Fig. 3

Metacells improve cell type annotation, gene correlation, imputation and RNA velocity. a–b AUC of recovery of CD4 (top) and CD8 (bottom) T cells from the Tcells dataset using single markers (a) or signatures defined form bulk (b) consisting of the top 5 or top 50 genes for metacells computed with SuperCell, MetaCell_def, MetaCell_SC and random grouping, or after subsampling. c Expression of CD4 (top) and CD8A (bottom) in T cells from the Tcells dataset at the single-cell and the metacell (\(\gamma =100\)) levels. d Gene correlation at the single-cell (top) and the metacell (\(\gamma =50\)) (bottom) levels for selected gene pairs in the Cd8_TILs dataset, with the corresponding sample-weighted Pearson correlation (ρ). e Comparison of the GO similarity of metacell and single-cell top correlated genes identified for individual cell lines from the cell_lines dataset. The y-axis shows the ratio between mean GO match scores of the top correlated genes at the metacell and the single-cell levels. f Mean Spearman correlation between bulk and MAGIC-imputed data in each cell line of the cell_lines dataset. The dashed lines show the correlation between the pseudo-bulk (i.e., averaged gene expression within a cell line) and bulk gene expression. g Joint tSNE visualization of RNA velocity for the brain_cells dataset (\(N = 3396\)) for single cells (left) and metacells (\(\gamma =10\)) (right) colored by cell type annotation. h, Velocity purity in metacells (defined as the cosine similarity of single-cell velocities within each metacell). i Number of genes with valid estimated equilibrium slope values. j Pearson correlation of gene equilibrium slope values obtained in single-cell and metacell RNA velocities. k Cosine similarity between 2D single-cell and metacell RNA velocities shown in (g). For the subsampling and random grouping, the center of the error bars denotes the median, and the extrema denotes the 1st and 3rd quartiles (obtained with different random seeds)