Cytosplore is an interactive visual analysis system for understanding how the immune system works. The goal of the analysis framework is to provide a clear picture of the immune systems cellular composition and the cells’ corresponding properties and functionality. Cytosplore is targeted at the analysis of mass cytometry (CyTOF) data. Mass cytometry is a novel technique to determine the properties of single-cells with unprecedented detail. This amount of detail allows for much finer differentiation but also comes at the cost of more complex analysis. Cytosplore incorporates state-of-the art clustering and dimensionality reduction techniques, such as Approximated-tSNE (up to 100x faster than standard t-SNE without loss in precision) and a custom implementation of the SPADE clustering algorithm. Cytosplore implements progressive visual analytics and visualization techniques to provide a highly engaging user experience through direct feedback and linked views. Try Cytosplore yourself and sign up for our mailing list for latest news.
TU Delft / LUMC
Lead Developer/Project Liaison
Join the team! We have open projects within the Cytosplore project including a project on classification of cellular expression data through dimensionality reduction. If you are interested in working with us on this or other projects involving biological visualization for your master thesis project at the TU Delft or LUMC, please contact Thomas Höllt or Anna Vilanova to discuss the possibilities.
Publications & Media
The Delta published an article covering our latest results including HSNE in November 2017: New tool identifies a few cells among millions
The Cytosplore team was interviewed for an article in Medical Delta in December 2016: Exploring new dimensions of the immune system.
Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types. Nature Communications, 2017.
Abstract: In this work, we present Cytosplore+HSNE. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We applied HSNE to several available mass cytometry datasets. We found that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling in a fraction of the time.
Cytosplore: Interactive Immune Cell Phenotyping for Large Single-Cell Datasets. Computer Graphics Forum, 2016.
Abstract: In this work, we present Cytosplore, implementing an interactive workflow to analyze mass cytometry data in an integrated system, providing multiple linked views, showing different levels of detail and enabling the rapid definition of known and unknown cell types, based on mass cytometry (CyTOF). Cytosplore handles millions of cells, each represented as a high-dimensional data point, facilitates hypothesis generation and confirmation, and provides a significant speed up of the current workflow.
Approximated and User Steerable tSNE for Progressive Visual Analytics. Transactions on Computer Graphics and Visualization, 2017.
Abstract: We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets.
Mass Cytometry of the Human Mucosal Immune System Identifies Tissue- and Disease-Associated Immune Subsets. Immunity, 2016.
Abstract: Inflammatory intestinal diseases are characterized by abnormal immune responses and affect distinct locations of the gastrointestinal tract. Although the role of several immune subsets in driving intestinal pathology has been studied, a system-wide approach that simultaneously interrogates all major lineages on a single-cell basis is lacking. We used high-dimensional mass cytometry to generate a system-wide view of the human mucosal immune system in health and disease ...
Disclaimer: Cytosplore is a research project between TU Delft and Leiden University Medical Center. It is not a commercial software product and not licensed for clinical use. We do our best to provide support in our free time but cannot guarantee it.
If you have any suggestions, problems, want to share your success stories or papers published using Cytosplore, we would love to get feedback! Please do not hesitate to get in touch.
Cytosplore is free for academic and non-commercial use, however, if you use Cytosplore within the scope of a scientific article you must cite the original publications:
T. Höllt, N. Pezzotti, V. van Unen, F. Koning, E. Eisemann, B. Lelieveldt, and A. Vilanova. Cytosplore: Interactive Immune Cell Phenotyping for Large Single-Cell Datasets. Computer Graphics Forum (Proceedings of EuroVis), 35(3): pp. 171—180, 2016.
V. van Unen, T, Höllt, N. Pezzotti, N. Li, M. Reinders, E. Eisemann, A. Vilanova, F. Koning, and B. Lelieveldt. Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types. Nature Communications, 2017