Cell-Type Specific Aging Clocks for Immune Cells
GRNimmuneClock provides pre-trained aging clocks for immune cell types, built using gene regulatory network (GRN) analysis. Predict biological age from gene expression data with cell-type specific models trained on multiple cohorts.
- 🔬 Cell-Type Specific: Separate models for CD4T and CD8T cells
- 📊 High Performance: Trained on multiple cohorts with Spearman corr > 0.8.
- 🧬 GRN-Based: Uses gene regulatory network-informed features
- 🔗 Network Analysis: Access GRNs for TF-target exploration
- 🎨 Visualization Tools: Built-in plotting functions for analysis
- 🚀 Easy to Use: Simple Python API
- 🔧 Training Pipeline: Tools to train custom aging clocks
pip install grnimmuneclockOr install from source:
git clone https://github.com/janursa/GRNimmuneClock.git
cd GRNimmuneClock
pip install -e .from grnimmuneclock import AgingClock, load_example_data
import grnimmuneclock.plotting as gplot
# Load pre-trained clock for CD4T cells
clock = AgingClock(cell_type='CD4T')
# Load example data
adata = load_example_data()
# Predict biological age
adata_predicted = clock.predict(adata)
print(adata_predicted.obs['predicted_age'])
# Visualize predictions
gplot.plot_predicted_vs_actual(adata_predicted, hue='sex')See the tutorial.ipynb for more.
CD4T: CD4+ T cellsCD8T: CD8+ T cells
All models are:
- Algorithm: Ridge regression with StandardScaler
- Features: Gene expression values (target genes from GRN analysis)
- Training: Multiple cohorts (European, Korean, Japanese, Chinese)
- Age Range: 20-80 years
- Species: Human
- Tissue: Peripheral blood
If you use GRNimmuneClock in your research, please cite:
@article{nourisa2025grnimmuneclock,
title={TBD},
author={Nourisa, Jalil and others},
journal={TBD},
year={2025}
}MIT License - see LICENSE file for details.
Contributions are welcome! Please feel free to submit a Pull Request.
For questions and issues, please open an issue on GitHub.