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VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics
Deep learning architectures such as variational autoencoders have revolutionized the analysis of transcriptomics data. However, the latent space of these variational autoencoders offers little to no interpretability. To provide further biological insights, we introduce a novel sparse Variational Aut...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478947/ https://www.ncbi.nlm.nih.gov/pubmed/34584103 http://dx.doi.org/10.1038/s41467-021-26017-0 |