SC2ATmd: a tool for integration of the figure of merit with cluster analysis for gene expression data

dc.contributor.authorOlex, Amy L.
dc.contributor.authorFetrow, Jacquelyn S.
dc.date.accessioned2014-01-10T21:14:18Z
dc.date.available2014-01-10T21:14:18Z
dc.date.issued2011
dc.description.abstractSummary: Standard and Consensus Clustering Analysis Tool for Microarray Data (SC2ATmd) is a MATLAB-implemented application specifically designed for the exploration of microarray gene expression data via clustering. Implementation of two versions of the clustering validation method figure of merit allows for performance comparisons between different clustering algorithms, and tailors the cluster analysis process to the varying characteristics of each dataset. Along with standard clustering algorithms this application also offers a consensus clustering method that can generate reproducible clusters across replicate experiments or different clustering algorithms. This application was designed specifically for the analysis of gene expression data, but may be used with any numerical data as long as it is in the right format. Availability: SC2ATmd may be freely downloaded from http://www.compbiosci.wfu.edu/tools.htm.en_US
dc.identifier.citationOlex, A.L.; Fetrow, J.S. (2011). "SC2ATmd: a tool for integration of the figure of merit with cluster analysis for gene expression data." Bioinformatics, 27(9):1330-31. Available at http://dx.doi.org/10.1093/bioinformatics/btr115.en_US
dc.identifier.urihttp://dx.doi.org/10.1093/bioinformatics/btr115
dc.identifier.urihttps://wakespace.lib.wfu.edu/handle/10339/39106
dc.publisherOxforden_US
dc.sourceBioinformaticsen_US
dc.titleSC2ATmd: a tool for integration of the figure of merit with cluster analysis for gene expression dataen_US
dc.typeArticleen_US
zsrdc.citation.issue9en_US
zsrdc.citation.volume27en_US

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