This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data.
We aim at discovering mechanisms of biological dysfunction in complex diseases by pursuing a systems view that includes molecular, cellular, tissue, organism and population level aspects. Hereby we focus on the analysis and functional mining of large and heterogeneous data which includes different kinds of high throughput ‘OMICs’, phenotypic and clinical data.
In particular, we develop and implement bioinformatical algorithms suited for high-dimensional data. A central approach are self-organizing maps (SOMs), which are utilized to reduce the feature and sample space dimensions while preserving the information richness contained, and which we complemented with algorithms for preprocessing, data integration, feature selection, function mining and visualization.
Scientific Speaker Institution