SpaDC enables sequence-based integrative analysis and regulatory inference of spatial chromatin accessibility data

Spatial ATAC-seq enables simultaneous profiling of cellular locations and chromatin accessibility in intact tissues but faces challenges from high dimensionality, noise, and sparsity. Moreover, existing methods often overlook DNA sequence information, which contains critical regulatory motifs. To address these limitations, we introduce SpaDC, a graph-regularized convolutional neural network that integrates spatial location, chromatin accessibility, and DNA sequence. SpaDC employs a triplet loss function to integrate multiple spatial ATAC-seq datasets and remove batch effects. Benchmark analyses on real datasets demonstrate state-of-the-art performance in spatial domain identification, data denoising, and gene regulatory network (GRN) inference. Applied to mouse embryonic brain spatial ATAC-seq data, SpaDC accurately identified known brain structures and recovered chromatin accessibility signals. On P22 mouse brain spatial multi-omics data, SpaDC revealed spatial domain-specific cis-regulatory elements and GRNs. Collectively, SpaDC provides a powerful, sequence-based solution for spatial ATAC-seq analysis, enabling more accurate and robust investigation of tissue architecture and chromatin organization. SpaDC is a computational method that integrates DNA sequence and spatial location to denoise spatial epigenomics data, thereby enhancing spatial domain identification and gene regulatory network inference.