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Methylation and High Dimensional Data Integration
Tu, Robin
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https://hdl.handle.net/2142/115367
Description
- Title
- Methylation and High Dimensional Data Integration
- Author(s)
- Tu, Robin
- Issue Date
- 2022-04-12
- Director of Research (if dissertation) or Advisor (if thesis)
- Zhao, Sihai D
- Doctoral Committee Chair(s)
- Zhao, Sihai D
- Committee Member(s)
- Foss, Alexander H
- Li, Bo
- Simpson, Douglas G
- Department of Study
- Statistics
- Discipline
- Statistics
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- Ph.D.
- Degree Level
- Dissertation
- Keyword(s)
- Dimension Reduction
- Contrastive
- Methylation
- Bioinformatics
- Statistics
- Methodology
- Abstract
- Data integration challenges in bioinformatics are multifaceted. This paper aims to motivate methods and techniques to handle multiple data types and data sources. We integrate multiple sources of data to explore the usefulness of proxy blood methylation as a substitute for brain tissue. We then propose a likelihood based dimension reduction method to handle non-quantitative data. Lastly, we propose a tuning-free method that identifies low-dimensional representations of a target dataset relative to one or more comparison datasets which also is computationally efficient even with large numbers of features.
- Graduation Semester
- 2022-05
- Type of Resource
- Thesis
- Handle URL
- https://hdl.handle.net/2142/115367
- Copyright and License Information
- Copyright 2022 Robin Tu
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Graduate Dissertations and Theses at Illinois PRIMARY
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