Aarhus University Seal / Aarhus Universitets segl

Peter Sørensen

Analysis of the real EADGENE data set: Comparison of methods and guidelines for data normalisation and selection of differentially expressed genes

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Dokumenter

  • GSE 39

    Forlagets udgivne version, 283 KB, PDF-dokument

DOI

  • Florence Jaffrézic, INRA, UR337, Frankrig
  • Dirk-Jan de Koning, Roslin Institute, Storbritannien
  • Paul J Boettcher, PTP, Jamaica
  • Agnès Bonnet, INRA, UMMR444, Frankrig
  • Bart Buitenhuis
  • Rodrigue Closset, University of Liege, Belgien
  • Sébastien Déjean, Université Paul Sabatier, Frankrig
  • Céline Delmas, INRA UMR444, Frankrig
  • Johannes C Detilleux, University of Liege, Belgien
  • Peter Dove, University of Ljubljana, Slovenien
  • Mylène Duval, INRA, UR631, Frankrig
  • Jean-Louis Foulley, INRA, UR337, Frankrig
  • Jakob Hedegaard
  • Henrik Hornshøj, Danmark
  • Ina B Hulsegge, Animal Breeding and Genomics Centre, ABGC, Holland
  • Luc Janss
  • Kirsty Jensen, Roslin Institute, Storbritannien
  • Li Jiang, Danmark
  • Miha Lavric, University of Ljubljana, Slovenien
  • Kim-Anh Lê Cao, INRA, UR631, Frankrig
  • Mogens Sandø Lund, Danmark
  • Roberto Malinverni, PTP, Italien
  • Guillemette Marot, INRA, UR337, Frankrig
  • Haisheng Nie, University and Research Centre Wageningen, Holland
  • Wolfram Petzl, Ludwig-Maximilians University, Tyskland
  • Marco H Pool, Animal Breeding and Genomics Centre, ABGC, Holland
  • Chrisstèle Robert-Granié, INRA, UR631, Frankrig
  • Magali San Christobal, INRA, UMR444, Frankrig
  • Evert M van Schothorst, Wageningen University and Research Centre, Holland
  • Hans-Joachim Schuberth, University of Veterinary Medicine, Hannover, Tyskland
  • Peter Sørensen
  • Alessandra Stella, PTP, Italien
  • Gwwenola Tosser-Klopp, INRA, UMR444, Frankrig
  • Dave Waddington, Roslin Institute, Storbritannien
  • Michael Watson, Institute for Animal Health, Compton, Storbritannien
  • Wei Yang, Research Institute for the Biology of Farm Anmals, Dummerstorf, Tyskland
  • Holme Zerbe, Ludwig-Maximilians-University, Munich, Tyskland
  • Hans-Martin Seyfert, Research Institute for the Biology of Farm Animals, Dummerstoft, Tyskland
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
OriginalsprogEngelsk
TidsskriftGenetics Selection Evolution
Nummer39
Sider (fra-til)633-650
Antal sider13
ISSN0999-193X
DOI
StatusUdgivet - 2007

Se relationer på Aarhus Universitet Citationsformater

Download-statistik

Ingen data tilgængelig

ID: 508662