A common approach in data-driven knowledge discovery is to prioritize a collection of items, such as genes, cell lines, and tissue samples, based on a rich set of experimental data and metadata. Applications include, for instance, selecting the most appropriate cell line for an experiment or identifying genes that could serve as potential drug targets or biomarkers. This can be challenging due to the heterogeneity and size of the data as well as the fact that multiple attributes need to be considered in combination. Advanced visual exploration tools – going beyond static spreadsheet tools such as Microsoft Excel – are needed to aid this prioritization process. To address this task, we developed Ordino, an open-source, web-based visual analysis tool for flexible ranking, filtering, and exploring of cancer genomics data.
Ordino: visual analysis tool for ranking and exploring genes, cell lines, and tissue samples
bioRxiv preprint, doi:10.1101/277848, 2018.
We thank Christian Lehner for contributions to the implementation of the tool as well as Daniel Gerlach, Markus Bauer, and Anita Steiner for their contributions to data preparation and data handling. This work was supported by the Austrian Science Fund (P27975-NBL), the State of Upper Austria (FFG 851460), and Boehringer Ingelheim RCV.