Fig. 1
From: Metacells untangle large and complex single-cell transcriptome networks
Simplifying single-cell RNA-seq data with SuperCell. a Overview of the SuperCell coarse-graining pipeline, including the following steps. (1) A single-cell network is constructed from the single-cell gene expression matrix using k-nearest neighbors (kNN) algorithm. (2) Densely connected cells are merged into metacells at a user-defined graining level (\(\gamma\)). (3) A gene expression matrix of metacells is computed by averaging gene expression within each metacell. (4) The metacell gene expression matrix can be used for visualization and downstream analyses such as clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. b–e Examples of metacell networks at several graining levels. For comparison, the network of clusters is shown on the right. b Five cancer cell lines (cell_lines, \(N=3918\)) shown with different colors. c Tumor-infiltrating immune cells (TIICs, \(N=\mathrm{15,939}\)). d T cells sorted from PBMC (Tcells, \(N=\mathrm{40,560}\)). e Tumor-infiltrating CD8 T lymphocytes (Cd8_TILs, \(N=3574)\)