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Modeling gene regulatory networks using neural network architectures

Nature Computational Science volume 1, pages 491–501 (2021)Cite this article

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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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Fig. 1: Overview of DeepSEM.
Fig. 2: The neural network architecture of DeepSEM.
Fig. 3: Summary of the GRN prediction performance in terms of EPR.
Fig. 4: Validating GRN prediction using epigenetic data.
Fig. 5: Single-cell clustering and embedding.
Fig. 6: Simulation performance of DeepSEM compared with cscGAN and scGAN.

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Data availability

We provide all datasets generated or analyzed during this study. The gene experimental scRNA-seq datasets were downloaded from Gene Expression Omnibus with the accession numbers GSE81252 (hHEP dataset65), GSE75748 (hESC dataset66), GSE98664 (mESC dataset62), GSE48968 (mDC dataset63), GSE81682 (mHSC dataset64), GSE115746 (mouse cortex dataset33), GSE60361 (Zeisel dataset41), GSE85241 (Muraro dataset78), GSE81861 (Li dataset79), and GSE45719 (Deng dataset80). The other experimental scRNA-seq dataset were downloaded from ArrayExpress with the accession number E-MTAB-5061 (Segerstolpe dataset81), NCBI Sequence Read Archive (SRA) with accession number SRP041736 (Pollen dataset42), GitHub repositories (https://github.com/LuyiTian/sc_mixology) (CellBench dataset82) and the website for x10genomics (https://support.10xgenomics.com/single-cell-gene-expression/datasets/) (PBMC dataset43). The scATAC-seq and snmC-seq for mouse cortex were downloaded from Gene Expression Omnibus with the accession numbers GSE126724 (scATAC-seq35) and GSE97179 (snmC-seq34). More information for these datasets could be found in Methods. We also summarize the accession and download links in Supplementary Tables 1, 2, 5 and 9. Source Data for Figs. 3–6 are available with this manuscript.

Code availability

The codes generated during this study are available on GitHub (https://github.com/HantaoShu/DeepSEM) and in Zenodo83.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61872216, 81630103), the Turing AI Institute of Nanjing to J. Zeng. We also acknowledge the National Natural Science Foundation of China (31900862) for funding support to D.Z..

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Authors and Affiliations

  1. Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China

    Hantao Shu, Han Li, Dan Zhao & Jianyang Zeng

  2. Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA

    Jingtian Zhou

  3. Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA

    Jingtian Zhou

  4. UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, China

    Qiuyu Lian

  5. Department of Automation, Shanghai Jiao Tong University, Shanghai, China

    Qiuyu Lian

  6. Institute for Artificial Intelligence, Peking University, Beijing, China

    Jianzhu Ma

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Contributions

J. Zeng and J.M. designed the study and developed the conceptual ideas. H.S. and Q.L. implemented the main algorithms. H.S. performed the model training and experimental validation task. H.S. and J. Zhou collected all the input data sources and interpreted the results. H.S., J. Zhou, H.L., D.Z., J. Zeng and J.M. wrote the manuscript with support from all authors.

Corresponding authors

Correspondence to Jianyang Zeng or Jianzhu Ma.

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Competing interests

J. Zeng is founder and CTO of Silexon AI Technology Co. Ltd. and has an equity interest. The remaining authors declare no competing interests.

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Peer review information Nature Computational Science thanks Jun Ding, Yafei Lyu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Handling editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

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Supplementary Information

Supplementary Figs. 1–17, Sections 1–7 and Tables 1–10.

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Shu, H., Zhou, J., Lian, Q. et al. Modeling gene regulatory networks using neural network architectures. Nat Comput Sci 1, 491–501 (2021). https://doi.org/10.1038/s43588-021-00099-8

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