Leveraging structure in the analysis of high-dimensional biological data
Abstract/Contents
- Abstract
- With the advancement of technologies to collect data automatically, researchers now have access to large datasets unimaginable until recently. In biology and medicine, multiple types of data have been generated in massive quantities with the promise to explain complex biological phenomena. While previously data acquisition was the most costly process, the main bottleneck today has become data analysis because many datasets also contain hyper- informative details which result in high dimensionality. Detecting signals in such data is like searching for a needle in a haystack. Fortunately, certain structures underlying the data can provide opportunities to make the analysis statistically powerful and computationally efficient. Borrowing insights from different structures in the data, I have developed efficient, principled, and interpretable methods to analyze high-dimensional data, with a focus on the modern biological applications. In this thesis, I first introduce a method to analyze high-throughput single-cell data, combining gene expression and immuno-sequencing data. Next, I propose GLISS, a novel framework that integrates Spatial Gene Expression data with single-cell RNA-sequencing data to simultaneously select spatial gene features and identify hidden spatial cellular structures. GLISS utilizes a graph-based feature selection method that is sensitive to non-monotonic associations to determine spatial genes. Finally, I present AEGIS, an exploratory data analysis method for Gene Ontology applications. AEGIS entails new visualization strategies that leverage the Directed Acyclic Graph structure of the Gene Ontology to facilitate information retrieval and power calculations for research study design
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Zhu, Junjie (Jason) |
|---|---|
| Degree supervisor | Sabatti, Chiara |
| Thesis advisor | Sabatti, Chiara |
| Thesis advisor | Horowitz, Mark (Mark Alan) |
| Thesis advisor | Weissman, Tsachy |
| Degree committee member | Horowitz, Mark (Mark Alan) |
| Degree committee member | Weissman, Tsachy |
| Associated with | Stanford University, Department of Electrical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Junjie Zhu |
|---|---|
| Note | Submitted to the Department of Electrical Engineering |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Junjie Zhu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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