Constructing Gene Regulatory Network using Chatterjee's Rank Correlation with Single-cell Transcriptomic Data

Discovering gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data is critical for understanding cellular function. Still, existing methods are limited by strong theoretical assumptions or high computational complexity. We introduce a multiple testing framework for GRN construction using Chatterjee's rank correlation coefficient, a nonparametric measure of dependence. Our approach overcomes the limitations of traditional methods while offering a transparent, scalable, and computationally efficient alternative to recent black-box machine learning models. Crucially, to address the non-independence of cellular observations inherent to scRNA-seq, we develop a data-driven algorithm for estimating robust testing cutoffs. Furthermore, we exploit the asymmetric nature of Chatterjee's correlation to propose a new test for active regulation, enabling the construction of biologically meaningful and directionally informed GRNs. We demonstrate that our method matches or outperforms state-of-the-art approaches in recovering true gene-gene dependencies and directed regulatory interactions from both simulated and real datasets, particularly for complex, non-linear dependencies, providing a powerful tool for dissecting complex GRNs.