IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v9y2010i1n25.html
   My bibliography  Save this article

A Random Coefficients Model for Regional Co-Expression Associated with DNA Copy Number

Author

Listed:
  • van Wieringen Wessel N

    (VU University Medical Center, & VU University Amsterdam)

  • Berkhof Johannes

    (VU University Medical Center)

  • van de Wiel Mark A

    (VU University Medical Center & VU University Amsterdam)

Abstract

Regional co-expression refers to the phenomenon of contiguous genes exhibiting similar expression patterns. Among others, DNA copy number aberrations may be causally involved in regional co-expression. We propose a random coefficients model to explain regional co-expression from DNA copy number information, while modeling residual co-expression due to other causes by a correlated error structure. We show how the model parameters may be estimated (computationally efficient and consistently) from high-dimensional data, and suggest several robustifications of the estimation procedure. From the model we are able to assess whether there is a shared effect on expression levels due to the DNA copy number aberrations, but also whether this effect is homogeneous across genes. In two examples we use the proposed methodology to investigate the association between DNA copy number aberrations and regional co-expression.

Suggested Citation

  • van Wieringen Wessel N & Berkhof Johannes & van de Wiel Mark A, 2010. "A Random Coefficients Model for Regional Co-Expression Associated with DNA Copy Number," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-30, June.
    • Handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:25
    DOI: 10.2202/1544-6115.1531
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1544-6115.1531
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1544-6115.1531?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wessel N. van Wieringen & Mark A. van de Wiel, 2009. "Nonparametric Testing for DNA Copy Number Induced Differential mRNA Gene Expression," Biometrics, The International Biometric Society, vol. 65(1), pages 19-29, March.
    2. Oberhofer, W & Kmenta, J, 1974. "A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models," Econometrica, Econometric Society, vol. 42(3), pages 579-590, May.
    3. Schäfer Juliane & Strimmer Korbinian, 2005. "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-32, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chaturvedi Nimisha & Menezes Renée X. de & Goeman Jelle J. & Wieringen Wessel van, 2018. "A test for detecting differential indirect trans effects between two groups of samples," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(5), pages 1-11, October.
    2. van Wieringen Wessel N. & van de Wiel Mark A., 2014. "Penalized differential pathway analysis of integrative oncogenomics studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(2), pages 141-158, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hannart, Alexis & Naveau, Philippe, 2014. "Estimating high dimensional covariance matrices: A new look at the Gaussian conjugate framework," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 149-162.
    2. Avagyan, Vahe & Nogales, Francisco J., 2015. "D-trace Precision Matrix Estimation Using Adaptive Lasso Penalties," DES - Working Papers. Statistics and Econometrics. WS 21775, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Aslanidis, Nektarios, 2007. "Business Cycle Regimes in CEECs Production: A Threshold SURE Approach," Working Papers 2072/5318, Universitat Rovira i Virgili, Department of Economics.
    4. Hansen, Peter Reinhard, 2003. "Structural changes in the cointegrated vector autoregressive model," Journal of Econometrics, Elsevier, vol. 114(2), pages 261-295, June.
    5. Jianqing Fan & Xu Han, 2017. "Estimation of the false discovery proportion with unknown dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1143-1164, September.
    6. Wang Xiaoming & Dinu Irina & Liu Wei & Yasui Yutaka, 2011. "Linear Combination Test for Hierarchical Gene Set Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-18, March.
    7. Christian Genthon, 2008. "International diversification, performance and offshoring : the case of the computer services industry," Post-Print halshs-00348198, HAL.
    8. Seunghwan Lee & Sang Cheol Kim & Donghyeon Yu, 2023. "An efficient GPU-parallel coordinate descent algorithm for sparse precision matrix estimation via scaled lasso," Computational Statistics, Springer, vol. 38(1), pages 217-242, March.
    9. Bala Rajaratnam & Dario Vincenzi, 2016. "A theoretical study of Stein's covariance estimator," Biometrika, Biometrika Trust, vol. 103(3), pages 653-666.
    10. Wang, Christina Dan & Chen, Zhao & Lian, Yimin & Chen, Min, 2022. "Asset selection based on high frequency Sharpe ratio," Journal of Econometrics, Elsevier, vol. 227(1), pages 168-188.
    11. Viet Anh Nguyen & Daniel Kuhn & Peyman Mohajerin Esfahani, 2018. "Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator," Papers 1805.07194, arXiv.org.
    12. Christian Bongiorno, 2020. "Bootstraps Regularize Singular Correlation Matrices," Working Papers hal-02536278, HAL.
    13. van Wieringen, Wessel N. & Stam, Koen A. & Peeters, Carel F.W. & van de Wiel, Mark A., 2020. "Updating of the Gaussian graphical model through targeted penalized estimation," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    14. Mr. Jorge A Chan-Lau, 2017. "Variance Decomposition Networks: Potential Pitfalls and a Simple Solution," IMF Working Papers 2017/107, International Monetary Fund.
    15. Boulesteix Anne-Laure, 2006. "Reader's Reaction to "Dimension Reduction for Classification with Gene Expression Microarray Data" by Dai et al (2006)," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-7, June.
    16. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2018. "Calculating joint confidence bands for impulse response functions using highest density regions," Empirical Economics, Springer, vol. 55(4), pages 1389-1411, December.
    17. Korbinian Strimmer, 2008. "Comments on: Augmenting the bootstrap to analyze high dimensional genomic data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(1), pages 25-27, May.
    18. M. Angeles Carnero & Siem Jan Koopman & Marius Ooms, 2003. "Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 03-071/4, Tinbergen Institute.
    19. He, Changli & Kang, Jian & Silvennoinen, Annastiina & Teräsvirta, Timo, 2024. "Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model," Journal of Econometrics, Elsevier, vol. 239(1).
    20. Pan-Jun Kim & Nathan D Price, 2011. "Genetic Co-Occurrence Network across Sequenced Microbes," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-9, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:25. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.