European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom; Department of Dermatology, Medical University of Vienna, Vienna, Austria.
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom.
Mol Cell Proteomics. 2020 Dec;19(12):2115-2125. doi: 10.1074/mcp.TIR120.002155. Epub 2020 Sep 9.
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.
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相当的生物信息学知识。在这里,我们提出了新的 ReactomeGSA 资源,用于多组学数据集的比较通路分析。ReactomeGSA 可以通过 Reactome 现有的网络界面和新的带有明确支持 scRNA-seq 数据的 ReactomeGSA R Bioconductor 包使用。来自不同物种的数据自动映射到一个共同的通路空间。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、比较通路分析的技术门槛。我们使用 ReactomeGSA 来描述 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 细胞的肺腺癌样本缺乏通常通过 NFkappaB 进行的激活。这可能与 scRNA-seq 数据中存在特定的肿瘤相关 IgG+浆细胞亚群有关,这些浆细胞在人类黑色素瘤中缺乏 NFkappaB 激活。这展示了 ReactomeGSA 如何通过整合大型多组学数据集来获得新的生物医学见解。