differentially expressed genes or proteins) than statistically expected in networks integrated with user-supplied contextual data (e.g. Here, we introduce the Contextual Hub Analysis Tool (CHAT), a Cytoscape App that identifies hub nodes that interact with more "contextual" nodes (e.g. A more appropriate analysis is to determine which nodes interact with relevant nodes in the network (which we term contextual nodes) more than is statistically expected. Analysis of these degree-based hubs, for example identifying what biological processes or pathways these nodes are enriched in, tells us little about the experimental context of interest and more about the properties of highly connected nodes in general. Identifying hubs in these networks, however, is biased towards identifying nodes that are highly connected in general such as promiscuous, ubiquitous or well-studied nodes, because nodes with many interactions in the query database have a higher probability of being included in the network by chance alone. Analysis of these networks can, for example, identify subnetworks that are enriched in (but do not exclusively consist of) differentially expressed genes, or identify non-differentially expressed nodes that are topologically important in the network, both of which would otherwise not be identified. This approach to constructing a network is useful because it identifies a more fully connected network for analysis than would be the case if one restricted interactions to only those that occur between nodes in the gene list. differentially expressed genes, and then identify the high degree nodes in this network. To construct a network, users frequently query interaction databases to identify the interactors of a list of genes of interest, e.g. All of the applications available to date identify hubs based on node connectivity (degree) in a network of interest. Integrating contextual information, such as gene or protein expression data, with standard network analysis can provide insight into what are the most relevant network features in a particular study or contextĬytoscape has a number of applications to identify hubs in networks including cytoHubbaġ7, however, only the latter two are compatible with Cytoscape 3+. the network present in a specific cell type at a particular point in timeĨ. Biological networks, such as the human interactome, however, are not static entitiesĦ, and the extent to which a node acts as a hub can change depending on the biological context e.g. Hubs have also been found to be preferentially targeted by both bacterial and viral pathogensĤ and may be master regulators of biological processesĥ. The deletion of genes encoding hub proteins, for example, has been shown to correlate with lethality in yeast (the centrality-lethality rule)ģ. The identification of such highly connected nodes, termed hubs, is often of interest as hubs have been shown to be topologically and functionally important. ![]() Biological networks (and many other types of networks) have been shown to have a power law distribution of node connectivity, with most nodes having few connections and a few nodes being highly connectedĢ. Network analysis has emerged as a powerful approach to elucidate biological and disease processesġ.
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