ArticleNature communications2023
Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- CauTrigger: deciphering biological system state transitions by hierarchical causal decomposition.Nature methods · 2026Article
- CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Inferring Gene Regulatory Networks From Single-Cell RNA Sequencing Data by Dual-Role Graph Contrastive Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deciphering hierarchical regulatory network of cell fate via an epigenetics-informed heterogeneous graph transformer on single-cell multi-omics data.Briefings in bioinformatics · 2025Article
- CellNavi predicts genes directing cellular transitions by learning a gene graph-enhanced cell state manifold.Nature cell biology · 2025Article
- Engineering Cell Fate with Adaptive Feedback Control.ACS synthetic biology · 2025Article
- DualNetM: an adaptive dual network framework for inferring functional-oriented markers.BMC biology · 2025Article
- scPRINT: pre-training on 50 million cells allows robust gene network predictions.Nature communications · 2025Article
- MulNet: a scalable framework for reconstructing intra- and intercellular signaling networks from bulk and single-cell RNA-seq data.Briefings in bioinformatics · 2025Article
- Review
- Target control of linear directed networks based on the path cover problem.Scientific reports · 2024Article
- Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON.Nature communications · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.
Indexed as
Identifiers
What OpenQuestion holds
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.