Analysis and Visualization

Feature Selection from Enhanced Dataset:

Example of Feature Selection from Enhanced Dataset

import pandas as pd
from bioneuralnet.analysis.feature_selector import FeatureSelector
from bioneuralnet.network_embedding import GNNEmbedding

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

    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]
    })

    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]
    })

    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]
    })

    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'])

    gnn_embed = GNNEmbedding(
        omics_list=[omics_df1.set_index('SampleID'), omics_df2.set_index('SampleID')],
        phenotype_df=phenotype_df.set_index('SampleID'),
        clinical_data_df=clinical_data_df.set_index('SampleID'),
        adjacency_matrix=adjacency_matrix,
        model_type='GCN'
    )
    gnn_embeddings = gnn_embed.run()
    print("\nGNN Embeddings:")
    print(gnn_embeddings['graph'].head())

    combined_omics_data = pd.merge(omics_df1, omics_df2, on='SampleID')
    phenotype_series = phenotype_df.set_index('SampleID')['Phenotype']

    feature_selector = FeatureSelector(
        enhanced_omics_data=combined_omics_data.set_index('SampleID'),
        phenotype_data=phenotype_series,
        num_features=20,
        selection_method='lasso'
    )
    selected_features = feature_selector.run_feature_selection()
    print("\nSelected Multi-Omics Features:")
    print(selected_features.head())

if __name__ == "__main__":
    main()

Static Visualization:

Example of Static Visualization

import pandas as pd
from bioneuralnet.analysis import StaticVisualizer

def main():
    adjacency_matrix = pd.read_csv('input/adjacency_matrix.csv', index_col=0)
    
    static_vis = StaticVisualizer(
        adjacency_matrix=adjacency_matrix,
        layout='spring',  
        node_size=300,
        node_color='skyblue',
        edge_color='gray',
        linewidths=1.0,
        font_size=10,
        output_dir='visualizations/static',
        output_filename='static_network.png'
    )
    
    G = static_vis.generate_graph()
    static_vis.visualize(G)
    print("Static visualization workflow completed successfully.")

if __name__ == "__main__":
    main()

Dynamic Visualization:

Example of Dynamic Visualization

import pandas as pd
from bioneuralnet.analysis import DynamicVisualizer

def main():
    adjacency_matrix = pd.read_csv('input/adjacency_matrix.csv', index_col=0)
    
    dynamic_vis = DynamicVisualizer(
        adjacency_matrix=adjacency_matrix,
        layout='spring', 
        notebook=False,  
        bgcolor='#ffffff',
        font_color='black',
        output_dir='visualizations/dynamic',
        output_filename='dynamic_network.html',
        width="100%",
        height="800px"
    )
    
    G = dynamic_vis.generate_graph()
    dynamic_vis.visualize(G)
    print("Dynamic visualization workflow completed successfully.")

if __name__ == "__main__":
    main()