title: Revealing functionally coherent subsets using a spectral clustering and an information integration approach.
abstract: BACKGROUND
  Contemporary high-throughput analyses often produce lengthy lists of genes or proteins. It is desirable to divide the genes into functionally coherent subsets for further investigation, by integrating heterogeneous information regarding the genes. Here we report a principled approach for managing and integrating multiple data sources within the framework of graph-spectrum analysis in order to identify coherent gene subsets.
  
  
  RESULTS
  We investigated several approaches to integrate information derived from different sources that reflect distinct aspects of gene functional relationships including: functional annotations of genes in the form of the Gene Ontology, co-mentioning of genes in the literature, and shared transcription factor binding sites among genes. Given a list of genes, we construct a graph containing the genes in each information space; then the graphs were kernel transformed so they could be integrated; finally functionally coherent subsets were identified using a spectral clustering algorithm. In a series of simulation experiments, known functionally coherent gene sets were mixed and recovered using our approach.
  
  
  CONCLUSIONS
  The results indicate that spectral clustering approaches are capable of recovering coherent gene modules even under noisy conditions, and that information integration serves to further enhance this capability. When applied to a real-world data set, our methods revealed biologically sensible modules, and highlighted the importance of information integration. The implementation of the statistical model is provided under the GNU general public license, as an installable Python module, at: http://code.google.com/p/spectralmix.
authors.affiliation: Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA. adam.richards@stat.duke.edu
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Richards
authors.forename: John H
authors.initials: JH
authors.lastname: Schwacke
authors.forename: Bärbel
authors.initials: B
authors.lastname: Rohrer
authors.forename: L Ashley
authors.initials: LA
authors.lastname: Cowart
authors.forename: Xinghua
authors.initials: X
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D000818:Animals; D035843:Biomedical Research; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D015870:Gene Expression; D053263:Gene Regulatory Networks; D005800:Genes, Fungal; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D051379:Mice; D046228:Microarray Analysis; D015233:Models, Statistical; D012441:Saccharomyces cerevisiae / Q000235:genetics; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D014157:Transcription Factors