Integration and transfer learning of single-cell transcriptomes via cFIT
Large, comprehensive collections of single-cell RNA sequencing (scRNA-seq) datasets have been generated that allow for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. As new methods arise to measure distinct cellular modalities, a...
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| Published in: | Proceedings of the National Academy of Sciences - PNAS Vol. 118; no. 10 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
09.03.2021
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| Subjects: | |
| ISSN: | 1091-6490, 1091-6490 |
| Online Access: | Get more information |
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