Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)
Mass cytometry enables an unprecedented number of parameters to be measured in individual cells at a high throughput, but the large dimensionality of the resulting data severely limits approaches relying on manual “gating.” Clustering cells based on phenotypic similarity comes at a loss of single-ce...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
National Academy of Sciences (U.S.)
2014
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| Online Access: | http://hdl.handle.net/1721.1/89083 https://orcid.org/0000-0003-1268-9602 |