ArticleCommunications biology2023
RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- CFM-GP: unified conditional flow matching to learn gene perturbation across cell types.NAR genomics and bioinformatics · 2026Article
- scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.BMC genomics · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- EnsembleRegNet: Interpretable deep learning for transcriptional network inference from single-cell RNA-seq.Computational biology and chemistry · 2026Article
- Gene regulatory network inference with popInfer reveals the dynamic regulation of hematopoietic stem cell quiescence.iScience · 2025Article
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- GRiNS: a python library for simulating gene regulatory network dynamics.BMC bioinformatics · 2025Article
- CellNavi predicts genes directing cellular transitions by learning a gene graph-enhanced cell state manifold.Nature cell biology · 2025Article
- Computational modeling of single-cell dynamics data.Briefings in bioinformatics · 2025Review
- In silico screening system based on a transcription factors regulatory network only using transcriptomic data.PloS one · 2025Article
- Virus Infection Induces Immune Gene Activation with CTCF-anchored Enhancers and Chromatin Interactions in Pig Genome.Genomics, proteomics & bioinformatics · 2024Article
- A mini-review on perturbation modelling across single-cell omic modalities.Computational and structural biotechnology journal · 2024Review
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Authors and funding
13 authors.
Funding
Abstract
Single-cell RNA-seq analysis coupled with CRISPR-based perturbation has enabled the inference of gene regulatory networks with causal relationships. However, a snapshot of single-cell CRISPR data may not lead to an accurate inference, since a gene knockout can influence multi-layered downstream over time. Here, we developed RENGE, a computational method that infers gene regulatory networks using a time-series single-cell CRISPR dataset. RENGE models the propagation process of the effects elicited by a gene knockout on its regulatory network. It can distinguish between direct and indirect regulations, which allows for the inference of regulations by genes that are not knocked out. RENGE therefore outperforms current methods in the accuracy of inferring gene regulatory networks. When used on a dataset we derived from human-induced pluripotent stem cells, RENGE yielded a network consistent with multiple databases and literature. Accurate inference of gene regulatory networks by RENGE would enable the identification of key factors for various biological systems.
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Registered trials
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