The VRNetzer platform enables interactive network analysis in Virtual Reality

Sebastian Pirch, Felix Müller, Eugenia Iofinova, Julia Pazmandi, Christiane V. R. Hütter, Martin Chiettini, Celine Sin, Kaan Boztug, Iana Podkosova, Hannes Kaufmann and Jörg Menche ()
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Sebastian Pirch: CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences
Felix Müller: CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences
Eugenia Iofinova: CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences
Julia Pazmandi: CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences
Christiane V. R. Hütter: CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences
Martin Chiettini: CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences
Celine Sin: CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences
Kaan Boztug: CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences
Iana Podkosova: Institute of Visual Computing and Human-Centered Technology, TU Wien
Hannes Kaufmann: Institute of Visual Computing and Human-Centered Technology, TU Wien
Jörg Menche: CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: Abstract Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting in proverbial ‘hairballs’. Here, we introduce a Virtual Reality (VR) platform that overcomes these limitations by facilitating the thorough visual, and interactive, exploration of large networks. Our platform allows maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features. As a proof of concept, we show how our platform can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development. Our platform represents a general purpose, VR-based data exploration platform for large and diverse data types by providing an interface that facilitates the interaction between human intuition and state-of-the-art analysis methods.

Date: 2021
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DOI: 10.1038/s41467-021-22570-w

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