title: Dynamically weighted clustering with noise set.
abstract: BACKGROUND
  Various clustering methods have been applied to microarray gene expression data for identifying genes with similar expression profiles. As the biological annotation data accumulated, more and more genes have been organized into functional categories. Functionally related genes may be regulated by common cellular signals, thus likely to be co-expressed. Consequently, utilizing the rapidly increasing functional annotation resources such as Gene Ontology (GO) to improve the performance of clustering methods is of great interest. On the opposite side of clustering, there are genes that have distinct expression profiles and do not co-express with other genes. Identification of these scattered genes could enhance the performance of clustering methods.
  
  
  RESULTS
  We developed a new clustering algorithm, Dynamically Weighted Clustering with Noise set (DWCN), which makes use of gene annotation information and allows for a set of scattered genes, the noise set, to be left out of the main clusters. We tested the DWCN method and contrasted its results with those obtained using several common clustering techniques on a simulated dataset as well as on two public datasets: the Stanford yeast cell-cycle gene expression data, and a gene expression dataset for a group of genetically different yeast segregants.
  
  
  CONCLUSIONS
  Our method produces clusters with more consistent functional annotations and more coherent expression patterns than existing clustering techniques.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics at University of California, Los Angeles, CA 90095, USA.
authors.forename: Yijing
authors.initials: Y
authors.lastname: Shen
authors.forename: Wei
authors.initials: W
authors.lastname: Sun
authors.forename: Ker-Chau
authors.initials: KC
authors.lastname: Li
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D015966:Gene Expression Regulation, Fungal; D005800:Genes, Fungal; D012441:Saccharomyces cerevisiae / Q000235:genetics