field of microarray data analysisisnow thrivingand growing at a remarkable pace. Biologists, geneticists, and computer scientists as well as statisticians all need an accessible, systematic treatment of the techniques used for analyzing the vast amounts of data generated by large-scale gene expression studies. And there is arguably no group better qualified to do so than the authors of this book.Statistical Analysis of Gene Expression Microarray Data promises to become the definitive basic reference in the field. Under the editorship of Terry Speed, some of the world's most pre-eminent authorities have joined forces to present the tools, features, and problems associated with the analysis of genetic microarray data. These include::"Model-based analysis of oligonucleotide arrays, including expression index computation, outlier detection, and standard error applications"Design and analysis of comparative experiments involving microarrays, with focus on \ two-color cDNA or long oligonucleotide arrays on glass slides "Classification issues, including the statistical foundations of classification and an overview of different classifiers"Clustering, partitioning, and hierarchical methods of analysis, including techniques related to principal components and singular value decompositionAlthough the technologies used in large-scale, high throughput assays will continue to evolve, statistical analysis will remain a cornerstone of their success and future development. Statistical Analysis of Gene Expression Microarray Data will help you meet the challenges of large, complex datasets and contribute to new methodological and computational advances.

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