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September 2018 Estimating a common covariance matrix for network meta-analysis of gene expression datasets in diffuse large B-cell lymphoma
Anders Ellern Bilgrau, Rasmus Froberg Brøndum, Poul Svante Eriksen, Karen Dybkær, Martin Bøgsted
Ann. Appl. Stat. 12(3): 1894-1913 (September 2018). DOI: 10.1214/18-AOAS1136


The estimation of covariance matrices of gene expressions has many applications in cancer systems biology. Many gene expression studies, however, are hampered by low sample size and it has therefore become popular to increase sample size by collecting gene expression data across studies. Motivated by the traditional meta-analysis using random effects models, we present a hierarchical random covariance model and use it for the meta-analysis of gene correlation networks across 11 large-scale gene expression studies of diffuse large B-cell lymphoma (DLBCL). We suggest to use a maximum likelihood estimator for the underlying common covariance matrix and introduce an EM algorithm for estimation. By simulation experiments comparing the estimated covariance matrices by cophenetic correlation and Kullback–Leibler divergence the suggested estimator showed to perform better or not worse than a simple pooled estimator. In a posthoc analysis of the estimated common covariance matrix for the DLBCL data we were able to identify novel biologically meaningful gene correlation networks with eigengenes of prognostic value. In conclusion, the method seems to provide a generally applicable framework for meta-analysis, when multiple features are measured and believed to share a common covariance matrix obscured by study dependent noise.


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Anders Ellern Bilgrau. Rasmus Froberg Brøndum. Poul Svante Eriksen. Karen Dybkær. Martin Bøgsted. "Estimating a common covariance matrix for network meta-analysis of gene expression datasets in diffuse large B-cell lymphoma." Ann. Appl. Stat. 12 (3) 1894 - 1913, September 2018.


Received: 1 June 2016; Revised: 1 June 2017; Published: September 2018
First available in Project Euclid: 11 September 2018

zbMATH: 06979656
MathSciNet: MR3852702
Digital Object Identifier: 10.1214/18-AOAS1136

Rights: Copyright © 2018 Institute of Mathematical Statistics


Vol.12 • No. 3 • September 2018
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