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ReactomeGSA-Efficient Multi-Omics Comparative Pathway Analysis
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DOI：10.1074/mcp.TIR120.002155.png)
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Abstract
En 中文
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. 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.
Keywords:
Pathway analysis
data evaluation
bioinformatics software
melanoma
cancer biology*
cancer immunology
multi-omics data integration
tumor microenvironment
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