A way to enhance our understanding of the development and progression of complex diseases is to investigate the influence of cellular environments on gene co-expression (i.e. gene-pair correlations). Often, changes in gene co-expression are investigated across two or more biological conditions defined by categorizing a continuous covariate. However, the selection of arbitrary cut-off points may have an influence on the results of an analysis. To address this issue, we use a general linear model (GLM) for correlated data to study the relationship between gene-module co-expression and a covariate like metabolite concentration. The GLM specifies the gene-pair correlations as a function of the continuous covariate. The use of the GLM allows for investigating different (linear and non-linear) patterns of co-expression. Furthermore, the modeling approach offers a formal framework for testing hypotheses about possible patterns of co-expression. In our paper, a simulation study is used to assess the performance of the GLM. The performance is compared with that of a previously proposed GLM that utilizes categorized covariates. The versatility of the model is illustrated by using a real-life example. We discuss the theoretical issues related to the construction of the test statistics and the computational challenges related to fitting of the proposed model.

中文翻译：

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用于研究基因模块共表达与连续协变量之间关联的多元线性模型。

增强我们对复杂疾病的发生和发展的理解的一种方法是研究细胞环境对基因共表达（即基因对相关性）的影响。通常，在通过对连续协变量进行分类而定义的两个或多个生物学条件下，研究基因共表达的变化。但是，选择任意的截止点可能会影响分析结果。为了解决这个问题，我们使用通用线性模型（GLM）来获取相关数据，以研究基因模块共表达与代谢物浓度等协变量之间的关系。GLM将基因对相关性指定为连续协变量的函数。使用GLM可以调查不同的（线性和非线性）共表达模式。此外，建模方法提供了一个正式的框架，用于测试有关共表达可能模式的假设。在我们的论文中，通过仿真研究来评估GLM的性能。将该性能与先前提出的利用分类协变量的GLM进行了比较。通过一个真实的例子来说明模型的多功能性。我们讨论与测试统计数据的构建有关的理论问题，以及与拟议模型的拟合有关的计算挑战。通过一个真实的例子来说明模型的多功能性。我们讨论与测试统计数据的构建有关的理论问题，以及与拟议模型的拟合有关的计算挑战。通过一个真实的例子来说明模型的多功能性。我们讨论与测试统计数据的构建有关的理论问题以及与拟议模型的拟合有关的计算挑战。