title: Vector analysis as a fast and easy method to compare gene expression responses between different experimental backgrounds.
abstract: BACKGROUND
  Gene expression studies increasingly compare expression responses between different experimental backgrounds (genetic, physiological, or phylogenetic). By focusing on dynamic responses rather than a direct comparison of static expression levels, this type of study allows a finer dissection of primary and secondary regulatory effects in the various backgrounds. Usually, results of such experiments are presented in the form of Venn diagrams, which are intuitive and visually appealing, but lack a statistical foundation.
  
  
  RESULTS
  Here we introduce Vector Analysis (VA) as a simple, yet principled, approach to comparing expression responses in different experimental backgrounds. VA enables the automatic assignment of genes to response prototypes and provides statistical significance estimates to eliminate spurious response patterns. The application of VA to a real dataset, comparing nutrient starvation responses in wild type and mutant Arabidopsis plants, reveals that consistent patterns of expression behavior are present in the data and are reliably detected by the algorithm.
  
  
  CONCLUSIONS
  Vector analysis is a flexible, easy-to-use technique to compare gene expression patterns in different experimental backgrounds. It compares favorably with the classical Venn diagram approach and can be implemented manually using spreadsheets, such as Excel, or automatically by using the supplied software.
authors: Molecular Plant Science Group, Institute of Biomedical and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK. R.Breitling@bio.gla.ac.uk
authors: Rainer
authors: R
authors: Breitling
authors: Patrick
authors: P
authors: Armengaud
authors: Anna
authors: A
authors: Amtmann
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D003626:Data Display; D020869:Gene Expression Profiling / Q000379:methods*; D018506:Gene Expression Regulation, Plant; D015233:Models, Statistical