| --- |
| license: mit |
| library_name: pyaging |
| tags: |
| - pyaging |
| - aging-clock |
| - biology |
| - dna-methylation |
| --- |
| |
| # pcgrimage |
|
|
| Principal-component proxy trained to reproduce the original DNAm GrimAge score; age and sex are additional model inputs. |
|
|
| | | | |
| |---|---| |
| | **Predicts** | mortality risk | |
| | **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, ["pcgrimage"]) |
| ``` |
|
|
| 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 |
|
|
