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Nucleic Acids Research 2005 33(6):e53; doi:10.1093/nar/gni050
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Published online 30 March 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oupjournals.org

Methods Online

Analysis of host response to bacterial infection using error model based gene expression microarray experiments

Dov J. Stekel, Donatella Sarti, Victor Trevino, Lihong Zhang2, Mike Salmon1, Chris D. Buckley1, Mark Stevens3, Mark J. Pallen2, Charles Penn and Francesco Falciani*

School of Biosciences, The University of Birmingham Birmingham B15 2TT, UK 1MRC Centre for Immune Regulation, Division of Immunity and Infection, The University of Birmingham Birmingham B15 2TT, UK 2Bacterial Pathogenesis and Genomics Unit, Division of Immunity and Infection, Medical School, The University of Birmingham Birmingham B15 2TT, UK 3Institute of Animal Health Compton, UK

*To whom correspondence should be addressed. Tel: +44 121 4143037; Fax: +44 121 4145925; Email: f.falciani{at}bham.ac.uk

Received October 27, 2004. Revised December 8, 2004. Accepted March 1, 2005.

A key step in the analysis of microarray data is the selection of genes that are differentially expressed. Ideally, such experiments should be properly replicated in order to infer both technical and biological variability, and the data should be subjected to rigorous hypothesis tests to identify the differentially expressed genes. However, in microarray experiments involving the analysis of very large numbers of biological samples, replication is not always practical. Therefore, there is a need for a method to select differentially expressed genes in a rational way from insufficiently replicated data. In this paper, we describe a simple method that uses bootstrapping to generate an error model from a replicated pilot study that can be used to identify differentially expressed genes in subsequent large-scale studies on the same platform, but in which there may be no replicated arrays. The method builds a stratified error model that includes array-to-array variability, feature-to-feature variability and the dependence of error on signal intensity. We apply this model to the characterization of the host response in a model of bacterial infection of human intestinal epithelial cells. We demonstrate the effectiveness of error model based microarray experiments and propose this as a general strategy for a microarray-based screening of large collections of biological samples.

The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors


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