VeTra: a tool for trajectory inference based on RNA velocity

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VeTra : a tool for trajectory inference based on RNA velocity. / Weng, Guangzheng; Kim, Junil; Won, Kyoung Jae.

In: Bioinformatics, Vol. 37, No. 20, btab364, 2021, p. 3509-3513.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Weng, G, Kim, J & Won, KJ 2021, 'VeTra: a tool for trajectory inference based on RNA velocity', Bioinformatics, vol. 37, no. 20, btab364, pp. 3509-3513. https://doi.org/10.1093/bioinformatics/btab364

APA

Weng, G., Kim, J., & Won, K. J. (2021). VeTra: a tool for trajectory inference based on RNA velocity. Bioinformatics, 37(20), 3509-3513. [btab364]. https://doi.org/10.1093/bioinformatics/btab364

Vancouver

Weng G, Kim J, Won KJ. VeTra: a tool for trajectory inference based on RNA velocity. Bioinformatics. 2021;37(20):3509-3513. btab364. https://doi.org/10.1093/bioinformatics/btab364

Author

Weng, Guangzheng ; Kim, Junil ; Won, Kyoung Jae. / VeTra : a tool for trajectory inference based on RNA velocity. In: Bioinformatics. 2021 ; Vol. 37, No. 20. pp. 3509-3513.

Bibtex

@article{b4905e7c5afb44dfac61be08e2bdbf3f,
title = "VeTra: a tool for trajectory inference based on RNA velocity",
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.",
author = "Guangzheng Weng and Junil Kim and Won, {Kyoung Jae}",
note = "{\textcopyright} The Author(s) 2021. Published by Oxford University Press.",
year = "2021",
doi = "10.1093/bioinformatics/btab364",
language = "English",
volume = "37",
pages = "3509--3513",
journal = "Computer Applications in the Biosciences",
issn = "1471-2105",
publisher = "Oxford University Press",
number = "20",

}

RIS

TY - JOUR

T1 - VeTra

T2 - a tool for trajectory inference based on RNA velocity

AU - Weng, Guangzheng

AU - Kim, Junil

AU - Won, Kyoung Jae

N1 - © The Author(s) 2021. Published by Oxford University Press.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

U2 - 10.1093/bioinformatics/btab364

DO - 10.1093/bioinformatics/btab364

M3 - Journal article

C2 - 33974009

VL - 37

SP - 3509

EP - 3513

JO - Computer Applications in the Biosciences

JF - Computer Applications in the Biosciences

SN - 1471-2105

IS - 20

M1 - btab364

ER -

ID: 274273772