High order expression dependencies finely resolve cryptic states and subtypes in single cell data
Abstract Single cells are typically typed by clustering into discrete locations in reduced dimensional transcriptome space. Here we introduce Stator, a data-driven method that identifies cell (sub)types and states without relying on cells’ local proximity in transcriptome space. Stator labels the sa...
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| Format: | Article |
| Language: | English |
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Springer Nature
2025-01-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.1038/s44320-024-00074-1 |
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| author | Abel Jansma Yuelin Yao Jareth Wolfe Luigi Del Debbio Sjoerd V Beentjes Chris P Ponting Ava Khamseh |
| author_facet | Abel Jansma Yuelin Yao Jareth Wolfe Luigi Del Debbio Sjoerd V Beentjes Chris P Ponting Ava Khamseh |
| author_sort | Abel Jansma |
| collection | DOAJ |
| description | Abstract Single cells are typically typed by clustering into discrete locations in reduced dimensional transcriptome space. Here we introduce Stator, a data-driven method that identifies cell (sub)types and states without relying on cells’ local proximity in transcriptome space. Stator labels the same single cell multiply, not just by type and subtype, but also by state such as activation, maturity or cell cycle sub-phase, through deriving higher-order gene expression dependencies from a sparse gene-by-cell expression matrix. Stator’s finer resolution is clear from analyses of mouse embryonic brain, and human healthy or diseased liver. Rather than only coarse-scale labels of cell type, Stator further resolves cell types into subtypes, and these subtypes into stages of maturity and/or cell cycle phases, and yet further into portions of these phases. Among cryptically homogeneous embryonic cells, for example, Stator finds 34 distinct radial glia states whose gene expression forecasts their future GABAergic or glutamatergic neuronal fate. Further, Stator’s fine resolution of liver cancer states reveals expression programmes that predict patient survival. We provide Stator as a Nextflow pipeline and Shiny App. |
| format | Article |
| id | doaj-art-64c51b07beb44a51a910a6e990929ced |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-64c51b07beb44a51a910a6e990929ced2025-02-09T13:00:49ZengSpringer NatureMolecular Systems Biology1744-42922025-01-0121217320710.1038/s44320-024-00074-1High order expression dependencies finely resolve cryptic states and subtypes in single cell dataAbel Jansma0Yuelin Yao1Jareth Wolfe2Luigi Del Debbio3Sjoerd V Beentjes4Chris P Ponting5Ava Khamseh6MRC Human Genetics Unit, Institute of Genetics & Cancer, University of EdinburghMRC Human Genetics Unit, Institute of Genetics & Cancer, University of EdinburghMRC Human Genetics Unit, Institute of Genetics & Cancer, University of EdinburghHiggs Centre for Theoretical Physics, School of Physics & Astronomy, University of EdinburghMRC Human Genetics Unit, Institute of Genetics & Cancer, University of EdinburghMRC Human Genetics Unit, Institute of Genetics & Cancer, University of EdinburghMRC Human Genetics Unit, Institute of Genetics & Cancer, University of EdinburghAbstract Single cells are typically typed by clustering into discrete locations in reduced dimensional transcriptome space. Here we introduce Stator, a data-driven method that identifies cell (sub)types and states without relying on cells’ local proximity in transcriptome space. Stator labels the same single cell multiply, not just by type and subtype, but also by state such as activation, maturity or cell cycle sub-phase, through deriving higher-order gene expression dependencies from a sparse gene-by-cell expression matrix. Stator’s finer resolution is clear from analyses of mouse embryonic brain, and human healthy or diseased liver. Rather than only coarse-scale labels of cell type, Stator further resolves cell types into subtypes, and these subtypes into stages of maturity and/or cell cycle phases, and yet further into portions of these phases. Among cryptically homogeneous embryonic cells, for example, Stator finds 34 distinct radial glia states whose gene expression forecasts their future GABAergic or glutamatergic neuronal fate. Further, Stator’s fine resolution of liver cancer states reveals expression programmes that predict patient survival. We provide Stator as a Nextflow pipeline and Shiny App.https://doi.org/10.1038/s44320-024-00074-1Higher-order Gene Expression DependenciesSingle-cell TranscriptomicsStructure LearningCell StateCell Cycle Phases |
| spellingShingle | Abel Jansma Yuelin Yao Jareth Wolfe Luigi Del Debbio Sjoerd V Beentjes Chris P Ponting Ava Khamseh High order expression dependencies finely resolve cryptic states and subtypes in single cell data Molecular Systems Biology Higher-order Gene Expression Dependencies Single-cell Transcriptomics Structure Learning Cell State Cell Cycle Phases |
| title | High order expression dependencies finely resolve cryptic states and subtypes in single cell data |
| title_full | High order expression dependencies finely resolve cryptic states and subtypes in single cell data |
| title_fullStr | High order expression dependencies finely resolve cryptic states and subtypes in single cell data |
| title_full_unstemmed | High order expression dependencies finely resolve cryptic states and subtypes in single cell data |
| title_short | High order expression dependencies finely resolve cryptic states and subtypes in single cell data |
| title_sort | high order expression dependencies finely resolve cryptic states and subtypes in single cell data |
| topic | Higher-order Gene Expression Dependencies Single-cell Transcriptomics Structure Learning Cell State Cell Cycle Phases |
| url | https://doi.org/10.1038/s44320-024-00074-1 |
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