The impacts of active and self-supervised learning on efficient annotation of single-cell expression data
Abstract A crucial step in the analysis of single-cell data is annotating cells to cell types and states. While a myriad of approaches has been proposed, manual labeling of cells to create training datasets remains tedious and time-consuming. In the field of machine learning, active and self-supervi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-45198-y |