Cited by 816Gene Regulatory Network AnalysisSingle-cell and spatial transcriptomicsMicrobial Metabolic Engineering and Bioproduction

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Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

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Pratapa, Aditya, Jalihal, Amogh P., Law, Jeffrey N., Bharadwaj, Aditya, Murali, T. M.·2020·Nature Methods

DOI 10.1038/s41592-019-0690-6PMC PMC7098173

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Abstract

We present a systematic evaluation of state-of-the-art algorithms for inferring gene regulatory networks from single-cell transcriptional data. As the ground truth for assessing accuracy, we use synthetic networks with predictable trajectories, literature-curated Boolean models and diverse transcriptional regulatory networks. We develop a strategy to simulate single-cell transcriptional data from synthetic and Boolean networks that avoids pitfalls of previously used methods. Furthermore, we collect networks from multiple experimental single-cell RNA-seq datasets. We develop an evaluation framework called BEELINE. We find that the area under the precision-recall curve and early precision of the algorithms are moderate. The methods are better in recovering interactions in synthetic networks than Boolean models. The algorithms with the best early precision values for Boolean models also perform well on experimental datasets. Techniques that do not require pseudotime-ordered cells are generally more accurate. Based on these results, we present recommendations to end users. BEELINE will aid the development of gene regulatory network inference algorithms.

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Additional file 10 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
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Additional file 9 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
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Additional file 7 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
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Additional file 8 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
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Additional file 2 of Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
Ranek, J. S., Stanley, N., & Purvis, J. E. (2022) · Figshare · doi:10.6084/m9.figshare.20968169
Additional file 3 of Gene regulation network inference using k-nearest neighbor-based mutual information estimation: revisiting an old DREAM
Shachaf, L., Roberts, E., Cahan, P., & Xiao, J. (2024) · Figshare · doi:10.6084/m9.figshare.26573176
Additional file 2 of Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios
Duo, H., Li, Y., Lan, Y., Tao, J., Yang, Q., Xiao, Y., Sun, J., Li, L., Nie, X., Zhang, X., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25963290
Additional file 1 of scEvoNet: a gradient boosting-based method for prediction of cell state evolution
Kotov, A., Zinovyev, A., & Monsoro-Burq, A. H. (2024) · Figshare · doi:10.6084/m9.figshare.26573131
Additional file 1 of Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios
Duo, H., Li, Y., Lan, Y., Tao, J., Yang, Q., Xiao, Y., Sun, J., Li, L., Nie, X., Zhang, X., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25963076
Additional file 1 of Gene regulation network inference using k-nearest neighbor-based mutual information estimation: revisiting an old DREAM
Shachaf, L., Roberts, E., Cahan, P., & Xiao, J. (2024) · Figshare · doi:10.6084/m9.figshare.26573170
Additional file 3 of Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios
Duo, H., Li, Y., Lan, Y., Tao, J., Yang, Q., Xiao, Y., Sun, J., Li, L., Nie, X., Zhang, X., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26729284
Additional file 3 of PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization
Gibbs, C. S., Mahmood, O., Bonneau, R., & Cho, K. (2024) · Figshare · doi:10.6084/m9.figshare.26703682
Additional file 1 of NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity
Su, K., Katebi, A., Kohar, V., Clauss, B., Gordin, D., Qin, Z. S., Karuturi, R. K. M., Li, S., & Lu, M. (2023) · Figshare · doi:10.6084/m9.figshare.22606120
Additional file 1 of siVAE: interpretable deep generative models for single-cell transcriptomes
Choi, Y., Li, R., & Quon, G. (2023) · Figshare · doi:10.6084/m9.figshare.22615894
Additional file 4 of NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity
Su, K., Katebi, A., Kohar, V., Clauss, B., Gordin, D., Qin, Z. S., Karuturi, R. K. M., Li, S., & Lu, M. (2023) · Figshare · doi:10.6084/m9.figshare.22606129
Additional file 1 of Commonly used software tools produce conflicting and overly-optimistic AUPRC values
Chen, W., Chen, M., Zhang, Z., Fung, C. S., Wang, R., Chen, Y., Qian, Y., Cheng, L., Yip, K. Y., Tsui, S. K., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25815087
Additional file 3 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
Li, S., Schmid, K., de Vries, D. H., Korshevniuk, M., Losert, C., Oelen, R., van Blokland, I. V., Groot, H. E., Swertz, M. A., van der Harst, P., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22655402
Additional file 2 of NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity
Su, K., Katebi, A., Kohar, V., Clauss, B., Gordin, D., Qin, Z. S., Karuturi, R. K. M., Li, S., & Lu, M. (2023) · Figshare · doi:10.6084/m9.figshare.22606123
Additional file 2 of siVAE: interpretable deep generative models for single-cell transcriptomes
Choi, Y., Li, R., & Quon, G. (2023) · Figshare · doi:10.6084/m9.figshare.22615897
Additional file 5 of PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization
Gibbs, C. S., Mahmood, O., Bonneau, R., & Cho, K. (2024) · Figshare · doi:10.6084/m9.figshare.26703688
Additional file 3 of Associating transcription factors to single-cell trajectories with DREAMIT
Maulding, N. D., Seninge, L., & Stuart, J. M. (2024) · Figshare · doi:10.6084/m9.figshare.26777389
Additional file 1 of DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification
Decamps, C., Arnaud, A., Petitprez, F., Ayadi, M., Baurès, A., Armenoult, L., Escalera, S., Guyon, I., Nicolle, R., Tomasini, R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16726873
Additional file 1 of Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
Ranek, J. S., Stanley, N., & Purvis, J. E. (2022) · Figshare · doi:10.6084/m9.figshare.20968166
Additional file 16 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
Li, S., Schmid, K., de Vries, D. H., Korshevniuk, M., Losert, C., Oelen, R., van Blokland, I. V., Groot, H. E., Swertz, M. A., van der Harst, P., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22655444
Additional file 2 of PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization
Gibbs, C. S., Mahmood, O., Bonneau, R., & Cho, K. (2024) · Figshare · doi:10.6084/m9.figshare.25566382
Additional file 1 of Kernelized multiview signed graph learning for single-cell RNA sequencing data
Karaaslanli, A., Saha, S., Maiti, T., & Aviyente, S. (2023) · Figshare · doi:10.6084/m9.figshare.22617763
Additional file 2 of Commonly used software tools produce conflicting and overly-optimistic AUPRC values
Chen, W., Chen, M., Zhang, Z., Fung, C. S., Wang, R., Chen, Y., Qian, Y., Cheng, L., Yip, K. Y., Tsui, S. K., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25815607

