Predicting lung aging using scRNA-Seq data.
Age prediction based on single cell RNA-Sequencing data (scRNA-Seq) can provide information for patients' susceptibility to various diseases and conditions. In addition, such analysis can be used to identify aging related genes and pathways. To enable age prediction based on scRNA-Seq data, we...
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012632 |
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| _version_ | 1832540306566807552 |
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| author | Qi Song Alex Singh John E McDonough Taylor S Adams Robin Vos Ruben De Man Greg Myers Laurens J Ceulemans Bart M Vanaudenaerde Wim A Wuyts Xiting Yan Jonas Schupp James S Hagood Naftali Kaminski Ziv Bar-Joseph |
| author_facet | Qi Song Alex Singh John E McDonough Taylor S Adams Robin Vos Ruben De Man Greg Myers Laurens J Ceulemans Bart M Vanaudenaerde Wim A Wuyts Xiting Yan Jonas Schupp James S Hagood Naftali Kaminski Ziv Bar-Joseph |
| author_sort | Qi Song |
| collection | DOAJ |
| description | Age prediction based on single cell RNA-Sequencing data (scRNA-Seq) can provide information for patients' susceptibility to various diseases and conditions. In addition, such analysis can be used to identify aging related genes and pathways. To enable age prediction based on scRNA-Seq data, we developed PolyEN, a new regression model which learns continuous representation for expression over time. These representations are then used by PolyEN to integrate genes to predict an age. Existing and new lung aging data we profiled demonstrated PolyEN's improved performance over existing methods for age prediction. Our results identified lung epithelial cells as the most significant predictors for non-smokers while lung endothelial cells led to the best chronological age prediction results for smokers. |
| format | Article |
| id | doaj-art-280f2023590e49f3a592ecc53710a21a |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-280f2023590e49f3a592ecc53710a21a2025-02-05T05:30:39ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-12-012012e101263210.1371/journal.pcbi.1012632Predicting lung aging using scRNA-Seq data.Qi SongAlex SinghJohn E McDonoughTaylor S AdamsRobin VosRuben De ManGreg MyersLaurens J CeulemansBart M VanaudenaerdeWim A WuytsXiting YanJonas SchuppJames S HagoodNaftali KaminskiZiv Bar-JosephAge prediction based on single cell RNA-Sequencing data (scRNA-Seq) can provide information for patients' susceptibility to various diseases and conditions. In addition, such analysis can be used to identify aging related genes and pathways. To enable age prediction based on scRNA-Seq data, we developed PolyEN, a new regression model which learns continuous representation for expression over time. These representations are then used by PolyEN to integrate genes to predict an age. Existing and new lung aging data we profiled demonstrated PolyEN's improved performance over existing methods for age prediction. Our results identified lung epithelial cells as the most significant predictors for non-smokers while lung endothelial cells led to the best chronological age prediction results for smokers.https://doi.org/10.1371/journal.pcbi.1012632 |
| spellingShingle | Qi Song Alex Singh John E McDonough Taylor S Adams Robin Vos Ruben De Man Greg Myers Laurens J Ceulemans Bart M Vanaudenaerde Wim A Wuyts Xiting Yan Jonas Schupp James S Hagood Naftali Kaminski Ziv Bar-Joseph Predicting lung aging using scRNA-Seq data. PLoS Computational Biology |
| title | Predicting lung aging using scRNA-Seq data. |
| title_full | Predicting lung aging using scRNA-Seq data. |
| title_fullStr | Predicting lung aging using scRNA-Seq data. |
| title_full_unstemmed | Predicting lung aging using scRNA-Seq data. |
| title_short | Predicting lung aging using scRNA-Seq data. |
| title_sort | predicting lung aging using scrna seq data |
| url | https://doi.org/10.1371/journal.pcbi.1012632 |
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