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Bioinformatics Advance Access published online on July 24, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl392
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 2, 2006
Revised June 13, 2006
Accepted July 11, 2006

Article

Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer

Antonio Reverter 1 *, Aaron Ingham 1, Sigrid A. Lehnert 1, Siok-Hwee Tan 1, Yonghong Wang 1, Abhirami Ratnakumar 1, and Brian P. Dalrymple 1

1 CSIRO Livestock Industries, Queensland Bioscience Precinct, 306 Carmody Rd., Brisbane, Queensland 4067, Australia

* To whom correspondence should be addressed.
Antonio Reverter, E-mail: Tony.Reverter-Gomez{at}csiro.au


  Abstract

Motivation: Biological differences between classes are reflected in transcriptional changes which in turn affect the levels by which essential genes are individually expressed and collectively connected. The purpose of this communication is to introduce an analytical procedure to simultaneously identify genes that are differentially expressed (DE) as well as differentially connected (DC) in two or more classes of interest.

Results: Our procedure is based on a two-step approach: Firstly mixed-model equations are applied to obtain the normalized expression levels of each gene in each class treatment. These normalized expressions form the basis to compute a measure of (possible) DE as well as the correlation structure existing among genes. Second, a two-component mixture of bi-variate distributions is fitted to identify the component that encapsulates those genes that are DE and/or DC. We demonstrate our approach using three distinct data sets including a human systemic inflammation oligonucleotide data; a spotted cDNA data dealing with bovine in vitro adipogenesis; and SAGE database on cancerous and normal tissue samples.

Associate Editor: Chris Stoeckert
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