Victoria BOURGEAIS / GraphGONet

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GraphGONet

Project code of GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expression (submitted to Bioinformatics)

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7843e646 add notebooks to get the interpretation of a prediction and to build the GO laye… · by Victoria BOURGEAIS

GraphGONet

From the article entitled GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expression (submitted to Bioinformatics) by Victoria Bourgeais, Farida Zehraoui, and Blaise Hanczar.

Description

GraphGONet is a self-explaining neural network integrating the Gene Ontology into its hidden layers.

Get started

The code is implemented in Python (3.6.7) using PyTorch v1.7.1 and PyTorch-geometric v1.6.3 (see requirements.txt for more details about the additional packages used).

Dataset

The full microarray dataset can be downloaded on ArrayExpress database under the id E-MTAB-3732.

TCGA dataset can be downloaded from GDC portal.

Here, you can find the pre-processed training, validation and test sets with additional files for NN architecture to test the network: https://entrepot.ibisc.univ-evry.fr/d/d4764174275347f09862/

Usage

Example on TCGA dataset:

Train

python3 scripts/GraphGONet.py --save --n_inputs=18427 --n_nodes=10636 --n_nodes_annotated=8288 --n_classes=12 --selection_op="top" --selection_ratio=0.001 --n_epochs=50 --es --patience=5 --class_weight

Comparison with random selection

python scripts/GraphGONet.py --save --n_inputs=18427 --n_nodes=10636 --n_nodes_annotated=8288 --n_classes=12 --selection_op="random" --selection_ratio=0.001 --n_epochs=50 --es --patience=5 --class_weight

Comparison with no selection

python scripts/GraphGONet.py --save --n_inputs=18427 --n_nodes=10636 --n_nodes_annotated=8288 --n_classes=12 --n_epochs=50 --es --patience=5 --class_weight

Train the model with a small number of training samples

python scripts/GraphGONet.py --save --n_samples=50 --n_inputs=18427 --n_nodes=10636 --n_nodes_annotated=8288 --n_classes=12 --selection_op="top" --selection_ratio=0.001 --n_epochs=50 --es --patience=5 --class_weight

Help

All the details about the command line flags can be provided by the following command:

python scripts/GraphGONet.py --help

For most of the flags, the default values can be employed. dir_data, dir_files, and dir_log can be set to your own repositories. Only the flags in the command lines displayed have to be adjusted to reproduce the results from the paper. If you have enough GPU memory, you can choose to switch to the entire GO graph (argument type_graph set to "entire"). The graph can be reconstructed by following the notebooks: Build_GONet_graph_part{1,2,3}.ipynb located in the notebooks directory. Then, you should change the value of the arguments n_nodes and n_nodes_annotated in the command line.

Interpretation tool

Please see the notebook entitled Interpretation_tool.ipynb (located in the notebooks directory) to perform the biological interpretation of the results.