Fig. 1

Overview of scMC. a scMC takes multiple single-cell genomics datasets as input. Datasets and cell types are represented by different shapes and colors, respectively. b scMC identifies putative cell clusters for each dataset using a Leiden algorithm-based consensus approach and defines a set of confident cells in each cell cluster, as indicated by the cells inside the dashed lines. c scMC deconvolutes technical variation by identifying all pairs of shared cell clusters across any two datasets based on their similarity. The differences between any other pairs of cell clusters are attributed to both technical and biological variation, as indicated by the dashed lines. d scMC learns a shared embedding of cells by subtracting the inferred technical variation using variance analysis. In this shared embedding space, cells are grouped by cell types rather than dataset batches, allowing detection of dataset-shared and dataset-specific biological signals