Sync pcgrimage
Browse files- README.md +38 -0
- config.json +35 -0
- pcgrimage.pt +3 -0
README.md
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---
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license: bsd-3-clause
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library_name: pyaging
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tags:
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- pyaging
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- aging-clock
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- biology
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- dna-methylation
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---
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# pcgrimage
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Principal-component proxy trained to reproduce the original DNAm GrimAge score; age and sex are additional model inputs.
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|---|---|
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| **Predicts** | mortality risk |
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| **Species** | Homo sapiens |
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| **Tissue** | whole blood |
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| **Data type** | DNA methylation |
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| **Model type** | PCA + elastic net regression |
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| **Year** | 2022 |
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## Use with pyaging
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```python
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import pyaging as pya
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pya.pred.predict_age(adata, ["pcgrimage"])
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```
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Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io).
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## Citation
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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.
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https://doi.org/10.1038/s43587-022-00248-2
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config.json
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{
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"approved_by_author": "\u231b",
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"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\u2013661.",
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"citations": 497,
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"citations_date": "2026-07-05",
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"clock_name": "pcgrimage",
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"data_type": "DNA methylation",
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"doi": "https://doi.org/10.1038/s43587-022-00248-2",
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"journal": "Nature Aging",
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"last_author": "Morgan E. Levine",
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"model_type": "PCA + elastic net regression",
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"n_features": 78466,
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"notes": "Principal-component proxy trained to reproduce the original DNAm GrimAge score; age and sex are additional model inputs.",
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"platform": [
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"Illumina 450K"
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],
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"population": "adults",
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"predicts": [
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"mortality risk"
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],
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"reference_values": true,
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"research_only": null,
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"species": "Homo sapiens",
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"tissue": [
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"whole blood"
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],
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"training_target": [
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"DNAm GrimAge output"
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],
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"unit": [
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"years"
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],
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"version": "v0.3.0",
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"year": 2022
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}
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pcgrimage.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c549ab51ab1a1b20694a40a419ed96bc6f4d50f975f8e575270f8c653a9715d
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size 1216334287
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