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Peer-reviewedCC BYPLoS Computational Biology

Challenges and progress in RNA velocity: Comparative analysis across multiple biological contexts

  • Sarah Ancheta1,2,3
  • Leah C. Dorman1,3
  • Guillaume Le Treut1,3
  • Abel Gurung1,3
  • Greg Huber1,4,3
  • Löıc A. Royer1,5
  • Alejandro A. Granados1,6,3
  • Merlin Lange1,7
7 affiliations
  1. 1Biohub, San Francisco, United States of America
  2. 2Present address Biological and Medical Informatics Graduate Program, University of California, San Francisco, California, United States of America
  3. 3Chan Zuckerberg Biohub SF
  4. 4University of California, San Francisco, United States of America
  5. 5Calico Life Sciences LLC
  6. 6Present address Calico Life Sciences LLC, South San Francisco, California, United States of America
  7. 7Institut de la Vision, Sorbonne Universite´, INSERM, CNRS, Paris, France
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Abstract

Single-cell RNA sequencing is revolutionizing our understanding of cell state dynamics, allowing researchers to capture and quantify the transcriptomic profile of a single cell at a specific timepoint. Among the computational techniques used to predict cellular trajectories, RNA velocity has emerged as a predominant tool for modeling transcriptional dynamics. RNA velocity leverages the mRNA maturation process to generate velocity vectors that predict the likely future state of a cell, offering insights into cellular differentiation, aging, and disease progression. Although this technique has shown promise across biological fields, the performance accuracy varies depending on the RNA velocity method and dataset. We established a comparative pipeline and analyzed the performance of five RNA velocity methods on three datasets based on local consistency, method agreement, identification of driver genes, and robustness to sequencing depth. This benchmark provides a resource for scientists to understand the strengths and limitations of different RNA velocity methods.

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Cite

BibTeXRIS
BibTeX
@article{ancheta2026challenges,
  title = {{Challenges and progress in RNA velocity: Comparative analysis across multiple biological contexts}},
  author = {Sarah Ancheta and Leah C. Dorman and Guillaume Le Treut and Abel Gurung and Greg Huber and Löıc A. Royer and Alejandro A. Granados and Merlin Lange},
  journal = {PLoS Computational Biology},
  year = {2026},
  month = jun,
  doi = {10.1371/journal.pcbi.1014303},
  url = {https://doi.org/10.1371/journal.pcbi.1014303},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Challenges and progress in RNA velocity: Comparative analysis across multiple biological contexts
AU  - Sarah Ancheta
AU  - Leah C. Dorman
AU  - Guillaume Le Treut
AU  - Abel Gurung
AU  - Greg Huber
AU  - Löıc A. Royer
AU  - Alejandro A. Granados
AU  - Merlin Lange
PY  - 2026
DA  - 2026/06/01
JO  - PLoS Computational Biology
DO  - 10.1371/journal.pcbi.1014303
UR  - https://doi.org/10.1371/journal.pcbi.1014303
AB  - Single-cell RNA sequencing is revolutionizing our understanding of cell state dynamics, allowing researchers to capture and quantify the transcriptomic profile of a single cell at a specific timepoint. Among the computational techniques used to predict cellular trajectories, RNA velocity has emerged as a predominant tool for modeling transcriptional dynamics. RNA velocity leverages the mRNA maturation process to generate velocity vectors that predict the likely future state of a cell, offering insights into cellular differentiation, aging, and disease progression. Although this technique has shown promise across biological fields, the performance accuracy varies depending on the RNA velocity method and dataset. We established a comparative pipeline and analyzed the performance of five RNA velocity methods on three datasets based on local consistency, method agreement, identification of driver genes, and robustness to sequencing depth. This benchmark provides a resource for scientists to understand the strengths and limitations of different RNA velocity methods.
SN  - 1553-734X
SN  - 1553-7358
ER  -