{
 "approved_by_author": "\u231b",
 "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).",
 "citations": 302,
 "citations_date": "2026-10-02",
 "clock_name": "twelvecelldeconvolutebloodepictreg",
 "data_type": "DNA methylation",
 "doi": "https://doi.org/10.1038/s41467-021-27864-7",
 "journal": "Nature Communications",
 "last_author": "Brock C. Christensen",
 "model_type": "reference-based constrained deconvolution",
 "n_features": 240,
 "notes": "Reference-based constrained deconvolution returning the regulatory T-cell 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\u2019s 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.",
 "platform": [
  "Illumina EPIC"
 ],
 "population": "adults",
 "predicts": [
  "regulatory T-cell proportion"
 ],
 "preprocess": "fill_with_reference_means",
 "reference_values": true,
 "research_only": null,
 "species": "Homo sapiens",
 "tissue": [
  "purified blood leukocytes"
 ],
 "training_target": [
  "cell-type proportions"
 ],
 "unit": [
  "proportion"
 ],
 "version": "0.5.7",
 "year": 2022
}