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Differential network analysis via lasso penalized D-trace loss

Author

Listed:
  • Huili Yuan
  • Ruibin Xi
  • Chong Chen
  • Minghua Deng

Abstract

SummaryBiological networks often change under different environmental and genetic conditions. In this paper, we model network change as the difference of two precision matrices and propose a novel loss function called the D-trace loss, which allows us to directly estimate the precision matrix difference without attempting to estimate the precision matrices themselves. Under a new irrepresentability condition, we show that the D-trace loss function with the lasso penalty can yield consistent estimators in high-dimensional settings if the difference network is sparse. A very efficient algorithm is developed based on the alternating direction method of multipliers to minimize the penalized loss function. Simulation studies and a real-data analysis show that the proposed method outperforms other methods.

Suggested Citation

  • Huili Yuan & Ruibin Xi & Chong Chen & Minghua Deng, 2017. "Differential network analysis via lasso penalized D-trace loss," Biometrika, Biometrika Trust, vol. 104(4), pages 755-770.
    • Handle: RePEc:oup:biomet:v:104:y:2017:i:4:p:755-770.
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    File URL: http://hdl.handle.net/10.1093/biomet/asx049
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    Citations

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    Cited by:

    1. Zhou Tang & Zhangsheng Yu & Cheng Wang, 2020. "A fast iterative algorithm for high-dimensional differential network," Computational Statistics, Springer, vol. 35(1), pages 95-109, March.
    2. Ghazinoory, Sepehr & Aghaei, Parvaneh, 2021. "Differences between policy assessment & policy evaluation; a case study on supportive policies for knowledge-based firms," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    3. Pan, Yuqing & Mai, Qing, 2020. "Efficient computation for differential network analysis with applications to quadratic discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    4. Jarod Smith & Mohammad Arashi & Andriëtte Bekker, 2022. "Empowering differential networks using Bayesian analysis," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-19, January.

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