MetaClock workflow

This repository contains workflows for chronological-age prediction, cross-domain generalization, biological-age and disease analysis, functional enrichment, and intervention analysis. GEO workflows download inputs into repository-local data directories. The workflow READMEs describe the required files and preparation steps.

Project overview

MetaClock is an integrated framework that combines predictions from multiple established epigenetic clocks into meta models. This repository organizes the reproducible downstream workflows used to evaluate chronological-age prediction, quantify generalization across array platforms, tissues, and species, and study biological-age associations with disease and interventions.

Environment

Install uv and create the locked Python 3.11 environment from the repository root:

uv sync --frozen

MetaClock Python package

The repository also provides the importable metaclock package

python -m pip install -e .

Input contract

metaclock.fit() accepts an in-memory anndata.AnnData object or an .h5ad file in pyaging's standard orientation:

• rows are samples;
• columns are DNAm or phenotype features;
• feature names are stored in adata.var_names;
• the numeric training response is stored in adata.obs["age"] by default.

Clocks that use phenotype covariates, such as GrimAge, require those covariates in the input matrix and adata.var_names according to pyaging's input rules.

Fit and predict

Pass any ordered list of pyaging component-model names. The fitted result is an sklearn-compatible pipeline whose predict() method first runs the same components in the same order and then applies the trained meta-regressor:

import metaclock

component_model_list = [
    "horvath2013",
    "hannum",
    "dnamphenoage",
]

model = metaclock.fit(
    train_adata,
    component_model_list,
    "elastic_net",
    target="age",
)

predicted_age = model.predict(test_adata)

The three positional arguments are the input data, component-model list, and model architecture. Set pyaging_dir when using a specific local pyaging weight cache. The component ordering used for fitting is retained for every later prediction.

To run the component clocks without fitting a meta-model:

component_outputs = metaclock.predict_components(
    test_adata,
    component_model_list,
)

Supported architectures are elastic_net, lasso, ridge, linear_regression, random_forest, svr, mlp, and lightgbm. The mlp architecture uses StandardScaler followed by sklearn's MLPRegressor; its network parameters can be overridden through model_params. LightGBM is part of the standard locked environment and uses its sklearn-compatible LGBMRegressor interface.

Fitted models can be saved with joblib and loaded for later prediction:

import joblib

joblib.dump(model, model_output_path)
loaded_model = joblib.load(model_output_path)
predicted_age = loaded_model.predict(test_adata)