tagemortality

Elastic Net over 10,487 mouse-Entrez genes, multispecies multi-tissue, scaleddiff variant. Cohort-relative: predict_age runs the tAge cohort preprocessing on the raw RNA-seq counts itself, so a prediction is a hazard shift against the reference group rather than an absolute risk. Name the cohort's species with a 0/1 column among var_names (mouse, rat, macaque or human; absent or all-zero means mouse) and the samples to centre against with a truthy adata.obs["tage_reference_group"] (absent centres on the whole cohort); at least two samples are needed. The published pipeline's SimpleImputer, mean-only StandardScaler and pass-through SelectKBest are folded into the packaged linear layer, and the imputer medians are carried as reference_values so a gene the sample does not measure contributes its training median. Output is log10(hazard ratio) -- base 10, not the natural log the 'log hazard' unit label usually implies -- and unlike the chronological clocks it is never rescaled by species maximum lifespan, so it is directly comparable across species. Released under the MGB Open Access License 1.0: non-commercial academic research use only.

Predicts	mortality risk
Species	multiple species
Tissue	multi-tissue
Data type	transcriptomics
Model type	elastic net regression
Year	2026

Use with pyaging

import pyaging as pya

pya.pred.predict_age(adata, ["tagemortality"])

Browse every clock in the pyaging Clock Catalogue.

Citation

Tyshkovskiy, Alexander, et al. "Universal transcriptomic hallmarks of mammalian ageing and mortality." Nature 654 (2026): 173-188.

https://doi.org/10.1038/s41586-026-10542-3