Webb-Laboratory/CellBiAge

CellBiAge

Webb laboratory and Singh laboratory @Brown

Using Brown's HPC (OSCAR)

ML/DL applications in predicting cellcular age using mouse brain single cell/nuclei RNA-seq.

1. Models tested:

• Logistic regression with regularization
• Tree-based models
• Support Vector Machine Classifier (SVC)
• Multilayer Perceptron (MLP)

2. Virtual Enviroments:

MLP: requirments_mlp.txt

Other required packages for GPU:

module load cuda/11.7.1
module load cudnn/8.2.0

ML models (logistic regression, tree-based models, and SVC): environment_ml.yml

3. Implementation:

MLP: .py files in the /scripts folder

In terminal: To implement MLP KerasTuner for group-based cross validation:

cd scripts
python3 mlp_kt_4cv_console.py

To implement the best MLP over 10 random seeds:

python3 mlp_rs_console.py

ML: .ipynb jupyter notebooks in the /scripts folder

• 0x: different preprocessing methods
• 1x: hypothalamus all-cell models
• 2x: hypothalamus cell type-specific models
• 3x: SVZ all-cell models
• 4x: SVZ cell type-specific models
• 5x: Bechmarking results
• 6x: Batch integration and misc

4. Datasets

Hajdarovic, K. H., Yu, D., Hassell, L. A., Evans, S. A., Packer, S., Neretti, N., & Webb, A. E. (2022). Single-cell analysis of the aging female mouse hypothalamus. Nature Aging, 2(7), 662-678.

Buckley, M. T., Sun, E. D., George, B. M., Liu, L., Schaum, N., Xu, L., ... & Brunet, A. (2023). Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain. Nature Aging, 3(1), 121-137.