irGSEA: the integration of single-cell rank-based gene set enrichment analysis

irGSEA is an R package designed to assess the outcomes of various gene set scoring methods when applied to single-cell RNA sequencing data. This package incorporates six distinct scoring methods that rely on the expression ranks of genes, emphasizing relative expression levels over absolute values. The implemented methods include AUCell, UCell, singscore, ssGSEA, JASMINE and Viper. Previous studies have demonstrated the robustness of these methods to variations in dataset size and composition, generating enrichment scores based solely on the relative gene expression of individual cells. By employing the robust rank aggregation algorithm, irGSEA amalgamates results from all six methods to ascertain the statistical significance of target gene sets across diverse scoring methods. The package prioritizes user-friendliness, allowing direct input of expression matrices or seamless interaction with Seurat objects. Furthermore, it facilitates a comprehensive visualization of results. The irGSEA package and its accompanying documentation are accessible on GitHub (https://github.com/chuiqin/irGSEA). Graphical AbstractThe workflow of irGSEA. Individual cells were independently scored using AUCell, UCell, singscore, ssGSEA, JASMINE and Viper. Differential gene sets were calculated for each cell cluster using distinct enrichment score matrices, with significance determined at an adjusted P-value of <= 0.05 (Wilcoxon test). The 'Up' or 'Down' designation indicates whether the enrichment degree of the differential gene set in the cell cluster is higher or lower compared to other clusters. The robust rank aggregation algorithm was employed to filter statistically significant gene sets that exhibited similar levels of enrichment across all six methods.