STORM: spectral sparsification helps restore the spatial structure at single-cell resolution

Single-cell RNA sequencing thoroughly quantifies the individual cell transcriptomes but renounces the spatial structure. Conversely, recently emerged spatial transcriptomics technologies capture the cellular spatial structure but skimp cell or gene resolutions. Cell-cell affinity estimated by ligand-receptor interactions can partially reconstruct the quasi-structure of cells but falsely include the pseudo affinities between distant or indirectly interacting cells. Here, we develop a software package, STORM, to reconstruct the single-cell resolution quasi-structure from the spatial transcriptome with diminished pseudo affinities. STORM first curates the representative single-cell profiles for each spatial spot from a candidate library, then reduces the pseudo affinities in the intercellular affinity matrix by partial correlation, spectral graph sparsification, and spatial coordinates refinement. STORM embeds the estimated interactions into a low-dimensional space with the cross-entropy objective to restore the intercellular quasi-structures, which facilitates the discovery of dominant ligand-receptor pairs between neighboring cells at single-cell resolution. STORM reconstructed structures achieved shape Pearson correlations ranging from 0.91 to 0.97 on the mouse hippocampus and human organ tumor microenvironment datasets. Furthermore, STORM can solely de novo reconstruct the quasi-structures at single-cell resolution, i.e., reaching the cell-type proximity correlations 0.68 and 0.89 between reconstructed and immunohistochemistry-informed spatial structures on a human developing heart dataset and a tumor microenvironment dataset, respectively.