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    Decoupling Linear and Nonlinear Associations of Gene Expression

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    ThesisAlanItakura.pdf
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    ThesisAlanItakura
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    Type
    Thesis
    Authors
    Itakura, Alan
    Advisors
    Ravasi, Timothy cc
    Committee members
    Gao, Xin cc
    Ryu, Taewoo
    Program
    Bioscience
    KAUST Department
    Biological and Environmental Science and Engineering (BESE) Division
    Date
    2013-05
    Embargo End Date
    2014-05-31
    Permanent link to this record
    http://hdl.handle.net/10754/292462
    
    Metadata
    Show full item record
    Access Restrictions
    At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2014-05-31.
    Abstract
    The FANTOM consortium has generated a large gene expression dataset of different cell lines and tissue cultures using the single-molecule sequencing technology of HeliscopeCAGE. This provides a unique opportunity to investigate novel associations between gene expression over time and different cell types. Here, we create a MatLab wrapper for a powerful and computationally intensive set of statistics known as Maximal Information Coefficient, and then calculate this statistic for a large, comprehensive dataset containing gene expression of a variety of differentiating tissues. We then distinguish between linear and nonlinear associations, and then create gene association networks. Following this analysis, we are then able to identify clusters of linear gene associations that then associate nonlinearly with other clusters of linearity, providing insight to much more complex connections between gene expression patterns than previously anticipated.
    Citation
    Itakura, A. (2013). Decoupling Linear and Nonlinear Associations of Gene Expression. KAUST Research Repository. https://doi.org/10.25781/KAUST-9616Z
    DOI
    10.25781/KAUST-9616Z
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-9616Z
    Scopus Count
    Collections
    Biological and Environmental Science and Engineering (BESE) Division; Bioscience Program; MS Theses

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