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    • University of Missouri-Columbia
    • Graduate School - MU Theses and Dissertations (MU)
    • Theses and Dissertations (MU)
    • Dissertations (MU)
    • 2015 Dissertations (MU)
    • 2015 MU dissertations - Freely available online
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    Bayesian analysis of fMRI data and RNA-Seq time course experiment data

    Cheng, Yuan
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    [PDF] research.pdf (11.24Mb)
    [PDF] short.pdf (50.39Kb)
    Date
    2015
    Format
    Thesis
    Metadata
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    Abstract
    The present dissertation contains two parts. In the first part, we develop a new Bayesian analysis of functional MRI data. We propose a novel triple gamma Hemodynamic Response Function (HRF) including the component to describe the initial dip. We use HRF to inform voxel-wise neuronal activities. Then we devise a new model selection procedure with a nonlocal pMOM prior for joint detection of neuronal activation and estimation of HRF, in order to time the activation time difference between visual and motor areas in the brain. In the second part, we develop a new Bayesian analysis of RNA-Seq Time Course experiments data. We propose to use Bayesian Principal Component regression model and based on that, devise a model selection procedure by using nonlocal piMOM prior in order to identify differentially expressed genes. Most current existing methods for RNA-Seq Time Course experiments data are from static view of point and cannot predict temporal patterns. Our method estimate the posterior differentially expressed probability for each gene by borrowing information across all subjects. Use of nonlocal prior in the model selection procedure reduces false discovered differentially expressed genes.
    URI
    https://hdl.handle.net/10355/57756
    https://doi.org/10.32469/10355/57756
    Degree
    Ph. D.
    Thesis Department
    Statistics (MU)
    Rights
    OpenAccess.
    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
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    • 2015 MU dissertations - Freely available online
    • Statistics electronic theses and dissertations (MU)

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