irGSEA

irGSEA integrates and evaluates rank-based gene set scoring methods for single-cell RNA sequencing data analysis.

Key Features:

• Six Scoring Methods: Incorporates AUCell, UCell, singscore, ssGSEA, JASMINE, and Viper, all relying on gene expression ranks rather than absolute expression values.
• Robust Rank Aggregation: Amalgamates results from all six methods to determine statistical significance of target gene sets across diverse scoring approaches.
• Seurat Object Compatibility: Accepts direct input of expression matrices or seamless interaction with Seurat objects.
• Comprehensive Visualization: Provides visualization capabilities for result analysis.

Scientific Applications:

• Gene Set Enrichment Analysis: Assesses outcomes of gene set scoring methods applied to single-cell RNA sequencing data.
• Cross-Method Validation: Determines statistical significance of target gene sets by combining results from multiple rank-based scoring methods.
• Robustness Assessment: Leverages methods demonstrated to be robust to variations in dataset size and composition.

Methodology:

irGSEA employs the robust rank aggregation algorithm to combine results from six rank-based scoring methods (AUCell, UCell, singscore, ssGSEA, JASMINE, and Viper), generating enrichment scores based solely on the relative gene expression of individual cells.