title: A general strategy to determine the congruence between a hierarchical and a non-hierarchical classification.
abstract: BACKGROUND
  Classification procedures are widely used in phylogenetic inference, the analysis of expression profiles, the study of biological networks, etc. Many algorithms have been proposed to establish the similarity between two different classifications of the same elements. However, methods to determine significant coincidences between hierarchical and non-hierarchical partitions are still poorly developed, in spite of the fact that the search for such coincidences is implicit in many analyses of massive data.
  
  
  RESULTS
  We describe a novel strategy to compare a hierarchical and a dichotomic non-hierarchical classification of elements, in order to find clusters in a hierarchical tree in which elements of a given "flat" partition are overrepresented. The key improvement of our strategy respect to previous methods is using permutation analyses of ranked clusters to determine whether regions of the dendrograms present a significant enrichment. We show that this method is more sensitive than previously developed strategies and how it can be applied to several real cases, including microarray and interactome data. Particularly, we use it to compare a hierarchical representation of the yeast mitochondrial interactome and a catalogue of known mitochondrial protein complexes, demonstrating a high level of congruence between those two classifications. We also discuss extensions of this method to other cases which are conceptually related.
  
  
  CONCLUSIONS
  Our method is highly sensitive and outperforms previously described strategies. A PERL script that implements it is available at http://www.uv.es/~genomica/treetracker.
authors.affiliation: Departamento de Genética, Universidad de Valencia, Burjassot, Spain. marcasan@uv.es
authors.forename: Antonio
authors.initials: A
authors.lastname: Marco
authors.forename: Ignacio
authors.initials: I
authors.lastname: Marín
mesh_terms: D001185:Artificial Intelligence; D002965:Classification / Q000379:methods; D016000:Cluster Analysis; D030562:Databases, Protein; D003663:Decision Trees; D024101:Mitochondrial Proteins / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated / Q000379:methods; D025941:Protein Interaction Mapping / Q000145:classification* / Q000379:methods; D015203:Reproducibility of Results; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism; D020539:Sequence Analysis, Protein; D014584:User-Computer Interface
chemical_list: D024101:Mitochondrial Proteins; D029701:Saccharomyces cerevisiae Proteins