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Visual Analytics of Patterns of Gene Expression in the Developing Mammalian Brains

Li, Qihang
http://orcid.org/0000-0001-5281-7475
http://rave.ohiolink.edu/etdc/view?acc_num=osu1492643127036346

Abstract Details

2017, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
Understanding the inherent genomic characteristic in the developing brain is a critical topic in developing neuroscience and translational bioinformatics. Since the brain is one of the longest and most complex developing structure, the exploration of the comprehensive patterns that describe the changes during growth poses significant challenges. With advances in data generation technologies, the Allen Developing Mouse Brain Atlas (ADMBA) and Allen Developing Human Brain Atlas (ADHBA) projects became an invaluable resource for neuroscientists and developmental biologists for exploring interesting spatial and temporal patterns of gene expression. However, given the extremely large amounts of data, it is desirable to apply visualization techniques to their access, analysis, and interpretation. This dissertation proposes an extensible visual analytics framework for the spatiotemporal pattern exploration in the developing mouse and human brain. Targeting on the ADMBA and ADHBA data, I developed three visual analytics components: the spatial pattern exploration, the region-based temporal pattern exploration, and the integrative gene gradient-based spatiotemporal pattern exploration. The spatial pattern exploration component, HOS-Tree system, uses a tree-layout visualization to present the structural hierarchy and uses colors to indicate the developing orientations. Also, the region-based temporal pattern exploration component uses data-mining approaches to provide interactive pattern presentation among genes and structures. In addition, we use the gradient of gene expression to define the spatiotemporal genomic characteristic, and also design a 3-D visualization component to provide the exploration of the spatiotemporal patterns. For each visualization component, I investigate the visual analytics result by seeking the biological interpretation of the explored patterns. The investigation shows that several explorations are well-interpreted by development ground truth. These explored patterns could lead to future studies potentially. Finally, the proposed visual analytics framework and the containing approaches can be extended to generalized tools and applications in which exploration and integration of spatiotemporal data are needed. This dissertation also provides high-level design considerations for future researchers and practitioners about the conceptual methodologies in integrative visual analytics in spatiotemporal pattern exploration.
Raghu Machiraju (Committee Chair)
Kun Huang (Committee Co-Chair)
Christopher Bartlett (Committee Member)
176 p.
Bioinformatics; Computer Science
spatiotemporal, gene expression, gene gradients, developing brain, novel visualization for neuroscience

Recommended Citations

Citations

  • Li, Q. (2017). Visual Analytics of Patterns of Gene Expression in the Developing Mammalian Brains [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492643127036346

    APA Style (7th edition)

  • Li, Qihang. Visual Analytics of Patterns of Gene Expression in the Developing Mammalian Brains. 2017. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1492643127036346.

    MLA Style (8th edition)

  • Li, Qihang. "Visual Analytics of Patterns of Gene Expression in the Developing Mammalian Brains." Doctoral dissertation, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492643127036346

    Chicago Manual of Style (17th edition)

osu1492643127036346
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© 2017, some rights reserved.Creative Commons License Visual Analytics of Patterns of Gene Expression in the Developing Mammalian Brains by Qihang Li is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.