Sync pcgrimagepai1

README.md

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+---
+library_name: pyaging
+tags:
+- pyaging
+- aging-clock
+- biology
+- dna-methylation
+---
+
+# pcgrimagepai1
+
+Standalone principal-component proxy trained to reproduce the original DNAm PAI1 score. Coefficients are composed exactly from the author PCA rotation and proxy regression. CpGs use the author GSE40279 reference. Sex (female=1, male=0) and chronological age must be supplied when selected by the original component regression; missing demographic inputs are rejected. Selected demographic inputs: female.
+
+Model weights retain the original authors' terms; the pyaging software license does not relicense them.
+
+| | |
+|---|---|
+| **Predicts** | PAI-1 |
+| **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, ["pcgrimagepai1"])
+```
+
+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

config.json

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+{
+ "approved_by_author": "\u231b",
+ "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.",
+ "citations": 546,
+ "citations_date": "2026-10-02",
+ "clock_name": "pcgrimagepai1",
+ "data_type": "DNA methylation",
+ "doi": "https://doi.org/10.1038/s43587-022-00248-2",
+ "journal": "Nature Aging",
+ "last_author": "Morgan E. Levine",
+ "model_type": "PCA + elastic net regression",
+ "n_features": 78465,
+ "notes": "Standalone principal-component proxy trained to reproduce the original DNAm PAI1 score. Coefficients are composed exactly from the author PCA rotation and proxy regression. CpGs use the author GSE40279 reference. Sex (female=1, male=0) and chronological age must be supplied when selected by the original component regression; missing demographic inputs are rejected. Selected demographic inputs: female.",
+ "platform": [
+  "Illumina 450K"
+ ],
+ "population": "adults",
+ "predicts": [
+  "PAI-1"
+ ],
+ "reference_values": true,
+ "research_only": null,
+ "species": "Homo sapiens",
+ "tissue": [
+  "whole blood"
+ ],
+ "training_target": [
+  "DNAm PAI1 output"
+ ],
+ "unit": [
+  "picograms per milliliter"
+ ],
+ "version": "0.5.4",
+ "year": 2022
+}

pcgrimagepai1.provenance.json

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+{
+  "created": "2026-10-02",
+  "authors": {
+    "PCBrainAge": {
+      "repository": "https://github.com/MorganLevineLab/calcPCBrainAge",
+      "commit": "8a4a0bf93fe715e48d495af46edf17f10d988afa",
+      "doi": "10.18632/aging.204196",
+      "license_declaration": "MIT License",
+      "license_source": "DESCRIPTION",
+      "separate_license_file": false
+    },
+    "PCGrimAge": {
+      "repository": "https://github.com/MorganLevineLab/PC-Clocks",
+      "commit": "5e65bce1eeb600949fce0853e46ed2aa6210051d",
+      "doi": "10.1038/s43587-022-00248-2",
+      "weights_url": "https://yale.app.box.com/s/kq0b0a7lxckxjvaz7x5n4keaug7tewry",
+      "weights_file_id": "904007593153",
+      "license_declaration": null,
+      "license_note": "No explicit code or weight license was found in the pinned original PC-Clocks repository."
+    }
+  },
+  "source_sha256": {
+    "calcPCBrainAge/DESCRIPTION": "06dba30e2195b2aa53f45f4d69da536a6897831aa307dfabe8d91a4c62150941",
+    "calcPCBrainAge/NAMESPACE": "736cb2c31c983b3a07d06d09b8ef93d89972f312c1db584ff253a0281d082bac",
+    "calcPCBrainAge/R/PCBrainAge_Model.R": "676330cfa311e478b1aa8b23eb91a9aa92f1a680957e114b4d656f44dd73fb1e",
+    "calcPCBrainAge/R/calcPCBrainAge.R": "963eb2ad14b1445bd184e5d48e65d9c41581cd5cac162a1664c85631a21ff621",
+    "calcPCBrainAge/R/imputeMissingBrainCpGs.R": "6c8edc0416d5e0dcc61776a5b3944c8a89aec61eed193ec5f1c29b2251d943b2",
+    "calcPCBrainAge/R/meanImpute.R": "7efc2fc20957100316c0e44be15bcc0c71e5b869027e42983750cee7f91301ba",
+    "calcPCBrainAge/README.Rmd": "35f64d3ccadf7cb22854f270ea049c35efa6a8f97dcdebfbd58ba2f3da78a720",
+    "calcPCBrainAge/README.md": "8fc4f6c3f31850a74c9d664d88bd09bf5c06aa18b85eb701c373a180bf403094",
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+    "calcPCBrainAge/data/rotation1.rda": "7d9b2e33315b72a0b77e6d33f156fce44bbd08c5534d9039a450813d673c8550",
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+    "calcPCBrainAge/man/PCBrainAge_Model.Rd": "52ccf8bb87216383b14f12f30b31057c1182474bee63531e72cc8099137843c0",
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+    "calcPCBrainAge/man/imputeMissingBrainCpGs.Rd": "13b10c7ff9480747749fb79dd8db65a28ade35b8cf215a7055802f646df03fae",
+    "calcPCBrainAge/man/meanImpute.Rd": "fff910dd28f75e40dfd3714a06418d93d207d00052dc347307fe14643c6f5088",
+    "PC-Clocks/run_calcPCClocks.R": "ebdaface80c0ca7f4da7f88aa774d85d7c3e0894b9d35b5cf9939f3067287591",
+    "PC-Clocks/README.rtf": "deaf9e8c10093894bfb76c050f0f1feffb985393592446d4809b11c18e50da08",
+    "PC-Clocks/CITATION.cff": "e80c90605fc29bcfa07687c1618ce0f109c4bcf3d6e52bbbf97181e40c35ae82",
+    "CalcAllPCClocks.RData": "3136f589644056913104da92633f4dc06d76e82a03880472e27c6ea5dacdb4a5"
+  },
+  "derivation": "Original double-precision PCA rotation multiplied by original regression; subtract centered weighted mean from original intercept. Selected sex and age coefficients retained unchanged.",
+  "reference_engine": "R 4.5.3 (2026-03-11), arm64",
+  "oracle": "PCBrainAge: unmodified author calcPCBrainAge function; PCGrimAge components: unmodified PCA and eight prediction assignment lines from author run_calcPCClocks.R",
+  "third_party_reimplementations_used": false,
+  "input_contract": "PCBrainAge includes all 357,852 original cg/ch probes. PCGrimAge proxies require finite supplied age/female only when selected by the original component, with no demographic defaults. Missing methylation probes use original author reference means.",
+  "clock_name": "pcgrimagepai1",
+  "source_model": "PCPAI1",
+  "artifact_sha256": "feaf45f19b7fbde621ec9c9a355e9d1fca80956dcc95857c2edb1db1ffe0baf1",
+  "coefficient_archive_sha256": "fa7ef1945d2736fafeee7010b32ab76675fd3db9d9c4e5774c80918e67bd6ab3"
+}

pcgrimagepai1.pt

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