Modeling gene regulatory networks using neural network architectures

doi:10.1038/s43588-021-00099-8

. 2021 Jul;1(7):491-501.

doi: 10.1038/s43588-021-00099-8. Epub 2021 Jul 22.

Modeling gene regulatory networks using neural network architectures

Hantao Shu¹, Jingtian Zhou^{2

3}, Qiuyu Lian^{4

5}, Han Li¹, Dan Zhao¹, Jianyang Zeng⁶, Jianzhu Ma⁷

Affiliations

¹ Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
² Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
³ Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA.
⁴ UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, China.
⁵ Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
⁶ Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China. zengjy321@mail.tsinghua.edu.cn.
⁷ Institute for Artificial Intelligence, Peking University, Beijing, China. majianzhu@pku.edu.cn.

PMID: 38217125
DOI: 10.1038/s43588-021-00099-8

Modeling gene regulatory networks using neural network architectures

Hantao Shu et al. Nat Comput Sci. 2021 Jul.

. 2021 Jul;1(7):491-501.

doi: 10.1038/s43588-021-00099-8. Epub 2021 Jul 22.

Authors

Hantao Shu¹, Jingtian Zhou^{2

3}, Qiuyu Lian^{4

5}, Han Li¹, Dan Zhao¹, Jianyang Zeng⁶, Jianzhu Ma⁷

Affiliations

¹ Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
² Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
³ Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA.
⁴ UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, China.
⁵ Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
⁶ Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China. zengjy321@mail.tsinghua.edu.cn.
⁷ Institute for Artificial Intelligence, Peking University, Beijing, China. majianzhu@pku.edu.cn.

PMID: 38217125
DOI: 10.1038/s43588-021-00099-8

Abstract

Gene regulatory networks (GRNs) encode the complex molecular interactions that govern cell identity. Here we propose DeepSEM, a deep generative model that can jointly infer GRNs and biologically meaningful representation of single-cell RNA sequencing (scRNA-seq) data. In particular, we developed a neural network version of the structural equation model (SEM) to explicitly model the regulatory relationships among genes. Benchmark results show that DeepSEM achieves comparable or better performance on a variety of single-cell computational tasks, such as GRN inference, scRNA-seq data visualization, clustering and simulation, compared with the state-of-the-art methods. In addition, the gene regulations predicted by DeepSEM on cell-type marker genes in the mouse cortex can be validated by epigenetic data, which further demonstrates the accuracy and efficiency of our method. DeepSEM can provide a useful and powerful tool to analyze scRNA-seq data and infer a GRN.

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References

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[1] Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013). - DOI

[2] Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013). - DOI

[3] Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). - DOI

[4] Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). - DOI

[5] Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-seq. Genome Biol. 17, 77 (2016). - DOI

[6] Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-seq. Genome Biol. 17, 77 (2016). - DOI

[7] Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016). - DOI

[8] Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016). - DOI

[9] Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014). - DOI

[10] Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014). - DOI

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Modeling gene regulatory networks using neural network architectures

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Modeling gene regulatory networks using neural network architectures

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