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Cited by 133Single-cell and spatial transcriptomicsGene Regulatory Network AnalysisBioinformatics and Genomic Networks

The DOI matches its Crossref record; 62 of the 63 references it cites were found in a registry or an index.

Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells

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Cannoodt, Robrecht, Saelens, Wouter, Deconinck, Louise, Saeys, Yvan·2021·Nature Communications
DOI 10.1038/s41467-021-24152-2PMC PMC8225657

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Abstract

We present dyngen, a multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. dyngen is more flexible than current single-cell simulation engines, and allows better method development and benchmarking, thereby stimulating development and testing of computational methods. We demonstrate its potential for spearheading computational methods on three applications: aligning cell developmental trajectories, cell-specific regulatory network inference and estimation of RNA velocity.

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Fig. 1 Showcase of dyngen functionality. A Changes in abundance levels are driven strictly by gene regulatory reactions. B The input Gene Regulatory Network (GRN) is defined such that it models a dynamic process of interest. C The reactions define how abundance levels of molecules change at any particular time point. D Firing many reactions can significantly alter the cellular state over time. E dyngen keeps track of the likelihood of a reaction firing during small intervals of time, called the propensity, as well as the actual number of firings. F Similarly, dyngen can also keep track of the regulatory activity of every interaction. G A benchmark of trajectory inference methods has already been performed using the cell state ground-truth21. H The cell state ground-truth enables evaluating trajectory alignment methods. I The reaction propensity ground-truth enables evaluating RNA velocity methods. J The cellwise regulatory network ground-truth enables evaluating cell-specific gene regulatory network inference methods.
Fig. 1 Showcase of dyngen functionality. A Changes in abundance levels are driven strictly by gene regulatory reactions. B The input Gene Regulatory Network (GRN) is defined such that it models a dynamic process of interest. C The reactions define how abundance levels of molecules change at any particular time point. D Firing many reactions can significantly alter the cellular state over time. E dyngen keeps track of the likelihood of a reaction firing during small intervals of time, called the propensity, as well as the actual number of firings. F Similarly, dyngen can also keep track of the regulatory activity of every interaction. G A benchmark of trajectory inference methods has already been performed using the cell state ground-truth21. H The cell state ground-truth enables evaluating trajectory alignment methods. I The reaction propensity ground-truth enables evaluating RNA velocity methods. J The cellwise regulatory network ground-truth enables evaluating cell-specific gene regulatory network inference methods.CC BY · PubMed Central
Fig. 2 dyngen provides ground-truth data for a variety of applications (left), which can be used to quantitatively evaluate methods (right). Box plots denote the Q0 to Q4 quartile values. A Trajectory alignment aligns two trajectories between samples. We evaluate Dynamic Time Warping (DTW) and cellAlign when aligning two linear trajectories with different kinetic parameters based on the area differences between the worst possible alignment and the predicted alignment (Area Between Worst And Prediction, or ABWAP). B RNA velocity calculates for each cell the direction in which the expression of each gene is moving. We evaluated scVelo and velocyto by comparing these vectors with the known velocity vector (velocity correlation) and with the known direction of the cellular trajectory in a dimensionality reduction (velocity arrow cosine). C Cell-specific network inference (CSNI) predicts the regulatory network of every individual cell. We evaluate each cell-specific regulatory network with typical metrics for network inference: the Area Under the Receiver Operating Characteristics-curve (AUROC) and Area Under the Precision-Recall curve (AUPR). We evaluate three CSNI methods by computing the mean AUROC and AUPR across all cells.
Fig. 2 dyngen provides ground-truth data for a variety of applications (left), which can be used to quantitatively evaluate methods (right). Box plots denote the Q0 to Q4 quartile values. A Trajectory alignment aligns two trajectories between samples. We evaluate Dynamic Time Warping (DTW) and cellAlign when aligning two linear trajectories with different kinetic parameters based on the area differences between the worst possible alignment and the predicted alignment (Area Between Worst And Prediction, or ABWAP). B RNA velocity calculates for each cell the direction in which the expression of each gene is moving. We evaluated scVelo and velocyto by comparing these vectors with the known velocity vector (velocity correlation) and with the known direction of the cellular trajectory in a dimensionality reduction (velocity arrow cosine). C Cell-specific network inference (CSNI) predicts the regulatory network of every individual cell. We evaluate each cell-specific regulatory network with typical metrics for network inference: the Area Under the Receiver Operating Characteristics-curve (AUROC) and Area Under the Precision-Recall curve (AUPR). We evaluate three CSNI methods by computing the mean AUROC and AUPR across all cells.CC BY · PubMed Central

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Research works printed but not linked: 1

  1. Kuijjer, M. L., Tung, M., Yuan, G., Quackenbush, J. & Glass, K. Estimating sample-specific regulatory networks. iScience. 14, 226–240 https://doi.org/10.1016/j.isci.2019.03.021 (2019). · DOI

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Metadata

Authors
Cannoodt, R., Saelens, W., Deconinck, L., & Saeys, Y.
Affiliations
  • Ghent University (BE)
  • Vlaams Instituut voor Biotechnologie (BE)
Corresponding author
Saeys, Yvan
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2021-06-24
Volume
12
Issue
1
Pages
3942-3942
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-021-24152-2
PMC
PMC8225657
OpenAlex
W3174608384
Cited by
124
References
63
Citations by year
2026: 16 · 2025: 19 · 2024: 28 · 2023: 27 · 2022: 17 · 2021: 11
FWCI
7.06
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Silicon Valley Community Foundation
  • Chan Zuckerberg Initiative
  • Vlaamse regering
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
single-cell simulation, regulatory network inference, RNA velocity, single-cell omics
MeSH terms
Algorithms, Computer Simulation, Computational Biology, Benchmarking, Gene Expression Profiling, Gene Regulatory Networks, Single-Cell Analysis

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