Nonparametric methods for functional genomics and applications to immune aging
Abstract/Contents
- Abstract
- Rapid advances in DNA sequencing technology have facilitated the development of assays interrogating numerous attributes of cellular identity, including chromatin structure, DNA methylation, and DNA/protein interactions. As recent work has demonstrated the roles of transcription factor (TF) binding and chromatin organization as "leading indicators" of disease and development, considerable effort has been devoted to cataloging the various epigenomic markers of cell state, increasingly within the context of large, multi-institutional projects. However, the analysis methodologies in widespread use largely date to the early days of microarrays and RNA-seq, and are designed for small-scale functional genomics studies based on gene expression data. Such methods require the user to explicitly encode, in the form of a linear model, the structure of the experimental design. This constraint forces the investigator to decide, a priori, which experimental covariates are expected to affect the gene expression measurements, and how those covariates interact with one another. Additionally, because these methods are intended for use with gene expression data, they incorporate parametric assumptions that are particular to that data type and are not necessarily applicable to epigenomic data. In this dissertation, I present my work addressing these analytical shortcomings and discuss the use of epigenomic data for functional genomics: First, I introduce a statistical technique for associating principal components (PCs) with experimental covariates, enabling assumption-free quantification of confounding and correction for nuisance variables. Second, I describe an extension to the association between PCs and covariates, allowing for nonparametric differential analysis. Third, I demonstrate the application of functional genomics to chromatin structure data, establishing putative molecular mechanisms associated with CD8 T cell differentiation and aging in humans. Altogether, this dissertation facilitates and demonstrates the application of the functional genomics paradigm to the broad scope of assay types enabled by recent advances in high-throughput sequencing technologies.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Moskowitz, David M |
|---|---|
| Associated with | Stanford University, Department of Biomedical Informatics. |
| Primary advisor | Greenleaf, William James |
| Thesis advisor | Greenleaf, William James |
| Thesis advisor | Kundaje, Anshul, 1980- |
| Thesis advisor | Pritchard, Jonathan D |
| Advisor | Kundaje, Anshul, 1980- |
| Advisor | Pritchard, Jonathan D |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | David M. Moskowitz. |
|---|---|
| Note | Submitted to the Department of Biomedical Informatics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by David Maurice Moskowitz
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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