Detection of 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, Descartes, for spatially variable peaks identification based on the graph of inter-cellular correlations. Through the comprehensive benchmarking for spatially variable peaks identification, we demonstrate the superiority of Descartes in revealing cellular heterogeneity and capturing tissue structure. In terms of computational efficiency, Descartes also outperforms existing methods with spatial assumptions. Utilizing the graph of inter-cellular correlations, Descartes denoises and imputes data via the neighboring relationships, enhancing the precision of downstream analysis. We further demonstrate the ability of Descartes for peak module identification by using peak-peak correlations within the graph. When applied to spatial multi-omics data, Descartes show its potential to detect gene-peak interactions, offering valuable insights into the construction of gene regulatory networks.