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irGSEA：单细胞基于排名的基因集富集分析的整合。

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

机构信息

Department of Hematology and Oncology, Shenzhen Children's Hospital of China Medical University, Shenzhen 518038, China.

Department of Obstetrics and Gynecology; Department of Pediatrics; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine, The Third Affi1iated Hospital of Guangzhou Medical University, Guangzhou 510150, China.

出版信息

Brief Bioinform. 2024 May 23;25(4). doi: 10.1093/bib/bbae243.

DOI:10.1093/bib/bbae243
PMID:38801700
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11129768/
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).

摘要

irGSEA 是一个 R 包，旨在评估六种不同的基因集评分方法在单细胞 RNA 测序数据上的应用结果。该包采用了六种不同的评分方法，这些方法依赖于基因的表达排序，强调相对表达水平而不是绝对值。所实现的方法包括 AUCell、UCell、singscore、ssGSEA、JASMINE 和 Viper。先前的研究表明，这些方法对数据集大小和组成的变化具有稳健性，仅基于单个细胞的相对基因表达生成富集评分。irGSEA 采用稳健的秩聚合算法，将所有六种方法的结果合并，以确定目标基因集在不同评分方法中的统计学意义。该包注重用户友好性，允许直接输入表达矩阵或与 Seurat 对象无缝交互。此外，它还方便了结果的全面可视化。irGSEA 包及其附带的文档可在 GitHub（https://github.com/chuiqin/irGSEA）上获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ec/11129768/10b1ccf2f1bc/bbae243f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ec/11129768/d360cace8db3/bbae243ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ec/11129768/bf927e44b20d/bbae243f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ec/11129768/496503856865/bbae243f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ec/11129768/10b1ccf2f1bc/bbae243f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ec/11129768/d360cace8db3/bbae243ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ec/11129768/bf927e44b20d/bbae243f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ec/11129768/496503856865/bbae243f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ec/11129768/10b1ccf2f1bc/bbae243f3.jpg

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