scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data
Fig 5
scDSSC is scalable and robust for batch effect.
Plot A represents the cell visualization result of Human2 dataset, in which the 14 names indicate 14 different cell types. And plot B is the results of differential expression analysis on Human2. The rows and columns indicate the gene names and cell type names, respectively, and the brightness of the color reflects the average expression of the gene. In Plot C, we used ARI to evaluate the clustering performance of eight methods on six small datasets containing batch effects. Plot D shows the performance of scDSSC assessed by ACC, NMI and ARI on a large dataset Macosko_mouse. Plot C and Plot D use the same legend.