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

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, Lê Cao, Kim-Anh, Nie, Haisheng, Petzl, Wolfram, Pool, Marco H., Robert-Granié, Christèle, San Cristobal, Magali, Lund, Mogens Sandø, van Schothorst, Evert M., Schuberth, Hans-Joachim, Seyfert, Hans-Martin, Tosser-Klopp, Gwenola, Waddington, David, Watson, Michael, Yang, Wei and Zerbe, Holm (2007) Analysis of the real EADGENE data set: Multivariate approaches and post analysis (Open Access publication). Genetics Selection Evolution, 39 6: 651-668. doi:10.1051/gse:2007030

Author 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
Lê Cao, Kim-Anh
Nie, Haisheng
Petzl, Wolfram
Pool, Marco H.
Robert-Granié, Christèle
San Cristobal, Magali
Lund, Mogens Sandø
van Schothorst, Evert M.
Schuberth, Hans-Joachim
Seyfert, Hans-Martin
Tosser-Klopp, Gwenola
Waddington, David
Watson, Michael
Yang, Wei
Zerbe, Holm
Title Analysis of the real EADGENE data set: Multivariate approaches and post analysis (Open Access publication)
Journal name Genetics Selection Evolution   Check publisher's open access policy
ISSN 0999-193X
Publication date 2007-12-01
Sub-type Article (original research)
DOI 10.1051/gse:2007030
Open Access Status DOI
Volume 39
Issue 6
Start page 651
End page 668
Total pages 18
Place of publication Paris, France
Publisher EDP Sciences
Language eng
Subject 1001 Agricultural Biotechnology
0104 Statistics
0604 Genetics
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. © INRA.
Keyword Bovine annotation
Bovine microarray
Gene set analysis
Multivariate approaches
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
Institute for Molecular Bioscience - Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 2 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Sat, 29 May 2010, 01:07:56 EST by Mary-Anne Marrington on behalf of Institute for Molecular Bioscience