| --- |
| license: mit |
| library_name: pyaging |
| tags: |
| - pyaging |
| - aging-clock |
| - biology |
| - transcriptomics |
| --- |
| |
| # 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 |
| |
| ```python |
| import pyaging as pya |
| |
| pya.pred.predict_age(adata, ["tagemortality"]) |
| ``` |
| |
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). |
| |
| ## 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 |
| |
