Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
By V. van Unen, Thomas Höllt, Nicola Pezzotti, Na Li, Marcel J. T. Reinders, Elmar Eisemann, F. Koning, Anna Vilanova, Boudewijn P. F. Lelieveldt. 2017
208 citations recorded by the source corpus.
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. Single cell profiling yields high dimensional data of very large numbers of cells, posing challenges of visualization and analysis. Here the authors introduce a method for analysis of mass cytometry data that can handle very large datasets and allows their intuitive and hierarchical exploration.
Subfields: Immunology, Molecular Biology
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In Immunology
In Molecular Biology
- Fabian J Theis
- F. Alexander Wolf
- Lukas M. Simon
- Berthold Göttgens
- Matthew J. Hirn
- Zheng Wang
- Ronald R. Coifman
- Nikolaus Rajewsky
Citations in the corpus
- SOM-based embedding improves efficiency of high-dimensional cytometry data analysis (2019, methodology): “While embedding has been already used for hierarchical sample dissection [23] as a way to provide highly informative and comprehensible overview of the high-dimensional data that can precisely guide the analysis, EmbedSOM improves the analysis performance by removing unnecessary delays and interrupt”
- Visualizing structure and transitions in high-dimensional biological data (2019, background): “The third branch was constructed similarly except the progression occurs in dimensions 9–12 instead of dimensions 5–8.”
- Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2017, background): “[58] — published during the revision of the present paper — presents an idea for measuring “overlap between influence regions” of clusters obtained from”