ArticleCell systems2020
Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe.
Article in Cell systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 2 of them syntheses 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
104 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Robot-Assisted Therapy for Upper Limb Rehabilitation After Stroke: Umbrella Review.Journal of medical Internet research · 2026Pooled it
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- CoReGRN: a context-aware post-processing framework for gene regulatory network inference revealing regulatory signatures in HIV-Leishmaniasis.BMC genomics · 2026Article
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- AutoGERN: single-cell RNA-seq gene regulatory network inference via explicit link modeling and adaptive architectures.Bioinformatics (Oxford, England) · 2026Article
- Generative models of cell dynamics: from Neural ODEs to flow matching.Communications biology · 2026Review
- EnsembleRegNet: Interpretable deep learning for transcriptional network inference from single-cell RNA-seq.Computational biology and chemistry · 2026Article
- CellCraft: an extensible visual programming application for gene regulatory network inference.Bioinformatics (Oxford, England) · 2026Article
- TDAGENE: Inference of Gene Regulatory Network Based on Topological Data Analysis and Graph Attention Network for Single-Cell RNA Sequencing Data.Computational and structural biotechnology journal · 2026Article
- DeepCE: a deep learning framework for correlation-enhanced gene regulatory network inference in single-cell RNA sequencing data.Bioinformatics advances · 2026Article
- Gene regulatory network inference with popInfer reveals the dynamic regulation of hematopoietic stem cell quiescence.iScience · 2025Article
- LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data.Briefings in bioinformatics · 2025Article
- Multi-omic network inference from time-series data.NPJ systems biology and applications · 2025Article
- InterVelo: a mutually enhancing model for estimating pseudotime and RNA velocity in multi-omic single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Inferring causal trajectories from spatial transcriptomics using CASCAT.Nucleic acids research · 2025Article
- Inference of gene coexpression networks from single-cell transcriptome data based on variance decomposition analysis.Briefings in bioinformatics · 2025Article
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data.Nature communications · 2025Article
- Optimal transport reveals dynamic gene regulatory networks via gene velocity estimation.PLoS computational biology · 2025Article
- Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.Molecular systems biology · 2025Review
44 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for "pseudotime"-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as "RNA velocity" restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.
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.