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Albert Johannes Buitenhuis

Analysis of the real EADGENE data set: Multivariate approaches and post analysis

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

Dokumenter

  • GSE 39

    Forlagets udgivne version, 560 KB, PDF-dokument

DOI

  • Peter Sørensen
  • Agnès Bonnet, INRA, UMR 444, Frankrig
  • Bart Buitenhuis
  • Rodrigue Closset, University of Liege, Belgien
  • Sébastien Déjean, Université Paul Sabatier, Frankrig
  • Céline Delmas, INRA, Frankrig
  • Mylène Duval, INRA, Frankrig
  • Gwenola Tosser-Klopp, INRA, UMR 444, Frankrig
  • Jakob Hedegaard
  • Henrik Hornshøj, Danmark
  • Ina B Hulsegge, Animal Breeding and Genomics Centre, ABGC, Holland
  • Florence Jaffrézic, INRA, UR337, Frankrig
  • Kirsty Jensen, Roslin Institute, Storbritannien
  • Li Jiang, Danmark
  • Dirk-Jan de Koning, Roslin Institute, Storbritannien
  • Kim-Anh Lê Cao, INRA, Frankrig
  • Haisheng Nie, Wageningen University and Research Centre, Holland
  • Wolfram Petzl, Ludwig-Maximilians University, USA
  • Marco H Pool, Animal Breeding and Genomics Centre, ABGC, Holland
  • Christéle Robert-Granié, INRA, Frankrig
  • Magali San Cristobal, INRA, UMR 444, Frankrig
  • Evert M van Schothorst, Wageningen University and Research Centre, Holland
  • Hans-Joachim Schuberth, University of Veterinary Medicine, Hannover, Tyskland
  • Hans-Martin Seyfert, Research Institute for the Biology of Farm Animals, Dummerstorf, Tyskland
  • Dave Waddington, Roslin Instiute, Storbritannien
  • Michael Watson, Institute for Animal Health, Compton, Storbritannien
  • Wei Yang, Research Institute for the Biology of Farm Animals, Dummerstorf, Tyskland
  • Holm Zerbe, Ludwig-Maximilians University, Tyskland
  • Mogens Sandø Lund
  • Institut for Genetik og Bioteknologi
  • Biostatistik
  • Molekylær Genetik og Systembiologi
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
OriginalsprogEngelsk
TidsskriftGenetics Selection Evolution
Nummer39
Sider (fra-til)651-668
Antal sider13
ISSN0999-193X
DOI
StatusUdgivet - 2007

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