lucascamillomd/pyaging-data

pyaging data

This public repository contains the model weights and data files used by lucascamillomd/pyaging.

Contents

• Clock model weights live in one repository per clock under the pyaging organization (e.g. pyaging/horvath2013); each repo carries the weight file, the audited clock metadata as config.json, and a model card. Root-level *.pt files here are compatibility copies used when a per-clock repository or revision is unavailable.
• all_clock_metadata.pt is the live aggregate clock catalog. Retired clock files may remain here to preserve earlier releases; file presence is not current catalogue membership.
• Root-level example files support the pyaging tutorials.
• PAD000022_subset.pkl contains 32 real plasma samples and 134 Olink Explore 3072 assays for the OrganAge tutorial. Its data card documents the CC0 source, selection, normalization and limitations.
• supporting_files/ contains dependencies used to construct or document clocks.

Files used by the Python package are intentionally stored at the repository root and downloaded through the standard Hugging Face cache. The main branch is the live data release and may change independently of the Python package version.

Versioning and reproducibility

Each pyaging release tags this repository with the matching package version (e.g. v0.3.1), so a release tag captures the exact data files that shipped with that version of the package. By default pyaging downloads from main; set the PYAGING_DATA_REVISION environment variable to a release tag (or any commit) to pin downloads to that revision:

PYAGING_DATA_REVISION=v0.3.1 python my_analysis.py

Release tags only exist for pyaging versions published after the tagging scheme was introduced.

Harmonized clock metadata

The clock catalogue uses controlled, multi-valued metadata so clocks can be filtered consistently:

• tissue records the biological material used to develop or train the model.
• platform records the measurement platform used for model development.
• predicts describes how to interpret the value returned by the packaged model.
• training_target records the outcome used to fit or derive the model.
• unit records the physical or statistical unit of the returned, postprocessed value.

Each of these fields is an array of controlled terms, even when a clock has only one value. Precise wording from the paper, supplement, implementation, or author communication is retained in the notebooks' same-line metadata comments and in the field-level evidence ledger. The canonical clock_metadata.json registry and evidence_ledger.jsonl are maintained in the pyaging repository.

Clinical PhenoAge correction

Use pyaging >=0.5.7 for clinical phenoage. The corrected Gompertz parameter is gamma = 0.0076927; earlier package versions used the Cox variable-selection penalty 0.0192 instead. That inflated finite estimates by approximately 9.619365 years for the same log hazard. Recalculate prior clinical PhenoAge results after upgrading. The formula is implemented in the Python class, so a new weight download alone cannot fix an older package. DNAm PhenoAge and the separately fitted Sao Paulo model are unaffected. The original supplementary methods distinguish the two parameters on pages 1 and 2.

Licensing and provenance

This is a mixed-provenance research collection, so the repository license is other. The pyaging MIT license does not grant additional rights to third-party clock weights or source datasets. Consult each clock's embedded metadata, cited publication, and notes before use. Some clocks are marked research-only or have separate commercial terms.

Security

Clock files are trusted Python/PyTorch objects loaded by pyaging with torch.load(..., weights_only=False). Loading a malicious pickle can execute code. Only load these files from this official repository and review unexpected repository changes.

Publishing policy

The repository is maintained solely by Lucas Paulo de Lima Camillo (lucascamillomd). Weights are uploaded before aggregate metadata so the catalog never advertises a missing clock file. Public users need no Hugging Face token to download files.

Proteomic inputs

The 0.5.6 catalogue contains HPS, PAOPAC Conventional, PAC and 46 full Olink Explore 3072 OrganAge models. Their input units and protein identifiers are model-specific. OrganAge uses case-sensitive original symbols; HPS and PAC use lowercase symbols plus age in years. PAOPAC follows its original interface's NPX exponentiation, cohort standardization and LOWESS age-bias correction. Its predictions depend on the submitted cohort and require chronological age. NPX is log2 relative abundance, not concentration; cross-platform and serum/plasma harmonization are external preparation decisions. See the proteomics tutorial.

The reduced Olink Explore 1536 OrganAge models leave the current catalogue, and full-model names drop olink3000. Existing 0.5.4 tags and files remain intact. ProtAge and ipfP3GPT are not executable pyaging entries in this release; the guide explains the unavailable or restricted author assets. HPS returns a 0–1 healthspan probability (higher is healthier), not an age in years.