title: Systematic interpretation of microarray data using experiment annotations.
abstract: BACKGROUND
  Up to now, microarray data are mostly assessed in context with only one or few parameters characterizing the experimental conditions under study. More explicit experiment annotations, however, are highly useful for interpreting microarray data, when available in a statistically accessible format.
  
  
  RESULTS
  We provide means to preprocess these additional data, and to extract relevant traits corresponding to the transcription patterns under study. We found correspondence analysis particularly well-suited for mapping such extracted traits. It visualizes associations both among and between the traits, the hereby annotated experiments, and the genes, revealing how they are all interrelated. Here, we apply our methods to the systematic interpretation of radioactive (single channel) and two-channel data, stemming from model organisms such as yeast and drosophila up to complex human cancer samples. Inclusion of technical parameters allows for identification of artifacts and flaws in experimental design.
  
  
  CONCLUSIONS
  Biological and clinical traits can act as landmarks in transcription space, systematically mapping the variance of large datasets from the predominant changes down toward intricate details.
authors.affiliation: Department of Functional Genome Analysis, German Cancer Research Center, PO 101949, D-69009 Heidelberg, Germany. k.fellenberg@dkfz.de
authors.forename: Kurt
authors.initials: K
authors.lastname: Fellenberg
authors.forename: Christian H
authors.initials: CH
authors.lastname: Busold
authors.forename: Olaf
authors.initials: O
authors.lastname: Witt
authors.forename: Andrea
authors.initials: A
authors.lastname: Bauer
authors.forename: Boris
authors.initials: B
authors.lastname: Beckmann
authors.forename: Nicole C
authors.initials: NC
authors.lastname: Hauser
authors.forename: Marcus
authors.initials: M
authors.lastname: Frohme
authors.forename: Stefan
authors.initials: S
authors.lastname: Winter
authors.forename: Jürgen
authors.initials: J
authors.lastname: Dippon
authors.forename: Jörg D
authors.initials: JD
authors.lastname: Hoheisel
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D005163:Factor Analysis, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data*; D015203:Reproducibility of Results