Phenotypic-specific gene module discovery using a diagnostic tree and caBIG VISDA.

Yitan Zhu, Zuyi Wang, Yuanjian Feng, Jianhua Xuan, David J. Miller, Eric P. Hoffman, Yue Wang

    Research output: Contribution to journalArticle

    Abstract

    For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, visual statistical data analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic conditions. We then superimpose existing knowledge of gene regulation and gene function (ingenuity pathway analysis) to analyze the clustering results and generate novel hypotheses for further research on muscular dystrophies.

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Biomedical Engineering
    • Computer Vision and Pattern Recognition
    • Health Informatics

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