Researchers have developed CellDeathAnalysis (v0.4.0), an R package designed to provide a unified framework for analyzing 14 cell death pathways in bulk RNA-seq data, according to a Jul. 6 article publication in BIO Integration.
The package introduces two new algorithms: Crosstalk-Aware Pathway Scoring, which uses gene specificity weighting inspired by inverse document frequency and residual debiasing to address redundancy from inter-pathway gene overlap; and Cell Death Subtype Classification, which applies consensus clustering on pathway score profiles to identify biologically meaningful patient subtypes. The authors said the package integrates curated gene sets from FerrDb, MSigDB, KEGG, and primary literature sources. It also implements several scoring methods—including z-score, ssGSEA, GSVA, AUCell—and the novel crosstalk-aware method. Additional features include survival analysis, enrichment analysis, and publication-ready visualizations.
In their study using CellDeathAnalysis on 2,704 samples from The Cancer Genome Atlas across four cancer types (BRCA, LUAD, LIHC, and STAD), the researchers reported that the crosstalk-aware method reduced inter-pathway correlation with a mean reduction of 0.69 compared to z-score scoring. They found that the disulfidptosis score in LUAD showed a significant association with survival (hazard ratio = 2.19; adjusted P-value = 0.037). Consensus clustering identified clinically meaningful subtypes in LIHC (P = 0.017) and STAD (P = 0.028).
The authors said CellDeathAnalysis is the first dedicated toolkit for multi-pathway cell death analysis featuring both novel crosstalk-aware scoring and subtype classification capabilities.
They concluded that the package addresses a critical challenge of gene overlap between cell death pathways while enabling discovery of clinically relevant patient subtypes.
