Gene Regulatory Network Inference Based on Convolutional GRU

Time-course single-cell RNA sequencing (scRNA-seq) data reflect gene expression changes over time, offering a valuable resource for exploring dynamic gene interactions and building dynamic gene regulatory networks (GRNs). However, most existing methods are typically designed for bulk RNA sequencing (bulk RNA-seq) data and cannot be directly applied to time-course scRNA-seq data. Addressing this issue, we present CGGRN, an approach based on convolutional gated recurrent unit (GRU) for inferring GRNs. CGGRN transforms time-course data into images, including raw pairwise gene images and neighborhood images, and aggregates them with time point information into a four-dimensional tensor. The tensor is then fed into the convolutional GRU to capture features for each gene pair and reconstruct the GRN. We conducted trials on four time-course scRNA-seq datasets using CGGRN, and the outcomes show that CGGRN surpasses existing models in constructing GRN.