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

['Johannes Waschke', 'Max Planck Institute For Human Cognitive', 'Brain Sciences', 'Leipzig', 'Faculty Of Computer Science', 'Media', 'Leipzig University Of Applied Sciences', 'Mario Hlawitschka', 'Kerim Anlas', 'Embl Barcelona']

Date: 2022-01 

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.

Many of the processes that we study in biology are dynamic or interconnected. We can represent most of them as trajectories, being it connections between neurons in a brain or species in an ecosystem or motion traces of animals, cells or molecules. Modern experiments allow researchers to generate such trajectory data at unprecedented scales: think the parallel tracking of thousands of cells in a developing embryo over hours or days. However, visualising large-scale trajectory data is a challenge: the typical static visualisations result in excessive overplotting and often resemble the infamous hairballs. Simplification and interactivity are crucial strategies to deal with this problem. We present the lightweight tool linus that enables researchers to explore and share their trajectory data in an engaging way in web browsers from almost any device.

Funding: J.W. received funding from the International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity (Leipzig, Germany; https://imprs-neurocom.mpg.de ). K.A. and V.T. acknowledge funding from European Molecular Biology Laboratory (EMBL) Barcelona and Mesoscopic Imaging Facility, EMBL Barcelona for help with imaging. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Introduction

In biology, we often face large-scale trajectory data from dense spatial pathways, such as the brain connectivity obtained from diffusion MRI imaging [1], or tracking data such as cell trajectories [2] or animal trails [3]. Although this type of data is becoming increasingly prominent in biomedical research [4–6], exploring, sharing, and communicating patterns in such data are often cumbersome tasks requiring a set of different software that are often complex to install, learn and use. Recently, new tools have become available for efficiently visualising 3D volumetric data [7–9], and some of those allow the user to overlay tracking data to cross-check the quality of the results or to visualise simple predefined features (such as speed or time). However, given the more general-purpose design of such software, these are not ideal solutions to efficiently and collaboratively explore and share the visualisations.

Tools like CATMAID [10] or Neuroglancer (https://github.com/google/neuroglancer) impressively demonstrated the benefit of in-browser 3D visualisations for collaborative curation and visualisation of neuroimaging data [11]. In contrast to the specialised focus of these tools on volumetric neuroimaging data (e.g. reconstructing and visualising neural morphologies from electron microscopy images), we aimed to build a general-purpose, lightweight, and interactive visualisation of generic trajectory data across all fields of biology that might be challenging to visualise in static images otherwise (from animal tracks or static brain tractography to cellular or molecular motion). Here, interactive, scriptable, and easily shareable visualisation [12] open up novel ways of communicating and discussing experimental results and findings [13]. The analysis of complex trajectory data and the creation and testing of hypotheses could then be done collaboratively. Importantly, since such bioinformatics tools would be right at the interface of computational and life sciences, they need to be accessible and usable for scientists with little or no background in programming. Ideally, the data should be bundled with a guided, interactive presentation in one concise packet that can be passed to a collaborator. To address these challenges, we have developed our tool linus, making it easier to explore 3D trajectory data from any device without a local installation of specialised software. linus creates interactive visualisation packets that can be explored in a web browser while keeping data presentation straightforward and shareable, both offline and online (Fig 1A). In previous work, we explored cell trajectories during zebrafish gastrulation extracted from large-scale fluorescence microscopy datasets [2]. In these experiments, linus allowed us to interactively explore the tracks of around 11.000 cells (starting number) as they moved across the zebrafish embryo throughout 16 hrs. More importantly, it enabled us to share and discuss visualisations with collaborators from different backgrounds and to create figures for the manuscript.

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TIFF original image Download: Fig 1. Browser-based exploration and sharing of trajectory visualisations with linus. (a) Control workflow of linus. (Input data) linus can import tracking data from a variety of formats. (Preprocessing) The Python-converter additionally enriches the imported data with additional features (providing e.g. an edge-bundled version of the data, visual context, or a coordinate system) and prepares the visualisation packet. (Tour setup) The user can open the visualisation in a web browser and create an interactive presentation of the data. (Sharing) These visualisations can be shared via a URL, or a QR code and (Exploring) readily presented and explored across various devices. (b) Overview of the graphical user interface (GUI). The data can be visualised and explored in the browser. Different aspects of the data can be interactively highlighted (zoomed example on the right shows the effect of changing the degree of trajectory bundling). https://doi.org/10.1371/journal.pcbi.1009503.g001

By sharing this tool with the community, we hope to facilitate novel applications of visualising trajectories across all of biology. We have written this manuscript for a broad audience and thus mainly concentrate on describing how to create, use, and share the visualisations in the Results section from a user perspective. The Design and Implementation section and the S1 Text describe the technical details for readers who need to deploy the tool on their data. Finally, we refer readers interested in contributing new functionality or adapting the existing code (maintainers) to the technical documentation at our repository https://gitlab.com/imb-dev/linus.

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[1] Url: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009503

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