Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations

BACKGROUND: Unsupervised compression algorithms applied to gene expression data extract latent or hidden signals representing technical and biological sources of variation. However, these algorithms require a user to select a biologically appropriate latent space dimensionality. In practice, most re...

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Detalles Bibliográficos
Autores principales: Way, Gregory P., Zietz, Michael, Rubinetti, Vincent, Himmelstein, Daniel S., Greene, Casey S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7212571/
https://www.ncbi.nlm.nih.gov/pubmed/32393369
http://dx.doi.org/10.1186/s13059-020-02021-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7212571/
https://www.ncbi.nlm.nih.gov/pubmed/32393369
http://dx.doi.org/10.1186/s13059-020-02021-3

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