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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...
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| Hauptverfasser: | , , , |
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| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
National Academy of Sciences
2014
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3890841/ https://ncbi.nlm.nih.gov/pubmed/24344260 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.1321405111 |
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