Aug 2026· Frontiers in Bioinformatics· Vol 6· 1 citation· 45 references
Medicine
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.
A structure-aware interleaved-attention graph learning framework, termed IAGRN, is proposed for GRN inference from scRNA-seq data that interleaves topology-constrained local attention with distance-aware global attention, enabling effective integration of structural priors and long-range regulatory signals.
Yue Wang, Si-Cheng Tian, Dan Li· International Journal of Mol...· 0 citations
Accurate inference of gene regulatory networks (GRNs) from single-cell gene expression data is challenging due to noise, data sparsity, and variability in gene-gene associations across cells. We propose CoReGRN (Contextual Refinement of Gene Regulatory Networks), a nonparametric, context-aware post-processing framework that refines inferred GRNs by reweighting candidate regulatory interactions using Mutual Information based association strength and local network context. The method uses empirical cumulative distribution function (ECDF) based scores to assess how unusual each interaction is relative to the connectivity patterns of the genes it connects. Then combines this contextual information with the original edge confidence scores. We evaluate the CoReGRN framework on gold-standard datasets from the BEELINE benchmark suite across multiple state-of-the-art GRN inference algorithms. The results show consistent performance improvements, with average absolute gains of 0.097 in AUROC, 0.130 in AUPR, 0.133 in MCC, and 0.095 in F1-Score. We further apply the framework to a single-cell HIV-Leishmaniasis dataset, where the refined networks support the analysis of disease-specific regulatory hubs and interactions. Comparison with existing biological knowledge identifies both known and potentially novel regulatory relationships across HIV infection, HIV-Leishmaniasis, and HIV-Visceral Leishmaniasis conditions. The analysis includes hub gene identification, interaction network analysis, and literature based validation using GeneMANIA.
E. M., Jereesh A S, G. S. Kumar· BMC Genomics· 0 citations
Inferring gene regulatory networks (GRNs) from single-cell transcriptomic data is crucial for biological discovery, yet existing approaches suffer from a fundamental misalignment with real-world needs. Researchers typically seek a small set of high-confidence regulatory interactions for experimental validation, often involving previously unseen genes. However, current benchmarks rely on transductive splits with global classification metrics, while prevailing models struggle to generalize under inductive settings. To bridge this gap, we reformulate GRN inference as an inductive, ranking-centric graph completion problem and introduce \textbf{\benchmark}, a new benchmark that incorporates an inductive gene-holdout split together with knowledge graph completion metrics to better evaluate top-ranked predictions. Building on this, we propose \textbf{\method}, the first co-evolutionary discrete diffusion framework that jointly models biologically coherent discretized gene expression states and regulatory interactions for robust inductive generalization and improved top-ranked regulatory discovery. We further introduce TF-ALL Subgraph Sampling (TASS) for scalable training. Extensive experiments on {\benchmark} show that {\method} establishes new state-of-the-art performance, significantly outperforming existing methods in novel regulatory discovery, and ablation studies further verify the effectiveness of our design.
Jiaze Song, Runhao Zhao, Minghao Xu et al.· 0 citations
Understanding gene regulation at single-cell resolution is crucial for unraveling development, disease, and cellular identity. We introduce single-cell regulatory graph attention network (scReGAT), a deep learning framework that integrates prior knowledge of cis-regulatory element (cRE)-gene and transcription factor-gene interactions to reconstruct cell-specific regulatory networks. Central to scReGAT is a knowledge-guided regulatory graph (kRG), which combines experimentally validated regulatory interactions with cell-resolved chromatin accessibility profiles. These graphs serve as the foundation for training a Graph Attention Network (GAT) to predict gene expression and quantify the contribution of specific regulatory interactions using an interpretable regulatory score for each edge. In benchmarking across five single-cell multi-omics datasets, scReGAT successfully recapitulates known cell-type-specific cRE-gene interactions. In both neuroblastoma and osteogenic differentiation systems, it uncovers dynamic regulatory rewiring that predicts transcriptional transitions. Furthermore, by integrating genome-wide association studies loci from Alzheimer's disease, multiple sclerosis, and schizophrenia, scReGAT identifies disease-associated cell types and uncovers candidate regulatory mechanisms underlying complex trait associations. These results position scReGAT as a robust and generalizable framework for decoding long-range gene regulation at single-cell resolution. The source code of scReGAT can be accessed at https://github.com/TianLab-Bioinfo/scReGAT/ and https://ngdc.cncb.ac.cn/biocode/tool/BT008081.
Reconstruction of gene regulatory networks (GRNs) is essential for uncovering regulatory relationships between transcription factors (TFs) and target genes. With advances in single-cell RNA sequencing (scRNA-seq), cell-type-specific GRN inference has become an important direction in systems biology; however, existing deep learning methods still struggle with noisy expression, symmetric link scoring that cannot capture regulatory direction, and difficulty separating co-expression and co-regulation from direct regulation. Built upon the variational autoencoder (VAE)-graph attention network (GAT) framework of GRANet [1], we propose DMVD-GRN with enhanced VAE multi-view denoising, skew-symmetric directed decoding, and structure-aware joint decoding, integrating expression correlation, second-order adjacency, and Jaccard co-regulation priors. On 14 tasks of the STRING benchmark, DMVD-GRN achieves superior AUROC and AUPRC, with more pronounced AUPRC gains, thereby improving identification of true regulatory edges under extreme class imbalance.
Abstract Motivation Inferring gene networks from single-cell RNA sequencing data is challenging due to high sparsity, dimensionality, and technical noise. Current pipelines lack the multi-dataset integration and comprehensive post-processing analysis. Results scGraphVerse is an R package that integrates multiple algorithms (GENIE3, GRNBoost2, ZILGM, PCzinb, and JRF) with extensive evaluation and visualization tools. Its modular workflow supports early, late, and joint integration strategies for multi-dataset analysis, providing standardized input/output interfaces and biological interpretation tools, including community detection, pathway enrichment, and literature mining. Benchmarking on simulated data showed model-based methods (PCzinb and ZILGM) perform well with limited sample sizes, while JRF performs best as the network size and dataset numbers increase. A PBMC case study demonstrates JRF’s ability to identify literature-supported regulatory communities across donors. Availability and implementation The package is available in Bioconductor 3.22 at https://bioconductor.org/packages/release/bioc/html/scGraphVerse.html. Code and examples: https://github.com/ngsFC/scGV_analysis.
Francesco Cecere, D. De Canditiis, Annamaria Carissimo et al.· Bioinformatics Advances· 0 citations
