clusterMaker: a multi-algorithm clustering plugin for Cytoscape

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 cluster...

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Vydané v:BMC bioinformatics Ročník 12; číslo 1; s. 436
Hlavní autori: Morris, John H, Apeltsin, Leonard, Newman, Aaron M, Baumbach, Jan, Wittkop, Tobias, Su, Gang, Bader, Gary D, Ferrin, Thomas E
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 09.11.2011
Springer Nature B.V
BMC
Predmet:
Algorithms
Animals
Bioinformatics
Biomedical and Life Sciences
Cluster Analysis
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Genomics
Life Sciences
Mice
Microarrays
Protein Interaction Maps
Racemases and Epimerases - genetics
Saccharomyces cerevisiae - enzymology
Saccharomyces cerevisiae - genetics
Software
Transcriptome analysis
Yeasts
Tree View
Uncentered Correlation
Cluster Algorithm
Genetic Interaction Data
Fuzzy Cluster
ISSN:1471-2105, 1471-2105
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Shrnutí: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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-12-436