ArticlePLoS computational biology2021
Optimal transport analysis reveals trajectories in steady-state systems.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
- Global StationaryOT: trajectory inference for aging time courses of single-cell snapshots.Bioinformatics (Oxford, England) · 2026Article
- CellRank: consistent and data view agnostic fate mapping for single-cell genomics.Nature protocols · 2026Review
- SupeRJump: Determining normal and leukemic differentiation fate through semi-supervised jump diffusion modeling.bioRxiv : the preprint server for biology · 2026Article
- DestinyNet: A deep-learning framework for cell-fate analysis from lineage-tracing single-cell RNA sequencing data.Patterns (New York, N.Y.) · 2026Article
- Vascular smooth muscle cell state trajectories mediate molecular mechanisms of coronary disease risk.Nature communications · 2026Article
- Branched Schrödinger Bridge Matching.ArXiv · 2026Article
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 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
- VIP-OT: Dissecting Single-Cell Biochemical State Dynamics under Perturbation via Vibrational Painting and Optimal Transport.bioRxiv : the preprint server for biology · 2025Article
- Single-cell-level digital twins for preterm birth prevention strategies.bioRxiv : the preprint server for biology · 2025Article
- Learning stochastic processes with intrinsic noise from cross-sectional biological data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Dynamic gene regulatory network inference from single-cell data using optimal transport.Bioinformatics (Oxford, England) · 2025Article
- Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis.Entropy (Basel, Switzerland) · 2025Review
- Gene trajectory inference for single-cell data by optimal transport metrics.Nature biotechnology · 2025Article
- The dynamics of hematopoiesis over the human lifespan.Nature methods · 2025Article
- Computing the distance between unbalanced distributions: the flat metric.Machine learning · 2025Article
- Cellular proliferation biases clonal lineage tracing and trajectory inference.Bioinformatics (Oxford, England) · 2024Article
- Modeling single cell trajectory using forward-backward stochastic differential equations.PLoS computational biology · 2024Article
- Reconstructing growth and dynamic trajectories from single-cell transcriptomics data.Nature machine intelligence · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding how cells change their identity and behaviour in living systems is an important question in many fields of biology. The problem of inferring cell trajectories from single-cell measurements has been a major topic in the single-cell analysis community, with different methods developed for equilibrium and non-equilibrium systems (e.g. haematopoeisis vs. embryonic development). We show that optimal transport analysis, a technique originally designed for analysing time-courses, may also be applied to infer cellular trajectories from a single snapshot of a population in equilibrium. Therefore, optimal transport provides a unified approach to inferring trajectories that is applicable to both stationary and non-stationary systems. Our method, StationaryOT, is mathematically motivated in a natural way from the hypothesis of a Waddington's epigenetic landscape. We implement StationaryOT as a software package and demonstrate its efficacy in applications to simulated data as well as single-cell data from Arabidopsis thaliana root development.
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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 OpenQuestion graph.