Authors: Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M.
Affiliations: Virginia Tech (US)
Corresponding author: Murali, T. M.
ORCID: Pratapa, Aditya
Jalihal, Amogh P.
Law, Jeffrey N.
Bharadwaj, Aditya
Murali, T. M.
Venue: Nature Methods
Publisher: Springer Science and Business Media LLC
Published: 2020-01-06
Volume: 17
Issue: 2
Pages: 147-154
ISSN: 1548-7091 (ISSN-L)
1548-7105
DOI: 10.1038/s41592-019-0690-6
PMC: PMC7098173
OpenAlex: W2997831639
Cited by: 792
References: 63
Citations by year: 2026: 135 · 2025: 146 · 2024: 135 · 2023: 142 · 2022: 101 · 2021: 94 · 2020: 36
FWCI: 31.76
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by
www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Methods (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Science Foundation (CCF-1617678, DBI-1759858)
U.S. Department of Health & Human Services | NIH | National Cancer Institute (UH2CA203768)
ODNI | Intelligence Advanced Research Projects Activity (W911NF-17-2-0105)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Keywords: gene regulatory network inference, single-cell transcriptomics, single-cell RNA sequencing, algorithm benchmarking, synthetic gene regulatory networks, Boolean model, data simulation, pseudotime, precision and recall
MeSH terms: Algorithms, Sequence Analysis, RNA, Gene Regulatory Networks, Single-Cell Analysis, Transcriptome, Datasets as Topic

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Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

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