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

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.

在代谢组学（metabolomics）与转录组学（transcriptomics）整合分析中，探究代谢物是否调控预设基因模块（gene module）的共表达（co-expression），是一项相关核心研究问题。本文针对代谢物与基因模块共表达间的相关性问题，采用多元统计模型与统计检验方法，对两类高维数据集开展整合分析。针对相关数据，我们提出的广义线性模型（General Linear Model, GLM）通过由代谢子集特异性通用方差-协方差块构成的块对角方差-协方差结构，对校正后的基因表达值间的相关性进行建模。本研究通过模拟实验，对用于条件共表达（conditional co-expression）推断的统计检验方法的性能展开评估。我们将所提方法应用于前期已完成表征的脂质-白细胞模块（lipid-leukocyte module）的基因表达数据。研究结果表明，相较于既往方法，本研究提出的GLM方法更不易检出虚假条件共表达，性能更优异。