Show simple item record

dc.contributor.authorZhu, Yitanen_US
dc.contributor.authorLi, Huaien_US
dc.contributor.authorMiller, David Jen_US
dc.contributor.authorWang, Zuyien_US
dc.contributor.authorXuan, Jianhuaen_US
dc.contributor.authorClarke, Roberten_US
dc.contributor.authorHoffman, Eric Pen_US
dc.contributor.authorWang, Yueen_US
dc.identifier.citationBMC Bioinformatics. 2008 Sep 18;9(1):383en_US
dc.description.abstractAbstract 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.en_US
dc.rightsAttribution 4.0 United States*
dc.titlecaBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic dataen_US
dc.typeJournal articleen_US
dc.description.versionPeer Revieweden_US
dc.rights.holderYitan Zhu et al.; licensee BioMed Central Ltd.en_US

Files in this item


This item appears in the following Collection(s)

  • BioMed Central [381]
    BioMed Central publications by Virginia Tech authors.

Show simple item record

Attribution 4.0 United States
Except where otherwise noted, this item's license is described as Attribution 4.0 United States