Computational and Statistical Methods for Analysis of Spatial Transcriptomics Data
Spatial transcriptomics technologies are an emerging class of high-throughput sequencing methodologies for measuring gene expression at near single-cell resolution at spatiallydefined measurement spots across a biological tissue. We show how measuring cells in their native environment has the potent...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/152008 |
| _version_ | 1826205280664289280 |
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| author | Cable, Dylan Maxwell |
| author2 | Irizarry, Rafael A. |
| author_facet | Irizarry, Rafael A. Cable, Dylan Maxwell |
| author_sort | Cable, Dylan Maxwell |
| collection | MIT |
| description | Spatial transcriptomics technologies are an emerging class of high-throughput sequencing methodologies for measuring gene expression at near single-cell resolution at spatiallydefined measurement spots across a biological tissue. We show how measuring cells in their native environment has the potential to identify spatial patterns of cell types, cell-to-cell interactions, and spatial variation in cellular behavior. However, several technical challenges necessitate the development of appropriate statistical methods, including additive mixtures of single cells, overdispersion, and technical platform effects across technologies. The key contributions of this thesis include developing a statistical framework accounting for these challenges to identify cell types within spatial transcriptomics datasets. We extend this approach to a general regression framework that can, accounting for multiple replicates, learn cell type-specific differential gene expression (DE) across many scenarios including DE across spatial regions and due to cell-to-cell interactions. We apply our framework to a metastatic tumor clone and discover an association between immune cell localization and an epithelial-mesenchymal transition of cancer cells. We also extend our approach to identify cell types from spatial transcriptomics data in an unsupervised manner, drawing from our other algorithms. |
| first_indexed | 2024-09-23T13:10:30Z |
| format | Thesis |
| id | mit-1721.1/152008 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:10:30Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1520082023-09-01T03:27:49Z Computational and Statistical Methods for Analysis of Spatial Transcriptomics Data Cable, Dylan Maxwell Irizarry, Rafael A. Chen, Fei Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Spatial transcriptomics technologies are an emerging class of high-throughput sequencing methodologies for measuring gene expression at near single-cell resolution at spatiallydefined measurement spots across a biological tissue. We show how measuring cells in their native environment has the potential to identify spatial patterns of cell types, cell-to-cell interactions, and spatial variation in cellular behavior. However, several technical challenges necessitate the development of appropriate statistical methods, including additive mixtures of single cells, overdispersion, and technical platform effects across technologies. The key contributions of this thesis include developing a statistical framework accounting for these challenges to identify cell types within spatial transcriptomics datasets. We extend this approach to a general regression framework that can, accounting for multiple replicates, learn cell type-specific differential gene expression (DE) across many scenarios including DE across spatial regions and due to cell-to-cell interactions. We apply our framework to a metastatic tumor clone and discover an association between immune cell localization and an epithelial-mesenchymal transition of cancer cells. We also extend our approach to identify cell types from spatial transcriptomics data in an unsupervised manner, drawing from our other algorithms. Ph.D. 2023-08-30T15:59:09Z 2023-08-30T15:59:09Z 2023-06 2023-07-13T14:16:23.776Z Thesis https://hdl.handle.net/1721.1/152008 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Cable, Dylan Maxwell Computational and Statistical Methods for Analysis of Spatial Transcriptomics Data |
| title | Computational and Statistical Methods for Analysis of Spatial Transcriptomics Data |
| title_full | Computational and Statistical Methods for Analysis of Spatial Transcriptomics Data |
| title_fullStr | Computational and Statistical Methods for Analysis of Spatial Transcriptomics Data |
| title_full_unstemmed | Computational and Statistical Methods for Analysis of Spatial Transcriptomics Data |
| title_short | Computational and Statistical Methods for Analysis of Spatial Transcriptomics Data |
| title_sort | computational and statistical methods for analysis of spatial transcriptomics data |
| url | https://hdl.handle.net/1721.1/152008 |
| work_keys_str_mv | AT cabledylanmaxwell computationalandstatisticalmethodsforanalysisofspatialtranscriptomicsdata |