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Nat Biotechnol. 2019 May;37(5):547-554. doi: 10.1038/s41587-019-0071-9. Epub 2019 Apr 1.

A comparison of single-cell trajectory inference methods.

Author information

1
Data mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.
2
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
3
Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium.
4
Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
5
Centre International de Recherche en Infectiologie, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Université de Lyon, Lyon, France.
6
Data mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium. yvan.saeys@ugent.be.
7
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium. yvan.saeys@ugent.be.

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.

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PMID:
30936559
DOI:
10.1038/s41587-019-0071-9
[Indexed for MEDLINE]

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