Attention-based deep clustering method for scRNA-seq cell type identification
Fig 4
AttentionAE-sc construction better relationships among cells (Muraro).
a. The trend of the number of cell groups predicted and ARI scores changing with training epochs during the clustering stage. The better clustering performance was obtained, when the cluster centers were adaptively chosen with each iteration and redundant cluster centers were discarded. Other datasets were shown in S4 Fig. b. The visualization of cell embeddings. c. the heatmap of relative cosine distance between cells calculated by the embeddings of multi-head attention layer. d. the heatmap of relative cosine distance between cells calculated from the input expression matrix. On the right subfigure, we sorted the dataset follow per cell groups to get an intuitive visualization of among cells distance (c, d, e). e. the heatmap of relative cosine distance between cells calculated by ordinary denoising autoencoder (DAE) and the corresponding visualization.