| library_name: pyaging | |
| tags: | |
| - pyaging | |
| - aging-clock | |
| - biology | |
| - dna-methylation | |
| # pcdnamtl | |
| Principal-component proxy trained to reproduce the original DNAmTL clock output; the returned score remains in kilobases. Figure-level base-pair deviations are a separate analysis-scale conversion. | |
| Model weights retain the original authors' terms; the pyaging software license does not relicense them. | |
| | | | | |
| |---|---| | |
| | **Predicts** | leukocyte telomere length | | |
| | **Species** | Homo sapiens | | |
| | **Tissue** | whole blood | | |
| | **Data type** | DNA methylation | | |
| | **Model type** | PCA + elastic net regression | | |
| | **Year** | 2022 | | |
| ## Use with pyaging | |
| ```python | |
| import pyaging as pya | |
| pya.pred.predict_age(adata, ["pcdnamtl"]) | |
| ``` | |
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). | |
| ## Citation | |
| Higgins-Chen, Albert T., et al. "A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking." Nature Aging 2 (2022): 644–661. | |
| https://doi.org/10.1038/s43587-022-00248-2 | |