Aggregating GRNs via multiplex link prediction

Gene regulatory networks (GRNs) are essential in studying cell differentiation and development. However, existing GRN inference models often disregard the network nature of GRNs, undermining the potential for GRN inference from scRNA-seq data. Popular GRN models such as PIDC and GENIE3 only consider at most triplet interactions among genes, significantly underestimating the regulation complexity in real biological systems. To showcase the importance of the network structure in GRNs, we proposed a novel GRN aggregation method, MuxGRN, which leverages multiplex link prediction to aggregate GRNs inferred from various state-of-the-art models and extracts the information propagated within different layers of GRNs. We tested MuxGRN on 14 real scRNA-seq datasets aggregated from 9 GRN models and compared it with 2 baseline edge-level aggregation methods. Our evaluation shows that MuxGRN achieves a top-tier averaged AUPRC among individual GRN models and discovers up to 26.55% new true positive edges, significantly outperforming baselines. This highlights the potential of link prediction in GRN aggregation for mining weak signals from inferred GRNs, and suggests that future development of GRN models should emphasize the network nature of GRNs.