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XGDP

Environment

conda env create --file=environment.yml

Data Preparation

Download the raw data

Download the drug response data in IC50 called PANCANCER_IC from GDSC. And download the gene expression data called CCLE_expression from CCLE under mRNA expression.

Preprocess the data

  • Create a folder in your project directory called root_folder.
mkdir root_folder
  • Place the PANCANCER_IC data under folder data/GDSC and place the CCLE_expression data under folder data/CCLE. Run the following command to preprocess the data. The data will be saved under root_folder/<branch_num>.
python load_data.py <branch_num>

Train the model

python train.py \
        --model <model_num>
        --branch <branch_num>
        --do_cv
        --do_attn
  • Available models: 0:GCN, 1:GAT, 2:GAT_Edge, 3:GATv2, 4:SAGE, 5:GIN, 6:GINE, 7:WIRGAT, 8:ARGAT, 9:RGCN, 10:FiLM

Explain the model

Attribute the chemical structures with GNNExplainer

python gnnexplainer.py \
        --model <model_num>
        --branch <branch_num>
        --do_attn
        --explain_type <type>
python draw_gnnexplainer.py \
        --model <model_num>
        --branch <branch_num>
        --explain_type <type>
        --annotation <type>
  • Available explaining types: 0:model, 1:phenomenon
  • Available annotation types: 0:numbers, 1:heatmap, 2:both, 3:functional group-level heatmap

Attribute the gene expression values with Integrated Gradients

python integrated_gradients.py \
        --model <model_num>
        --branch <branch_num>
        --do_attn
        --iqr_baseline

Pathway Analysis

  • Download the gene sets from MSigDB and place them under data/.
  • Refer to pathway_analysis.ipynb for the pathway analysis experiments based on the gene saliency scores.