title: oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on bioconductor.
abstract: BACKGROUND
  Comprehensive analysis of genome-wide molecular data challenges bioinformatics methodology in terms of intuitive visualization with single-sample resolution, biomarker selection, functional information mining and highly granular stratification of sample classes. oposSOM combines those functionalities making use of a comprehensive analysis and visualization strategy based on self-organizing maps (SOM) machine learning which we call 'high-dimensional data portraying'. The method was successfully applied in a series of studies using mostly transcriptome data but also data of other OMICs realms.
  
  
  METHODS
  oposSOM is now publicly available as Bioconductor R package.
  
  
  BACKGROUND
  wirth@izbi.uni-leipzig.de
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig 04107, Germany.
authors.forename: Henry
authors.initials: H
authors.lastname: Löffler-Wirth
authors.affiliation: Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig 04107, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Kalcher
authors.affiliation: Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig 04107, Germany.
authors.forename: Hans
authors.initials: H
authors.lastname: Binder
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D003196:Computer Graphics; D015894:Genome, Human; D023281:Genomics / Q000379:methods; D006801:Humans; D016393:Lymphoma, B-Cell / Q000378:metabolism*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
chemical_list: D014408:Biomarkers, Tumor