Simulating Gene Regulatory Networks for Validation of scRNA-seq Data Alignment Methods

Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.

Uncovering Cellular Resolution in scRNAseq via Unbiased Cell and Gene Network Analysis

The results indicate that GmGM provides a unified, reproducible framework for joint cell clustering and gene-network inference, capable of revealing cellular structure beyond that captured by conventional pipelines.

scFlowReport: A Reproducible Workflow for Comparative Downstream Biological Analysis of Single-Cell RNA-seq Data

Single-cell RNA sequencing (scRNA-seq) studies are frequently organized around comparisons—disease versus control, treatment response, or genetic perturbation—yet biological interpretation still depends on integrating multiple independent downstream analyses for differential expression, functional enrichment, regulatory network inference, and cell–cell communication analysis. Applying these tools consistently across comparisons typically requires substantial custom scripting, and their heterogeneous outputs must be manually harmonized before the results can be compared or reported together. We present scFlowReport, a lightweight, configuration-driven workflow that propagates a single user-defined comparison across cell-level and sample-aware pseudobulk differential expression, over-representation and ranked functional enrichment, transcription-factor regulon export for SCENIC, and group-resolved LIANA cell–cell communication analysis, starting from an already annotated Seurat object. The workflow automatically compiles complementary downstream results into standardized figures, summary tables, and a self-contained static HTML report that can be readily inspected and shared without requiring a persistent server. Application of scFlowReport to a publicly available Atopic Dermatitis scRNA-seq dataset demonstrated its utility by enabling researchers to obtain complementary biological evidence from multiple established downstream analyses. By coordinating complementary downstream analyses under a shared comparison framework, scFlowReport provides a practical and reproducible workflow for systematic interpretation of comparative single-cell transcriptomic data.

Benchmarking methods for inferring single-cell transcription factor activity using large-scale perturbation sequencing data

Abstract Transcription factor activity (TFA) is determined not solely by the expression level of the transcription factor (TF) gene itself, but is also modulated by a series of post-transcriptional regulatory processes. Although numerous computational methods have been developed to infer TFA from single-cell transcriptomic data by constructing gene regulatory networks (GRNs), a systematic and unified evaluation of these methods using high-quality experimental data remains lacking in the field. In this study, we conducted a comprehensive evaluation of eight mainstream TFA inference methods spanning three categories—prior GRN-based, de novo GRN-based, and integrated GRN-based approaches—using large-scale, high-quality single-cell perturbation sequencing (Perturb-seq) datasets. Our results demonstrate that metaTF, which employs an integrated GRN, achieves the best performance across multiple metrics, including TF coverage, predictive accuracy for perturbed cells, and accuracy for perturbed TFs. Among de novo GRN-based methods, pySCENIC exhibits predictive accuracy second only to metaTF but with lower TF coverage; meanwhile, decoupleR, a prior GRN-based method, ranks highly across all evaluated metrics. Further investigation reveals that the enrichment of reconstructed regulons within differentially expressed genes, the selection of prior GRNs and TFA scoring algorithms, and the perturbation types of target TFs are all critical factors influencing the accuracy of TFA inference. This study provides practical recommendations for the application and development of TFA inference methods.

scGraphVerse: a modular workflow for single-cell gene network inference

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.

FERRET: Framework to Evaluate Robustness in Regulatory Networks Using Heterogeneous Cell Types

Techniques for evaluating gene regulatory network (GRN) inference methods typically focus on recovering small ground-truth networks or on benchmarking against simulated data. However, both approaches have important limitations and fail to capture the biological variability present in real datasets. FERRET is a framework for benchmarking single-cell GRN inference methods based on a simple biological assumption: independent estimates of the regulatory network from the same cellular state should resemble one another more closely than estimates from distinct cellular states. Rather than relying on incomplete or simulated ground truth, FERRET quantifies network robustness using two complementary metrics: Robustness Area Under the Curve (RAUC), an AUC-like measure of within-cell-type network similarity relative to between-cell-type similarity, and Monotonicity, which assesses the consistency of network similarity across edge-weight cutoffs. FERRET also supports biological validation through pathway enrichment analysis. We validate FERRET using experimentally derived ChIP-seq networks from B lymphocytes and fibroblasts as positive controls and randomly generated networks as negative controls, showing that biologically related networks receive high robustness scores whereas randomly generated networks receive scores consistent with chance. Finally, we apply FERRET to multiple GRN inference methods on real single-cell RNA-sequencing datasets to identify methods that produce the most robust, biologically informative regulatory networks. GRAPHICAL ABSTRACT