New R package advances multi-pathway cell death analysis

Announcing a new article publication in BIO Integration. The authors of this article developed CellDeathAnalysis (v0.4.0), an R package providing a unified framework for analyzing 14 cell death pathways in bulk RNA-seq data. The package introduces two novel algorithms: Crosstalk-Aware Pathway Scoring, which uses gene specificity weighting (inverse document frequency-inspired) and residual debiasing to reduce redundancy from inter-pathway gene overlap; and Cell Death Subtype Classification, which uses consensus clustering on pathway score profiles to identify biologically meaningful patient subtypes. The package integrates curated gene sets from FerrDb, MSigDB, KEGG, and the primary literature, and implements multiple scoring methods (z-score, ssGSEA, GSVA, and AUCell, and the novel crosstalk-aware method), survival analysis, enrichment analysis, and publication-ready visualizations.

By applying CellDeathAnalysis to 2704 The Cancer Genome Atlas (TCGA) samples across four cancer types (BRCA, LUAD, LIHC, and STAD), the crosstalk-aware method reduced inter-pathway correlation (mean reduction = 0.69) compared to z-score scoring. The disulfidptosis score in LUAD exhibited a significant survival association (HR = 2.19, P_adj = 0.037). Consensus clustering identified clinically meaningful subtypes in LIHC (P = 0.017) and STAD (P = 0.028).

CellDeathAnalysis provides the first dedicated toolkit for multi-pathway cell death analysis with novel crosstalk-aware scoring and subtype classification capabilities. The package addresses the critical challenge of gene overlap between cell death pathways and enables discovery of clinically meaningful patient subtypes.