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

Shuwen Hu; Yi Jing; Tao Li; You-Gan Wang; Zhenyu Liu; Jing Gao; Yu-Chu Tian

doi:10.1186/s12859-023-05458-y

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

BMC Bioinformatics. 2023 Sep 26;24(1):362. doi: 10.1186/s12859-023-05458-y.

Authors

Shuwen Hu^#^{1

2}, Yi Jing^#³, Tao Li^#⁴, You-Gan Wang⁵, Zhenyu Liu⁶, Jing Gao⁷, Yu-Chu Tian⁸

Affiliations

¹ School of Computer Science, Queensland University of Technology, Brisbane, QLD, 4001, Australia.
² Agriculture and Food, CSIRO, St Lucia, QLD, 4067, Australia.
³ Faculty of Science, The University of New South Wales, Sydney, 2052, Australia.
⁴ School of Life Sciences, Inner Mongolia Agricultural University, Hohhot, 010018, China.
⁵ Institute for Learning Sciences and Teacher Education, Australian Catholic University, Brisbane, QLD, 4000, Australia.
⁶ School of Computer and Information Engineering, Inner Mongolia Agriculture University, Hohhot, 010018, China.
⁷ School of Computer and Information Engineering, Inner Mongolia Agriculture University, Hohhot, 010018, China. gaojing@imau.edu.cn.
⁸ School of Computer Science, Queensland University of Technology, Brisbane, QLD, 4001, Australia. y.tian@qut.edu.au.

^# Contributed equally.

Abstract

Background: 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.

Methods: 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.

Conclusion: 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.

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

MeSH terms

Algorithms
Animals
Circadian Rhythm / genetics
Gene Expression
Gene Expression Profiling* / methods
Gene Expression Regulation*
Mammals / genetics
Rats
Transcription Factors / metabolism

Substances

Transcription Factors

Abstract

MeSH terms

Substances

Grants and funding