Spatially varying cell-specific gene regulation network inference

Abstract

Gene regulatory networks (GRNs), involving interactions between large numbers of genes, govern expression levels of mRNA and their resulting proteins to control cell functions. As many new sequencing technologies in single-cell and spatial resolutions are raised, the construction of GRNs gains opportunities to be generalized to the cell-specific level. Here we propose SVGRN, a deep learning model to infer cell-specific GRNs using spatial transcriptomics data. We model the gene expression, GRN matrix, and spatial coordinates of cells in the structural equation model (SEM) to learn gene interactions in an unsupervised way. Conditioned on the target cell position, the model is able to tune the whole tissue GRN to the target cell through also borrowing information from neighborhood cells. Results on simulated datasets show that SVGRN achieves better performance in cases with more noises, genes, or cells, which illustrates its ability to solve more complex situations. Our model was further applied to spatial transcriptomics datasets generated using different technologies and resolutions, including a seqFISH-based mouse embryo dataset and two Visium-based datasets from human cutaneous squamous cell carcinoma (cSCC) and fallopian tube tissues. The GRNs predicted by SVGRN from these datasets reveal dynamic gene regulatory patterns in mouse organogenesis, tumor development and the functional organization of the fallopian tube, highlighting the efficiency and broad applicability of our model.