MOTIVATION: Trajectory inference (TI) for single cell RNA sequencing (scRNAseq) data is a powerful approach to interpret dynamic cellular processes such as cell cycle and development. Still, however, accurate inference of trajectory is challenging. Recent development of RNA velocity provides an approach to visualize cell state transition without relying on prior knowledge.
RESULTS: To perform TI and group cells based on RNA velocity we developed VeTra. By applying cosine similarity and merging weakly connected components, VeTra identifies cell groups from the direction of cell transition. Besides, VeTra suggests key regulators from the inferred trajectory. VeTra is a useful tool for TI and subsequent analysis.
AVAILABILITY AND IMPLEMENTATION: The Vetra is available at https://github.com/wgzgithub/VeTra.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
