caBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data

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dc.contributor.author Zhu, Yitan
dc.contributor.author Li, Huai
dc.contributor.author Miller, David J
dc.contributor.author Wang, Zuyi
dc.contributor.author Xuan, Jianhua
dc.contributor.author Clarke, Robert
dc.contributor.author Hoffman, Eric P
dc.contributor.author Wang, Yue
dc.date.accessioned 2012-08-24T11:54:10Z
dc.date.available 2012-08-24T11:54:10Z
dc.date.issued 2008-09-18
dc.identifier.citation BMC Bioinformatics. 2008 Sep 18;9(1):383
dc.identifier.uri http://hdl.handle.net/10919/18881
dc.description.abstract Abstract Background The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables. Results In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA) for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive) hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy) and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample clustering, and phenotype clustering (wherein phenotype labels for samples are known), albeit with minor algorithm modifications customized to each of these tasks. Conclusion VISDA achieved robust and superior clustering accuracy, compared with several benchmark clustering schemes. The model order selection scheme in VISDA was shown to be effective for high dimensional genomic data clustering. On muscular dystrophy data and muscle regeneration data, VISDA identified biologically relevant co-expressed gene clusters. VISDA also captured the pathological relationships among different phenotypes revealed at the molecular level, through phenotype clustering on muscular dystrophy data and multi-category cancer data.
dc.title caBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data
dc.type Journal Article
dc.date.updated 2012-08-24T11:54:10Z
dc.description.version Peer Reviewed
dc.language.rfc3066 en
dc.rights.holder Yitan Zhu et al.; licensee BioMed Central Ltd.
dc.identifier.doi http://dx.doi.org/10.1186/1471-2105-9-383

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