ArticleGenomics, proteomics & bioinformatics2021
scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy.
Article in Genomics, proteomics & bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 42 citations in OpenAlex.
- DNFE: Directed network flow entropy for detecting tipping points during biological processes.PLoS computational biology · 2025Article
- scRDEN: single-cell dynamic gene rank differential expression network and robust trajectory inference.Scientific reports · 2025Article
- "Weibing" in traditional Chinese medicine-biological basis and mathematical representation of disease-susceptible state.Acta pharmaceutica Sinica. B · 2025Review
- Identification of Critical States in Complex Biological Systems Using Cell-Specific Causal Network Entropy.Research (Washington, D.C.) · 2025Article
- The network structural entropy for single-cell RNA sequencing data during skin aging.Briefings in bioinformatics · 2024Article
- FateNet: an integration of dynamical systems and deep learning for cell fate prediction.Bioinformatics (Oxford, England) · 2024Article
- Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.Briefings in bioinformatics · 2024Article
- CPMI: comprehensive neighborhood-based perturbed mutual information for identifying critical states of complex biological processes.BMC bioinformatics · 2024Article
- Edge-based relative entropy as a sensitive indicator of critical transitions in biological systems.Journal of translational medicine · 2024Article
- MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy.BMC bioinformatics · 2024Article
- Distance covariance entropy reveals primed states and bifurcation dynamics in single-cell RNA-Seq data.iScience · 2022Article
- Identifying the critical state of complex biological systems by the directed-network rank score method.Bioinformatics (Oxford, England) · 2022Article
- Detecting the Critical States of Type 2 Diabetes Mellitus Based on Degree Matrix Network Entropy by Cross-Tissue Analysis.Entropy (Basel, Switzerland) · 2022Article
- A novel method to detect the early warning signal of COVID-19 transmission.BMC infectious diseases · 2022Article
- Identifying the critical states and dynamic network biomarkers of cancers based on network entropy.Journal of translational medicine · 2022Article
- Advanced Single-cell Omics Technologies and Informatics Tools for Genomics, Proteomics, and Bioinformatics Analysis.Genomics, proteomics & bioinformatics · 2021Article
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5 authors at 2 institutions in 1 country.
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Abstract
During early embryonic development, cell fate commitment represents a critical transition or "tipping point" of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene-gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the "dark genes" that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes.The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.
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