Motivation: Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks. Results: We present the mathematical construction of a statistical procedure for testing hypothesis regarding gene regulatory network activation. The real probability distribution for the test statistic is evaluated by a permutation based study. To illustrate the functionality of the proposed methodology, we also present a simple example based on a small hypothetical network and the activation measuring of two KEGG networks, both based on gene expression data collected from gastric and esophageal samples. The two KEGG networks were also analyzed for a public database, available through NCBI-GEO, presented as Supplementary Material. Availability: This method was implemented in an R package that is available at the BioConductor project website under the name maigesPack.

中文翻译：

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一种测量基因调控网络激活的统计方法

动机：基因表达数据分析对于现代分子生物学非常重要，因为我们有能力测量数千个基因的表达谱并支持植根于系统生物学的研究。在这项工作中，我们提出了一个简单的统计模型，用于基因调控网络的激活测量，而不是传统的基因共表达网络。结果：我们提出了用于检验基因调控网络激活假设的统计程序的数学结构。检验统计量的真实概率分布由基于排列的研究评估。为了说明所提出方法的功能，我们还提出了一个基于小型假设网络和两个 KEGG 网络的激活测量的简单示例，两者均基于从胃和食道样本中收集的基因表达数据。还对两个 KEGG 网络进行了分析，以获取可通过 NCBI-GEO 获得的公共数据库，作为补充材料提供。可用性：这个方法是在一个R可在 BioConductor 项目网站上以名称获取的软件包麦格斯包.