ArticleNature communications2021
Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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Who cites it
70 citing papers in PubMed, 99 citations in OpenAlex.
- scGEN: Leveraging an Adaptive Weighting Strategy for Challenging Cell Clustering in Single-Cell RNA-seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
- Dynamic Landscape Analysis of cell fate decisions provides predictive models of neural development from single-cell data.PLoS biology · 2026Article
- SupeRJump: Determining normal and leukemic differentiation fate through semi-supervised jump diffusion modeling.bioRxiv : the preprint server for biology · 2026Article
- HNPP: Higher-order network-based personalized PageRank for detecting critical phase in complex biological systems.PLoS computational biology · 2026Article
- Molecular noise modulates transitions in the cell-fate differentiation landscape.NPJ systems biology and applications · 2026Article
- Article
- Inferring stochastic dynamics by biophysical Neural ODE using single-cell transcriptomics.Nature communications · 2026Article
- Deciphering functional intra-tumoral heterogeneity in BRAFOncogene · 2026Article
- Dissecting reversible and irreversible single cell state transitions from gene regulatory networks.Molecular systems biology · 2026Article
- Network hypermotifs in biological systems: structure, dynamics, and functional implications.NPJ systems biology and applications · 2026Review
- Unifying theories in high-dimensional biophysics: approaches, challenges and opportunities.NPJ systems biology and applications · 2026Article
- Navigating the landscape of direct cellular reprogramming with DiReG.NPJ systems biology and applications · 2026Article
- Perspectives on integrating artificial intelligence and single-cell omics for cellular plasticity research.Quantitative biology (Beijing, China) · 2025Article
- Branch-specific gene discovery in cell differentiation using multi-omics graph attention.PLoS computational biology · 2025Article
- Reconstructing gene network structure and dynamics from single cell data.Bioinformatics (Oxford, England) · 2025Article
- Review
- Cell and tissue reprogramming: Unlocking a new era in medical drug discovery.Pharmacological reviews · 2025Review
- A message passing framework for precise cell state identification with scClassify2.Genome biology · 2025Article
10 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
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. The method is consistent with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.
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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.