Inferring circadian gene regulatory relationships from gene expression data with a hybrid framework

BMC Bioinformatics

Research

Yi Jing¹  You-Gan Wang²  Yu-Chu Tian³  Shuwen Hu⁴  Jing Gao⁵  Zhenyu Liu⁵  Tao Li⁶ 

[1] Faculty of Science, The University of New South Wales, 2052, Sydney, Australia;Institute for Learning Sciences and Teacher Education, Australian Catholic University, 4000, Brisbane, QLD, Australia;School of Computer Science, Queensland University of Technology, 4001, Brisbane, QLD, Australia;School of Computer Science, Queensland University of Technology, 4001, Brisbane, QLD, Australia;Agriculture and Food, CSIRO, 4067, St Lucia, QLD, Australia;School of Computer and Information Engineering, Inner Mongolia Agriculture University, 010018, Hohhot, China;School of Life Sciences, Inner Mongolia Agricultural University, 010018, Hohhot, China;

关键词: Circadian gene;    Gene regulatory relationships;    Gene expression data;    Fuzzy c-means clustering;    Dynamic time warping;   

DOI  :  10.1186/s12859-023-05458-y

 received in 2022-12-21, accepted in 2023-08-30,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe 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.MethodsIn 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.ConclusionThe 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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