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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2020
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| Materias: | |
| 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 |