| library_name: pyaging | |
| tags: | |
| - pyaging | |
| - aging-clock | |
| - biology | |
| - dna-methylation | |
| # twelvecelldeconvolutebloodepicbas | |
| Reference-based constrained deconvolution returning the basophil proportion from EPIC-array blood methylation. The published EPIC IDOL-Ext library used 1,200 CpGs selected to optimize recovery of known artificial-mixture cell-type proportions; pyaging instead inherits Biolearn’s undocumented 240-CpG replacement, whose rows reproducibly comprise 10 positive and 10 negative maximal cell-versus-other methylation contrasts per subtype and are not a subset of the published 1,200 probes. | |
| Model weights retain the original authors' terms; the pyaging software license does not relicense them. | |
| | | | | |
| |---|---| | |
| | **Predicts** | basophil proportion | | |
| | **Species** | Homo sapiens | | |
| | **Tissue** | purified blood leukocytes | | |
| | **Data type** | DNA methylation | | |
| | **Model type** | reference-based constrained deconvolution | | |
| | **Year** | 2022 | | |
| ## Use with pyaging | |
| ```python | |
| import pyaging as pya | |
| pya.pred.predict_age(adata, ["twelvecelldeconvolutebloodepicbas"]) | |
| ``` | |
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). | |
| ## Citation | |
| Salas, L.A., Zhang, Z., Koestler, D.C. et al. Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling. Nature Communications 13, 761 (2022). | |
| https://doi.org/10.1038/s41467-021-27864-7 | |