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Inference of Gene Regulatory Networks from Genetic Perturbations with Linear Regression Model

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  • Zijian Dong
  • Tiecheng Song
  • Chuang Yuan

Abstract

It is an effective strategy to use both genetic perturbation data and gene expression data to infer regulatory networks that aims to improve the detection accuracy of the regulatory relationships among genes. Based on both types of data, the genetic regulatory networks can be accurately modeled by Structural Equation Modeling (SEM). In this paper, a linear regression (LR) model is formulated based on the SEM, and a novel iterative scheme using Bayesian inference is proposed to estimate the parameters of the LR model (LRBI). Comparative evaluations of LRBI with other two algorithms, the Adaptive Lasso (AL-Based) and the Sparsity-aware Maximum Likelihood (SML), are also presented. Simulations show that LRBI has significantly better performance than AL-Based, and overperforms SML in terms of power of detection. Applying the LRBI algorithm to experimental data, we inferred the interactions in a network of 35 yeast genes. An open-source program of the LRBI algorithm is freely available upon request.

Suggested Citation

  • Zijian Dong & Tiecheng Song & Chuang Yuan, 2013. "Inference of Gene Regulatory Networks from Genetic Perturbations with Linear Regression Model," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-9, December.
    • Handle: RePEc:plo:pone00:0083263
    DOI: 10.1371/journal.pone.0083263
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    References listed on IDEAS

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    1. Renhua Li & Shirng-Wern Tsaih & Keith Shockley & Ioannis M Stylianou & Jon Wergedal & Beverly Paigen & Gary A Churchill, 2006. "Structural Model Analysis of Multiple Quantitative Traits," PLOS Genetics, Public Library of Science, vol. 2(7), pages 1-12, July.
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