Abstract (Expand)
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.
Authors: H. Loffler-Wirth, M. Kalcher, H. Binder
Date Published: 1st Oct 2015
Publication Type: Not specified
PubMed ID: 26063839
Citation: Bioinformatics. 2015 Oct 1;31(19):3225-7. doi: 10.1093/bioinformatics/btv342. Epub 2015 Jun 10.