IntroGRN: Gene Regulatory Network Inference from Single-Cell RNA Data Based on Introspective VAE

The inference of gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data enables describing the regulatory relationships among genes from a cellular perspective, and revealing the essence of various life phenomena. However, the high sparsity of scRNA-seq data poses new challenges to the inference of GRNs. In this study, an introspective adversarial gene regulatory network unsupervised inference model, called IntroGRN, is designed based on variational autoencoder and structural equation model. IntroGRN attempts to produce better reconstruction samples via adversarial training, and introduce the structural equation model into the process of adversarial. Compared with eight state-of-the-art methods, the proposed IntroGRN method can infer the best gene regulatory networks in most benchmark datasets, which has been verified through extensive experiments. The source code of method IntroGRN can be downloaded from https://github.com/lryup/IntroGRN.