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Comparative Study
. 2020 Dec;19(12):2115-2125.
doi: 10.1074/mcp.TIR120.002155. Epub 2020 Sep 9.

ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis

Johannes Griss  1 , Guilherme Viteri  2 , Konstantinos Sidiropoulos  2 , Vy Nguyen  3 , Antonio Fabregat  2 , Henning Hermjakob  4
Affiliations
Comparative Study

ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis

Johannes Griss et al. Mol Cell Proteomics. 2020 Dec.

Abstract

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; bioinformatics software; cancer biology*; cancer immunology; data evaluation; melanoma; multi-omics data integration; tumor microenvironment.

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Conflict of interest statement

Conflict of interest—The authors declare that they have no conflicts of interest with the contents of this article.

Figures

None
Graphical abstract
Fig. 1.
Fig. 1.
Schema of the ReactomeGSA system. All requests are sent to a public web-based API through the ReactomeGSA Bioconductor R package or Reactome's web-based PathwayBrowser. The system is a Kubernetes application based on the microservices architecture. All requests are distributed through an internal message queue using RabbitMQ. Worker nodes are responsible for the complete pathway analysis, including identifier mapping and the creation of the visualization data in Reactome's pathway browser. Data nodes are responsible to load data from external resources such as ExpressionAtlas. Finally, report nodes create PDF and Microsoft Excel files as a static report of the analysis results. All data are stored in a central Redis instance. All nodes are Docker containers that are orchestrated by Kubernetes and automatically scaled based on current demand. Thereby, the application can dynamically adapt to changing usage levels.
Fig. 2.
Fig. 2.
ReactomeGSA is fully integrated into the web-based Reactome pathway browser (https://reactome.org). Users can either upload their own datasets or import public data from ExpressionAtlas. The gene set analysis is performed through the ReactomeGSA API. Results are visualized in Reactome's interactive pathway browser and sent as static reports in PDF and Microsoft Excel format via E-mail.
Fig. 3.
Fig. 3.
The ReactomeGSA Bioconductor R package can directly process data from the most commonly used data structures for 'omics analyses. The pathway analysis is performed through the ReactomeGSA analysis system and made available through a native R object. Convenient plotting functions give a quick overview of how well two datasets correlate on the pathway level. Volcano plots further highlight the magnitude of the observed changes in individual datasets. Additionally, pathway analysis of scRNA-seq data are simplified through the single “analyse_sc_clusters” function.
Fig. 4.
Fig. 4.
Comparison of TIPB-high versus -low samples from TCGA studies on melanoma (TCGA Mel), ovarian cancer (TCGA Ovarian), lung adenocarcinoma study (TCGA Lung), lung squamous cell carcinoma (TCGA Lung SCC), and breast cancer (TCGA Breast). A, Overall survival of patients with high (blue line) or low (red line) expression of the TIPB signature (split by the median expression in the data set). B, Average gene fold-changes per pathway. Only pathways significantly regulated (FDR < 0.1) in the TCGA melanoma and the TCGA lung adenocarcinoma cohort with a different direction of regulation in these two cohorts are shown. Shades of yellow represent a down-regulation, shades of blue an up-regulation.
Fig. 5.
Fig. 5.
Analysis of B cell subtypes from the data set by Jerby-Arnon et al. (32) A, UMAP plot of the identified B cell clusters. Cell type annotations are based on canonical B cell markers (33). B, ReactomeGSA gene set variation based pathway-level expression in the identified B cell clusters of the Jerby-Arnon et al. Data set. Expression values were z-score normalized by pathway. C, Expression of IgG estimated through FCGRT abundance in the B cell clusters.
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