Visualizing Uncertainty for Comparing Genomic Pediatric Brain Cancer Data

State of the art genomic methods produce an abundance of data which ultimately increases the quantity within and of data repositories. Thereby, open data is of much importance to boost scientific studies for combating disease. More and more often, there are several data sources available, offering diverse sample data. Particularly, interpretation of genomic data remains a challenge due to data size and differing quality. We present an approach for visualizing uncertainty of heterogeneous data sources on mutation rates in genomic data for pediatric cancer analysis. Visualization as method for knowledge discovery will be of great importance in order to put inhomogeneity of sample data into perspective.