Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data

We motivate and present biVI, which combines the variational autoencoder framework of scVI with biophysically motivated, bivariate models for nascent and mature RNA distributions. While previous approaches to integrate bimodal data via the variational autoencoder framework ignore the causal relation...

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Detalles Bibliográficos
Autores principales: Carilli, Maria, Gorin, Gennady, Choi, Yongin, Chari, Tara, Pachter, Lior
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882246/
https://www.ncbi.nlm.nih.gov/pubmed/36712140
http://dx.doi.org/10.1101/2023.01.13.523995

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882246/
https://www.ncbi.nlm.nih.gov/pubmed/36712140
http://dx.doi.org/10.1101/2023.01.13.523995

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