Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations
*** While the ProtAge and ProtAge20 models are not available in this repository, we are in the process of developing a Python package for users to calculate the ProtAge and ProtAge20 models in non-UKB proteomic data for non-commercial use. Please contact Austin Agentieri ([email protected]) to discuss early access to these models if you have a pressing time deadline and we can help you with early access. Otherwise, this repository will be updated with a link to the corresponding Python package repository when ready. ***
This directory contains the code used for data preparation, analysis, tables, and figure creation for the publication "Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations." Published in Nature Medicine (2024). DOI: 10.1038/s41591-024-03164-7.
This repository was created on Sun July 14 2024.
R and Python code for each stage of our data preparation and analysis are contained in these files. This includes:
- Importing raw proteomics data
- Training machine learning models for the ProtAge proteomic age clock
- Conducting mortality and incident disease analyses
- Creating figures, tables, and plots
The following is a description of the various files and directories found within this project.
| Directory | Description |
|---|---|
files/ |
Files and coding tables that are called in scripts. |
code/ |
Code used for all analyses and for creating figures/tables. |
Files are in R, R Markdown, Python, and Jupyter Notebook format. R Markdown scripts are not currently written or optimized to be knit and published via R Markdown without additional configuration in the code.
This project is licensed under a dual-license model:
- Academic Use: Free under the MIT License for academic and non-commercial use.
- Commercial Use: Requires a commercial license.
For more information on licensing and to obtain a commercial license, please contact Austin Argentieri ([email protected]).
Please contact Austin Argentieri ([email protected]) with any questions, comments, or concerns.
