Stability of Cell-Specific Gene Regulatory Networks Inferred by ScReNI

Abstract

Gene regulatory networks (GRNs) inferred at single-cell resolution offer insight into regulatory mechanisms underlying complex diseases such as Alzheimer's disease, but the reliability of the GRNs produced by methods such as ScReNI is not fully explored. This work investigates the stability and reliability of GRNs inferred by a Python reimplementation of ScReNI applied to the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) and a mouse retinal dataset. Three experiments were conducted. First, gene-level prediction accuracy, measured via random forest out-of-bag (OOB) R², was found to vary across genes and to be driven primarily by expression level rather than network position. Second, two main candidate cell-level reliability metrics were evaluated against precision and recall: OOB R² showed no significant association with precision once network density and expression were statistically controlled for, whereas edge weight variance across repeated inference runs retained significant correlation with both, making it the more promising candidate metric identified. Third, comparing the GRNs of cells with high versus low edge weight variance within cell types revealed that such networks share similar topological structure but diverge substantially in edge weight magnitude. These findings indicate that out-of-bag accuracy alone is an insufficient proxy for GRN reliability, and that edge weight variance, while more informative, does not yet provide a fully cell-type-consistent reliability metric, motivating further work on alternative approaches to quantifying single-cell GRN stability.