Cytosplore: Interactive Immune Cell Phenotyping for Large Single-Cell Datasets
To understand how the immune system works, one needs to have a clear picture of its cellular compositon and the cells' corresponding properties and functionality. Mass cytometry is a novel technique to determine the properties of single‐cells with unprecedented detail. This amount of detail all...
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| Published in | Computer graphics forum Vol. 35; no. 3; pp. 171 - 180 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2016
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| Subjects | |
| Online Access | Get full text |
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| Abstract | To understand how the immune system works, one needs to have a clear picture of its cellular compositon and the cells' corresponding properties and functionality. 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. 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. 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. We show the effectiveness of Cytosplore in a case study evaluation. |
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| AbstractList | To understand how the immune system works, one needs to have a clear picture of its cellular compositon and the cells' corresponding properties and functionality. 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. 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. 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. We show the effectiveness of Cytosplore in a case study evaluation. |
| Author | Koning, F. Vilanova, A. Höllt, T. Eisemann, E. van Unen, V. Pezzotti, N. Lelieveldt, B. |
| Author_xml | – sequence: 1 givenname: T. surname: Höllt fullname: Höllt, T. organization: TU Delft, Delft, The Netherlands – sequence: 2 givenname: N. surname: Pezzotti fullname: Pezzotti, N. organization: TU Delft, Delft, The Netherlands – sequence: 3 givenname: V. surname: van Unen fullname: van Unen, V. organization: Leiden University Medical Center, Leiden, The Netherlands – sequence: 4 givenname: F. surname: Koning fullname: Koning, F. organization: Leiden University Medical Center, Leiden, The Netherlands – sequence: 5 givenname: E. surname: Eisemann fullname: Eisemann, E. organization: TU Delft, Delft, The Netherlands – sequence: 6 givenname: B. surname: Lelieveldt fullname: Lelieveldt, B. organization: TU Delft, Delft, The Netherlands – sequence: 7 givenname: A. surname: Vilanova fullname: Vilanova, A. organization: TU Delft, Delft, The Netherlands |
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| Copyright | 2016 The Author(s) Computer Graphics Forum © 2016 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2016 The Eurographics Association and John Wiley & Sons Ltd. |
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| References_xml | – reference: Ornatsky O.I., Kinach R., Bandura D.R., Lou X., Tanner S.D., Baranov V.I., Nitz M., Winnik M.A.: Development of analytical methods for multiplex bio-assay with inductively coupled plasma mass spectrometry. Journal of Analytical Atomic Spectrometry 23 (2008), 463-469. doi: 10.1039/B710510J. 2 – reference: Sedlmair M., Munzner T., Tory M.: Empirical guidance on scatterplot and dimension reduction technique choices. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 2634-2643. doi: 10.1109/TVCG.2013.153. 5 – reference: Brehmer M., Munzner T.: A multi-level typology of abstract visualization tasks. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 2376-2385. doi: 10.1109/TVCG.2013.124. 2, 4 – reference: Lex A., Streit M., Schulz H., Partl C., Schmalstieg D., Park P.J., Gehlenborg N.: StratomeX: visual analysis of large-scale heterogeneous genomics data for cancer subtype characterization. Computer Graphics Forum 31, 3 (2012), 1175-1184. doi: 10.1111/j.1467-8659.2012.03110.x. 3 – reference: Qiu P., Simonds E.F., Bendall S.C., Gibbs Jr K.D., Bruggner R.V., Linderman M.D., Sachs K., Nolan G.P., Plevritis S.K.: Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nature Biotechnology 29 (2011), 886-891. doi: 10.1038/nbt.1991. 3, 4 – reference: Rothenberg E.V.: Lineage determination in the immune system. Immunological Reviews 238, 1 (2010), 5-11. doi: 10.1111/j.1600-065X.2010.00965.x. 4 – reference: Shekhar K., Brodin P., Davis M.M., Chakraborty A.K.: Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE). Proceedings of the National Academy of Sciences 111, 1 (2014), 202-207. doi: 10.1073/pnas.1321405111. 3, 6 – reference: Lujan E., Zunder E.R., Ng Y.H., Goronzy I.N., Nolan G.P., Wernig M.: Early reprogramming regulators identified by prospective isolation and mass cytometry. 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