Fig. 6: The results of mouse bone marrow data generated by Paul et al.21.
From: Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
a The t-SNE plot showing the maximum probabilities of cluster assignments of cells. The maximum probability is the probability for the cluster that is assigned with the highest probability by DESC. b The t-SNE plots of clustering results by DESC and scVI. Compared with scVI, DESC yields more accurate clustering result for DC, lymph, and Mk. In scVI, the clustering result is more diffused, and Mk cells are mixed together with GMP cells. In contrast, DESC clearly separated DC, Lymph, and Mk cells from the other cell clusters. c, d The t-SNE plots of true cell-type labels (obtained from the original publication) for DESC and scVI. e, f The t-SNE plots of true cell-type labels with pseudotime ordering (obtained from the original publication) for DESC and scVI. Ery erythrocyte, MEP megakaryocyte/erythrocyte progenitors, Mk megakaryocyte, GMP granulocyte/macrophage progenitors, DC dendritic cell, Baso basophils, Mo monocyte, Neu neutrophils, Eos eosinophils; Lymph lymphocyte.
