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...

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Detalles Bibliográficos
Autores principales: Seninge, Lucas, Anastopoulos, Ioannis, Ding, Hongxu, Stuart, Joshua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
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

Internet

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

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