Fig 1.
Transitive dependency structure of PathVisio 3.
The application consists of eight modules each providing specific functionality. The modules core and data are independent modules (colored in blue) that function as libraries that can be reused outside of PathVisio (PV). Especially the core module is often used as a PV library for reading and writing of pathway files. Other modules in red, gui, desktop and visualization, provide functionality that is used by other modules. Green modules, gex, statistics and plugin manager, are not used by other PV modules but can be used by PV plugins. The PV JavaApplet version integrated in WikiPathways uses the core and gui modules.
Fig 2.
Plugin extension and installation system of PathVisio 3.
The plugin repository stores all plugin files and their dependencies. The RepoIndex library is used to create a repository.xml file which contains the dependency indexes of all plugins. Metadata about plugins is stored in the PathVisio plugin database which is then exported into a pathvisio.xml file. The PathVisio 3 plugin manager retrieves data from both files to facilitate the installation of plugins in PathVisio 3.
Table 1.
PathVisio 3 feature table.
Fig 3.
PathVisio 3, a full-powered pathway editor.
(A) The basic drawing palette contains data nodes, interactions, graphical elements, cellular compartments and a few templates. Simple drag-and-drop mechanism allows users to add the elements in the pathway diagram. (B) The ACE inhibitor pathway on WikiPathways (http://www.wikipathways.org/instance/WP554) was drawn in PathVisio describing the downstream effects of angiotensin-converting-enzyme (ACE) inhibtors. (C) The entities and interactions in the pathways can be annotated with external identifiers. In this example the pathway author annotated the KNG1 gene with the Entrez Gene identifier 3827. PathVisio utilizes the BridgeDb identifier mapping framework to free the user from manual identifier mapping steps.
Fig 4.
Multi-omics visualization in PathVisio.
Two transcriptomics datasets are visualized together with a metabolomics dataset on the Kennedy pathway from WikiPathways (http://www.wikipathways.org/instance/WP1771). The log2FC is visualized in the first column of the data node boxes using a gradient from blue over white to red. In the second column three levels of p-values are visualized (p-value < 0.01, < 0.05 and > 0.05). The expression data for a selected gene or metabolite is shown in the “Data” tab on the right side. In the red rectangle the expression data for the selected Cept1 gene is shown. There are two measurements for the gene from the two transcriptomics datasets, therefore the gene box in the pathway is split horizontally into two rows.
Fig 5.
Pathway statistics result in PathVisio.
The user defines the criterion for significantly changed genes with an absolute log2FC > 1 (A). A Z-Score is calculated for each pathway in the pathway collection and in the result table the pathways are ranked based on their Z-Score (B). A high Z-Score indicates that the pathway is more affected than expected based on the overall dataset. The user can click on each pathway to open the pathway with the data visualized on it.