The Detection of Metabolite-Mediated Gene Module Co-Expression Using Multivariate Linear Models

Trishanta Padayachee, Tatsiana Khamiakova, Ziv Shkedy, Markus Perola, Perttu Salo and Tomasz Burzykowski

PLOS ONE, 2016, vol. 11, issue 2, 1-17

Abstract: Investigating whether metabolites regulate the co-expression of a predefined gene module is one of the relevant questions posed in the integrative analysis of metabolomic and transcriptomic data. This article concerns the integrative analysis of the two high-dimensional datasets by means of multivariate models and statistical tests for the dependence between metabolites and the co-expression of a gene module. The general linear model (GLM) for correlated data that we propose models the dependence between adjusted gene expression values through a block-diagonal variance-covariance structure formed by metabolic-subset specific general variance-covariance blocks. Performance of statistical tests for the inference of conditional co-expression are evaluated through a simulation study. The proposed methodology is applied to the gene expression data of the previously characterized lipid-leukocyte module. Our results show that the GLM approach improves on a previous approach by being less prone to the detection of spurious conditional co-expression.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0150257

DOI: 10.1371/journal.pone.0150257

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