Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks
Visualization algorithms are fundamental tools for interpreting single-cell data. However, standard methods, such as t-stochastic neighbor embedding (t-SNE), are not scalable to datasets with millions of cells and the resulting visualizations cannot be generalized to analyze new datasets. Here we in...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Cell Press
2019
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| Online Access: | https://hdl.handle.net/1721.1/122802 |