title: Reordering hierarchical tree based on bilateral symmetric distance.
abstract: BACKGROUND
  In microarray data analysis, hierarchical clustering (HC) is often used to group samples or genes according to their gene expression profiles to study their associations. In a typical HC, nested clustering structures can be quickly identified in a tree. The relationship between objects is lost, however, because clusters rather than individual objects are compared. This results in a tree that is hard to interpret.
  
  
  RESULTS
  This study proposes an ordering method, HC-SYM, which minimizes bilateral symmetric distance of two adjacent clusters in a tree so that similar objects in the clusters are located in the cluster boundaries. The performance of HC-SYM was evaluated by both supervised and unsupervised approaches and compared favourably with other ordering methods.
  
  
  CONCLUSIONS
  The intuitive relationship between objects and flexibility of the HC-SYM method can be very helpful in the exploratory analysis of not only microarray data but also similar high-dimensional data.
authors.affiliation: Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, United States of America.
authors.forename: Minho
authors.initials: M
authors.lastname: Chae
authors.forename: James J
authors.initials: JJ
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics