GRNNLink: Predicting gene regulatory links from single-cell RNA-seq data using graph recurrent neural network

Single-cell RNA sequencing (scRNA-seq) technology offers unprecedented opportunities for inferring gene regulatory networks (GRNs) at the genome level. However, scRNA-seq data is highly sparse and has a low signal-to-noise ratio with significant dropout. Many unsupervised or self-supervised models have been proposed to infer GRNs from large RNA-seq datasets, but few are suitable for scRNA-seq data. Recent research confirms that transcription factor (TF)-DNA binding data enables supervised GRN inference. In this paper, we propose a novel framework called GRNNLink, which leverages known GRNs to infer potential regulatory relationships between genes. First, we preprocess the raw scRNA-seq data. Then, we introduce an interactive graph encoder based on a graph recurrent neural network (GRNN) to refine gene features by capturing the correlations between network nodes. Finally, matrix completion is performed using the node correlation features to predict GRNs. To evaluate model performance, we compare GRNNLink with six existing GRN reconstruction methods across seven scRNA-seq datasets. The results demonstrate that our method exhibits high robustness and accuracy.