Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.
The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes such as the regulation of gene transcription. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expressi...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012962 |
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| author | Alexander Morin Ching Pan Chu Paul Pavlidis |
| author_facet | Alexander Morin Ching Pan Chu Paul Pavlidis |
| author_sort | Alexander Morin |
| collection | DOAJ |
| description | The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes such as the regulation of gene transcription. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TR's coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use. |
| format | Article |
| id | doaj-art-adb486da1a164d4a9b15425b784e7d43 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-adb486da1a164d4a9b15425b784e7d432025-08-20T03:53:42ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-04-01214e101296210.1371/journal.pcbi.1012962Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.Alexander MorinChing Pan ChuPaul PavlidisThe proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes such as the regulation of gene transcription. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TR's coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use.https://doi.org/10.1371/journal.pcbi.1012962 |
| spellingShingle | Alexander Morin Ching Pan Chu Paul Pavlidis Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics. PLoS Computational Biology |
| title | Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics. |
| title_full | Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics. |
| title_fullStr | Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics. |
| title_full_unstemmed | Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics. |
| title_short | Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics. |
| title_sort | identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics |
| url | https://doi.org/10.1371/journal.pcbi.1012962 |
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