Statistical Analysis of Gene Expression Microarray Data

Statistical Analysis of Gene Expression Microarray Data

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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 decomposition Although 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 more

Product details

  • Hardback | 240 pages
  • 157.48 x 236.22 x 20.32mm | 498.95g
  • Taylor & Francis Inc
  • CRC Press Inc
  • Bosa Roca, United States
  • English
  • 99 black & white illustrations, 48 colour illustrations, 20 black & white tables
  • 1584883278
  • 9781584883272
  • 1,852,842

Review quote

"The 10 authors are among the world's authorities on the statistical analysis of this new class of biotechnology . What I like best about this stimulating book is that it allows a simplified logical view of large complex multivariate data sets. I highly recommend this book for library purchase, and for individuals in the field ." - Journal of the Royal Statistical Society, Series A, Vol. 157 (3) "Analysis for gene expression data is the latest hot new topic in statistical data analysis...[this book] deals with microarray experiments: design and analysis for a comparative study, classification methods for data analysis, and clustering for data analysis. Scientists whose work concerns this type of data will want to get a copy of the book." -Technometrics, 2003 "This book is a milestone, documenting major significant advances in the statistical methodology. The four chapters, though independent, share common foci with issues of design, robustness, and the freely available associated software. The statistical ideas are introduced succinctly. The book is especially valuable for research scientists in the field seeking an understanding of the related statistical developments." - Short Book Reviews of the ISIshow more

Table of contents

MODEL-BASED ANALYSIS OF OLIGONUCLEOTIDE ARRAYS AND ISSUES IN cDNA MICROARRAY ANALYSIS, Cheng Li, George C. Tseng, and Wing Hung Wong Model-Based Analysis of Oligonucleotide Arrays Issues in cDNA Microarray Analysis Acknowledgments DESIGN AND ANALYSIS OF COMPARATIVE MICROARRAY EXPERIMENTS, Yee Hwa Yang and Terry Speed Introduction Experimental Design Two-Sample Comparisons Single-Factor Experiments with more than Two Levels Factorial Experiments Some Topics for Further Research CLASSIFICATION IN MICROARRAY EXPERIMENTS, \ Sandrine Dudoit and Jane Fridlyand Introduction Overview of Different Classifiers General Issues in Classification Performance Assessment Aggregating Predictors Datasets Results Discussion Software and Datasets Acknowledgments CLUSTERING MICROARRAY DATA, Hugh Chipman, Trevor J. Hastie, and Robert Tibshirani An Example Dissimilarity Clustering Methods Partitioning Methods Hierarchical Methods Two-Way Clustering Principal Components, the SVD, and Gene Shaving Other Approaches Software REFERENCES INDEXshow more

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