A flexible gene regulatory network reconstruction method based on autoencoder and graph attention network

Reconstruction of gene regulatory networks from gene expression profile have been an important challenge task in system biology for decades. Recently, with the advancement of single cell RNA-seq technology, the studies in this field turn from bulk gene expression data to scRNA-seq data. However, the complexity of regulatory relationships and high noise in scRNA-seq introduce further challenges in addressing this issue. In this study, we proposed a flexible gene regulatory network reconstruction method based on autoencoder and graph attention network, called scGiant. scGiant incorporates autoencoder to capture the non-linear representation of genes with graph attention network to learn regulatory relationships among genes. To evaluate the performance of scGiant, we compared it with seven state-of-the-art GRN inference algorithms on four real single-cell RNA sequencing datasets, and the results demonstrate that scGiant is superior in accuracy and scalability. The inferred core GRN of CD8+ naïve T cells also demonstrates its potential in practical biological applications.