Analysis of Microarray Gene Expression Data
Published on 30. April 2004
Book
Hardback
XVI, 377 pages
978-0-7923-7087-1 (ISBN)
Description
After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis. The contribution of this book is to provide readers with an integrated presentation of various topics on analyzing microarray data.
Reviews / Votes
From the reviews:
"This book aims to be a comprehensive work on statistical techniques for analysis of microarray data. . the book contains an elaborate discussion on several variants of useful ANOVA models. . Standard multiple testing and permutation methods are well illustrated. . In conclusion, the book is a successful attempt to be a complete reference work for microarray data analysis. It is certainly a rich source of references." (M. A. van de Wiel, Kwantitatieve Methoden, Issue 3, 2006)
More details
Edition
2004 ed.
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
18 s/w Abbildungen
XVI, 377 p. 18 illus.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 28 mm
Weight
837 gr
ISBN-13
978-0-7923-7087-1 (9780792370871)
DOI
10.1007/b129531
Schweitzer Classification
Other editions
Additional editions
Mei-Ling Ting Lee
Analysis of Microarray Gene Expression Data
Book
05/2013
Springer
€160.49
Shipment within 15-20 days
Mei-Ling Ting Lee
Analysis of Microarray Gene Expression Data
E-Book
05/2007
Springer
€149.79
Available for download
Content
DNA, RNA, Protein, and Gene Expression.- Microarray Technology.- Inherent Variability in Microarray Data.- Background Noise.- Transformation and Normalization.- Missing Values in Microarray Data.- Saturated Intensity Readings in Microarray Data.- Experimental Design.- Anova Models for Michrorray Data.- Multiple Testing in Microarray Studies.- Permutation Tests in Microarray Data.- Bayesian Methods for Microarray Data.- Power and Sample Size Considerations at the Planning Stage.- Cluster Analysis.- Principal Components and Singular Value Decomposition.- Self-organizing Maps.- Discrimination and Classification.- Artificial Neural Networks.- Support Vector Machines.