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clusterMaker: a multi-algorithm clustering plugin for Cytoscape

John H Morris1*, Leonard Apeltsin1, Aaron M Newman2, Jan Baumbach3, Tobias Wittkop4, Gang Su56, Gary D Bader78 and Thomas E Ferrin19

Author Affiliations

1 Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA

2 Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California, USA

3 Max Planck Institute for Informatics, Saarbr├╝cken, Germany

4 Buck Institute for Age Research, Novato, California, USA

5 Bioinformatics Program, University of Michigan, Ann Arbor, MI, USA

6 National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI, USA

7 The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada

8 Department of Molecular Genetics, University of Toronto, Ontario, Canada

9 Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA

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BMC Bioinformatics 2011, 12:436  doi:10.1186/1471-2105-12-436

Published: 9 November 2011

Abstract

Background

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. 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. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. 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.

Results

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. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section.

Conclusions

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. Several of these visualizations and algorithms are only available to Cytoscape users through the clusterMaker plugin. clusterMaker is available via the Cytoscape plugin manager.