ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis.
ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis.
复制标题
ReactomeGSA-有效的多摩斯比较途径分析。
DOI：
10.1074/mcp.tir120.002155
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Hermjakob H
中科院分区：
文献类型：
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作者：
Griss J;Viteri G;Sidiropoulos K;Nguyen V;Fabregat A;Hermjakob H
我们提出了新的 ReactomeGSA 资源，用于多组学数据集的比较路径分析。 ReactomeGSA 可通过 Reactome 的 Web 界面和新颖的 ReactomeGSA R Bioconductor 软件包访问，明确支持 scRNA-seq 数据。我们通过表征 B 细胞在抗肿瘤免疫中的作用来展示 ReactomeGSA 的功能。结合五项 TCGA 研究的多组学数据揭示了 B 细胞在不同癌症中的显着相反作用。这展示了 ReactomeGSA 如何通过集成大型多组学数据集快速获得新颖的生物医学见解。亮点 ReactomeGSA 是一种用于多物种、多组学途径分析的新型工具。其定量路径分析方法具有很高的统计能力。综合五项 TCGA 研究的数据表明，B 细胞在癌症中具有相反的作用。 ReactomeGSA 揭示了转录水平和蛋白质水平之间关键途径的差异。通路分析是分析“组学实验”的关键方法。然而，整合来自不同组学技术和不同物种的数据仍然需要大量的生物信息学知识。在这里，我们介绍了用于多组学数据集比较路径分析的新型 ReactomeGSA 资源。 ReactomeGSA 可以通过 Reactome 现有的 Web 界面和新颖的 ReactomeGSA R Bioconductor 包来使用，明确支持 scRNA-seq 数据。来自不同物种的数据会自动映射到共同的路径空间。来自 ExpressionAtlas 和 Single Cell ExpressionAtlas 的公共数据可以直接集成到分析中。 ReactomeGSA 大大降低了多组学、跨物种、比较途径分析的技术障碍。我们使用 ReactomeGSA 来表征 B 细胞在抗肿瘤免疫中的作用。我们比较了来自五个癌症基因组图谱 (TCGA) 转录组学和两个临床蛋白质组肿瘤分析联盟 (CPTAC) 蛋白质组学研究的富含 B 细胞和缺乏 B 细胞的人类癌症样本。富含 B 细胞的肺腺癌样品缺乏通过 NFkappaB 进行的激活。这可能与人类黑色素瘤的 scRNA-seq 数据中缺乏 NFkappaB 激活的肿瘤相关 IgG+ 浆细胞的特定子集的存在有关。这展示了 ReactomeGSA 如何通过集成大型多组学数据集获得新颖的生物医学见解。
We present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA is accessible through Reactome's web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. We showcase ReactomeGSA's functionality by characterizing the role of B cells in anti-tumour immunity. Combining multi-omics data of five TCGA studies reveals marked opposing effects of B cells in different cancers. This showcases how ReactomeGSA can quickly derive novel biomedical insights by integrating large multi-omics datasets. Highlights ReactomeGSA is a novel tool for multi-species, multi-omics pathway analysis. Its quantitative pathway analysis methods offer high statistical power. Combining data of five TCGA studies shows B cells have opposing effects in cancers. ReactomeGSA reveals differences in key pathways between transcript- and protein-level. Pathway analyses are key methods to analyze 'omics experiments. Nevertheless, integrating data from different 'omics technologies and different species still requires considerable bioinformatics knowledge. Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome's existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell ExpressionAtlas can be directly integrated in the analysis. ReactomeGSA greatly reduces the technical barrier for multi-omics, cross-species, comparative pathway analyses. We used ReactomeGSA to characterize the role of B cells in anti-tumor immunity. We compared B cell rich and poor human cancer samples from five of the Cancer Genome Atlas (TCGA) transcriptomics and two of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics studies. B cell-rich lung adenocarcinoma samples lacked the otherwise present activation through NFkappaB. This may be linked to the presence of a specific subset of tumor associated IgG+ plasma cells that lack NFkappaB activation in scRNA-seq data from human melanoma. This showcases how ReactomeGSA can derive novel biomedical insights by integrating large multi-omics datasets.