Bayesian information sharing enhances detection of regulatory associations in rare cell types
Abstract Motivation: Recent advances in single-cell RNA-sequencing (scRNA-seq) technologies promise to enable the study of gene regulatory associations at unprecedented resolution in diverse cellular contexts. However, identifying unique regulatory associations observed only in specific cell type...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Oxford University Press (OUP)
2022
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| Online Access: | https://hdl.handle.net/1721.1/145604 |
| _version_ | 1811094388150894592 |
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| author | Wu, Alexander P Peng, Jian Berger, Bonnie Cho, Hyunghoon |
| author2 | Massachusetts Institute of Technology. Department of Mathematics |
| author_facet | Massachusetts Institute of Technology. Department of Mathematics Wu, Alexander P Peng, Jian Berger, Bonnie Cho, Hyunghoon |
| author_sort | Wu, Alexander P |
| collection | MIT |
| description | Abstract
Motivation: Recent advances in single-cell RNA-sequencing (scRNA-seq) technologies promise to enable the study
of gene regulatory associations at unprecedented resolution in diverse cellular contexts. However, identifying
unique regulatory associations observed only in specific cell types or conditions remains a key challenge; this is particularly so for rare transcriptional states whose sample sizes are too small for existing gene regulatory network inference methods to be effective.
Results: We present ShareNet, a Bayesian framework for boosting the accuracy of cell type-specific gene regulatory
networks by propagating information across related cell types via an information sharing structure that is adaptively
optimized for a given single-cell dataset. The techniques we introduce can be used with a range of general network
inference algorithms to enhance the output for each cell type. We demonstrate the enhanced accuracy of our approach on three benchmark scRNA-seq datasets. We find that our inferred cell type-specific networks also uncover
key changes in gene associations that underpin the complex rewiring of regulatory networks across cell types, tissues and dynamic biological processes. Our work presents a path toward extracting deeper insights about cell typespecific gene regulation in the rapidly growing compendium of scRNA-seq datasets. |
| first_indexed | 2024-09-23T15:59:15Z |
| format | Article |
| id | mit-1721.1/145604 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:59:15Z |
| publishDate | 2022 |
| publisher | Oxford University Press (OUP) |
| record_format | dspace |
| spelling | mit-1721.1/1456042022-10-04T03:04:25Z Bayesian information sharing enhances detection of regulatory associations in rare cell types Wu, Alexander P Peng, Jian Berger, Bonnie Cho, Hyunghoon Massachusetts Institute of Technology. Department of Mathematics Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Abstract Motivation: Recent advances in single-cell RNA-sequencing (scRNA-seq) technologies promise to enable the study of gene regulatory associations at unprecedented resolution in diverse cellular contexts. However, identifying unique regulatory associations observed only in specific cell types or conditions remains a key challenge; this is particularly so for rare transcriptional states whose sample sizes are too small for existing gene regulatory network inference methods to be effective. Results: We present ShareNet, a Bayesian framework for boosting the accuracy of cell type-specific gene regulatory networks by propagating information across related cell types via an information sharing structure that is adaptively optimized for a given single-cell dataset. The techniques we introduce can be used with a range of general network inference algorithms to enhance the output for each cell type. We demonstrate the enhanced accuracy of our approach on three benchmark scRNA-seq datasets. We find that our inferred cell type-specific networks also uncover key changes in gene associations that underpin the complex rewiring of regulatory networks across cell types, tissues and dynamic biological processes. Our work presents a path toward extracting deeper insights about cell typespecific gene regulation in the rapidly growing compendium of scRNA-seq datasets. 2022-09-28T17:04:34Z 2022-09-28T17:04:34Z 2021 2022-09-28T16:52:02Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/145604 Wu, Alexander P, Peng, Jian, Berger, Bonnie and Cho, Hyunghoon. 2021. "Bayesian information sharing enhances detection of regulatory associations in rare cell types." Bioinformatics, 37 (Supplement_1). en 10.1093/BIOINFORMATICS/BTAB269 Bioinformatics Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Oxford University Press (OUP) Oxford University Press |
| spellingShingle | Wu, Alexander P Peng, Jian Berger, Bonnie Cho, Hyunghoon Bayesian information sharing enhances detection of regulatory associations in rare cell types |
| title | Bayesian information sharing enhances detection of regulatory associations in rare cell types |
| title_full | Bayesian information sharing enhances detection of regulatory associations in rare cell types |
| title_fullStr | Bayesian information sharing enhances detection of regulatory associations in rare cell types |
| title_full_unstemmed | Bayesian information sharing enhances detection of regulatory associations in rare cell types |
| title_short | Bayesian information sharing enhances detection of regulatory associations in rare cell types |
| title_sort | bayesian information sharing enhances detection of regulatory associations in rare cell types |
| url | https://hdl.handle.net/1721.1/145604 |
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