linus: Conveniently explore, share, and present large-scale biological 
trajectory data in a web browser

Waschke, Johannes; Hlawitschka, Mario; Anlas, Kerim; Trivedi, Vikas; Roeder, Ingo; Huisken, Jan; Scherf, Nico

doi:10.1371/journal.pcbi.1009503

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linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser

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Waschke, Johannes
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Faculty of Computer Science and Media, University of Applied Sciences, Leipzig, Germany;

/persons/resource/persons201756

Scherf, Nico
Institute for Medical Informatics and Biometry, University Hospital Carl Gustav Carus, Dresden, Germany;
Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI, Leipzig, Germany;
Method and Development Group Neural Data Science and Statistical Computing, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Waschke_2021.pdf
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引用

Waschke, J., Hlawitschka, M., Anlas, K., Trivedi, V., Roeder, I., Huisken, J., & Scherf, N. (2021). linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser. PLoS Computational Biology, 17(11):. doi:10.1371/journal.pcbi.1009503.

引用: https://hdl.handle.net/21.11116/0000-0009-99E9-4

要旨

In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data can be a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise packet that makes it straightforward to create and test hypotheses collaboratively. To address these challenges, we have developed a tool, linus, which makes the process of exploring and sharing 3D trajectories as easy as browsing a website. We provide a python script that reads trajectory data, enriches them with additional features such as edge bundling or custom axes, and generates an interactive web-based visualisation that can be shared online. linus facilitates the collaborative discovery of patterns in complex trajectory data.