Fig. 2

Metacells preserve clustering and differential expression results, and reveal genes specifically expressed in dendritic cell subtypes. a Median purity of metacells computed with SuperCell, MetaCell_def and MetaCell_SC at different graining levels for the four datasets shown in Fig. 1b–e (cell_lines, TIICs, Tcells, Cd8_TILs). As a lower bound, the purity after random grouping of cells is shown in gray. b Consistency between the hierarchical clustering of metacells or after subsampling and the one of single cells (see Additional file 1: Fig. S4a for results with other clustering algorithms). The blue line shows the range of ARI values when other clustering algorithms are applied to the single-cell data (median shown with “X”). c Proportion of the cluster-specific DE genes (based on weighted t-test) found at the single-cell level and recovered at the metacell level or after subsampling. d Proportion of the condition-specific DE genes found in bulk RNA-seq and recovered at the metacell level or after subsampling in the Mouse_DE dataset. e Expression of genes coding for trans-membrane proteins in single cells (top) and metacells (bottom) that are more differentially expressed (i.e., better ranking) between cDCs and pDCs at the metacell level. The number following the ‘#’ sign indicates the ranking of each gene among the top differentially expressed ones. f Flow cytometry analysis of DCs from murine KP1.9 lung adenocarcinoma (\(N=7\)). g Median fluorescence intensity of proteins coded by the genes from (e). All comparison shown in e and g pass statistical significance based on two-tailed unpaired Student’s t-test (p values < 0.05)