Interactive Visual Analysis of Mass Cytometry Data by ... · by Hierarchical Stochastic Neighbor...

Events / Area of Influence (AoI)

Hierarchy Construction (high-dimensional space)

Neighborhood Graph Embeddings and Clustering

Hierarchy Exploration (two-dimensional space)

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Interactive Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding

T Höllt1,5, V van Unen3, N Pezzotti1, N Li3, M Reinders2, E Eisemann1, F Koning3, A Vilanova1, B Lelieveldt2,4

1Computer Graphics and Visualization Group, 2Pattern Recognition and Bioinformatics Group, TU Delft,3Dept. of Immunohematology and Blood Transfusion, 4Div. of Image Processing, Department of Radiology,

5Computational Biology Center, LUMC,

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HSNE(2 levels)

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AoI(# Events)

Overview Level Data Level

Concept: Non-Linear Hierarchy (Abstract to Single-Cell)

Hierarchy Construction and Exploration

Results● State-of-the-art precision in a fraction of the time

Advantages

References[1] van Unen, et al. Mass Cytometry of the Human Mucosal Immune System Identifies Tissue- and Disease-Associated Immune Subsets. Immunity 44, 1227–1239 (2016).[2] Pezzotti et al. Hierarchical Stochastic Neighbor Embedding. Comput. Graph. Forum 35, 21–30 (2016).[3] Höllt, et al. Cytosplore: Interactive Immune Cell Phenotyping for Large Single-Cell Datasets. Comput. Graph. Forum 35, 171–180 (2016).[4] Samusik et al. Automated mapping of phenotype space with single-cell data. Nat. Methods 13, 493–496 (2016).

AcknowledgmentsThe research leading to these results has received funding from Leiden University Medical Center, the Netherlands Organization for Scientific Research (ZonMW grant 91112008) and the NWO Applied and Engineering Sciences grants 12720 (VAnPIRe) and 17721 (Genes in Space).

Contact: T.Hoellt@lumc.nl http://www.cytosplore.org

● Millions(!) of cells● Non-linear distances, based on single cell resolution throughout the hierarchy● Full data access, zoom into single-cell resolution● Ineractive, integrated software Cytosplore[3]

IntroductionImmunesystem-wide CyTOF studies[1] produce millions of cells, which most computational tools cannot handle with-out downsampling, potentially leading to data loss.HSNE[2] builds a hierarchy of non-linear similarities, allowing exploration of large scale datasets at full resolution interac-tively and, through that, efficient discor-very of rare cell populations.

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CD8+T cells

CD4+T cells

NKT & γδ T cells

Eosinophils

pDCCD4+

CD4-

IgD+IgM+

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Plasma cells

NK cells

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B cells

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Macrophages

mDCs

Basophils

Nonclassicalmonocytes

Intermediatemonocytes

Classicalmonocytes

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Branching point between classicaland nonclassical pathways

HSNE - 5 minutes Vortex[4] - 22 hours