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Integrating biological information into the statistical analysis and design of microarray experiments*

Published online by Cambridge University Press:  05 October 2009

G. J. M. Rosa  and
A. I. Vazquez
G. J. M. Rosa*
Affiliation:
Department of Dairy Science, University of Wisconsin, Madison, WI 53706, USA
A. I. Vazquez
Affiliation:
Department of Dairy Science, University of Wisconsin, Madison, WI 53706, USA
*
†E-mail: [email protected]
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Abstract

Microarray technology is a powerful tool for animal functional genomics studies, with applications spanning from gene identification and mapping, to function and control of gene expression. Microarray assays, however, are complex and costly, and hence generally performed with relatively small number of animals. Nevertheless, they generate data sets of unprecedented complexity and dimensionality. Therefore, such trials require careful planning and experimental design, in addition to tailored statistical and computational tools for their appropriate data mining. In this review, we discuss experimental design and data analysis strategies, which incorporate prior genomic and biological knowledge, such as genotypes and gene function and pathway membership. We focus the discussion on the design of genetical genomics studies, and on significance testing for detection of differential expression. It is shown that the use of prior biological information can improve the efficiency of microarray experiments.

Keywords

microarrayexperimental designdifferential expressionbiological knowledge
Type
Full Paper
Information
animal , Volume 4 , Issue 2 , February 2010 , pp. 165 - 172
Copyright
Copyright © The Animal Consortium 2009

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Footnotes

*

This paper has been presented at the session “Genomics selection and bioinformatics” of the 59th Annual meeting of the European Association for Animal Production held in Vilnius (Lithuania), 24 to 27 August 2008. Dr A. Maki-Tanila acted as a guest editor.

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