Fig. 3

siVAE generates accurate and fast interpretations. a Heatmap indicating the mean pairwise correlation between the interpretations (loadings) of siVAE, Gene Relevance, and three neural net feature attribution methods (saliency maps, grad × input, and DeepLIFT), where correlations have been averaged over each of the 2 embedding dimensions for the fetal liver atlas dataset. b Bar plot indicating the time required to train siVAE, compared to training a canonical VAE and executing one of the feature attribution methods on the LargeBrainAtlas dataset. For feature attribution methods, the run times were extrapolated due to infeasibly long run times; additional experiments demonstrate the feasibility of extrapolation (Additional file 1: Figure S10). c Line plot indicating the time required to train siVAE compared to training a canonical VAE and executing a feature attribution method on the LargeBrainAtlas dataset, when the number of embedding dimensions for siVAE is varied and the number of features is fixed at 28k. d Line plot indicating the time required to train siVAE compared to training a canonical VAE and executing a feature attribution method on the BrainCortex dataset, when the number of features is being varied and the number of embedding dimensions is fixed at 20