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Abstract Single‐cell chromatin conformation capture (scHi‐C) techniques have evolved to provide significant insights into the structural organization and regulatory mechanisms in individual cells. Although many scHi‐C protocols have been developed, they often involve intricate procedures and the resulting data are sparse, leading to computational challenges for systematic data analysis and limited applicability. This review provides a comprehensive overview, quantitative evaluation of thirteen protocols and practical guidance on computational topics. It is first assessed the efficiency of these protocols based on the total number of contacts recovered per cell and thecis/transratio. It is then provided systematic considerations for scHi‐C quality control and data imputation. Additionally, the capabilities and implementations of various analysis methods, covering cell clustering, A/B compartment calling, topologically associating domain (TAD) calling, loop calling, 3D reconstruction, scHi‐C data simulation and differential interaction analysis is summarized. It is further highlighted key computational challenges associated with the specific complexities of scHi‐C data and propose potential solutions. 

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. 

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. 

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.