Inferring spatial and signaling relationships between cells from single cell transcriptomic data.

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

SUBMITTER: Cang Z 

PROVIDER: S-EPMC7190659 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature