ArticleNature methods2024
Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Article
- mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.Bioinformatics (Oxford, England) · 2026Article
- CycleVI: isolating cell cycle variation with an interpretable deep generative model.Bioinformatics (Oxford, England) · 2026Article
- From noise to models to numbers: Evaluating negative binomial models and parameter estimations in single-cell RNA-seq.PLoS computational biology · 2026Article
- Graph Topology Reframes the Coherence of Cell-State Manifold Inference under Heterogeneous Single-Cell Observations.Computational and structural biotechnology journal · 2026Article
- A novel paradigm for single-cell annotation in stem cell research.Stem cell reports · 2025Review
- Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics.Bioactive materials · 2025Review
- Insights, opportunities, and challenges provided by large cell atlases.Genome biology · 2025Review
- InterVelo: a mutually enhancing model for estimating pseudotime and RNA velocity in multi-omic single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Quantifying Landscape and Flux from Single-Cell Omics: Unraveling the Physical Mechanisms of Cell Function.JACS Au · 2025Review
- GraphVelo allows for accurate inference of multimodal velocities and molecular mechanisms for single cells.Nature communications · 2025Article
- Evolutionarily recent transcription factors partake in human cell cycle regulation.Cell genomics · 2025Article
- Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review.Briefings in bioinformatics · 2025Review
- Single-cell and spatial genomic landscape of non-small cell lung cancer brain metastases.Nature medicine · 2025Article
- Trajectory inference from single-cell genomics data with a process time model.PLoS computational biology · 2025Article
- Uncovering cell cycle speed modulations with statistical inference.Nature methods · 2024Article
- Storm: Incorporating transient stochastic dynamics to infer the RNA velocity with metabolic labeling information.PLoS computational biology · 2024Article
Corrections and comments
- Update of
Authors and funding
14 authors.
Funding
Abstract
Across biological systems, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. While low-dimensional dynamics can be extracted using RNA velocity, these algorithms can be fragile and rely on heuristics lacking statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. To address these challenges, we introduce a Bayesian model of RNA velocity that couples velocity field and manifold estimation in a reformulated, unified framework, identifying the parameters of an explicit dynamical system. Focusing on the cell cycle, we implement VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validate its ability to infer cell cycle periods using live imaging. We also apply VeloCycle to reveal speed differences in regionally defined progenitors and Perturb-seq gene knockdowns. Overall, VeloCycle expands the single-cell RNA sequencing analysis toolkit with a modular and statistically consistent RNA velocity inference framework.
Indexed as
Identifiers
What OpenQuestion holds
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.