Interactive Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for data analysis. We introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for single-cell analysis, a computational approach that constructs a hierarchy of non-linear similarities, allowing the analysis of millions of cells via different levels of detail up to single-cell resolution within minutes. We integrated HSNE into the Cytosplore +HSNE framework to facilitate interactive exploration and analysis of the hierarchy by a set of corresponding two-dimensional plots with stepwise increase in detail up to the single-cell level. This divide and conquer approach minimizes computation time and, thereby, allows efficient and interactive visualization. We validated the discovery potential of Cytosplore+HSNE by re-analyzing a recent study on gastrointestinal disorders as well as two other publicly available mass cytometry datasets. We found that Cytosplore+HSNE efficiently identifies both abundant and rare cell populations, without resorting to downsampling of the data, including rare cell populations that were missed in a previous analysis due to downsampling. Taken together, Cytosplore +HSNE offers unprecedented possibilities for visual exploration and analysis of millions of cells measured in mass cytometry studies.

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@misc{ bib:2017_ksomics,
    author = {Thomas H{\"o}llt and Vincent van Unen and Nicola Pezzotti and Na Li and Marcel Reinders and Elmar Eisemann and Frits Koning and Anna Vilanova and Boudewijn Lelieveldt},
    title = { Interactive Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types },
    howpublished = { Poster presentation at Poster Presentation, Keystone Symposia on Molecular and Cellular Biology; Single Cell Omics, Stockholm, Swe },
    year = { 2017 },
}