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.