Graph Embeddings

BioNeuralNet multiple graph embedding approaches:

GNNEmbedding:

GCN - Graph Convolutional Network GAT - Graph Attention Network GraphSAGE - Graph Sample and Aggregation GIN - Graph Isomorphism Network

GNN Embedding Example:

Generating GNN-based Embeddings with correlation-based node features (optional).

import pandas as pd
from bioneuralnet.datasets import DatasetLoader
from bioneuralnet.network_embedding import GNNEmbedding
from bioneuralnet.external_tools import SmCCNet
from bioneuralnet.metrics import plot_embeddings

# Load example synthetic dataset
loader = DatasetLoader("example1")
omics1, omics2, phenotype, clinical = loader.load_data()

# Display dataset dimensions
print("Dataset Shapes:")
print(f"Omics1: {omics1.shape}")  # Expected: (358, 500)
print(f"Omics2: {omics2.shape}")  # Expected: (358, 100)
print(f"Phenotype: {phenotype.shape}")  # Expected: (358, 1)
print(f"Clinical: {clinical.shape}")  # Expected: (358, 6)")

# Merge omics data
merged_omics = pd.concat([omics1, omics2], axis=1)

# Generate global network using SmCCNet
smccnet = SmCCNet(
    phenotype_df=phenotype,
    omics_dfs=[omics1, omics2],
    data_types=["genes", "proteins"],
    kfold=3,
    subSampNum=500,
)
global_network, smccnet_clusters = smccnet.run()

# Initialize and run GNN Embedding
embeddings = GNNEmbedding(
    adjacency_matrix=global_network,
    omics_data=merged_omics,
    phenotype_data=phenotype,
    clinical_data=clinical,
    tune=True,
)

embeddings.fit()
embeddings_output = embeddings.embed(as_df=True)

# Display output shape
print(f"GNN Embeddings Shape: {embeddings_output.shape}")


global_node_labels = embeddings._prepare_node_labels()
embeddings_array = embeddings_output.values  
fig1 = plot_embeddings(embeddings_array, global_node_labels)

The resulting embeddings can be used for: - Clustering - Subject-level integration - Visualization - Disease prediction