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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.
Highlights
Omics technologies have rapidly advanced giving rise to an extensive amount of omics data with widespread availability
We focus on the integrative analysis of metabolomic and transcriptomic data to investigate the co-expression of a gene module (a set of coexpressed genes belonging to the same biological pathway) conditional on metabolic concentrations
Combining the Larntz & Perlman test with a suitable multiple-testing procedure should result in a testing framework that properly controls the family-wise error rate (FWER) or the false discovery rate (FDR)
Summary
Omics technologies have rapidly advanced giving rise to an extensive amount of omics (genomics, proteomics, metabolomics, transcriptomics, glycomics, and lipidomics) data with widespread availability. To obtain a comprehensive understanding of complex diseases, research is centring on the integrative analysis of omics data, necessitating more advanced methodological frameworks. We focus on the integrative analysis of metabolomic and transcriptomic data to investigate the co-expression of a gene module (a set of coexpressed (correlated) genes belonging to the same biological pathway) conditional on metabolic concentrations. Conditional co-expression is the observation of dependence of the correlation(s) (or other measure(s) of association) of gene expression levels on values of a covariate. It is investigated to gain insight into the regulatory mechanisms resulting in gene co-expression and, in turn, to PLOS ONE | DOI:10.1371/journal.pone.0150257. It is investigated to gain insight into the regulatory mechanisms resulting in gene co-expression and, in turn, to PLOS ONE | DOI:10.1371/journal.pone.0150257 February 26, 2016
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