Fig. 2
From: siVAE: interpretable deep generative models for single-cell transcriptomes
Accuracy evaluation of cell-embedding spaces. a 2D visualization of the inferred cell-embedding spaces of a canonical VAE, siVAE (γ = 0) (no regularization term), siVAE (γ = 0.05) (default regularization weight), and LDVAE. Each point represents a cell and is colored according to cell type. b Barplot indicating the balanced accuracy of a k-NN (k = 80) classifier predicting the cell type labels of single cells based on their inferred position in the cell-embedding space inferred by various methods trained on the fetal liver atlas dataset. Higher accuracies are interpreted as more accurate inferred cell-embedding spaces. c 2D UMAP visualization of the original NeurDiff dataset, without batch correction. Top row shows annotation based on cell type, and the bottom row shows annotation based on the batch. d Same as c, except visualization is a tSNE visualization of the siVAE-inferred cell-embedding space where siVAE corrects for batch within the model