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Abstract
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).
Academic Publication irGSEA: the integration of single-cell rank-based gene set enrichment analysis
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What is the core focus of the research titled 'irGSEA: the integration of single-cell rank-based gene set enrichment analysis'?
This literature focuses on: Abstract 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 ...
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Yes, highly correlated activity was mapped. An entry titled 'irGSEA: the integration of single-cell rank-based gene set enrichment analysis' discusses this: Abstract irGSEA is an R package designed to assess the outcomes of various gene set scoring methods when applied to single-cell RNA ...
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