Analysis of the real EADGENE data set: multivariate approaches and post analysis (open access publication).
Journal Article
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.
Full Text
Duke Authors
Cited Authors
- Sørensen, P; Bonnet, A; Buitenhuis, B; Closset, R; Déjean, S; Delmas, C; Duval, M; Glass, L; Hedegaard, J; Hornshøj, H; Hulsegge, I; Jaffrézic, F; Jensen, K; Jiang, L; de Koning, D-J; Lê Cao, K-A; Nie, H; Petzl, W; Pool, MH; Robert-Granié, C; San Cristobal, M; Lund, MS; van Schothorst, EM; Schuberth, H-J; Seyfert, H-M; Tosser-Klopp, G; Waddington, D; Watson, M; Yang, W; Zerbe, H
Published Date
- November 2007
Published In
Volume / Issue
- 39 / 6
Start / End Page
- 651 - 668
PubMed ID
- 18053574
Pubmed Central ID
- PMC2682812
International Standard Serial Number (ISSN)
- 0999-193X
Digital Object Identifier (DOI)
- 10.1186/1297-9686-39-6-651
Language
- eng
Conference Location
- France