Decoding apoptosis, ferroptosis, and inflammatory cell death in adenomyosis at single-cell resolution.

Abstract

Accurate pathway activity inference from single-cell RNA sequencing (scRNA-seq) data is hindered by sparsity, technical noise, and the weak yet coordinated nature of transcriptional programs. Existing methods typically aggregate expression values over predefined gene sets, which can obscure context-dependent regulatory structure. Here, we present Graph-based Pathway Activity Scoring (GraphPAS), a hierarchical graph learning framework for recovering coherent pathway-level structure from scRNA-seq data. Systematic benchmarking across scRNA-seq datasets showed that GraphPAS consistently achieved higher adjusted Rand index, normalized mutual information, and silhouette width than AUCell and scapGNN, while maintaining greater robustness under dropout and Gaussian noise perturbations. Applied to adenomyosis scRNA-seq data, GraphPAS revealed enrichment of programmed cell death programs in macrophages. Pain-associated samples showed elevated apoptosis, ferroptosis, and necroptosis signatures accompanied by inflammatory activation, implicating macrophage-centered cell death remodeling in the adenomyosis microenvironment.