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

Vincent van Unen, Thomas Höllt, Nicola Pezzotti, Na Li, Marcel J. T. Reinders, Elmar Eisemann, Frits Koning, Anna Vilanova, Boudewijn P. F. Lelieveldt
doi: https://doi.org/10.1101/169888
Vincent van Unen
1Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands;
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  • For correspondence: V.van_unen@lumc.nl B.P.F.Lelieveldt@lumc.nl
Thomas Höllt
2Computer Graphics and Visualization Group, Mekelweg 4, 2628 CD, Delft University of Technology, Delft, the Netherlands;
3Computational Biology Center, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands;
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Nicola Pezzotti
2Computer Graphics and Visualization Group, Mekelweg 4, 2628 CD, Delft University of Technology, Delft, the Netherlands;
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Na Li
1Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands;
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Marcel J. T. Reinders
4Pattern Recognition and Bioinformatics Group, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, the Netherland
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Elmar Eisemann
2Computer Graphics and Visualization Group, Mekelweg 4, 2628 CD, Delft University of Technology, Delft, the Netherlands;
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Frits Koning
1Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands;
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Anna Vilanova
2Computer Graphics and Visualization Group, Mekelweg 4, 2628 CD, Delft University of Technology, Delft, the Netherlands;
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Boudewijn P. F. Lelieveldt
4Pattern Recognition and Bioinformatics Group, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, the Netherland
5Division of Image Processing, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands;
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  • For correspondence: V.van_unen@lumc.nl B.P.F.Lelieveldt@lumc.nl
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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 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 analysed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry datasets. 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 a study on gastrointestinal disorders and three other available mass cytometry datasets. We found 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 datasets.

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Posted September 13, 2017.
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Interactive Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types
Vincent van Unen, Thomas Höllt, Nicola Pezzotti, Na Li, Marcel J. T. Reinders, Elmar Eisemann, Frits Koning, Anna Vilanova, Boudewijn P. F. Lelieveldt
bioRxiv 169888; doi: https://doi.org/10.1101/169888
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Interactive Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types
Vincent van Unen, Thomas Höllt, Nicola Pezzotti, Na Li, Marcel J. T. Reinders, Elmar Eisemann, Frits Koning, Anna Vilanova, Boudewijn P. F. Lelieveldt
bioRxiv 169888; doi: https://doi.org/10.1101/169888

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