Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics
Huang et al apply and benchmark multiple machine learning methods to align gene expression and electrophysiological data of single neuronal cells in the mouse brain from the Brain Initiative. Their approach reveals potential genome functions and gene regulatory mechanisms from gene expression to neu...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/216380d4edf44c03beae98a5f0ad5b06 |
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| Sumario: | Huang et al apply and benchmark multiple machine learning methods to align gene expression and electrophysiological data of single neuronal cells in the mouse brain from the Brain Initiative. Their approach reveals potential genome functions and gene regulatory mechanisms from gene expression to neuronal electrophysiology. |
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