ArticleNature communications2025
TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- T Cell Heterogeneity Shaped by Alternative Splicing Predicts Prognosis in Hepatocellular Carcinoma.Cancer science · 2026Article
- Article
- RNAPaceDB: a dedicated database to dissect RNA velocity across diverse cell types.Nucleic acids research · 2026Article
- Fundamental errors in RNA velocity arising from the omission of cell growth.bioRxiv : the preprint server for biology · 2025Article
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
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Authors and funding
5 authors.
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Abstract
RNA velocity inference is a valuable tool for understanding cell development, differentiation, and disease progression. However, existing RNA velocity inference methods typically rely on explicit assumptions of ordinary differential equations (ODE), which prohibits them from capturing complex transcriptomic expression patterns. In this study, we introduce TIVelo, a RNA velocity estimation approach that first determines the velocity direction at the cell cluster level based on trajectory inference, before estimating velocity for individual cells. TIVelo calculates an orientation score to infer the direction at the cluster level without an explicit ODE assumption, which effectively captures complex transcriptional patterns, avoiding potential inconsistencies in velocity estimation for genes that do not follow the simple ODE assumption. We validated the effectiveness of TIVelo by its application to 16 real datasets and the comparison with six benchmarking methods.
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