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Bioinformatics and Computational Biology Solutions Using R and BioconductorCell-Based Assays

Bioinformatics and Computational Biology Solutions Using R and Bioconductor: Cell-Based Assays [This chapter describes methods and tools for processing and visualizing data from high-throughput cell-based assays. Such assays are used to examine the contribution of genes to a biological process or phenotype (Carpenter and Sabatini, 2004). In principle, this can be done for any gene or combination of genes and for any biological process of interest. There is a variety of technologies, but all of them rely on the availability of genomic resources such as whole genome sequences, full-length cDNA libraries, siRNA collections; or on libraries of protein-specific ligands (compounds). Typically, all or at least large parts of the experimental procedures and data collection are automated. Cell-based assays offer the potential for clustering of genes based on their functional profiles (Piano et al., 2002) and epistatic analyses to elucidate complex genetic networks (Tong et al., 2004).] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Bioinformatics and Computational Biology Solutions Using R and BioconductorCell-Based Assays

Part of the Statistics for Biology and Health Book Series
Editors: Gentleman, Robert; Carey, Vincent J.; Huber, Wolfgang; Irizarry, Rafael A.; Dudoit, Sandrine
Huber, W.; Hahne, F.
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
71 –90
DOI
10.1007/0-387-29362-0_5
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter describes methods and tools for processing and visualizing data from high-throughput cell-based assays. Such assays are used to examine the contribution of genes to a biological process or phenotype (Carpenter and Sabatini, 2004). In principle, this can be done for any gene or combination of genes and for any biological process of interest. There is a variety of technologies, but all of them rely on the availability of genomic resources such as whole genome sequences, full-length cDNA libraries, siRNA collections; or on libraries of protein-specific ligands (compounds). Typically, all or at least large parts of the experimental procedures and data collection are automated. Cell-based assays offer the potential for clustering of genes based on their functional profiles (Piano et al., 2002) and epistatic analyses to elucidate complex genetic networks (Tong et al., 2004).]

Published: Jan 1, 2005

Keywords: Bivariate Normal Distribution; Epistatic Analysis; Rectangular Table; Complex Genetic Network; General Purpose Tool

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