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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Gorde:
Xehetasun bibliografikoak
Argitaratua izan da:Genome Biol
Egile Nagusiak: Way, Gregory P., Zietz, Michael, Rubinetti, Vincent, Himmelstein, Daniel S., Greene, Casey S.
Formatua: Artigo
Hizkuntza:Inglês
Argitaratua: BioMed Central 2020
Gaiak:
Research
Sarrera elektronikoa:https://ncbi.nlm.nih.gov/pmc/articles/PMC7212571/
https://ncbi.nlm.nih.gov/pubmed/32393369
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s13059-020-02021-3
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