AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations

Abstract

Networks often contain regions of tightly connected nodes, or clusters, that highlight their shared relationships. An effective way to create a visual summary of a network is to identify clusters and annotate them with an enclosing shape and a summarizing label. Cytoscape provides the ability to annotate a network with shapes and labels, however these annotations must be created manually one at a time, which can be a laborious process. AutoAnnotate is a Cytoscape 3 App that automates the process of identifying clusters and visually annotating them. It greatly reduces the time and effort required to fully annotate clusters in a network, and provides freedom to experiment with different strategies for identifying and labelling clusters. Many customization options are available that enable the user to refine the generated annotations as required. Annotated clusters may be collapsed into single nodes using the Cytoscape groups feature, which helps simplify a network by making its overall structure more visible. AutoAnnotate is applicable to any type of network, including enrichment maps, protein-protein interactions, pathways, or social networks.

Keywords: annotations; complexity reduction; cytoscape; enrichment map; modular networks; network analysis; network clustering; tag cloud.

Figure 1.. AutoAnnotate overview.

A network is clustered, textual annotation associated with each cluster is automatically summarized as a single cluster label, and the results are visualized. Users can customize the view by selectively collapsing and expanding clusters.

Figure 2.. The Create Annotation Set dialog.

Cluster options: ClusterMaker2 requires a clustering algorithm and edge weight attribute to be selected, otherwise a node attribute must be selected. Label options: User selects the node attribute to use for creating labels, the labelling algorithm to use, and the maximum number of words per label.

Figure 3.. The Display Options panel.

Changing parameters here automatically updates the display.

Figure 4.. The main Annotation Set panel.

Clusters and labels are shown. Clusters can be collapsed or expanded. An annotation can be customized via options available in a context sensitive (right click) menu.

Figure 5.. Event Flow.

1) Cytoscape events are fired using OSGi services ( e.g. SetCurrentNetworkViewEvent, NetworkViewAboutToBeDestroyedEvent). 2) The AutoAnnotate Model Manager reacts to Cytoscape events and updates the Data Model. 3) The ModelManager fires Model Events over the Guava EventBus ( e.g. AnnotationSetAddedEvent, ClusterAddedEvent). 4) The UI and Annotation Renderer modules each respond to a subset of the model events. There is no direct dependency between the UI and the Annotation Renderer. 5) The UI directly updates the Model, which causes Model Events to fire, which updates the UI and Annotations.

Figure 6.. AutoAnnotated enrichment map.

( A) Annotated enrichment map ( B) Collapsed enrichment map.

Figure 7.. Co-authorship network for article containing ‘Cytoscape’ in its title/abstract/keyword.

The most central node was annotated with an additional image representing the top words in the set of publications as generated by wordle.com.

Figure 8.. Running AutoAnnotate on a PPI network.

Four different annotations for the same base PPI network using parameters - WordCloud normalization of 0, previously created cluster designations, and A. protein names, B. GO biological process, C. GO molecular function, or D. GO cellular component was used for label calculation.