Subject Representation

Subject Representation using Graph Embedding:

Example of Subject Representation Workflow

import pandas as pd
from bioneuralnet.subject_representation import GraphEmbedding

def main():
    phenotype_df = pd.DataFrame({
        'SampleID': ['S1', 'S2', 'S3', 'S4'],
        'Phenotype': ['Control', 'Treatment', 'Control', 'Treatment']
    },index_col=0)

    omics_df1 = pd.DataFrame({
        'SampleID': ['S1', 'S2', 'S3', 'S4'],
        'GeneA': [1.2, 2.3, 3.1, 4.0],
        'GeneB': [2.1, 3.4, 1.2, 3.3],
        'GeneC': [3.3, 1.5, 2.2, 4.1]
    },index_col=0)

    omics_df2 = pd.DataFrame({
        'SampleID': ['S1', 'S2', 'S3', 'S4'],
        'GeneD': [4.2, 5.3, 6.1, 7.0],
        'GeneE': [5.1, 6.4, 4.2, 6.3],
        'GeneF': [6.3, 4.5, 5.2, 7.1]
    },index_col=0)

    # Example Clinical DataFrame
    clinical_data_df = pd.DataFrame({
        'SampleID': ['S1', 'S2', 'S3', 'S4'],
        'Age': [30, 40, 50, 60],
        'Sex': ['Male', 'Female', 'Female', 'Male'],
        'BMI': [25.0, 28.1, 30.2, 24.5]
    }, index_col=0)

    adjacency_matrix = pd.DataFrame({
        'GeneA': [1.0, 0.8, 0.3, 0.0],
        'GeneB': [0.8, 1.0, 0.4, 0.0],
        'GeneC': [0.3, 0.4, 1.0, 0.7],
        'GeneD': [0.0, 0.0, 0.7, 1.0]
    }, index=['GeneA', 'GeneB', 'GeneC', 'GeneD'], index_col=0)

    # You can also load the data from files directly

    # omics_files = [pd.read_csv('input/proteins.csv', index_col=0),
    #                pd.read_csv('input/metabolites.csv', index_col=0)]
    # phenotype_df = pd.read_csv('input/phenotype_data.csv', index_col=0)
    # clinical_data_df = pd.read_csv('input/clinical_data.csv', index_col=0)
    # adjacency_matrix = pd.read_csv('input/adjacency_matrix.csv', index_col=0)

    graph_embed = GraphEmbedding(
        adjacency_matrix=adjacency_matrix,
        omics_list=[omics_df1, omics_df2],
        phenotype_df=phenotype_df,
        clinical_data_df=clinical_data_df,
        embedding_method='GNNs'
    )

    enhanced_omics_data = graph_embed.run()

    enhanced_omics_data.to_csv('output/enhanced_omics_data.csv')
    print("Graph embedding workflow completed successfully.")

if __name__ == "__main__":
    main()