Browsing by Author "Pezzotti, Nicola"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Cytosplore: Interactive Visual Single-Cell Profiling of the Immune System
    (The Eurographics Association, 2019) Höllt, Thomas; Pezzotti, Nicola; van Unen, Vincent; Li, Na; Koning, Frits; Eisemann, Elmar; Lelieveldt, Boudewijn P. F.; Vilanova, Anna; Bruckner, Stefan and Oeltze-Jafra, Steffen
    Recent advances in single-cell acquisition technology have led to a shift towards single-cell analysis in many fields of biology. In immunology, detailed knowledge of the cellular composition is of interest, as it can be the cause of deregulated immune responses, which cause diseases. Similarly, vaccination is based on triggering proper immune responses; however, many vaccines are ineffective or only work properly in a subset of those who are vaccinated. Identifying differences in the cellular composition of the immune system in such cases can lead to more precise treatment. Cytosplore is an integrated, interactive visual analysis framework for the exploration of large single-cell datasets. We have developed Cytosplore in close collaboration with immunology researchers and several partners use the software in their daily workflow. Cytosplore enables efficient data analysis and has led to several discoveries alongside high-impact publications.
  • Thumbnail Image
    Item
    Focus+Context Exploration of Hierarchical Embeddings
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Höllt, Thomas; Vilanova, Anna; Pezzotti, Nicola; Lelieveldt, Boudewijn P. F.; Hauser, Helwig; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Hierarchical embeddings, such as HSNE, address critical visual and computational scalability issues of traditional techniques for dimensionality reduction. The improved scalability comes at the cost of the need for increased user interaction for exploration. In this paper, we provide a solution for the interactive visual Focus+Context exploration of such embeddings. We explain how to integrate embedding parts from different levels of detail, corresponding to focus and context groups, in a joint visualization. We devise an according interaction model that relates typical semantic operations on a Focus+Context visualization with the according changes in the level-of-detail-hierarchy of the embedding, including also a mode for comparative Focus+Context exploration and extend HSNE to incorporate the presented interaction model. In order to demonstrate the effectiveness of our approach, we present a use case based on the visual exploration of multi-dimensional images.