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Estimating time‐varying directed gene regulation networks

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  • Yunlong Nie
  • LiangLiang Wang
  • Jiguo Cao

Abstract

The problem of modeling the dynamical regulation process within a gene network has been of great interest for a long time. We propose to model this dynamical system with a large number of nonlinear ordinary differential equations (ODEs), in which the regulation function is estimated directly from data without any parametric assumption. Most current research assumes the gene regulation network is static, but in reality, the connection and regulation function of the network may change with time or environment. This change is reflected in our dynamical model by allowing the regulation function varying with the gene expression and forcing this regulation function to be zero if no regulation happens. We introduce a statistical method called functional SCAD to estimate a time‐varying sparse and directed gene regulation network, and simultaneously, to provide a smooth estimation of the regulation function and identify the interval in which no regulation effect exists. The finite sample performance of the proposed method is investigated in a Monte Carlo simulation study. Our method is demonstrated by estimating a time‐varying directed gene regulation network of 20 genes involved in muscle development during the embryonic stage of Drosophila melanogaster.

Suggested Citation

  • Yunlong Nie & LiangLiang Wang & Jiguo Cao, 2017. "Estimating time‐varying directed gene regulation networks," Biometrics, The International Biometric Society, vol. 73(4), pages 1231-1242, December.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:4:p:1231-1242
    DOI: 10.1111/biom.12685
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    1. Epskamp, Sacha & Cramer, Angélique O.J. & Waldorp, Lourens J. & Schmittmann, Verena D. & Borsboom, Denny, 2012. "qgraph: Network Visualizations of Relationships in Psychometric Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i04).
    2. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    3. Hulin Wu & Tao Lu & Hongqi Xue & Hua Liang, 2014. "Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 700-716, June.
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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    1. Baisen Liu & Liangliang Wang & Yunlong Nie & Jiguo Cao, 2021. "Semiparametric Mixed-Effects Ordinary Differential Equation Models with Heavy-Tailed Distributions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 428-445, September.

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