Data-driven transcriptomic modules
Abstract/Contents
- Abstract
- High throughput measurement of gene expression levels have dramatically increased the amount of biological information we collect from experiments, and presents a tantalizing possibility of gaining novel insights into human biology at the molecular level. Realization of this possibility is dependent on our ability to interpret the tens of thousands of numerical values, and to draw inferences from the patterns. Analysis of these data typically begins with the identification of genes that are differentially expressed between two or more groups. Unfortunately, this either entails testing all the genes (and hence suffer from low statistical power), or using pre-defined subsets that introduce researcher bias. In recent years, it is more common for researchers to perform Gene Set Enrichment Analysis (GSEA), which restores statistical power by testing at the gene set level. The choice of which gene sets and how many of them to test is, however, a source of researcher bias. Furthermore, the reliance on pre-defined gene sets means that we are, once again, limited to our current understanding of human biology. Other problems that have plagued transcriptomic analysis include inter-platform incompatibility and batch effects. This dissertation seeks to provide an alternative approach for transcriptomic research. Specifically, I describe a data-driven methodology for obtaining transcriptomic modules from a large compendium of microarray experiments, and demonstrate how these modules can be used to perform classification of samples and also to extract biologically-meaningful properties from gene expression datasets. I also describe an imputation method to allow cross-platform analysis between two of the most common microarray platforms in the Gene Expression Omnibus, and use this to transform the drug-gene database, Connectivity Map (CMap), into a compatible form for the transcriptomic module analysis. Finally, I demonstrate how drug repurposing could be achieved using CMap in the framework of these transcriptomic modules.
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 | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Zhou, Weizhuang |
|---|---|
| Degree supervisor | Altman, Russ |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Hastie, Trevor |
| Thesis advisor | Khatri, Purvesh |
| Degree committee member | Hastie, Trevor |
| Degree committee member | Khatri, Purvesh |
| Associated with | Stanford University, Department of Bioengineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Weizhuang Zhou. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Weizhuang Zhou
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...