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 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.
Authors
- Jonas C. Schupp (ORCID: https://orcid.org/0000-0002-7714-8076)
- Ruben De Man (ORCID: https://orcid.org/0000-0001-5468-0281)
- James S. Hagood (ORCID: https://orcid.org/0000-0003-3938-0330)
- Robin Vos (ORCID: https://orcid.org/0000-0002-3468-9251)
- John E. McDonough (ORCID: https://orcid.org/0000-0003-2497-0258)
- Qi Song (ORCID: https://orcid.org/0000-0001-5420-4031)
- Bart M. Vanaudenaerde (ORCID: https://orcid.org/0000-0001-6435-6901)
- Taylor Adams (ORCID: https://orcid.org/0000-0003-4280-9070)
- Ziv Bar‐Joseph (ORCID: https://orcid.org/0000-0003-3430-6051)
- Laurens J. Ceulemans (ORCID: https://orcid.org/0000-0002-4261-7100)
- Greg Myers
- Xiting Yan (ORCID: https://orcid.org/0000-0001-8688-9004)
- Wim Wuyts (ORCID: https://orcid.org/0000-0001-9648-3497)
- Naftali Kaminski (ORCID: https://orcid.org/0000-0001-5917-4601)
- A Singh (ORCID: https://orcid.org/0009-0005-5645-2203)
Institutions
- University of North Carolina at Chapel Hill (US)
- Yale University (US)
- Lung Institute (US)
- Carnegie Mellon University (US)
- McMaster University (CA)
- KU Leuven (BE)
Publication Details
- Journal
- PLoS Computational Biology
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1371/journal.pcbi.1012632
- Citations
- 2
- Primary Topic
- MicroRNA in disease regulation
- Type
- article
- Field-Weighted Citation Impact
- 0.57
Funders
- National Institutes of Health