oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on bioconductor

Bioinformatics. 2015 Oct 1;31(19):3225-7. doi: 10.1093/bioinformatics/btv342. Epub 2015 Jun 10.


Motivation: 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.

Availability and implementation: oposSOM is now publicly available as Bioconductor R package.

Contact: wirth@izbi.uni-leipzig.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Biomarkers, Tumor / metabolism*
  • Computer Graphics*
  • Genome, Human*
  • Genomics / methods
  • Humans
  • Lymphoma, B-Cell / metabolism*
  • Sequence Analysis, DNA / methods*
  • Software*


  • Biomarkers, Tumor