Abstract
Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however, crucial spatial information is often lost. We present SpaOTsc, a method relying on structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively small number of genes. A spatial metric for individual cells in scRNA-seq data is first established based on a map connecting it with the spatial measurements. The cell–cell communications are then obtained by “optimally transporting” signal senders to target signal receivers in space. Using partial information decomposition, we next compute the intercellular gene–gene information flow to estimate the spatial regulations between genes across cells. Four datasets are employed for cross-validation of spatial gene expression prediction and comparison to known cell–cell communications. SpaOTsc has broader applications, both in integrating non-spatial single-cell measurements with spatial data, and directly in spatial single-cell transcriptomics data to reconstruct spatial cellular dynamics in tissues.
Highlights
Single-cell RNA sequencing provides details for individual cells; crucial spatial information is often lost
spatially optimal transporting the single cells (SpaOTsc) method consists of two major components: (a) constructing a spatial metric for cells in scRNA-seq data and (b) reconstructing cell–cell communication networks and identifying intercellular regulatory relationships between genes (Fig. 1)
When we compared Wg and Dpp based cell–cell communications inferred by SpaOTsc with another inference method[5] which does not include spatial information, we found that SpaOTsc makes predictions that are more biologically feasible and more consistent with the prior knowledge (Supplementary Figs. 18–22)
Summary
Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; crucial spatial information is often lost. While single-cell transcriptomics has mainly been used to delineate cell subpopulations and their lineage relationships, recently computational tools have been developed to infer cell–cell communications from scRNA-seq data[2,3]. On the other hand, measuring gene expression in intact tissues provides spatial resolutions but the genes examined need to be selected in advance Is it possible to better infer communications between cells located in different positions in the intact tissues using single-cell transcriptomics data with the aid of additional spatial measurements?. At an individual cell level, similarity measurements based on correlation coefficients[10,11] or correspondence scores[12] between commonly examined genes in both spatial imaging data and scRNA-seq data were used to reconstruct spatial gene expression or map cells in scRNA-seq data to their potential spatial origins. Posterior probability estimates were carried out on spatial data described by a mixture model[13] or simplified to onedimensional bins[14] to assign spatial origins to individual cells
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