Fig. 1

The scABC framework for unsupervised clustering of scATAC-seq data. a Overview of scABC pipeline. scABC constructs a matrix of read counts over peaks, then weights cells by sample depth and applies a weighted K-medoids clustering. The clustering defines a set of K landmarks, which are then used to reassign cells to clusters. b Assignment of cells to landmarks by Spearman correlation, where each cell is highly correlated with just one landmark. The similarity measure used above is defined as the Spearman correlation of cells to landmarks, normalized by the mean of the absolute values across all landmarks for every cell. This allows us to better visualize the relative correlation across all cells. c Accessibility of peaks across all cells. The vast majority of peaks tend to be either common or cluster specific, allowing us to define cluster specific peaks