ArticleNature biotechnology2019
Inferring population dynamics from single-cell RNA-sequencing time series data.
Article in Nature biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
- Decoding cellular population dynamics through mechanistic modelling and statistical data analysis.NPJ systems biology and applications · 2026Review
- Predicting neuroblastoma prognosis using machine learning analysis of metabolism-related gene network perturbation.BMC pediatrics · 2026Article
- scPD: a Python package for inferring continuous population dynamics from single-cell snapshot data.Bioinformatics advances · 2026Article
- A novel paradigm for single-cell annotation in stem cell research.Stem cell reports · 2025Review
- Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data.NPJ systems biology and applications · 2025Review
- TemporalVAE: atlas-assisted temporal mapping of time-series single-cell transcriptomes during embryogenesis.Nature cell biology · 2025Article
- Integrating single-cell RNA sequencing and Mendelian randomization analysis to identify potential drug targets for dilated cardiomyopathy.Hereditas · 2025Article
- Reconstructing Waddington Landscape from Cell Migration and Proliferation.Interdisciplinary sciences, computational life sciences · 2025Article
- CLADES: a hybrid NeuralODE-Gillespie approach for unveiling clonal cell fate and differentiation dynamics.Nature communications · 2025Article
- Quantifying Landscape and Flux from Single-Cell Omics: Unraveling the Physical Mechanisms of Cell Function.JACS Au · 2025Review
- Adapting systems biology to address the complexity of human disease in the single-cell era.Nature reviews. Genetics · 2025Review
- diffGEK: differential gene expression kinetics.Bioinformatics (Oxford, England) · 2025Article
- Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity.NPJ systems biology and applications · 2025Article
- Article
- The prognostic model based on tumor-associated neutrophils contributes to the stromal landscape and influences metabolic reprogramming in colorectal cancer.Frontiers in immunology · 2025Article
- Advanced omics approaches in liver transplant settings: current applications and future prospectives.Frontiers in immunology · 2025Review
- Mapping lineage-traced cells across time points with moslin.Genome biology · 2024Article
- LineageVAE: reconstructing historical cell states and transcriptomes toward unobserved progenitors.Bioinformatics (Oxford, England) · 2024Article
- CellRank 2: unified fate mapping in multiview single-cell data.Nature methods · 2024Article
- A time- and single-cell-resolved model of murine bone marrow hematopoiesis.Cell stem cell · 2024Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Recent single-cell RNA-sequencing studies have suggested that cells follow continuous transcriptomic trajectories in an asynchronous fashion during development. However, observations of cell flux along trajectories are confounded with population size effects in snapshot experiments and are therefore hard to interpret. In particular, changes in proliferation and death rates can be mistaken for cell flux. Here we present pseudodynamics, a mathematical framework that reconciles population dynamics with the concepts underlying developmental trajectories inferred from time-series single-cell data. Pseudodynamics models population distribution shifts across trajectories to quantify selection pressure, population expansion, and developmental potentials. Applying this model to time-resolved single-cell RNA-sequencing of T-cell and pancreatic beta cell maturation, we characterize proliferation and apoptosis rates and identify key developmental checkpoints, data inaccessible to existing approaches.
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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.