pyaging 0.1.6

A Python-based compendium of GPU-optimized aging clocks.

🐍 pyaging: a Python-based compendium of GPU-optimized aging clocks

pyaging is a cutting-edge Python package designed for the longevity research community, offering a comprehensive suite of GPU-optimized biological aging clocks.

Installation - Clock gallery - Search, cite, get metadata and clock parameters - Illumina Human Methylation Arrays - Illumina Mammalian Methylation Arrays - RRBS DNA methylation - Bulk histone mark ChIP-Seq - Bulk ATAC-Seq - Bulk RNA-Seq - Blood chemistry - API Reference

With a growing number of aging clocks and biomarkers of aging, comparing and analyzing them can be challenging. pyaging simplifies this process, allowing researchers to input various molecular layers (DNA methylation, histone ChIP-Seq, ATAC-seq, transcriptomics, etc.) and quickly analyze them using multiple aging clocks, thanks to its GPU-backed infrastructure. This makes it an ideal tool for large datasets and multi-layered analysis.

❓ Can't find an aging clock?

If you have recently developed an aging clock and would like it to be integrated into pyaging, please email us. We aim to incorporate it within two weeks! We are also happy to adapt to any licensing terms for commercial entities.

💬 Community Discussion

For coding-related queries, feedback, and discussions, please visit our GitHub Issues page.

📖 Citation

To cite pyaging, please use the following:

@article {de_Lima_Camillo_pyaging,
	author = {Lucas Paulo de Lima Camillo},
	title = {pyaging: a Python-based compendium of GPU-optimized aging clocks},
	elocation-id = {2023.11.28.569069},
	year = {2023},
	doi = {10.1101/2023.11.28.569069},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2023/11/29/2023.11.28.569069},
	journal = {bioRxiv}
}

📝 To-Do List

• Incorporate more murine DNA methylation clocks
• Add torch data loader for age prediction of large datasets
• Move preprocessing/postprocessing step to the AnnLoader
• Add other blood chemistry biological age clocks (KD age)
• Incorporate proteomic clocks (and datasets)
• Integrate scAge clocks (this is proving to be difficult)
• Integrate scRNAseq clocks (and datasets)