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Bioinformatics and Computational Biology Solutions Using R and BioconductorPreprocessing Two-Color Spotted Arrays

Bioinformatics and Computational Biology Solutions Using R and Bioconductor: Preprocessing... [Preprocessing of two-color spotted arrays can be broadly divided in two main categories: quality assessment and normalization. In this chapter, we will focus on functions from the arrayQuality and marray packages that perform these tasks. The chapter begins by describing various data structures and tools available in these packages for reading and storing primary data from two-color spotted arrays. This is followed by descriptions of various exploratory tools such as MAplots, spatial plots, and boxplots to assess data quality of an array. Finally, algorithms available for performing appropriate normalization to remove sources of systematic variation are discussed. We will illustrate the above-mentioned functions using a case study.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Bioinformatics and Computational Biology Solutions Using R and BioconductorPreprocessing Two-Color Spotted Arrays

Part of the Statistics for Biology and Health Book Series
Editors: Gentleman, Robert; Carey, Vincent J.; Huber, Wolfgang; Irizarry, Rafael A.; Dudoit, Sandrine
Yang, Y. H.; Paquet, A. C.
Bioinformatics and Computational Biology Solutions Using R and Bioconductor — Jan 1, 2005
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Publisher
Springer New York
Copyright
© Springer Science+Business Media, Inc. 2005
ISBN
978-0-387-25146-2
Pages
49 –69
DOI
10.1007/0-387-29362-0_4
Publisher site
See Chapter on Publisher Site

Abstract

[Preprocessing of two-color spotted arrays can be broadly divided in two main categories: quality assessment and normalization. In this chapter, we will focus on functions from the arrayQuality and marray packages that perform these tasks. The chapter begins by describing various data structures and tools available in these packages for reading and storing primary data from two-color spotted arrays. This is followed by descriptions of various exploratory tools such as MAplots, spatial plots, and boxplots to assess data quality of an array. Finally, algorithms available for performing appropriate normalization to remove sources of systematic variation are discussed. We will illustrate the above-mentioned functions using a case study.]

Published: Jan 1, 2005

Keywords: Background Intensity; Target Information; Diagnostic Plot; Limma Package; Color Palette

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