MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-cell gene expression data. We also develop MichiGAN...

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Detalles Bibliográficos
Autores principales: Yu, Hengshi, Welch, Joshua D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139054/
https://www.ncbi.nlm.nih.gov/pubmed/34016135
http://dx.doi.org/10.1186/s13059-021-02373-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139054/
https://www.ncbi.nlm.nih.gov/pubmed/34016135
http://dx.doi.org/10.1186/s13059-021-02373-4

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