DNA Microarrays and Related Genomics Techniques: Design, Analysis, and Interpretation of Experiments (Chapman & Hall/CRC Biostatistics Series) Review

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DNA Microarrays and Related Genomics Techniques: Design, Analysis, and Interpretation of Experiments (Chapman & Hall/CRC Biostatistics Series) ReviewAn expansive tour of existing statistical methodologies used in microarray data analysis from contributed experts. What it does is deeply explore what people are using nowadays, cites references in each chapter beautifully, and gives the reader a solid footing as to the pros and cons of each technique presented. Each chapter delves deep enough, and by citing references extensively, one can go seek further detail from the source.
IMHO, this is probably one of the most professionally done books out there. By that I mean there is no fluff, just high quality content across the board. The intended audience is those having some computational background, particularly in statistics. However, each chapter begins with an introductory paragraph serving as a review so that anyone could pretty much gather what is going to be discussed.
A really nice feature of this compilation is that not only does it provide insight into the current methods, it outlines where further developments are needed and how they may help in filling in the gaps. With so many developments having been made in microarray data analysis recently, its nice to know that this book exists to help one gain a solid foundation on how to proceed with some sort of clarity. A five star rating for this one hands down. :)DNA Microarrays and Related Genomics Techniques: Design, Analysis, and Interpretation of Experiments (Chapman & Hall/CRC Biostatistics Series) OverviewConsidered highly exotic tools as recently as the late 1990s, microarrays are now ubiquitous in biological research. Traditional statistical approaches to design and analysis were not developed to handle the high-dimensional, small sample problems posed by microarrays. In just a few short years the number of statistical papers providing approaches to analyzing microarray data has gone from almost none to hundreds if not thousands. This overwhelming deluge is quite daunting to either the applied investigator looking for methodologies or the methodologist trying to keep up with the field. DNA Microarrays and Related Genomics Techniques: Design, Analysis, and Interpretation of Experiments consolidates discussions of methodological advances into a single volume.
The book's structure parallels the steps an investigator or an analyst takes when conducting and analyzing a microarray experiment from conception to interpretation. It begins with foundational issues such as ensuring the quality and integrity of the data and assessing the validity of the statistical models employed, then moves on to cover critical aspects of designing a microarray experiment. The book includes discussions of power and sample size, where only very recently have developments allowed such calculations in a high dimensional context, followed by several chapters covering the analysis of microarray data. The amount of space devoted to this topic reflects both the variety of topics and the effort investigators have devoted to developing new methodologies. In closing, the book explores the intellectual frontier - interpretation of microarray data. It discusses new methods for facilitating and affecting formalization of the interpretation process and the movement to make large high dimensional datasets public for further analysis, and methods for doing so.
There is no question that this field will continue to advance rapidly and some of the specific methodologies discussed in this book will be replaced by new advances. Nevertheless, the field is now at a point where a foundation of key categories of methods has been laid out and begun to settle. Although the details may change, the majority of the principles described in this book and the foundational categories it contains will stand the test of time, making the book a touchstone for researchers in this field.

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