Print Email Facebook Twitter Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types Title Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types Author van Unen, Vincent (Leiden University Medical Center) Höllt, T. (TU Delft Computer Graphics and Visualisation; Leiden University Medical Center) Pezzotti, N. (TU Delft Computer Graphics and Visualisation) Li, Na (Leiden University Medical Center) Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics) Eisemann, E. (TU Delft Computer Graphics and Visualisation) Koning, Frits (Leiden University Medical Center) Vilanova Bartroli, A. (TU Delft Computer Graphics and Visualisation) Lelieveldt, B.P.F. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Date 2017 Abstract 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 the data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analyzed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry data sets. 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 apply HSNE to a study on gastrointestinal disorders and three other available mass cytometry data sets. We find that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional data sets. Subject Computational biology and bioinformaticsFlow cytometryImmunology To reference this document use: http://resolver.tudelft.nl/uuid:7d5657e0-a8d6-4e2c-9c67-43f6949cc529 DOI https://doi.org/10.1038/s41467-017-01689-9 ISSN 2041-1723 Source Nature Communications, 8, 1-10 Part of collection Institutional Repository Document type journal article Rights © 2017 Vincent van Unen, T. Höllt, N. Pezzotti, Na Li, M.J.T. Reinders, E. Eisemann, Frits Koning, A. Vilanova Bartroli, B.P.F. Lelieveldt Files PDF 36287432.pdf 4.93 MB Close viewer /islandora/object/uuid:7d5657e0-a8d6-4e2c-9c67-43f6949cc529/datastream/OBJ/view