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Analysis of the real EADGENE data set: Multivariate approaches and post analysis (Open Access publication)

Author(s): Sørensen Peter | Bonnet Agnès | Buitenhuis Bart | Closset Rodrigue | Déjean Sébastien | Delmas Céline | Duval Mylène | Glass Liz | Hedegaard Jakob | Hornshøj Henrik | Hulsegge Ina | Jaffrézic Florence | Jensen Kirsty | Jiang Li | de Koning Dirk-Jan | Cao Kim-Anh | Nie Haisheng | Petzl Wolfram | Pool Marco | Robert-Granié Christèle | San Cristobal Magali | Lund Mogens | van Schothorst Evert | Schuberth Hans-Joachim | Seyfert Hans-Martin | Tosser-Klopp Gwenola | Waddington David | Watson Michael | Yang Wei | Zerbe Holm

Journal: Genetics Selection Evolution
ISSN 0999-193X

Volume: 39;
Issue: 6;
Start page: 651;
Date: 2007;
Original page

Keywords: bovine annotation | bovine microarray | gene set analysis | mastitis | multivariate approaches

Abstract The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed.
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