Descart: a method for detecting spatial chromatin accessibility patterns with inter-cellular correlations

Recent advances in spatial sequencing technologies enable simultaneous capture of spatial location and chromatin accessibility of cells within intact tissue slices. Identifying peaks that display spatial variation and cellular heterogeneity is the first and key analytic task for characterizing the spatial chromatin accessibility landscape of complex tissues. Here we propose an efficient and iterative model, Descart, for spatial variable peaks identification based on the graph of inter-cellular correlations. Through comprehensive benchmarking on 16 tissue slices from 4 published datasets, we demonstrate the superiority of Descart in accurately identifying spatial variable peaks from three perspectives, that is facilitating clustering performance, capturing domain-specific signals and maintaining spatial continuity. In terms of computational efficiency, Descart also outperforms existing methods with spatial assumptions. Utilizing the graph of inter-cellular correlations, Descart denoises and imputes data via the neighboring relationships, enhancing the precision of downstream analysis. We further demonstrate the ability of Descart for peak module identification by using peak-peak correlations within the graph. When applied to spatial multi-omics data, Descart show its potential to detect gene-peak interactions, offering valuable insights into the construction of gene regulatory networks. Here we propose a graph-based model, Descart, for spatial variable peaks identification based on the inter-cellular correlations.