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

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.

Fig. 1

Logical block diagram of the new hybrid framework

Fig. 2

The normalized standard deviation of 17,879 genes

Fig. 3

Cluster 6 of fuzzy C-means clustering results. The numbers on the x-axis (time, 0-480) correspond to the 480 time points during 24 h and y-axis represents the normalized expression value from the Mfuzz result

Fig. 4

The optimal smooth for the Aanat gene

Fig. 5

The corresponding ranks of genes in the selected cluster based on different distances. The red line is the straight line $y=x$ (reference line), which means the corresponding ranks of these 1,093 genes are 1 to 1,093

Fig. 6

The relationships based on the dynamic VAR. 150 top significant edges are presented. The numbers corresponding to the ID of genes and the complete list can be found in Additional file 1: Table 1. The blue solid line represents a positive correlation, while the black dashed line indicates a negative correlation between two genes

Fig. 7

The plot of related genes with the Aanat gene. The x-axis is the time points, and the y-axis is the expression values