Genet. Sel. Evol. 39 (2007) 651-668
DOI: 10.1051/gse:2007030

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

Peter Sørensen^a, Agnès Bonnet^b, Bart Buitenhuis^a, Rodrigue Closset^c, Sébastien Déjean^d, Céline Delmas^e, Mylène Duval^e, Liz Glass^f, Jakob Hedegaard^a, Henrik Hornshøj^a, Ina Hulsegge^g, Florence Jaffrézic^h, Kirsty Jensen^f, Li Jiang^a, Dirk-Jan de Koning^f, Kim-Anh Lê Cao^{d, e}, Haisheng Nieⁱ, Wolfram Petzl^j, Marco H. Pool^g, Christèle Robert-Granié^e, Magali San Cristobal^b, Mogens Sandø Lund^a, Evert M. van Schothorst^k, Hans-Joachim Schuberth^l, Hans-Martin Seyfert^m, Gwenola Tosser-Klopp^b, David Waddington^f, Michael Watsonⁿ, Wei Yang^m and Holm Zerbe<sup>j

a</sup>  University of Aarhus, Faculty of Agricultural Sciences, Dept. of Genetics and Biotechnology, P.O. Box 50 DK-8830 Tjele, Denmark;
^b  INRA, UMR 444 Laboratoire de génétique cellulaire, BP 52627, 31326 Castanet-Tolosan, France;
^c  Faculty of Veterinary Medicine, University of Liege, Liege, Belgium;
^d  Université Paul Sabatier, UMR 5219 Laboratoire de statistique et probabilités, 31062 Toulouse, France;
^e  INRA, UR631 Station d'amélioration génétique des animaux, BP 52627, 31326 Castanet-Tolosan, France;
^f  Roslin Institute, Department of Genetics and Genomics, Roslin Biocentre, Roslin, Midlothian, EH25 9PS, UK (RLN);
^g  Animal Sciences Group Wageningen UR, Lelystad, The Netherlands;
^h  INRA, UR337 Station de génétique quantitative et appliquée, Jouy-en-Josas, 78350, France;
ⁱ  Animal Breeding and Genomics Centre, Wageningen University and Research Centre, The Netherlands;
^j  Clinic for Ruminants, Ludwig-Maximilians-University, Munich, Germany;
^k  Food Bioactives Group, RIKILT-Institute of Food Safety, Wageningen University and Research Centre, Wageningen, The Netherlands;
^l  Immunology Unit, University of Veterinary Medicine, Hannover, Germany;
^m  Research Institute for the Biology of Farm Animals, Dummerstorf, Germany;
ⁿ  Informatics Group, Institute for Animal Health, Compton, Newbury, Berks RG20 7NN, UK

(Received 10 May 2007; accepted 4 July 2007; published online 6 December 2007)

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.

Key words: bovine annotation / bovine microarray / gene set analysis / mastitis / multivariate approaches

Correspondence and reprints: pso@agrsci.dk

© INRA, EDP Sciences 2007