MultiCausGRN: directed prior-guided graph attention model for multi-omics gene regulatory network inference
TL;DR
Direct prior-guided graph learning can improve the robustness and biological interpretability of GRN inference in data-limited settings and demonstrate that directed prior-guided graph learning can improve the robustness and biological interpretability of GRN inference in data-limited settings.
Abstract
Introduction Existing methods for gene regulatory network (GRN) inference rely primarily on gene expression data alone or on lower-resolution bulk sequencing data. Despite recent advances in integrating chromatin accessibility and RNA sequencing, inferring GRNs from paired single-cell multi-omics data remains challenging due to noise, sparsity, and complex nonlinear regulatory relationships. Methods We present MultiCausGRN, a graph attention network (GAT)-based framework for GRN inference from paired scRNA-seq and scATAC-seq data. The model incorporates directed prior-guided graph attention learning to capture biologically grounded regulatory directionality by integrating curated directed regulatory edges into graph representation learning. MultiCausGRN performs supervised transcription factor–target link prediction using integrated multi-omics features within a two-layer graph attention architecture. Results On the human PBMC multi-omics dataset, prior knowledge integration improved predictive stability and achieved a mean test AUPRC of 0.743 ± 0.049 and a mean AUROC of 0.682 ± 0.026 across five independent random seeds. Discussion These results demonstrate that directed prior-guided graph learning can improve the robustness and biological interpretability of GRN inference in data-limited settings. MultiCausGRN is publicly available at: https://github.com/nrr-90/MultiCausGRN.