E-Book, Englisch, 352 Seiten, E-Book
McLachlan / Do / Ambroise Analyzing Microarray Gene Expression Data
1. Auflage 2005
ISBN: 978-0-471-72612-8
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
E-Book, Englisch, 352 Seiten, E-Book
Reihe: Wiley Series in Probability and Statistics
ISBN: 978-0-471-72612-8
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
A multi-discipline, hands-on guide to microarray analysis ofbiological processes
Analyzing Microarray Gene Expression Data provides acomprehensive review of available methodologies for the analysis ofdata derived from the latest DNA microarray technologies. Designedfor biostatisticians entering the field of microarray analysis aswell as biologists seeking to more effectively analyze their ownexperimental data, the text features a unique interdisciplinaryapproach and a combined academic and practical perspective thatoffers readers the most complete and applied coverage of thesubject matter to date.
Following a basic overview of the biological and technicalprinciples behind microarray experimentation, the text provides alook at some of the most effective tools and procedures forachieving optimum reliability and reproducibility of researchresults, including:
* An in-depth account of the detection of genes that aredifferentially expressed across a number of classes of tissues
* Extensive coverage of both cluster analysis and discriminantanalysis of microarray data and the growing applications of bothmethodologies
* A model-based approach to cluster analysis, with emphasis onthe use of the EMMIX-GENE procedure for the clustering of tissuesamples
* The latest data cleaning and normalization procedures
* The uses of microarray expression data for providing importantprognostic information on the outcome of disease
Autoren/Hrsg.
Weitere Infos & Material
Preface.
1. Microarrays in Gene Expression Studies.
2. Cleaning and Normalization.
3. Some Cluster Analysis Methods.
4. Clustering of Tissue Samples.
5. Screening and Clustering of Genes.
6. Discriminant Analysis.
7. Supervised Classification of Tissue Samples.
8. Linking Microarray Data with Survival Analysis.
References.
Author Index.
Subject Index.