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1. scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets

   https://doi.org/10.3390/ijms241713339  

   Dautle, Madison; Zhang, Shaoqiang; Chen, Yong (September 2023, International Journal of Molecular Sciences)

   Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among gene modules. As state-of-the-art applications, we first applied scTIGER to scRNA-seq datasets of prostate cancer cells, and successfully identified the dynamic regulatory networks of AR, ERG, PTEN and ATF3 for same-cell type between prostatic cancerous and normal conditions, and two-cell types within the prostatic cancerous environment. We then applied scTIGER to scRNA-seq data from neurons with and without fear memory and detected specific regulatory networks for BDNF, CREB1 and MAPK4. Additionally, scTIGER demonstrates robustness against high levels of dropout noise in scRNA-seq data. 

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2. Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders

   https://doi.org/10.1093/bib/bbaf089  

   Chen, Liang; Dautle, Madison; Gao, Ruoying; Zhang, Shaoqiang; Chen, Yong (March 2025, Briefings in Bioinformatics)

   Abstract The development of single-cell RNA sequencing (scRNA-seq) technology provides valuable data resources for inferring gene regulatory networks (GRNs), enabling deeper insights into cellular mechanisms and diseases. While many methods exist for inferring GRNs from static scRNA-seq data, current approaches face challenges in accurately handling time-series scRNA-seq data due to high noise levels and data sparsity. The temporal dimension introduces additional complexity by requiring models to capture dynamic changes, increasing sensitivity to noise, and exacerbating data sparsity across time points. In this study, we introduce GRANGER, an unsupervised deep learning-based method that integrates multiple advanced techniques, including a recurrent variational autoencoder, GRANGER causality, sparsity-inducing penalties, and negative binomial (NB)-based loss functions, to infer GRNs. GRANGER was evaluated using multiple popular benchmarking datasets, where it demonstrated superior performance compared to eight well-known GRN inference methods. The integration of a NB-based loss function and sparsity-inducing penalties in GRANGER significantly enhanced its capacity to address dropout noise and sparsity in scRNA-seq data. Additionally, GRANGER exhibited robustness against high levels of dropout noise. We applied GRANGER to scRNA-seq data from the whole mouse brain obtained through the BRAIN Initiative project and identified GRNs for five transcription regulators: E2f7, Gbx1, Sox10, Prox1, and Onecut2, which play crucial roles in diverse brain cell types. The inferred GRNs not only recalled many known regulatory relationships but also revealed sets of novel regulatory interactions with functional potential. These findings demonstrate that GRANGER is a highly effective tool for real-world applications in discovering novel gene regulatory relationships. 

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3. EnsembleRegNet: Interpretable deep learning for transcriptional network inference from single-cell RNA-seq

   https://doi.org/10.1016/j.compbiolchem.2025.108702  

   Alawad, Duaa Mohammad; Katebi, Ataur; Hoque, Md Tamjidul (February 2026, Computational Biology and Chemistry)

   Gene regulatory networks (GRNs) govern gene expression and cellular identity, but accurately inferring their structure from high-dimensional single-cell RNA sequencing (scRNA-seq) data remains a major challenge. Here, we present EnsembleRegNet, a deep learning framework that infers transcription factor (TF)-target gene relationships by integrating an ensemble encoder-decoder and multilayer perceptron (MLP) architecture. EnsembleRegNet utilizes Hodges-Lehmann estimator (HLE)-based binarization, case-deletion analysis, motif enrichment using RcisTarget, and regulon activity scoring with AUCell to enhance both robustness and biological interpretability. Extensive evaluations across simulated and real scRNA-seq datasets demonstrate that EnsembleRegNet outperforms existing GRN inference methods, including SCENIC and SIGNET, in both clustering performance and regulatory accuracy. By uncovering cell-type-specific regulatory modules and enhancing interpretability, EnsembleRegNet offers a scalable and biologically grounded framework for exploring transcriptional regulation. Its demonstrated performance establishes a new benchmark for GRN inference and highlights its promise for applications in disease modeling, biomarker discovery, and cellular reprogramming. 

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4. A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

   https://doi.org/10.1002/aidi.202500247  

   Gupta, Nishi; Dautle, Madison; Zhang, Shaoqiang; Chen, Yong (April 2026, Advanced Intelligent Discovery)

   Accurately inferring gene regulatory networks (GRNs) from single‐cell RNA sequencing (scRNA‐seq) data is critical for understanding cellular dynamics in both normal development and disease. However, existing computational methods often suffer from low precision and high false‐positive rates due to the intrinsic noise and complex regulatory architecture in scRNA‐seq data. We introduce scTIGER2.0, a deep‐learning‐based framework that integrates expression correlation, pseudotime ordering, temporal causal discovery, and bootstrap‐based significance testing to infer high‐confidence, directional gene–gene interactions. Benchmarking against five popular GRN inference methods using large‐scale datasets, scTIGER2.0 consistently achieved superior specificity, especially in linear developmental trajectories. In real applications, scTIGER2.0 identified an APOE‐centered GRN from Alzheimer's disease scRNA‐seq data and uncovered interconnected GRNs for FOS, FOXP1, JUN, KLF6, NCOA4, and RUNX1 from acute myeloid leukemia data, where 87.5% of the predicted targets show promoter‐binding peaks in the corresponding ChIP‐seq data. These results demonstrate that scTIGER2.0 is a robust, accurate and fully integrated platform for uncovering biologically meaningful GRNs from noisy scRNA‐seq data. 

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5. GRNFormer: accurate gene regulatory network inference using graph transformer

   https://doi.org/10.1093/bioinformatics/btag144  

   Hegde, Akshata; Cheng, Jianlin (April 2026, Bioinformatics)

   Mathelier, Anthony (Ed.)

   Abstract MotivationDeciphering gene regulatory networks (GRNs) from single-cell transcriptomics data remains a fundamental challenge in computational biology. It is hindered by data sparsity, high dimensionality, and the lack of scalable, generalizable inference models. To address this, we present GRNFormer, a generalizable graph transformer framework for accurate GRN inference from transcriptomics data across species, cell types, and platforms without requiring cell-type annotations or prior regulatory information. ResultsGRNFormer integrates a transformer-based gene expression encoder (Gene-Transcoder) with a variational graph autoencoder (GraViTAE) employing pairwise attention to jointly learn the representations of genes (nodes) and their co-expression relationships (edges). Leveraging TF-Walker, a transcription factor-anchored subgraph sampling strategy, it effectively captures gene regulatory interactions from either single-cell or bulk RNA-seq datasets. Benchmarking on standard datasets demonstrates that GRNFormer outperforms existing traditional and deep learning state-of-the-art methods in blind evaluations, achieving average sampled area under the receiver operating characteristic curve (Sampled_AUROC) and sampled area under the precision–recall curve (Sampled_AUPRC) values between 0.90 and 0.98 as well as 0.87–0.98 average sampled F1 score. The model robustly recovers both known and novel regulatory networks, including pluripotency circuits in human embryonic stem cells (hESCs) and immune cell modules in peripheral blood mononuclear cells (PBMCs). The architecture enables scalable, biologically interpretable GRN inference across various datasets, cell types, and species, establishing GRNFormer as a robust and transferable tool for network biology. Availability and implementationGRNFormer is available on GitHub (https://github.com/BioinfoMachineLearning/GRNformer); the version used in this work is archived on Zenodo (https://doi.org/10.5281/zenodo.18868395), with evaluation resources for reproducibility. 

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