Colloquy Cycle on Friday, May 24, 2019 - 10:30

High dimensional single-cell data is nowadays collected routinely for multiple applications in biology. Standard tools for the analysis of these data do not scale well with regard to the number of dimensions or the number of cells. To tackle these issues, we have extended and created new dimensionality reduction techniques such as A-tSNE[1] and HSNE[2,3]. We have implemented these in our integrated single-cell analysis framework Cytosplore and created new interaction methods such as CyteGuide[4] and Focus+Context for HSNE[5].

This presentation will give an overview over the Cytosplore Visual Analytics framework and highlight some of its domain applications.

[1]Approximated and User Steerable tSNE for Progressive Visual Analytics, IEEE Transactions on Visualization and Computer Graphics, 2017
[2] Hierarchical Stochastic Neighbor Embedding, Computer Graphics Forum (Proceedings of EuroVis 2016), 2016
[3] Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types, Nature Communications, 2017
[4] CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis, IEEE Transactions on Visualization and Computer Graphics (Proceedings of IEEE InfoVis 2017), 2018
[5] Focus+Context Exploration of Hierarchical Embeddings, Computer Graphics Forum (Proceedings of EuroVis 2019), 2019

Assistant Professor for Visualization at Leiden Computational Biology Center at LUMC
Visiting Researcher at Computer Graphics and Visualization group at TU Delft

In virtual 3D environments, it is easy to lose orientation while navigating or changing the view with zooming and panning operations. In the real world, annotated maps are an established tool to orient oneself in large and unknown environments. The use of annotations and landmarks in traditional maps can also be transferred to virtual environments. But occlusions by three-dimensional structures have to be taken into account as well as performance considerations for an interactive real-time application. Furthermore, annotations should be discreetly integrated into the existing 3D environment and not distract the viewer's attention from more important features. In this paper, we present an implementation of automatic annotations based on open data to improve the spatial orientation in the highly interactive and dynamic decision support system Visdom. We distinguish between line and area labels for object-specific labeling, which facilitates a direct association of the labels with their corresponding objects or regions. The final algorithm provides clearly visible, easily readable and dynamically adapting annotations with continuous levels of detail integrated into an interactive real-time application.