ArticleBMC bioinformatics2024
scEGOT: single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
- Cell trajectory inference based on schrödinger problem and a mechanistic model of stochastic gene expression.NPJ systems biology and applications · 2026Article
- Branched Schrödinger Bridge Matching.ArXiv · 2026Article
- Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.Briefings in bioinformatics · 2026Review
- MultistageOT: Multistage optimal transport infers trajectories from a snapshot of single-cell data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data.NPJ systems biology and applications · 2025Review
- Dynamic gene regulatory network inference from single-cell data using optimal transport.Bioinformatics (Oxford, England) · 2025Article
- Optimal transport reveals dynamic gene regulatory networks via gene velocity estimation.PLoS computational biology · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundTime-series scRNA-seq data have opened a door to elucidate cell differentiation, and in this context, the optimal transport theory has been attracting much attention. However, there remain critical issues in interpretability and computational cost.
resultsWe present scEGOT, a comprehensive framework for single-cell trajectory inference, as a generative model with high interpretability and low computational cost. Applied to the human primordial germ cell-like cell (PGCLC) induction system, scEGOT identified the PGCLC progenitor population and bifurcation time of segregation. Our analysis shows TFAP2A is insufficient for identifying PGCLC progenitors, requiring NKX1-2. Additionally, MESP1 and GATA6 are also crucial for PGCLC/somatic cell segregation.
conclusionsThese findings shed light on the mechanism that segregates PGCLC from somatic lineages. Notably, not limited to scRNA-seq, scEGOT's versatility can extend to general single-cell data like scATAC-seq, and hence has the potential to revolutionize our understanding of such datasets and, thereby also, developmental biology.
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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 Socratic graph.