tAge

May 27, 2026 · View on GitHub

tAge logo

R package for transcriptomic biological age prediction from gene expression data.

Installation

devtools::install_github("Gladyshev-Lab/tAge")

Python dependency

tAge uses Python for model prediction. Set up a virtual environment with required packages:

python -m venv .venv
.venv/bin/pip install joblib pandas scikit-learn

Then in R, before running predictions:

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

Models availability

The current tAge models can be obtained here:

Transcriptomic clock models and rodent gene expression meta-dataset

Quick start

Bulk RNA-seq

library(tAge)

# Load data
exprs_data <- read.csv("expression_matrix.csv", row.names = 1)
metadata <- read.csv("metadata.csv", row.names = 1)
eset <- make_ExpressionSet(exprs_data, metadata)

# Preprocess
tAge_eset <- tAge_preprocessing(
  eset,
  species = "mouse",
  gene_mapping_type = "Gene.Symbol",
  control_group_column = "treatment",
  control_group_label = "control"
)

# Predict
model_paths <- list(
  scaled_diff = "path/to/EN_scaleddiff.pkl",
  yugene_diff = "path/to/EN_yugenediff.pkl"
)
Sys.setenv(RETICULATE_PYTHON = ".venv/bin/python")

results <- predict_tAge(tAge_eset, model_paths, species = "mouse", mode = "EN")

Single-cell (pseudobulk)

library(Seurat)

# Pseudobulk aggregation by sample and tissue
eset <- aggregate_on_obs_columns(
  seurat_obj,
  obs_column_names = c("sample_id", "tissue"),
  coverage_threshold = 1e7
)

# Remove outliers
eset_clean <- remove_outliers(eset, split_by = "tissue")

# Run tAge per 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"
)

Supported species

Mouse, human, rat, monkey. Non-mouse species are mapped to mouse orthologs internally.

Citation

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

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: