title: Comparing functional annotation analyses with Catmap.
abstract: BACKGROUND
  Ranked gene lists from microarray experiments are usually analysed by assigning significance to predefined gene categories, e.g., based on functional annotations. Tools performing such analyses are often restricted to a category score based on a cutoff in the ranked list and a significance calculation based on random gene permutations as null hypothesis.
  
  
  RESULTS
  We analysed three publicly available data sets, in each of which samples were divided in two classes and genes ranked according to their correlation to class labels. We developed a program, Catmap (available for download at http://bioinfo.thep.lu.se/Catmap), to compare different scores and null hypotheses in gene category analysis, using Gene Ontology annotations for category definition. When a cutoff-based score was used, results depended strongly on the choice of cutoff, introducing an arbitrariness in the analysis. Comparing results using random gene permutations and random sample permutations, respectively, we found that the assigned significance of a category depended strongly on the choice of null hypothesis. Compared to sample label permutations, gene permutations gave much smaller p-values for large categories with many coexpressed genes.
  
  
  CONCLUSIONS
  In gene category analyses of ranked gene lists, a cutoff independent score is preferable. The choice of null hypothesis is very important; random gene permutations does not work well as an approximation to sample label permutations.
authors.affiliation: Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden. thomas@thep.lu.se <thomas@thep.lu.se>
authors.forename: Thomas
authors.initials: T
authors.lastname: Breslin
authors.forename: Patrik
authors.initials: P
authors.lastname: Edén
authors.forename: Morten
authors.initials: M
authors.lastname: Krogh
mesh_terms: D002965:Classification / Q000379:methods; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D005796:Genes / Q000502:physiology*; D012984:Software