Example 1: SmCCNet + GNN Embeddings + Subject Representation

This tutorial illustrates how to:

1. Build: an adjacency matrix with SmCCNet.

2. Enhance Representation: Generate node embeddings using GNNEmbedding.

3. Integrate: Incorporate these embeddings into subject-level omics data using SubjectRepresentation.

Workflow:

1. Construct: - A multi-omics network adjacency using SmCCNet.

2. Generate: - Node embeddings with a Graph Neural Network (GNN).

3. Integrate: - These embeddings into subject-level omics data for enhanced representation.

Subject-level embeddings provide richer phenotypic and clinical context.

View full-size image: Subject Representation

Step-by-Step Instructions:

1. Data Setup: - Load omics data, phenotype data, and clinical data using DatasetLoader.

2. Network Construction (SmCCNet): - Call SmCCNet.run() to produce an adjacency matrix from multi-omics data.

3. GNN Embedding: - Pass the adjacency, omics data, and (optionally) clinical data to GNNEmbedding. - Use .fit() and .embed() to generate node embeddings.

4. Subject Representation: - Integrate these embeddings into omics data via SubjectRepresentation.

Below is a complete Python implementation:

import pandas as pd
from bioneuralnet.datasets import DatasetLoader
from bioneuralnet.external_tools import SmCCNet
from bioneuralnet.network_embedding import GNNEmbedding
from bioneuralnet.downstream_task import SubjectRepresentation

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

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

# 3) Generate adjacency matrix 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()

# 4) Generate embeddings using GNNEmbedding
gnn_embedding = GNNEmbedding(
    adjacency_matrix=global_network,
    omics_data=merged_omics,
    phenotype_data=phenotype,
    clinical_data=clinical,
    tune=True,
)
gnn_embedding.fit()
embeddings_output = gnn_embedding.embed(as_df=True)

print(f"GNN embeddings generated. Shape: {embeddings_output.shape}")

# 5) Perform subject representation using SubjectRepresentation
graph_embedding = SubjectRepresentation(
    omics_data=merged_omics,
    embeddings=embeddings_output,
    phenotype_data=phenotype,
    tune=True,
)

enhanced_data = graph_embedding.run()
print(f"Enhanced omics data shape: {enhanced_data.shape}")

# Save enhanced omics data
enhanced_data.to_csv("enhanced_omics_data.csv")

Results:

• Adjacency Matrix generated using SmCCNet.

• Node Embeddings from GNN.

• Enhanced Omics Data, integrating node embeddings for subject-level analysis.