Deciphering Cell Types by Integrating scATAC-seq Data with Genome Sequences

Abstract The single cell ATAC sequencing (scATAC-seq) technology provides insight into gene regulation and epigenetic heterogeneity at single-cell resolution, but cell annotation from scATAC-seq remains challenging due to high dimensionality and extreme sparsity within the data. Existing cell annotation methods mostly focused on cell peak matrix without fully utilizing the underlying genomic sequence. Here, we propose a method, SANGO, for accurate s ingle cell an notation by integrating g en o me sequences around the accessibility peaks within scATAC data. The genome sequences of peaks are encoded into low-dimensional embeddings, and then iteratively used to reconstruct the peak stats of cells through a fully-connected network. The learned weights are considered as regulatory modes to represent cells, and utilized to align the query cells and the annotated cells in the reference data through a graph transformer network for cell annotations. SANGO was demonstrated to consistently outperform competing methods on 55 paired scATAC-seq datasets across samples, platforms, and tissues. SANGO was also shown able to detect unknown tumor cells through attention edge weights learned by graph transformer. Moreover, according to the annotated cells, we found cell type-specific peaks that provide functional insights/ biological signals through expression enrichment analysis, cis-regulatory chromatin interactions analysis, and motif enrichment analysis.