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ArunTest.py

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+"""
+Example 3: Disease Prediction Using Graph Information (SmCCNet + Disease Prediction using Multi-Omics Networks (DPMON))
+======================================================================================================================
+
+This script demonstrates a workflow where we first generate a graph using Sparse Multiple Canonical Correlation Network
+(SmCCNet), and then use that network matrix to run Disease Prediction using Multi-Omics Networks (DPMON), a pipeline
+that leverages the power of Graph Neural Networks (GNNs) specifically designed to predict disease phenotypes.
+
+Steps:
+1. Generate an adjacency matrix using SmCCNet based on multi-omics and phenotype data.
+2. Utilize DPMON to predict disease phenotypes using the network information and omics data.
+"""
+
+import pandas as pd
+from bioneuralnet.graph_generation import SmCCNet
+from bioneuralnet.downstream_task import DPMON
+
+def run_smccnet_dpmon_workflow(omics_genes: pd.DataFrame,
+
+                               phenotype: pd.Series,
+                               clinical_data: pd.DataFrame) -> pd.DataFrame:
+    """
+    Executes the hybrid workflow combining SmCCNet for network generation and DPMON for disease prediction.
+
+    This function performs the following steps:
+        1. Generates an adjacency matrix using SmCCNet.
+        2. Initializes and runs DPMON for disease prediction based on the adjacency matrix.
+        3. Returns the disease prediction results.
+
+    Args:
+        omics_proteins (pd.DataFrame): DataFrame containing protein data.
+        omics_metabolites (pd.DataFrame): DataFrame containing metabolite data.
+        phenotype_data (pd.Series): Series containing phenotype information.
+        clinical_data (pd.DataFrame): DataFrame containing clinical data.
+
+    Returns:
+        pd.DataFrame: Disease prediction results from DPMON.
+    """
+    try:
+        smccnet_instance = SmCCNet(
+            phenotype_df=phenotype,
+            omics_dfs=[gene_names],
+            data_types=['genes'],
+            kfold=5,
+            summarization='PCA',
+            seed=732,
+        )
+        adjacency_matrix = smccnet_instance.run()
+        print("Adjacency matrix generated using SmCCNet.")
+
+        dpmon_instance = DPMON(
+            adjacency_matrix=adjacency_matrix,
+            omics_list=[omics_genes],
+            phenotype_data=phenotype,
+            features_data=clinical_data,
+            model='GCN',  
+            tune=False,  
+            gpu=False     
+        )
+
+        predictions_df = dpmon_instance.run()
+        if not predictions_df.empty:
+            print("DPMON workflow completed successfully. Predictions generated.")
+        else:
+            print("DPMON hyperparameter tuning completed. No predictions were generated.")
+
+        return predictions_df
+
+    except Exception as e:
+        print(f"An error occurred during the SmCCNet + DPMON workflow: {e}")
+        raise e
+
+if __name__ == "__main__":
+    try:
+        print("Starting SmCCNet + DPMON Hybrid Workflow...")
+
+        # omics_proteins = pd.DataFrame({
+        #     'protein_feature1': [0.1, 0.2],
+        #     'protein_feature2': [0.3, 0.4]
+        # }, index=['Sample1', 'Sample2'])
+
+        # omics_metabolites = pd.DataFrame({
+        #     'metabolite_feature1': [0.5, 0.6],
+        #     'metabolite_feature2': [0.7, 0.8]
+        # }, index=['Sample1', 'Sample2'])
+
+        # phenotype_data = pd.Series([1, 0], index=['Sample1', 'Sample2'])
+
+        # clinical_data = pd.DataFrame({
+        #     'clinical_feature1': [5, 3],
+        #     'clinical_feature2': [7, 2]
+        # }, index=['Sample1', 'Sample2'])
+
+        ## COPDGeneCounts.csv: 1st column is geneID_split containing the gene names
+        omics_genes = pd.read_csv("/Users/sarkara/Desktop/GitHub/BioNeuralNet/COPDGeneCounts.csv")
+
+        #gene_names = omics_genes["geneID_split"]
+        values = omics_genes[omics_genes.columns[0:337]]
+        gene_names = values.drop(["geneID"], axis=1)
+        gene_names_transposed = gene_names.reset_index().transpose()
+        print(gene_names_transposed)
+
+        md = pd.read_csv("/Users/sarkara/Desktop/GitHub/BioNeuralNet/COPDGeneMetadata.csv")
+        phenotype = md[["sid", "finalgold_visit"]].reset_index()
+        clinical_data = md[["sid", "age_visit", "gender", "smoking_status"]].reset_index()
+        print(clinical_data)
+        print(phenotype)
+        gene_names = gene_names.reset_index()
+        predictions = run_smccnet_dpmon_workflow(gene_names, phenotype, clinical_data)
+
+        print("DPMON Predictions:")
+        #print(predictions)
+
+        print("Hybrid Workflow completed successfully.\n")
+    except Exception as e:
+        print(f"An error occurred during the execution: {e}")
+        raise e