Deconvolution and inference of spatial communication through optimization algorithm for spatial transcriptomics.

Spatial transcriptomics technologies can capture gene expression at spatial loci. However, at certain resolutions, the obtained gene expression reflects the sum of either a heterogeneous or homogeneous set of cells, rather than individual cell. This limitation gives rise to the deconvolution algorithm to make cell-type inferences at each location. Yet, the vast majority of deconvolution methods that have been developed ignore the spatial information of the tissue and the communications between the cells or spots. To overcome these afflictions, we proposed a deconvolution method, non-negative least squares-based and optimization search-based deconvolution (NODE), that combines cell-type-specific information from single-cell RNA sequencing (scRNA-seq) and intercellular communications in tissue. NODE deconvolution algorithm, incorporating the spatial information of the tissue, allows us to quantify intercellular communications at the same instant. NODE can not only utilize optimization method to infer the deconvolution results of spatial transcriptomics data and reduce the probability of overfitting situations, but also make reasonable inferences for spatial communications. Subsequently, we applied NODE to four datasets to validate the correctness of the NODE deconvolution results and compare them with existing deconvolution algorithms. NODE also inferred spatial communications and validated them in tissue development of human heart.