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A benchmark study of simulation methods for single-cell RNA sequencing data

Yue Cao, Pengyi Yang, View ORCID ProfileJean Yee Hwa Yang
doi: https://doi.org/10.1101/2021.06.01.446157
Yue Cao
1Charles Perkins Centre, The University of Sydney, Sydney, Australia
2School of Mathematics and Statistics, The University of Sydney, Sydney, Australia
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Pengyi Yang
1Charles Perkins Centre, The University of Sydney, Sydney, Australia
2School of Mathematics and Statistics, The University of Sydney, Sydney, Australia
3Westmead Institute for Medical Research, The University of Sydney, Australia
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  • For correspondence: pengyi.yang@sydney.edu.au jean.yang@sydney.edu.au
Jean Yee Hwa Yang
1Charles Perkins Centre, The University of Sydney, Sydney, Australia
2School of Mathematics and Statistics, The University of Sydney, Sydney, Australia
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  • ORCID record for Jean Yee Hwa Yang
  • For correspondence: pengyi.yang@sydney.edu.au jean.yang@sydney.edu.au
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Abstract

Single-cell RNA-seq (scRNA-seq) data simulation is critical for evaluating computational methods for analysing scRNA-seq data especially when ground truth is experimentally unattainable. The reliability of evaluation depends on the ability of simulation methods to capture properties of experimental data. However, while many scRNA-seq data simulation methods have been proposed, a systematic evaluation of these methods is lacking. We developed a comprehensive evaluation framework, SimBench, including a novel kernel density estimation measure to benchmark 12 simulation methods through 36 scRNA-seq experimental datasets. We evaluated the simulation methods on a panel of data properties, ability to maintain biological signals and computational scalability. Our benchmark uncovered performance differences among the methods and highlighted the varying difficulties in simulating data characteristics. Furthermore, we identified several limitations including maintaining heterogeneity of distribution. These results, together with the framework and datasets made publicly available as R packages, will guide simulation methods selection and their future development.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 02, 2021.
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A benchmark study of simulation methods for single-cell RNA sequencing data
Yue Cao, Pengyi Yang, Jean Yee Hwa Yang
bioRxiv 2021.06.01.446157; doi: https://doi.org/10.1101/2021.06.01.446157
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A benchmark study of simulation methods for single-cell RNA sequencing data
Yue Cao, Pengyi Yang, Jean Yee Hwa Yang
bioRxiv 2021.06.01.446157; doi: https://doi.org/10.1101/2021.06.01.446157

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