scTrans: Sparse attention powers fast and accurate cell type annotation in single-cell RNA-seq data
Fig 5
Latent representation quality analysis. (a) The ARI, NMI and ASW evaluation metrics were calculated for the clustering results of latent representation generated by scTrans, scSemiGAN and scDeepSort, with each datasets running 5 times. The y-axis represents NMI, the x-axis represents ARI, different shapes represent different datasets, and shape size represents ASW. (b) The six graphs showed the UMAP visualization of latent representation on MCA Pancreas dataset generated by scTrans, scSemiGAN, and scDeepSort, including K-Means clustering results and true cell type. (c) Comparison of the clustering results ARI of all methods in mouse brain and mouse pancreas datasets. Error bars were based on mean and 95% confidence. (d) UMAP visualization results showed T cell development dataset, including gene expression variations at different developmental stages and batch information of three donors in the datasets. And pseudo time inference results were shown based on latent representations generated by scTrans, trVAE, and scVI methods.