9 of 9 people found the following review helpful:
4.0 out of 5 stars
Outstanding survey, September 12, 2004
This review is from: Statistical Analysis of Gene Expression Microarray Data (Hardcover)
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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8 of 8 people found the following review helpful:
4.0 out of 5 stars
Excellent book for data analyst, March 5, 2004
This review is from: Statistical Analysis of Gene Expression Microarray Data (Hardcover)
Thorough converage of statistics involved in microarray data analysis. It presents important knowledge for biologists who use data analysis tools but would like to know what is behind the scene. Understanding the book needs some statistical background and hence not a easy book for biologists and genetists who do not have that knowledge.
I would like to emphasize that experiment design issue is presented in a very clear way and should be read by all who plan to start project related to gene expression. Clustering and classification are two major analysis methods for microarray data, and the comprehensive discussion of the statistical mechanisms for each method in the last two chapters will help analysts to choose the right methods when mining the data. The first chapter seems to be a little out of the place, because it mainly discusses model-based genechip data analysis. This chapter touches a little about preprocessing and gene selection but far from complete.
A chapter with thorough discussion of pre-processing techniques and gene selection techniques would make this a prefect book. Overall it is a great reference for anyone who is interested in microarray data analysis!
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1 of 1 people found the following review helpful:
4.0 out of 5 stars
the wave of the future, June 11, 2009
This review is from: Statistical Analysis of Gene Expression Microarray Data (Hardcover)
This book looks like a text on statistical methods in microarray data edited and contributed by Terry Speed a leading researcher in the field from the Berkeley California Statistics department. There are several contributors to the book include Hastie and Tibshirani from Stanford and Tseng and Wong from Harvard. These are some of the top academic biostatisticians in the world who are currently heavily involved in microarray research.
Usually I write the first amazon review on books like this. But this book has been out since 2003 and so my friend wiredweird has already given a very good description of those chapter. All I would like to add is that the experimental design and the multiplicity of hypothesis tests are not what is typical seen by statisticians. So new methodology has been developed to improve the analysis of microarray data for gene expression. Chapter 1 deals with the modeling approaches used to deal with the idiosyncrasies of this type of data. The other three chapters deal with experimental design, classification and clustering and the underlying issues of image processing. The important issue of multiplicity of tests and the false detection rate (FDR) methodology is covered in Chapter 2 pages 62-66. I also find it very satisfying to see advanced statistical methods such as bootstrap, lasso, boosting, hierarchical linear models and Bayesian methods.
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