DECA: harnessing interpretable transformer model for cellular deconvolution of chromatin accessibility profile.

Abstract

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) identifies chromatin accessibility across the genome, crucial for gene expression regulating. However, bulk ATAC-seq obscures cellular heterogeneity, while single-cell ATAC-seq suffers from issues such as sparsity and costliness. To this end, we introduce DECA, a sophisticated deep learning model based on vision transformer to deconvolve cell type information from bulk chromatin accessibility profiles, utilizing single-cell ATAC-seq datasets as reference for enhanced precision and resolution. Notably, patch attention generated by DECA's multi-head attention mechanism aligns with chromatin interactions detected by Hi-C. Additionally, DECA predicted lineage-specific cell composition changes due to genetic perturbation. The chromatin accessibility signatures predicted by DECA are enriched with cell-type specific genetic variations. Ultimately, we applied DECA on pan-cancer ATAC-seq datasets and demonstrated its capability to deconvolve cell type proportions with clinical significance. Taken together, DECA deconvolves cellular proportions and predicts their chromatin accessibility profiles from bulk chromatin accessibility data, which enable exploring the gene regulatory programs in development and diseases.