Guangzheng Weng1,2, Junil Kim2,3,4, Kyoung Jae Won2,3. 1. Department of Biology, The bioinformatics Centre, University of Copenhagen, 2200 Copenhagen N, Denmark. 2. Biotech Research and Innovation Centre (BRIC), University of Copenhagen, 2200 Copenhagen N, Denmark. 3. Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, Faculty of Health and Medical Sciences, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen N, Denmark. 4. Department of Bioinformatics, School of Systems Biomedical Science, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, 06978 Seoul, South Korea.
Abstract
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: The Vetra is available at https://github.com/wgzgithub/VeTra. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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: The Vetra is available at https://github.com/wgzgithub/VeTra. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.