Transcriptional regulatory networks (TRNs) play a crucial role in exploring microbial life activities and complex regulatory mechanisms. The comprehensive reconstruction of TRNs requires the integration of large-scale experimental data, which poses significant challenges due to the complexity of regulatory relationships. The application of machine learning tools, such as clustering analysis, has been employed to investigate TRNs, but these methods have limitations in capturing both global and local co-expression effects. In contrast, Independent Component Analysis (ICA) has emerged as a powerful analysis algorithm for modularizing independently regulated gene sets in TRNs, allowing it to account for both global and local co-expression effects. In this review, we comprehensively summarize the application of ICA in unraveling TRNs and highlight the research progress in three key aspects: (1) extending TRNs with iModulon analysis; (2) elucidating the regulatory mechanisms triggered by environmental perturbation; and (3) exploring the mechanisms of transcriptional regulation triggered by changes in microbial physiological state. At the end of this review, we also address the challenges facing ICA in TRN analysis and outline future research directions to promote the advancement of ICA-based transcriptomics analysis in biotechnology and related fields.

中文翻译：

---

利用独立成分分析解开转录调控网络：批判性综述和未来方向


转录调控网络 （TRN） 在探索微生物生命活动和复杂的调控机制中起着至关重要的作用。TRN 的全面重建需要整合大规模实验数据，由于监管关系的复杂性，这带来了重大挑战。机器学习工具（如聚类分析）的应用已被用于研究 TRN，但这些方法在捕获全局和局部共表达效应方面存在局限性。相比之下，独立成分分析 （ICA） 已成为一种强大的分析算法，用于模块化 TRN 中独立调节的基因集，使其能够同时考虑全局和局部共表达效应。在本文中，我们全面总结了 ICA 在解开 TRN 中的应用，并重点介绍了三个关键方面的研究进展：（1） 用 iModulon 分析扩展 TRN;（2） 阐明环境扰动触发的调节机制;（3） 探索微生物生理状态变化触发转录调控的机制。在这篇综述的最后，我们还讨论了 ICA 在 TRN 分析中面临的挑战，并概述了未来的研究方向，以促进基于 ICA 的转录组学分析在生物技术和相关领域的进步。