ArticleNature communications2024
GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 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
- Insights Into Spatial Transcriptomics: Exploring Recent Technical Developments and Their Diverse Applications.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Review
- Explainable data generation from observed small samples by matrix tri-factorization.Nature communications · 2026Article
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Decoding plant physiology through systems biology: Integrative multi-omics and computational perspectives for next-generation crop design.Plant communications · 2026Review
- Big data approaches to understanding gene regulatory networks in the shoot apical meristem andFrontiers in plant science · 2026Review
- Network-informed deconvolution of bulk immune gene co-expression reveals single-cell programs and spatial organization.Frontiers in immunology · 2026Article
- GeneSNAKE: a Python package for simulation of gene regulatory networks and perturbation-induced expression data.Bioinformatics advances · 2026Article
- FastSCODE: an accelerated SCODE algorithm for inferring gene regulatory networks on manycore processors.Bioinformatics (Oxford, England) · 2025Article
- scRegulate: single-cell regulatory-embedded variational inference of transcription factor activity from gene expression.Bioinformatics (Oxford, England) · 2025Article
- Simultaneously infer cell pseudotime, velocity field, and gene interaction from multi-branch scRNA-seq data with scPN.NAR genomics and bioinformatics · 2025Article
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- MOSim: bulk and single-cell multilayer regulatory network simulator.Briefings in bioinformatics · 2025Article
- A mini-review on perturbation modelling across single-cell omic modalities.Computational and structural biotechnology journal · 2024Review
- Deciphering lineage-relevant gene regulatory networks during endoderm formation by InPheRNo-ChIP.Briefings in bioinformatics · 2024Article
- GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks.Nature communications · 2024Article
- Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based causal implicit generative model for simulating single-cell RNA-seq data, in silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on six experimental reference datasets, we show that our model captures non-linear TF-gene dependencies and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. GRouNdGAN can synthesize cells under new conditions to perform in silico TF knockout experiments. Benchmarking various GRN inference algorithms reveals that GRouNdGAN effectively bridges the existing gap between simulated and biological data benchmarks of GRN inference algorithms, providing gold standard ground truth GRNs and realistic cells corresponding to the biological system of interest.
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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.