Benchmarking evaluates RNA velocity methods' performance in accuracy, stability, and usability, suggesting best practices for users.
RNA velocity techniques offer great potential for unveiling trajectories of cell state transitions in different biological contexts. While diverse computational methods have been developed, there are no evidence-based guidelines for best-practice in RNA velocity inference. Here, we conduct a benchmark study of 15 existing RNA velocity methods across 17 independent datasets, incorporating multiple validation strategies. We evaluate performance across three key dimensions: accuracy, stability, and usability. Our data showed no single method exhibited superior performance in all the assessments, and unexpected underperformance was observed in certain cases. Based on these findings, we establish scenario-based suggestions of best-practice to assist users in selecting the method best suited to their data and analytical needs.
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Luo et al. (2026) studied this question.
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