Sync dnamphenoageprc

README.md

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+---
+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.
+
+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

config.json

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+{
+ "approved_by_author": "\u231b",
+ "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."
+ ],
+ "citations": 22,
+ "citations_date": "2026-10-02",
+ "clock_name": "dnamphenoageprc",
+ "data_type": "DNA methylation",
+ "doi": "https://doi.org/10.1186/s13148-022-01249-z",
+ "journal": "Clinical Epigenetics",
+ "last_author": "Morgan Levine",
+ "model_type": "weighted linear score",
+ "n_features": 55,
+ "notes": "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.",
+ "platform": [
+  "Illumina 27K",
+  "Illumina 450K",
+  "Illumina EPIC"
+ ],
+ "population": "adults",
+ "predicts": [
+  "polycomb-related PhenoAge contribution"
+ ],
+ "preprocess": "missing_beta_zero_contribution",
+ "research_only": null,
+ "species": "Homo sapiens",
+ "tissue": [
+  "whole blood"
+ ],
+ "training_target": [
+  "phenotypic age"
+ ],
+ "unit": [
+  "years"
+ ],
+ "version": "0.5.4",
+ "year": 2022
+}

dnamphenoageprc.provenance.json

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+{
+  "source_url": "https://raw.githubusercontent.com/MorganLevineLab/prcPhenoAge/0af213c60035d8dd568d9d9ba2df90cfbca19596/data-raw/Clock_Levine_PRC.csv",
+  "source_commit": "0af213c60035d8dd568d9d9ba2df90cfbca19596",
+  "source_sha256": "089e2a3661d604c2822f56710b4076d2e36e07e2d2ce64de61f2958b3e9d0043",
+  "intercept": "none, matching author calcPRCPhenoAge/calcnonPRCPhenoAge",
+  "imputation": "FALSE: absent CpGs and supplied NA beta values contribute zero"
+}

dnamphenoageprc.pt

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