The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. In particular, deep learning approac...

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Detalles Bibliográficos
Autores principales: Brombacher, Eva, Hackenberg, Maren, Kreutz, Clemens, Binder, Harald, Treppner, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Molecular Biosciences
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643784/
https://www.ncbi.nlm.nih.gov/pubmed/36387277
http://dx.doi.org/10.3389/fmolb.2022.962644

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643784/
https://www.ncbi.nlm.nih.gov/pubmed/36387277
http://dx.doi.org/10.3389/fmolb.2022.962644

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