#Cytoscape clustering software#
#Cytoscape clustering plus#

PanGIA (which stands for Physical and Genetic Interaction Alignment) is a Cytoscape plugin originally developed for Cytoscape 2, and which has now been released for Cytoscape 3.

#Cytoscape clustering software#

Most authors of Cytoscape 2 plugins will face similar challenges porting their software to work with Cytoscape 3, and this paper gives details of how the PanGIA port addressed them. In the process, the ability to control randomization was added to the GUI, a feature which was not only integral to the porting process, but which also ensures more easily reproducible scientific analysis for PanGIA users. In order to convert the PanGIA plugin to an app, various challenges, including those related to a transformed data model, concurrency, and randomization had to be overcome. PanGIA is a Cytoscape add-on () designed to analyze and visualize genetic interaction data in light of physical interaction data. In the transition from Cytoscape 2 to Cystoscape 3, some of the underlying assumptions upon which plugins relied changed, requiring a significant porting effort for plugins to work as Cytoscape 3 apps. clusterMaker is available via the Cytoscape plugin manager.Much of the biologically significant functionality in Cytoscape is contained within third-party add-ons, called plugins in Cytoscape 2 and apps in Cytoscape 3. Several of these visualizations and algorithms are only available to Cytoscape users through the clusterMaker plugin. The Cytoscape plugin clusterMaker provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Cytoscape session files for all three scenarios are provided in the Additional Files section. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering.

Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types the identification of protein complexes in the yeast Saccharomyces cerevisiae and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily.

#Cytoscape clustering plus#

clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them.