The statistics of RNA splicing in single cells
Abstract/Contents
- Abstract
- Although the amount of single-cell RNA-sequencing (scRNA-seq) data has exponentially increased in recent years, analysis of RNA splicing in these datasets remains virtually nonexistent, largely due to the sparsity and bias of the data. In this thesis, we introduce a new method of analyzing differential splicing at the single-cell level and apply this method to make new biological discoveries. We start by introducing the SpliZ, which quantifies the alternative splicing of each gene in a single number for each cell in the dataset. We verify the validity of the SpliZ through simulation, comparison with existing methods, and re-discovery of known true positives in the human lung. Next, we apply the SpliZ to over 200,000 cells from human, mouse, and mouse lemur to create a comprehensive atlas of cell-type-resolved alternative splicing, with experimental validation of two examples. We discover that unsupervised clustering of cells based only on the SpliZ scores of RPS24 and ATP5F1C accurately recapitulates division into stromal, immune, and epithelial compartments. Correlation of the SpliZ with developmental time reveals previously unknown conserved splicing changes throughout spermatogenesis. Finally, we apply the SpliZ to spatial transcriptomics data to discover spatially-resolved RNA splicing patterns in the mouse brain, which are more significantly localized than gene expression for Myl6 and Gng13. The SpliZ opens the door to widespread analysis of alternative splicing in scRNA-seq data.
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 | Olivieri, Julia Eve |
|---|---|
| Degree supervisor | Salzman, Julia |
| Thesis advisor | Salzman, Julia |
| Thesis advisor | Hastie, Trevor |
| Thesis advisor | Sabatti, Chiara |
| Degree committee member | Hastie, Trevor |
| Degree committee member | Sabatti, Chiara |
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Julia Eve Olivieri. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineerig. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/wd974qw1277 |
Access conditions
- Copyright
- © 2022 by Julia Eve Olivieri
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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