Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data

Abstract

Inferring time-varying gene regulatory networks from time-series single-cell RNA sequencing (scRNA-seq) data remains a challenging task. The existing methods have notable limitations as most are either designed for reconstructing time-varying networks from bulk microarray data or constrained to inferring stationary networks from scRNA-seq data, failing to capture the dynamic regulatory changes at the single-cell level. Furthermore, scRNA-seq data present unique challenges, including sparsity, dropout events, and the need to account for heterogeneity across individual cells. These challenges complicate the accurate capture of gene regulatory network dynamics over time. In this work, we propose a novel f-divergence-based dynamic gene regulatory network inference method (f-DyGRN), which applies f-divergence to quantify the temporal variations in gene expression across individual single cells. Our approach integrates a first-order Granger causality model with various regularization techniques and partial correlation analysis to reconstruct gene regulatory networks from scRNA-seq data. To infer dynamic regulatory networks at different stages, we employ a moving window strategy, which allows for the capture of dynamic changes in gene interactions over time. We applied this method to analyze both simulated and real scRNA-seq data from THP-1 human myeloid monocytic leukemia cells, comparing its performance with the existing approaches. Our results demonstrate that f-DyGRN, when equipped with a suitable f-divergence measure, outperforms most of the existing methods in reconstructing dynamic regulatory networks from time-series scRNA-seq data.

Keywords: Granger causality; f-divergence; gene regulatory network; regularization; single-cell RNA sequencing; time-series data; time-varying network.

Figure 1

AUROC curves for the 10-gene dataset showing gene regulatory network inference performance across different f-divergence measures using LASSO, SCAD ($a=3.7$), and MCP ($a=3$).

Figure 2

Jensen–Shannon (JS) networks of MCP-based f-DyGRN for the 10-gene dataset with $a=3$. Each node represents a gene, labeled with its corresponding gene number (1–10). The solid black arrows indicate activation, and red dashed arrows indicate inhibition.

Figure 3

AUROC curves of SCAD-based f-DyGRN for the 10-gene dataset across different a values.

Figure 4

AUROC curves for the 20-gene dataset across different f-divergences using LASSO, SCAD ($a=3.7$), and MCP ($a=3$) regularization.

Figure 5

Curves of mean similarity values for the 20-gene dataset across different f-divergence measures using LASSO, SCAD ($a=3.7$), and MCP ($a=3$) regularization penalties. The curves illustrate the temporal consistency of inferred networks across consecutive time windows.

Figure 6

Time-varying gene regulatory networks inferred by MCP-based f-DyGRN using symmetric KL divergence for the THP-1 dataset ($a=3$). The solid black arrows indicate activation, and red dashed arrows indicate inhibition.

Figure 7

AUROC curves for the THP-1 dataset across various f-divergence measures using LASSO, SCAD ($a=3.7$), and MCP ($a=3$) regularization penalties.