A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data
Fig 5
The semantic meaningfulness of the latent variables in the prior-based models, shown as the correlation between the biological priors and the latent μ of prior-based models on the test set.
The correlation of each dimension is shown in A: for priorVAE and B: for beta-priorVAE. Subplot C summarizes these correlations to directly compare the semantic interpretability of the two models.