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