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The power of network visualizations
Abstract
Virtually all processes in health and disease rely on the careful orchestration of a large number of diverse individual components ranging from molecules to cells and entire organs. Networks provide a powerful framework for describing and understanding these complex systems in a holistic fashion [1]. In addition to a plethora of mathematical and computational tools, networks also offer a uniquely intuitive visual interface to big data. However, a critical roadblock for meaningful network visualizations, in particular for larger, more complex networks, is the lack of layouts that allow for a straightforward interpretation of observed visual patterns. Indeed, standard layout algorithms are prone to generating proverbial ‘hairball’ visualizations that often obscure network structure, rather than elucidate it. As a result, it is often not possible to visually recognize connection patterns that can be analytically proven to be present in a particular network, or, conversely, to trust visual patterns to truly reflect an intrinsic structural feature of a network. In my presentation I will first introduce our recent work on network cartographs for creating interpretable network layouts [2]. Network cartographs use dimensionality reduction techniques to map network information directly into node positions. Any network information can be encoded and visualized in this fashion, including internal information, such as the structural importance of a particular node within the network, but also external node annotations, such as the similarity of nodes with respect to a given functional characteristic. The flexibility of our framework allows users to custom-tailor network visualizations for a specific application. For example, we designed a 3D interactome layout specifically for inspecting the biological functions of candidate genes that are suspected to cause rare diseases [3]. Finally, I will show how network layouts can be incorporated into our recently developed Virtual Reality (VR) network exploration platform VRNetzer [4]. The VRNetzer 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. Its design allows for 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.
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References
Caldera Michael, Buphamalai Pisanu, Müller Felix, Menche Jörg. Interactome-based approaches to human disease. Current Opinion in Systems Biology. Vol. 3:88–94. 2017. Elsevier BV. [Cross Ref]
Hütter Christiane V. R., Sin Celine, Müller Felix, Menche Jörg. Network cartographs for interpretable visualizations. Nature Computational Science. Vol. 2(2):84–89. 2022. Springer Science and Business Media LLC. [Cross Ref]
Buphamalai Pisanu, Kokotovic Tomislav, Nagy Vanja, Menche Jörg. Network analysis reveals rare disease signatures across multiple levels of biological organization. Nature Communications. Vol. 12(1)2021. Springer Science and Business Media LLC. [Cross Ref]
Pirch Sebastian, Müller Felix, Iofinova Eugenia, Pazmandi Julia, Hütter Christiane V. R., Chiettini Martin, Sin Celine, Boztug Kaan, Podkosova Iana, Kaufmann Hannes, Menche Jörg. The VRNetzer platform enables interactive network analysis in Virtual Reality. Nature Communications. Vol. 12(1)2021. Springer Science and Business Media LLC. [Cross Ref]