Bioinformatics Original Paper Simultaneous Identification of Differential Gene Expression and Connectivity in Inflammation, Adipogenesis and Cancer

Motivation: Biological differences between classes are reflected in transcriptional changeswhich in turnaffect the levels bywhichessential 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: First, mixedmodel 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 datasets 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. Contact: Tony.Reverter-Gomez@csiro.au Supplementary information: Supplementary data are available at Bioinformatics online.

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