Abstract
BackgroundWhen biological networks are studied, it is common to look for clusters, i.e. sets of nodes that are highly inter-connected. To understand the biological meaning of a cluster, the user usually has to sift through many textual annotations that are associated with biological entities.FindingsThe WordCloud Cytoscape plugin generates a visual summary of these annotations by displaying them as a tag cloud, where more frequent words are displayed using a larger font size. Word co-occurrence in a phrase can be visualized by arranging words in clusters or as a network.ConclusionsWordCloud provides a concise visual summary of annotations which is helpful for network analysis and interpretation. WordCloud is freely available at http://baderlab.org/Software/WordCloudPlugin
Highlights
When biological networks are studied, it is common to look for clusters, i.e. sets of nodes that are highly inter-connected
WordCloud provides a concise visual summary of annotations which is helpful for network analysis and interpretation
The font size of any word w in a tag cloud is directly proportional to: selw seltotk where selw is the number of selected nodes that contain the word w, seltot is the total number of selected nodes, netw is the number of nodes in the entire network that contain the word w, nettot is the total number of nodes in the network, and k is the network normalization coefficient, which can be tuned by the user through an interactive slider bar
Summary
Networks are widely used to represent relationships between biological entities, such as proteins and genes. A popular method for interpreting sub-networks using this information is enrichment analysis, where node and edge attributes are mined for statistically enriched text terms. A sub-network can be searched for enriched biological pathways associated with the list of nodes. Gene-set enrichment analysis is a popular approach to functionally characterize gene lists [10], including gene clusters from protein networks. Enrichment analysis can often produce long lists of enriched genesets, which are often redundant or interrelated, hindering the interpretation of the results. To overcome this problem, several visualization methods have been developed to arrange gene-sets as similarity networks, where clusters correspond to functionally related gene-sets. WordCloud can be effectively used to summarize these gene-set clusters (Figure 2)
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