E-raamat: Statistical Analysis of Gene Expression Microarray Data [Taylor & Francis e-raamat]

Four chapters describe statistical techniques for analyzing the genetic microarray data generated by large-scale, high throughput assays. The authors, who are affiliated with the University of California, Stanford, and Harvard, discuss the model-based analysis of oligonucleotide assays, different approaches for cDNA array data, the design and analysis of comparative microarray experiments, statistical issues arising in the classification of biological samples, and both partitioning methods and hierarchical methods for clustering data. The book will be of interest to biostatisticians and geneticists. Annotation (c) Book News, Inc., Portland, OR (booknews.com)

Although less than a decade old, the field of microarray data analysis is now thriving and 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.