title: Network clustering: probing biological heterogeneity by sparse graphical models.
abstract: BACKGROUND
  Networks and pathways are important in describing the collective biological function of molecular players such as genes or proteins. In many areas of biology, for example in cancer studies, available data may harbour undiscovered subtypes which differ in terms of network phenotype. That is, samples may be heterogeneous with respect to underlying molecular networks. This motivates a need for unsupervised methods capable of discovering such subtypes and elucidating the corresponding network structures.
  
  
  RESULTS
  We exploit recent results in sparse graphical model learning to put forward a 'network clustering' approach in which data are partitioned into subsets that show evidence of underlying, subset-level network structure. This allows us to simultaneously learn subset-specific networks and corresponding subset membership under challenging small-sample conditions. We illustrate this approach on synthetic and proteomic data.
  
  
  BACKGROUND
  go.warwick.ac.uk/sachmukherjee/networkclustering.
authors.affiliation: Department of Statistics, University of Warwick, Coventry, UK. s.n.mukherjee@warwick.ac.uk
authors.forename: Sach
authors.initials: S
authors.lastname: Mukherjee
authors.forename: Steven M
authors.initials: SM
authors.lastname: Hill
mesh_terms: D045744:Cell Line, Tumor; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D003196:Computer Graphics; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D015398:Signal Transduction