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Sparse principal component analysis based on genome network for correcting cell type heterogeneity in epigenome-wide association studies

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Abstract

In epigenome-wide association studies (EWAS), the mixed methylation expression caused by the combination of different cell types may lead the researchers to find the false methylation site related to the phenotype of interest. To correct the EWAS false discovery, some non-reference models based on sparse principal component analysis (sparse PCA) have been proposed. These models assume that all methylation sites have the same priori probability in each PC load. However, it is known that there already has gene network structure corresponding to the methylation site. How to integrate this genome network knowledge into the sparse PCA models to enhance the performance of existing models is an open research problem. We introduce GN-ReFAEWAS, a non-reference analysis model which integrates the prior gene network structure into the PCA framework to control the false discovery in EWAS. We used one simulated data set, three real data sets, and three additional tests for experiments and compared with four existing models. Experimental results show that the GN-ReFAEWAS model is better than the existing model by 2–90% in the indicators of sensitivity, specificity, genomic control factor λ, and correlation coefficient factor cov with known cell phenotype ratio.

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Data Availability

A sample code in R language is available at: https://github.com/mr1528126360/GNReFAEWAS.

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Acknowledgements

We are very grateful to the valuable comments by the anonymous reviewers.

Funding

This work is supported by the Macau Science and Technology Development Funds Grands No. 0056/2020/AFJ from the Macau Special Administrative Region of the People’s Republic of China, Key Project for University of Educational Commission of Guangdong Province of China Funds (Natural, Grant No. 2019GZDXM005), and Research and Demonstration of East and West Cerebral Infarction Recurrence Prediction Model Construction and Early Warning System Development Based on Multi-omics, Science and Technology Project of Guizhou Province, Project Number: Qian Ke He Support [2021] General 446.

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Authors and Affiliations

  1. Basic Teaching Department, ZhuHai Campus of ZunYi Medical University, Zhu Hai, China

    Rui Miao

  2. Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China

    Qi Dang, Jie Cai & Hai-Hui Huang

  3. Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou, 510006, China

    Sheng-Li Xie

  4. State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China

    Yong Liang

  5. Peng Cheng Laboratory, Shenzhen, China

    Yong Liang

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  1. Rui Miao
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Correspondence to Yong Liang.

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Miao, R., Dang, Q., Cai, J. et al. Sparse principal component analysis based on genome network for correcting cell type heterogeneity in epigenome-wide association studies. Med Biol Eng Comput 60, 2601–2618 (2022). https://doi.org/10.1007/s11517-022-02599-9

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  • DOI: https://doi.org/10.1007/s11517-022-02599-9

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