Single-cell ATAC-seq clustering and differential analysis by convolution-based approach

Single-cell ATAC-seq is a powerful tool to interrogate the epigenetic heterogeneity of cells. Here, we present a novel method to calculate the pairwise similarities between single cells by directly comparing their Tn5 insertion profiles instead of the binary accessibility matrix using a convolution-based approach. We demonstrate that our method retains the biological heterogeneity of single cells and is less affected by undesirable batch effects, which leads to more accurate results on downstream analyses such as dimension reduction and clustering. Based on the similarity matrix learned from epiConv, we develop an algorithm to infer differentially accessible peaks directly from heterogeneous cell population to overcome the limitations of conventional differential analysis through two-group comparisons.