AutoGERN

We present AutoGERN, a GNN framework tailored for GRN inference from scRNA-seq data.

Dependencies

cudatoolkit>=11.9
cudnn>=8.9
networkx>=3.1
numpy>=1.24
pandas>=2.0
python>=3.8
pytorch>=2.4
pyg>=2.6
scikit-learn>=1.3

Usage

We provided an example dataset under data/mDC/ for running gene regulatory inference using AutoGERN. It takes gene expression (N✖M) and an adjacent matrix (M✖M) representing the prior regulatory graph as input.

• For normal data separation, run python main_LP.py --dataset data/mDC/TF+500/
  

• For strict data separation, run python main_LP_hardsplit.py --dataset data/mDC_HS/TF+500/