Analysis of the real EADGENE data set: Comparison of methods and guidelines for data normalisation and selection of differentially expressed genes (Open Access publication)

Jaffrézic, Florence, de Koning, Dirk-Jan, Boettcher, Paul J., Bonnet, Agnès, Buitenhuis, Bart, Closset, Rodrigue, Déjean, Sébastien, Delmas, Céline, Detilleux, Johanne C., Dovč, Peter, Duval, Mylène, Foulley, Jean-Louis, Hedegaard, Jakob, Hornshøj, Henrik, Hulsegge, Ina, Janss, Luc, Jensen, Kirsty, Jiang, Li, Lavrič, Miha, Lê Cao, Kim-Ahn, Lund, Mogens Sandø, Malinverni, Roberto, Marot, Guillemette, Nie, Haisheng, Petzl, Wolfram, Pool, Marco H., Robert-Granié, Christèle, San Cristobal, Magali, van Schothorst, Evert M., Schuberth, Hans-Joachim, Sørensen, Peter, Stella, Alessandra, Tosser-Klopp, Gwenola, Waddington, David, Watson, Michael, Yang, Wei, Zerbe, Holm and Seyfert, Hans-Martin (2007) Analysis of the real EADGENE data set: Comparison of methods and guidelines for data normalisation and selection of differentially expressed genes (Open Access publication). Genetics Selection Evolution, 39 6: 633-650. doi:10.1051/gse:2007029


Author Jaffrézic, Florence
de Koning, Dirk-Jan
Boettcher, Paul J.
Bonnet, Agnès
Buitenhuis, Bart
Closset, Rodrigue
Déjean, Sébastien
Delmas, Céline
Detilleux, Johanne C.
Dovč, Peter
Duval, Mylène
Foulley, Jean-Louis
Hedegaard, Jakob
Hornshøj, Henrik
Hulsegge, Ina
Janss, Luc
Jensen, Kirsty
Jiang, Li
Lavrič, Miha
Lê Cao, Kim-Ahn
Lund, Mogens Sandø
Malinverni, Roberto
Marot, Guillemette
Nie, Haisheng
Petzl, Wolfram
Pool, Marco H.
Robert-Granié, Christèle
San Cristobal, Magali
van Schothorst, Evert M.
Schuberth, Hans-Joachim
Sørensen, Peter
Stella, Alessandra
Tosser-Klopp, Gwenola
Waddington, David
Watson, Michael
Yang, Wei
Zerbe, Holm
Seyfert, Hans-Martin
Title Analysis of the real EADGENE data set: Comparison of methods and guidelines for data normalisation and selection of differentially expressed genes (Open Access publication)
Journal name Genetics Selection Evolution   Check publisher's open access policy
ISSN 0999-193X
1297-9686
Publication date 2007-12-01
Sub-type Article (original research)
DOI 10.1051/gse:2007029
Open Access Status DOI
Volume 39
Issue 6
Start page 633
End page 650
Total pages 18
Place of publication Paris, France
Publisher EDP Sciences
Language eng
Subject 1001 Agricultural Biotechnology
0104 Statistics
0604 Genetics
Abstract A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies. © INRA.
Keyword Differentially expressed genes
Mastitis resistance
Microarray data
Normalisation
Quality control
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
 
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Created: Sat, 29 May 2010, 00:59:57 EST by Mary-Anne Marrington on behalf of Institute for Molecular Bioscience