School of Computer Science, Queensland University of Technology, Brisbane, QLD, 4001, Australia.
Agriculture and Food, CSIRO, St Lucia, QLD, 4067, Australia.
BMC Bioinformatics. 2023 Sep 26;24(1):362. doi: 10.1186/s12859-023-05458-y.
The central biological clock governs numerous facets of mammalian physiology, including sleep, metabolism, and immune system regulation. Understanding gene regulatory relationships is crucial for unravelling the mechanisms that underlie various cellular biological processes. While it is possible to infer circadian gene regulatory relationships from time-series gene expression data, relying solely on correlation-based inference may not provide sufficient information about causation. Moreover, gene expression data often have high dimensions but a limited number of observations, posing challenges in their analysis.
In this paper, we introduce a new hybrid framework, referred to as Circadian Gene Regulatory Framework (CGRF), to infer circadian gene regulatory relationships from gene expression data of rats. The framework addresses the challenges of high-dimensional data by combining the fuzzy C-means clustering algorithm with dynamic time warping distance. Through this approach, we efficiently identify the clusters of genes related to the target gene. To determine the significance of genes within a specific cluster, we employ the Wilcoxon signed-rank test. Subsequently, we use a dynamic vector autoregressive method to analyze the selected significant gene expression profiles and reveal directed causal regulatory relationships based on partial correlation.
The proposed CGRF framework offers a comprehensive and efficient solution for understanding circadian gene regulation. Circadian gene regulatory relationships are inferred from the gene expression data of rats based on the Aanat target gene. The results show that genes Pde10a, Atp7b, Prok2, Per1, Rhobtb3 and Dclk1 stand out, which have been known to be essential for the regulation of circadian activity. The potential relationships between genes Tspan15, Eprs, Eml5 and Fsbp with a circadian rhythm need further experimental research.
哺乳动物的生理机能包括睡眠、新陈代谢和免疫系统调节等，中央生物钟对这些机能都有调控作用。了解基因调控关系对于揭示各种细胞生物学过程的机制至关重要。虽然可以根据时间序列基因表达数据推断昼夜节律基因调控关系，但仅依赖相关性推断可能无法提供关于因果关系的充分信息。此外，基因表达数据通常具有较高的维度，但观察数量有限，这对其分析提出了挑战。
在本文中，我们引入了一种新的混合框架，称为昼夜节律基因调控框架（CGRF），用于从大鼠的基因表达数据中推断昼夜节律基因调控关系。该框架通过将模糊 C 均值聚类算法与动态时间扭曲距离相结合，解决了高维数据的挑战。通过这种方法，我们可以有效地识别与目标基因相关的基因簇。为了确定特定簇内基因的显著性，我们采用了 Wilcoxon 符号秩检验。随后，我们使用动态向量自回归方法来分析所选显著基因表达谱，并根据偏相关揭示基于部分相关的有向因果调控关系。
提出的 CGRF 框架为理解昼夜节律基因调控提供了一种全面而有效的解决方案。基于 Aanat 靶基因，从大鼠的基因表达数据中推断昼夜节律基因调控关系。结果表明，Pde10a、Atp7b、Prok2、Per1、Rhobtb3 和 Dclk1 等基因非常显著，这些基因已被证明对昼夜节律活动的调节至关重要。与昼夜节律相关的基因 Tspan15、Eprs、Eml5 和 Fsbp 之间的潜在关系需要进一步的实验研究。
