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A unified framework for integrative study of heterogeneous gene regulatory mechanisms

Nature Machine Intelligence volume 2, pages 447–456 (2020)Cite this article

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Matters Arising to this article was published on 08 July 2021

Abstract

Gene expression is regulated by a large variety of mechanisms. Previous studies attempting to model the quantitative relationships between gene expression levels and regulatory mechanisms have considered only one or a few mechanisms at a time, which cannot provide a full picture of the complex interactions among different mechanisms. This was partially due to the heterogeneity of the mechanisms, which involve different types of biological objects and data representations, making it hard to study them in a unified way. Here, we describe a flexible framework that can integrate very different types of data for studying their joint effects on gene expression. In this framework, domain knowledge is represented by metapaths, while the manifestations of their effects in actual data are summarized by an embedding of the biological objects in a latent space. We demonstrate the use of our framework in integrating several diverse types of data that are related to gene expression in different ways, including DNA contacts in three-dimensional genome architecture, protein–protein interactions, genomic neighbourhoods and broad chromatin accessibility domains. The modelling results reveal that these several types of data are able to model gene expression fairly well individually, but even better when integrated.

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Fig. 1: Schematic diagram of GEEK.
Fig. 2: Performance of the gene expression models.
Fig. 3: Comparing the performance of GEEK in the whole-genome and per-chromosome settings.
Fig. 4: Biological interpretations of the embeddings produced by GEEK.
Fig. 5: Performance of the gene expression models in the across-sample tests.

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Data availability

Example data for testing the source code are available at https://doi.org/10.24433/CO.1518993.v143. The public data used for producing the results in this study and the embeddings produced from the five cell lines can be downloaded from http://yiplab.cse.cuhk.edu.hk/geek/.

Code availability

Source codes are available at https://doi.org/10.24433/CO.1518993.v143.

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Acknowledgements

This work was supported by Hong Kong Research Grants Council General Research Funds 14170217 (C.C., D.L. and K.Y.Y.), 14203119 (A.S.L.C. and K.Y.Y.), 14200817, 15200715 and 15204116 (E.L.), Collaborative Research Funds 4045-18WF (A.S.L.C. and K.Y.Y.), 4054-16G (T.-L.L. and K.Y.Y.) and C4057-18EF (K.Y.Y.), Area of Excellence AoE/P-404/18 (E.L.), Theme-based Research Scheme T12C-714/14-R (K.Y.Y.), the Hong Kong Innovation and Technology Commission Innovative and Technology Fund ITS/310/18 (E.L.) and the Hong Kong Epigenomics Project (EpiHK). K.Y.Y. was also supported by CUHK Young Researcher Award, Outstanding Fellowship and Seed Funding for Strategic Areas.

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Author notes

  1. These authors contributed equally: Qin Cao, Zhenghao Zhang.

Authors and Affiliations

  1. Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Qin Cao, Zhenghao Zhang, Alexander Xi Fu, Qiong Wu, Eric Lo & Kevin Y. Yip

  2. School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China

    Qin Cao, Qiong Wu, Tin-Lap Lee & Alfred S. L. Cheng

  3. Department of Medicine, Baylor College of Medicine, Houston, TX, USA

    Chao Cheng

  4. Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China

    Danny Leung

  5. Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Hong Kong, China

    Kevin Y. Yip

  6. CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Hong Kong, China

    Kevin Y. Yip

  7. Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China

    Kevin Y. Yip

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  1. Qin Cao
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Contributions

K.Y.Y. conceived the study. Q.C. and Z.Z. designed and implemented GEEK. Q.C., Z.Z., A.X.F., Q.W. and K.Y.Y. performed data analyses. Q.C., A.X.F., T.-L.L., E.L., A.S.L.C., C.C., D.L. and K.Y.Y. interpreted the results. K.Y.Y. and Q.C. wrote the manuscript.

Corresponding author

Correspondence to Kevin Y. Yip.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary text (including algorithms 1 and 2), Table 1 and Figs. 1–23.

Supplementary Table 2

Functional enrichment analysis of gene clusters based on their embeddings. Each row represents the genes in a cluster that are annotated with the same functional term. The different columns specify the embedding setting, enrichment results and gene symbols, with their full descriptions provided in the file.

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Cao, Q., Zhang, Z., Fu, A.X. et al. A unified framework for integrative study of heterogeneous gene regulatory mechanisms. Nat Mach Intell 2, 447–456 (2020). https://doi.org/10.1038/s42256-020-0205-2

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