ArticleNature methods2020
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data.
Article in Nature methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 450 papers, 1 of them a synthesis that pooled it.
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Who cites it
450 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- scHGNN: A single-cell gene regulatory network inference method based on heuristic graph neural networks.Functional & integrative genomics · 2026Article
- CauTrigger: deciphering biological system state transitions by hierarchical causal decomposition.Nature methods · 2026Article
- Tunable population diversity through a noise-driven differentiation switch.bioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence for Plant Gene Regulatory Networks.International journal of molecular sciences · 2026Review
- A Method for Imputing scRNA-seq Data Based on Mobile Graph Convolutional Generative Adversarial Networks.Biochemical genetics · 2026Article
- BEAR-GRN: Systematic assessment of single-cell multi-omics-based gene regulatory network inference methods.Nature communications · 2026Article
- Joint inference of paired dynamical gene regulatory networks reveals distinct cell-state landscapes of neutrophil reprogramming.bioRxiv : the preprint server for biology · 2026Article
- A Gene-Program Architecture of Mouse T cells.bioRxiv : the preprint server for biology · 2026Article
- GLM-Prior: a genomic language model for transferable sequence-derived priors in gene regulatory network inference.Nature communications · 2026Article
- DDTRN: predicting bacterial transcriptional regulatory networks based on gene sequences using dual descriptor.Briefings in bioinformatics · 2026Article
- Benchmarking methods for inferring single-cell transcription factor activity using large-scale perturbation sequencing data.Briefings in bioinformatics · 2026Article
- Constructing cell-type-specific gene regulatory networks from cell-cell communication and a global gene regulatory network.Briefings in bioinformatics · 2026Article
- SignifiKANTE: efficient P-value computation for gene regulatory networks.Bioinformatics (Oxford, England) · 2026Article
- BLOBFISH: bipartite limited subnetworks from multiple observations using breadth-first search with constrained hops.Bioinformatics (Oxford, England) · 2026Article
- IAGRN: An Interleaved-Attention Graph Neural Network for Gene Regulatory Network Inference.International journal of molecular sciences · 2026Article
- A Framework for Benchmarking Pathway Reconstruction Algorithms.bioRxiv : the preprint server for biology · 2026Article
- Global StationaryOT: trajectory inference for aging time courses of single-cell snapshots.Bioinformatics (Oxford, England) · 2026Article
- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
- Hybrid origin and phenotype evolution of the modern maize.Journal of integrative plant biology · 2026Review
390 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
We present a systematic evaluation of state-of-the-art algorithms for inferring gene regulatory networks from single-cell transcriptional data. As the ground truth for assessing accuracy, we use synthetic networks with predictable trajectories, literature-curated Boolean models and diverse transcriptional regulatory networks. We develop a strategy to simulate single-cell transcriptional data from synthetic and Boolean networks that avoids pitfalls of previously used methods. Furthermore, we collect networks from multiple experimental single-cell RNA-seq datasets. We develop an evaluation framework called BEELINE. We find that the area under the precision-recall curve and early precision of the algorithms are moderate. The methods are better in recovering interactions in synthetic networks than Boolean models. The algorithms with the best early precision values for Boolean models also perform well on experimental datasets. Techniques that do not require pseudotime-ordered cells are generally more accurate. Based on these results, we present recommendations to end users. BEELINE will aid the development of gene regulatory network inference algorithms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.