title: The Detection of Metabolite-Mediated Gene Module Co-Expression Using Multivariate Linear Models.
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.
authors.affiliation: Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-Biostat), Hasselt University, Diepenbeek, Belgium.
authors.forename: Trishanta
authors.initials: T
authors.lastname: Padayachee
authors.affiliation: Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-Biostat), Hasselt University, Diepenbeek, Belgium.
authors.forename: Tatsiana
authors.initials: T
authors.lastname: Khamiakova
authors.affiliation: Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-Biostat), Hasselt University, Diepenbeek, Belgium.
authors.forename: Ziv
authors.initials: Z
authors.lastname: Shkedy
authors.affiliation: Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland.
authors.forename: Markus
authors.initials: M
authors.lastname: Perola
authors.affiliation: Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland.
authors.forename: Perttu
authors.initials: P
authors.lastname: Salo
authors.affiliation: Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-Biostat), Hasselt University, Diepenbeek, Belgium.
authors.forename: Tomasz
authors.initials: T
authors.lastname: Burzykowski
mesh_terms: D020155:3-Hydroxybutyric Acid / Q000097:blood; D008076:Cholesterol, HDL / Q000097:blood; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D053263:Gene Regulatory Networks / Q000235:genetics*; D007962:Leukocytes / Q000378:metabolism; D016014:Linear Models; D019787:Linoleic Acid / Q000097:blood; D008075:Lipoproteins, HDL / Q000097:blood; D055432:Metabolomics; D008957:Models, Genetic; D010316:Particle Size
chemical_list: D008076:Cholesterol, HDL; D008075:Lipoproteins, HDL; D019787:Linoleic Acid; D020155:3-Hydroxybutyric Acid