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. 2017 Jul 3;45(W1):W501-W508.
doi: 10.1093/nar/gkx372.

Pathview Web: user friendly pathway visualization and data integration

Weijun Luo  1   2 , Gaurav Pant  1   3 , Yeshvant K Bhavnasi  1   3 , Steven G Blanchard Jr  1   2 , Cory Brouwer  1   2
Affiliations

Pathview Web: user friendly pathway visualization and data integration

Weijun Luo et al. Nucleic Acids Res. .

Abstract

Pathway analysis is widely used in omics studies. Pathway-based data integration and visualization is a critical component of the analysis. To address this need, we recently developed a novel R package called Pathview. Pathview maps, integrates and renders a large variety of biological data onto molecular pathway graphs. Here we developed the Pathview Web server, as to make pathway visualization and data integration accessible to all scientists, including those without the special computing skills or resources. Pathview Web features an intuitive graphical web interface and a user centered design. The server not only expands the core functions of Pathview, but also provides many useful features not available in the offline R package. Importantly, the server presents a comprehensive workflow for both regular and integrated pathway analysis of multiple omics data. In addition, the server also provides a RESTful API for programmatic access and conveniently integration in third-party software or workflows. Pathview Web is openly and freely accessible at https://pathview.uncc.edu/.

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Figures

Figure 1.
Figure 1.
The Pathview Web server GUI. (A) The main analysis page with the user input web form; (B) the main result page with lists of outputs; (C) an example of interactive pathview graph viewed in browser. Important features are annotated by red tags and texts. Red dashed boxes mark the interactive features with hyperlinks and hover info.
Figure 2.
Figure 2.
The design of Pathview Web server: (A) architecture, (B) workflow and different modes. Note the workflow works with either gene data or compound data (No PA, Regular PA) or both together (No PA, Integrated PA). In the pathway analysis step, generally applicable gene-set enrichment (GAGE) is used for numeric data and over representation analysis for categorical data. In meta analysis step, P-values from regular pathway analysis are summarized into global P-values and pathways are then selected based on the latter.
Figure 3.
Figure 3.
Integrated pathway analysis and visualization of both gene expression and metabolomics data (Example 4 online). Shown here is the KEGG pathway hsa00190 Oxidative phosphorylation. The analysis statistics is shown in Table 2.
See this image and copyright information in PMC

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