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

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Chaturvedi Nimisha & Menezes Renée X. de & Wieringen Wessel van & Goeman Jelle J., 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.

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