ArticleNucleic acids research2021
TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Ten quick tips for causal analysis of biomedical omics data.PLoS computational biology · 2026Article
- scRAPID-web: a web server for predicting protein-RNA interactions from single-cell transcriptomics.BMC genomics · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- CellCraft: an extensible visual programming application for gene regulatory network inference.Bioinformatics (Oxford, England) · 2026Article
- Controllability of the gene regulatory network in zebrafish embryogenesis.Scientific reports · 2025Article
- Gene regulatory network inference with popInfer reveals the dynamic regulation of hematopoietic stem cell quiescence.iScience · 2025Article
- Inference of causal interaction networks of gut microbiota using transfer entropy.BMC genomics · 2025Article
- Trajectory Inference for Single Cell Omics.ArXiv · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Integrating Prior Knowledge Using Transformer for Gene Regulatory Network Inference.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- Single-microglia transcriptomic transition network-based prediction and real-world patient data validation identifies ketorolac as a repurposable drug for Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- FastTENET: an accelerated TENET algorithm based on manycore computing in Python.Bioinformatics (Oxford, England) · 2024Article
- Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- Prediction of protein-RNA interactions from single-cell transcriptomic data.Nucleic acids research · 2024Article
- TFvelo: gene regulation inspired RNA velocity estimation.Nature communications · 2024Article
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
- MICA: a multi-omics method to predict gene regulatory networks in early human embryos.Life science alliance · 2024Article
- Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON.Nature communications · 2023Article
- Network Analysis and Experimental Verification of the Mechanisms of Hydroxysafflor Yellow A in Ischemic Stroke Following Atherosclerosis.Molecules (Basel, Switzerland) · 2023Article
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4 authors.
Funding
Abstract
Accurate prediction of gene regulatory rules is important towards understanding of cellular processes. Existing computational algorithms devised for bulk transcriptomics typically require a large number of time points to infer gene regulatory networks (GRNs), are applicable for a small number of genes and fail to detect potential causal relationships effectively. Here, we propose a novel approach 'TENET' to reconstruct GRNs from single cell RNA sequencing (scRNAseq) datasets. Employing transfer entropy (TE) to measure the amount of causal relationships between genes, TENET predicts large-scale gene regulatory cascades/relationships from scRNAseq data. TENET showed better performance than other GRN reconstructors, in identifying key regulators from public datasets. Specifically from scRNAseq, TENET identified key transcriptional factors in embryonic stem cells (ESCs) and during direct cardiomyocytes reprogramming, where other predictors failed. We further demonstrate that known target genes have significantly higher TE values, and TENET predicted higher TE genes were more influenced by the perturbation of their regulator. Using TENET, we identified and validated that Nme2 is a culture condition specific stem cell factor. These results indicate that TENET is uniquely capable of identifying key regulators from scRNAseq data.
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