tAge

R package for transcriptomic biological age prediction from gene expression data, implementing the transcriptomic clocks from Tyshkovskiy et al. (2026), Nature.

The clocks predict two kinds of biological age from bulk RNA-seq (or pseudobulk single-cell) expression: chronological age and expected mortality. tAge handles preprocessing, normalisation, gene mapping across species, and prediction with the pre-trained Elastic Net (EN) and Bayesian Ridge (BR) models.

Installation

# install.packages("devtools")
devtools::install_github("Gladyshev-Lab/tAge")

Python dependency

tAge calls Python (via reticulate) to run the model predictions. Create a virtual environment with the packages listed in inst/python/requirements.txt:

python -m venv .venv
.venv/bin/pip install -r inst/python/requirements.txt   # joblib pandas scikit-learn numpy

Point R at that interpreter before calling predict_tAge():

# Linux / macOS
Sys.setenv(RETICULATE_PYTHON = ".venv/bin/python")
# Windows
Sys.setenv(RETICULATE_PYTHON = ".venv/Scripts/python.exe")

Clock models

Models are published on Zenodo (record 18763485). You can list and download them directly from R:

library(tAge)

# Browse available clocks (filter by type / outcome / species / tissue / scaling)
list_clocks(type = "EN", outcome = "Mortality")

# Download a chosen set; returns the table with a `path` column
clocks <- list_clocks(type = "EN", outcome = "Mortality",
                      species = "Multispecies", tissue = "Multi-Tissue")
clocks <- download_clocks(clocks, dest_dir = "clocks")

model_paths <- list(
  scaled_diff = clocks$path[clocks$scaling == "Scaled"],
  yugene_diff = clocks$path[clocks$scaling == "YuGene"]
)

Quick start

Bulk RNA-seq

library(tAge)
Sys.setenv(RETICULATE_PYTHON = ".venv/bin/python")

# 1. Load raw counts (genes x samples) and sample metadata (samples x variables)
exprs_data <- read.csv("expression_matrix.csv", row.names = 1)
metadata   <- read.csv("metadata.csv", row.names = 1)
eset <- make_ExpressionSet(exprs_data, metadata)

# 2. Preprocess: filter, map genes, RLE-normalise, log, scale, YuGene, centre
tAge_eset <- tAge_preprocessing(
  eset,
  species = "mouse",
  gene_mapping_type = "Gene.Symbol"    # or "Ensembl"
)

# 3. Predict
results <- predict_tAge(tAge_eset, model_paths, species = "mouse", mode = "EN")
head(results)

Single-cell (pseudobulk)

library(Seurat)

eset <- aggregate_on_obs_columns(
  seurat_obj,
  obs_column_names = c("sample_id", "tissue"),
  coverage_threshold = 1e7
)
eset_clean <- remove_outliers(eset, split_by = "tissue")

results <- tAge_by_group(
  eset_clean,
  split_by = "tissue",
  model_paths = model_paths,
  species = "mouse",
  mode = "EN",
  control_group_column = "age",
  control_group_label  = "young"
)

Interpreting the output

The units of the prediction depend on the clock outcome:

Outcome	Column	Units	Notes
Chronological	*_tAge	age (years for human, months for rodents)	normalised age rescaled by species max lifespan
Mortality	*_tAge	log10(hazard ratio)	not an age; higher = higher expected mortality
Normalized age	*_tAge	fraction of max lifespan	reported on its native scale

Only chronological clocks are rescaled to age units. Mortality clocks output a log10 hazard ratio and must not be interpreted in years — tAge derives this automatically from the clock registry.

Relative (differential) clocks and centring

All distributed clocks are relative (_scaleddiff / _yugenediff) models: they operate on expression centred against a reference group. tAge_preprocessing() centres the data automatically:

• No reference group given (default): centres on all samples (overall per-gene median). Use this for a general age/mortality readout of a cohort.
• Reference group given: pass control_group_column and control_group_label to centre on age-/sex-matched controls within your design (recommended when comparing treatment vs control).

Supported species

Mouse, human, rat, monkey. Non-mouse species are mapped to mouse orthologs internally, so all clocks operate in the mouse Entrez gene space.

Citation

Tyshkovskiy et al. (2026) Universal Transcriptomic Hallmarks of Mammalian Ageing and Mortality. Nature.

License

This package and the associated transcriptomic clock models are distributed under the MGB Open Access License 1.0. These materials may be used for non-commercial academic purposes, subject to the license terms. Commercial uses require a separate commercial license or agreement with Mass General Brigham.

For questions about commercial use, please contact MGB Innovation / Business Development & Licensing:

• innovation@partners.org
• licensing@partners.org