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Direct estimation of differential networks

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Listed:
  • Sihai Dave Zhao
  • T. Tony Cai
  • Hongzhe Li

Abstract

It is often of interest to understand how the structure of a genetic network differs between two conditions. In this paper, each condition-specific network is modelled using the precision matrix of a multivariate normal random vector, and a method is proposed to directly estimate the difference of the precision matrices. In contrast to other approaches, such as separate or joint estimation of the individual matrices, direct estimation does not require those matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Under the assumption that the true differential network is sparse, the direct estimator is shown to be consistent in support recovery and estimation. It is also shown to outperform existing methods in simulations, and its properties are illustrated on gene expression data from late-stage ovarian cancer patients.

Suggested Citation

  • Sihai Dave Zhao & T. Tony Cai & Hongzhe Li, 2014. "Direct estimation of differential networks," Biometrika, Biometrika Trust, vol. 101(2), pages 253-268.
    • Handle: RePEc:oup:biomet:v:101:y:2014:i:2:p:253-268.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu009
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    Cited by:

    1. Huang, Xianzheng & Zhang, Hongmei, 2021. "Tests for differential Gaussian Bayesian networks based on quadratic inference functions," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    2. Marinela - Daniela Manea, 2016. "Corporate Social Responsibility between the Aim and the Reality of Implementation in the Romanian Companies," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 335-340.
    3. Aaron Hudson & Ali Shojaie, 2022. "Covariate-Adjusted Inference for Differential Analysis of High-Dimensional Networks," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 345-388, June.
    4. Deepak Nag Ayyala & Santu Ghosh & Daniel F. Linder, 2022. "Covariance matrix testing in high dimension using random projections," Computational Statistics, Springer, vol. 37(3), pages 1111-1141, July.
    5. Wessel N. van Wieringen & Carel F. W. Peeters & Renee X. de Menezes & Mark A. van de Wiel, 2018. "Testing for pathway (in)activation by using Gaussian graphical models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1419-1436, November.
    6. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
    7. 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.
    8. Pircalabelu, Eugen, 2022. "WB-graphs: a within versus between group similarity interplay," LIDAM Discussion Papers ISBA 2022007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. Jiadong Ji & Yong He & Lei Liu & Lei Xie, 2021. "Brain connectivity alteration detection via matrix‐variate differential network model," Biometrics, The International Biometric Society, vol. 77(4), pages 1409-1421, December.
    10. Napoli, Philip M., 2015. "Social media and the public interest: Governance of news platforms in the realm of individual and algorithmic gatekeepers," Telecommunications Policy, Elsevier, vol. 39(9), pages 751-760.
    11. Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.
    12. Zhang, Hongmei & Huang, Xianzheng & Han, Shengtong & Rezwan, Faisal I. & Karmaus, Wilfried & Arshad, Hasan & Holloway, John W., 2021. "Gaussian Bayesian network comparisons with graph ordering unknown," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    13. Djordjilović, Vera & Chiogna, Monica, 2022. "Searching for a source of difference in graphical models," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    14. 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).

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