| library_name: pyaging | |
| tags: | |
| - pyaging | |
| - aging-clock | |
| - biology | |
| - dna-methylation | |
| # dnamphenoageprc | |
| Original Levine-lab polycomb-related PhenoAge contribution using 55 CpGs. Coefficients retain their original values; the 60.664 PhenoAge intercept is excluded from both partitions, so these scores are not standalone calibrated ages. The two contributions plus the original intercept reconstruct DNAmPhenoAge. Follows author imputation=FALSE: absent CpGs and supplied NA values contribute zero. The parent DNAmPhenoAge coefficients were fitted on InCHIANTI 450K whole-blood data; the selected sites also occur on 27K and EPIC arrays. | |
| Model weights retain the original authors' terms; the pyaging software license does not relicense them. | |
| | | | | |
| |---|---| | |
| | **Predicts** | polycomb-related PhenoAge contribution | | |
| | **Species** | Homo sapiens | | |
| | **Tissue** | whole blood | | |
| | **Data type** | DNA methylation | | |
| | **Model type** | weighted linear score | | |
| | **Year** | 2022 | | |
| ## Use with pyaging | |
| ```python | |
| import pyaging as pya | |
| pya.pred.predict_age(adata, ["dnamphenoageprc"]) | |
| ``` | |
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). | |
| ## Citation | |
| ['Rozenblit, M., et al. Evidence of accelerated epigenetic aging of breast tissues in patients with breast cancer is driven by CpGs associated with polycomb-related genes. Clinical Epigenetics 14, 30 (2022).', 'Levine, M. E., et al. "An epigenetic biomarker of aging for lifespan and healthspan." Aging 10.4 (2018): 573-591.'] | |
| https://doi.org/10.1186/s13148-022-01249-z | |