pyaging is a cutting-edge Python package designed for the longevity research community, offering a comprehensive suite of GPU-optimized biological aging clocks.
Installation - Search, cite, and get metadata - Bulk DNA methylation - Bulk histone mark ChIP-Seq - Bulk ATAC-Seq - Bulk RNA-Seq - API Reference
With a growing number of aging clocks, 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.
- Integrate
blackfor PEP8 compliant code formatting - Improve and expand
readthedocsdocumentation - Add option to specify download directory for datasets
- Rename datasets to reflect source publications
- Include mammalian array example datasets
- Add feature to control logging verbosity
- Publish to PyPi
- Add percentage of missing features in anndata after age prediction for each clock
- Add and populate "Notes" tab in metadata
- Add PhenoAge based on blood chemistry analysis (and datasets)
- Add table in readthedocs with all clock metadata
- Integrate scAge and scRNAseq clocks (and datasets)
- Incorporate murine DNA methylation and proteomic clocks (and datasets)
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!
For coding-related queries, feedback, and discussions, please visit our GitHub Issues page.