---
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