The applications listed here are available for use in the Discovery Environment and are documented in: Discovery Environment Manual.

Discovery Environment Applications List

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Comment: Just some minor grammar and added an additional citation

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High-throughput technologies such as whole genome transcriptional profiling have revolutionized molecular biology and provide an incredible amount of data. On the other hand, these techniques pose elementary methodological challenges simply by the huge and ever-increasing amount of data produced: researchers they produce. Researchers need adequate tools to extract the information content of the data in an effective and intelligent way. This includes algorithmic tasks such as data compression and filtering, feature selection, linkage with the to functional context, and proper visualization. EspeciallyIn particular, the this latter task is very important because an intuitive visualization of massive amounts of data clearly promotes quality control, the discovery of their the data's intrinsic structure, functional data mining and finally the generation of hypotheses.

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OposSOM bundles a series of sophisticated analysis methods with intuitive visualization options to study high-dimensional data with the special focus on gene-centered expression data. The algorithm transforms whole genome expression pattern of genes into a SOM coordinate system, which allows intuitive visualization of transcriptional activity of each sample in terms of mosaic portraits. This approach simultaneously searches for features which are differentially expressed and correlated in their profiles in the set of samples studied.

LoefflerLoffler-Wirth H, Kalcher M, Binder H (2015). “oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on Bioconductor.” Bioinformatics.

Wirth H, Loffler M, von Bergen M, Binder H (2011). "Expression cartography of human tissues using self organizing maps" BMC Bioinformatics.

A brief overview of the oposSOM workflow:

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