Experimental methods for quantifying gene expression have improved enormously in recent years. Today, the genome of every cell can be sequenced with unprecedented resolution and accuracy. However, the resulting data sets set new standards in terms of complexity, dimensionality and quantity, which require new methods for interpreting the underlying biology. This master's thesis therefore deals with interactive concepts for the visualization of high-dimensional RNA sequencing data, which, in combination with proven bioinformatics methods, should enable explorative data mining.

This project was developed in collaboration with the Helmholtz Institute for RNA-based Infection Research HIRI. The results were recently published in the journal NAR Genomics and Bioinformatics.