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

Sarah Ancheta, Leah Dorman, Guillaume Le Treut, Abel Gurung, Greg Huber, Loïc A Royer, Alejandro Granados and Merlin Lange

PLOS Computational Biology, 2026, vol. 22, issue 6, 1-20

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.Author summary: Single-cell RNA sequencing measures gene activity in individual cells, but each cell is only a snapshot in time. RNA velocity uses the balance of unspliced and spliced RNA to estimate where each cell is heading, helping reconstruct cell developmental trajectories. However, different velocity algorithms can yield different arrows on the same data, making results hard to interpret. We built a reproducible comparison pipeline and benchmarked five widely used RNA velocity methods across developmental datasets (mouse pancreas development and two zebrafish embryo datasets). We assessed (i) whether nearby, similar cells show consistent predicted directions, (ii) how strongly methods agree with one another, (iii) how disagreements propagate to downstream driver gene, and (iv) robustness when sequencing depth is reduced by read downsampling. All methods recovered known trajectories in some settings, but performance varied with biological complexity and read depth, and driver-gene rankings were often method-dependent. We provide practical guidelines and open code to help researchers choose, cross-check, and validate RNA velocity results as hypothesis-generating evidence.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014303

DOI: 10.1371/journal.pcbi.1014303

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