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Learning gene regulatory networks from next generation sequencing data

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  • Bochao Jia
  • Suwa Xu
  • Guanghua Xiao
  • Vishal Lamba
  • Faming Liang

Abstract

In recent years, next generation sequencing (NGS) has gradually replaced microarray as the major platform in measuring gene expressions. Compared to microarray, NGS has many advantages, such as less noise and higher throughput. However, the discreteness of NGS data also challenges the existing statistical methodology. In particular, there still lacks an appropriate statistical method for reconstructing gene regulatory networks using NGS data in the literature. The existing local Poisson graphical model method is not consistent and can only infer certain local structures of the network. In this article, we propose a random effect model‐based transformation to continuize NGS data and then we transform the continuized data to Gaussian via a semiparametric transformation and apply an equivalent partial correlation selection method to reconstruct gene regulatory networks. The proposed method is consistent. The numerical results indicate that the proposed method can lead to much more accurate inference of gene regulatory networks than the local Poisson graphical model and other existing methods. The proposed data‐continuized transformation fills the theoretical gap for how to transform discrete data to continuous data and facilitates NGS data analysis. The proposed data‐continuized transformation also makes it feasible to integrate different types of data, such as microarray and RNA‐seq data, in reconstruction of gene regulatory networks.

Suggested Citation

  • Bochao Jia & Suwa Xu & Guanghua Xiao & Vishal Lamba & Faming Liang, 2017. "Learning gene regulatory networks from next generation sequencing data," Biometrics, The International Biometric Society, vol. 73(4), pages 1221-1230, December.
  • Handle: RePEc:bla:biomet:v:73:y:2017:i:4:p:1221-1230
    DOI: 10.1111/biom.12682
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    References listed on IDEAS

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    1. Yoav Benjamini & Abba M. Krieger & Daniel Yekutieli, 2006. "Adaptive linear step-up procedures that control the false discovery rate," Biometrika, Biometrika Trust, vol. 93(3), pages 491-507, September.
    2. Genest, Christian & Nešlehová, Johanna, 2007. "A Primer on Copulas for Count Data," ASTIN Bulletin, Cambridge University Press, vol. 37(2), pages 475-515, November.
    3. Faming Liang & Qifan Song & Peihua Qiu, 2015. "An Equivalent Measure of Partial Correlation Coefficients for High-Dimensional Gaussian Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1248-1265, September.
    4. Faming Liang & Ick Hoon Jin & Qifan Song & Jun S. Liu, 2016. "An Adaptive Exchange Algorithm for Sampling From Distributions With Intractable Normalizing Constants," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 377-393, March.
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    Cited by:

    1. Duchwan Ryu & Devrim Bilgili & Önder Ergönül & Faming Liang & Nader Ebrahimi, 2018. "A Bayesian Generalized Linear Model for Crimean–Congo Hemorrhagic Fever Incidents," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 153-170, March.
    2. Rong Zhang & Zhao Ren & Wei Chen, 2018. "SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-14, August.
    3. Meichen Dong & Yiping He & Yuchao Jiang & Fei Zou, 2023. "Joint gene network construction by single‐cell RNA sequencing data," Biometrics, The International Biometric Society, vol. 79(2), pages 915-925, June.

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