Cytosplore

Interactive Immune Cell Phenotyping for Large Single-Cell Datasets

Journal Article (2016)
Author(s)

Thomas Hollt (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Nicola Pezzotti (TU Delft - Electrical Engineering, Mathematics and Computer Science)

V. van Unen (Leiden University Medical Center)

F. Koning (Leiden University Medical Center)

Elmar Eisemann (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Boudewijn P.F. Lelieveldt (Leiden University Medical Center, TU Delft - Electrical Engineering, Mathematics and Computer Science)

Anna Vilanova Bartroli (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Research Group
Computer Graphics and Visualisation
DOI related publication
https://doi.org/10.1111/cgf.12893 Final published version
More Info
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Publication Year
2016
Language
English
Research Group
Computer Graphics and Visualisation
Issue number
3
Volume number
35
Pages (from-to)
171-180
Event
EuroVis 2016 (2016-06-06 - 2016-06-10), Groningen, Netherlands
Downloads counter
206

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.