Methods for leveraging heterogeneity in gene expression and single-cell epigenetic data for systems immunology
Abstract/Contents
- Abstract
- Traditionally, biomedical and translational studies are designed to be run as a controlled experiment, in which biological, clinical, and technical heterogeneity is controlled. Consequently, such a study does not represent real-world heterogeneity. As such, many results from these studies fail to translate in clinical practice. Frequentist meta-analysis has been repeatedly shown to better leverage heterogeneity across independent datasets and identify robust molecular signatures of a disease, which in turn can accelerate translation to clinical practice. However, this class of techniques has several disadvantages, including, 1) a lack of robustness to outliers, 2) a large number of hyperparameters for gene set selection, and 3) a heavy reliance on hypothesis testing and p-values. To overcome these disadvantages, in Chapter 1, I will introduce a Bayesian meta-analysis approach that addresses these issues and is better able to account for heterogeneity across disparate studies. Heterogeneity is not limited to transcriptomic data but exists in epigenomic data as well. Historically, technologies such as ChIP-seq and ATAC-seq have been able to measure epigenetics at a bulk level. EpiTOF, a recently developed technology, is able to measure abundances of post-translational histone modifications at a single-cell level. This technology has been leveraged to profile hundreds of healthy individuals from around the world presenting unique methodological challenges for analysis, including understanding the heterogeneity of histone modifications in the single-cell data. In Chapters 2 and 3, I will address work developing methods to reduce technical heterogeneity, analyze biological heterogeneity and leverage EpiTOF data to learn about epigenetic regulation at a single-cell level. In addition, I will discuss comprehensive and cell-type specific networks of HPTM interactions and histone modifications profiles associated with the myeloid and lymphoid lineages, as well as cellular life span
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 | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Kalesinskas, Laurynas |
|---|---|
| Degree supervisor | Khatri, Purvesh |
| Thesis advisor | Khatri, Purvesh |
| Thesis advisor | Gaudilliere, Brice |
| Thesis advisor | Tian, Lu |
| Thesis advisor | Utz, Paul |
| Degree committee member | Gaudilliere, Brice |
| Degree committee member | Tian, Lu |
| Degree committee member | Utz, Paul |
| Associated with | Stanford University, School of Medicine, Department of Biomedical Data Science |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Laurynas Kalesinskas |
|---|---|
| Note | Submitted to the Department of Biomedical Data Science |
| Thesis | Thesis Ph.D. Stanford University 2022 |
| Location | https://purl.stanford.edu/hm365mt6037 |
Access conditions
- Copyright
- © 2022 by Laurynas Kalesinskas
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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