Searching for active modules, i.e., regions showing striking changes in molecular activity in biological networks is important to reveal regulatory and signaling mechanisms of biological systems. Most existing active modules identification methods are based on protein-protein interaction networks or metabolic networks, which require comprehensive and accurate prior knowledge. On the other hand, weighted gene co-expression networks (WGCNs) are purely constructed from gene expression profiles. However, existing WGCN analysis methods are designed for identifying functional modules but not capable of identifying active modules. There is an urgent need to develop an active module identification algorithm for WGCNs to discover regulatory and signaling mechanism associating with a given cellular response. To address this urgent need, we propose a novel algorithm called a ctive mo dules on the m u lti-layer weighted (co-expression gene) n e t work, based on a continuous opt i mizatio n approach (AMOUNTAIN). The algorithm is capable of identifying active modules not only from single-layer WGCNs but also from multilayer WGCNs such as cross-species and dynamic WGCNs. We first validate AMOUNTAIN on a synthetic benchmark dataset. We then apply AMOUNTAIN to WGCNs constructed from Th17 differentiation gene expression datasets of human and mouse, which include a single layer, a cross-species two-layer and a multilayer dynamic WGCNs. The identified active modules from WGCNs are enriched by known protein-protein interactions, and more importantly, they reveal some interesting and important regulatory and signaling mechanisms of Th17 cell differentiation.

中文翻译：

---

多层加权基因共表达网络的主动模块识别：一种连续优化方法

寻找活性模块，即在生物网络中显示出分子活性显着变化的区域，对于揭示生物系统的调节和信号传导机制很重要。现有的活性模块识别方法大多基于蛋白质-蛋白质相互作用网络或代谢网络，需要全面准确的先验知识。另一方面，加权基因共表达网络（WGCNs）纯粹由基因表达谱构建。然而，现有的WGCN分析方法是为识别功能模块而设计的，但不能识别活动模块。迫切需要开发一种用于 WGCN 的主动模块识别算法，以发现与给定细胞反应相关的调节和信号机制。为了解决这一迫切需求，积极的m 上的模块 多层加权（共表达基因）n e t 工作，基于连续选择 我mizatio n 方法（AMOUNTAIN）。该算法不仅能够从单层 WGCN 中识别活动模块，还能够从跨物种和动态 WGCN 等多层 WGCN 中识别活动模块。我们首先在合成基准数据集上验证 AMOUNTAIN。然后，我们将 AMOUNTAIN 应用于由人类和小鼠的 Th17 分化基因表达数据集构建的 WGCN，包括单层、跨物种两层和多层动态 WGCN。从 WGCNs 中鉴定出的活性模块富含已知的蛋白质-蛋白质相互作用，更重要的是，它们揭示了 Th17 细胞分化的一些有趣且重要的调节和信号传导机制。