BABEL: using deep learning to translate between single-cell datasets
Recent advances in sequencing and barcoding technologies have enabled researchers to simultaneously profile gene expression, chromatin accessibility, and/or protein levels in single cells. However, these multiomic techniques often pose technical and financial barriers that limit their practicality....
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| Formato: | article |
| Lenguaje: | EN |
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Nature Portfolio
2021
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| Acceso en línea: | https://doaj.org/article/4e0b2ba8f4ed4bae8a12fec4eec6f3c5 |
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| Sumario: | Recent advances in sequencing and barcoding technologies have enabled researchers to simultaneously profile gene expression, chromatin accessibility, and/or protein levels in single cells. However, these multiomic techniques often pose technical and financial barriers that limit their practicality. Kevin Wu and colleagues recently developed BABEL, a deep learning algorithm that can effectively translate between transcriptomic and chromatin profiles in single cells, thereby enabling researchers to perform multiomic analyses from an individual dataset. |
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