Advanced search
1 file | 5.90 MB

A comparison of single-cell trajectory inference methods

Wouter Saelens (UGent) , Robrecht Cannoodt (UGent) , Helena Todorov (UGent) and Yvan Saeys (UGent)
(2019) NATURE BIOTECHNOLOGY. 37(5). p.547-554
Author
Organization
Abstract
Trajectory inference approaches analyze genome-wide omics data from thousands of single cells and computationally infer the order of these cells along developmental trajectories. Although more than 70 trajectory inference tools have already been developed, it is challenging to compare their performance because the input they require and output models they produce vary substantially. Here, we benchmark 45 of these methods on 110 real and 229 synthetic datasets for cellular ordering, topology, scalability and usability. Our results highlight the complementarity of existing tools, and that the choice of method should depend mostly on the dataset dimensions and trajectory topology. Based on these results, we develop a set of guidelines to help users select the best method for their dataset. Our freely available data and evaluation pipeline (https://benchmark.dynverse.org) will aid in the development of improved tools designed to analyze increasingly large and complex single-cell datasets.
Keywords
NETWORK INFERENCE, RNA-SEQ

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 5.90 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Saelens, Wouter, Robrecht Cannoodt, Helena Todorov, and Yvan Saeys. 2019. “A Comparison of Single-cell Trajectory Inference Methods.” Nature Biotechnology 37 (5): 547–554.
APA
Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019). A comparison of single-cell trajectory inference methods. NATURE BIOTECHNOLOGY, 37(5), 547–554.
Vancouver
1.
Saelens W, Cannoodt R, Todorov H, Saeys Y. A comparison of single-cell trajectory inference methods. NATURE BIOTECHNOLOGY. 2019;37(5):547–54.
MLA
Saelens, Wouter et al. “A Comparison of Single-cell Trajectory Inference Methods.” NATURE BIOTECHNOLOGY 37.5 (2019): 547–554. Print.
@article{8619545,
  abstract     = {Trajectory inference approaches analyze genome-wide omics data from thousands of single cells and computationally infer the order of these cells along developmental trajectories. Although more than 70 trajectory inference tools have already been developed, it is challenging to compare their performance because the input they require and output models they produce vary substantially. Here, we benchmark 45 of these methods on 110 real and 229 synthetic datasets for cellular ordering, topology, scalability and usability. Our results highlight the complementarity of existing tools, and that the choice of method should depend mostly on the dataset dimensions and trajectory topology. Based on these results, we develop a set of guidelines to help users select the best method for their dataset. Our freely available data and evaluation pipeline (https://benchmark.dynverse.org) will aid in the development of improved tools designed to analyze increasingly large and complex single-cell datasets.},
  author       = {Saelens, Wouter and Cannoodt, Robrecht and Todorov, Helena and Saeys, Yvan},
  issn         = {1087-0156},
  journal      = {NATURE BIOTECHNOLOGY},
  keywords     = {NETWORK INFERENCE,RNA-SEQ},
  language     = {eng},
  number       = {5},
  pages        = {547--554},
  title        = {A comparison of single-cell trajectory inference methods},
  url          = {http://dx.doi.org/10.1038/s41587-019-0071-9},
  volume       = {37},
  year         = {2019},
}

Altmetric
View in Altmetric
Web of Science
Times cited: