Modeling gene regulatory networks using neural network architectures

Gene regulatory networks (GRNs) encode the complex molecular interactions that govern cell identity. Here we propose DeepSEM, a deep generative model that can jointly infer GRNs and biologically meaningful representation of single-cell RNA sequencing (scRNA-seq) data. In particular, we developed a neural network version of the structural equation model (SEM) to explicitly model the regulatory relationships among genes. Benchmark results show that DeepSEM achieves comparable or better performance on a variety of single-cell computational tasks, such as GRN inference, scRNA-seq data visualization, clustering and simulation, compared with the state-of-the-art methods. In addition, the gene regulations predicted by DeepSEM on cell-type marker genes in the mouse cortex can be validated by epigenetic data, which further demonstrates the accuracy and efficiency of our method. DeepSEM can provide a useful and powerful tool to analyze scRNA-seq data and infer a GRN.

Automated summary

DeepSEM is a deep generative model that can jointly infer gene regulatory networks (GRNs) and biologically meaningful representation of single-cell RNA sequencing (scRNA-seq) data. By developing a neural network version of the structural equation model (SEM), DeepSEM outperforms existing methods on various single-cell computational tasks and the predicted gene regulations can be validated by epigenetic data.