Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
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.
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2017-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-017-01689-9 |