Analysis of Microarray Gene Expression Data

AvMei-Ling Ting Lee

Inbunden, Engelska, 2004

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1 632 kr

Beskrivning

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.

Produktinformation

Utgivningsdatum:2004-04-30
Mått:155 x 235 x 28 mm
Vikt:837 g
Format:Inbunden
Språk:Engelska
Antal sidor:377
Upplaga:2004
Förlag:Kluwer Academic Publishers
ISBN:9780792370871

Utforska kategorier

Biovetenskap inom Naturvetenskap och teknik

Recensioner i media

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)

Innehållsförteckning

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.