Upload dataloader

app/dataloader.py

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+import pandas as pd
+import scanpy as sc
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+from pathlib import Path
+
+def getEntrezGeneSymbol(input_data_key,input_data_value):
+    BASE_PATH = Path(__file__).parent
+    file_path = str(BASE_PATH.parent / "Core data/SSC_all_Healthy_allproteins.csv")
+    mapping= pd.read_csv(file_path)
+    return mapping[mapping[input_data_key]==input_data_value]['EntrezGeneSymbol'].iloc[0]
+
+def load_singlecell_data(single_cell_data_path):
+    
+    return sc.read(f'{single_cell_data_path}/final_combined_simplified.h5ad')
+
+
+def load_data(metadata_path, proteins_path):
+    """
+    Load metadata and protein data from the provided file paths.
+    """
+    try:
+        metadata = pd.read_csv(metadata_path)
+        proteins = pd.read_csv(proteins_path)
+    except Exception as e:
+        raise ValueError(f"Error loading files: {e}")
+    return metadata, proteins
+
+
+def filter_data(proteins, metadata, protein_id, id_type):
+    """
+    Filter the proteins data for a specific protein ID based on the ID type
+    and retrieve corresponding metadata information.
+    """
+    valid_columns = {
+        "TargetFullName": "TargetFullName", #SSC all healthy all proteins
+        "Target": "Target", #SSC all healthy all proteins
+        "EntrezGeneID": "EntrezGeneID",
+        "EntrezGeneSymbol": "EntrezGeneSymbol"
+    }
+
+    if id_type not in valid_columns:
+        raise ValueError(f"Invalid ID type. Choose from {list(valid_columns.keys())}.")
+
+    column_name = valid_columns[id_type]
+
+    if column_name not in proteins.columns:
+        raise KeyError(f"Column '{column_name}' not found in proteins data.")
+
+    # Filter proteins data for the given protein ID
+    filtered_data = proteins[proteins[column_name] == protein_id]
+
+    if filtered_data.empty:
+        raise ValueError(f"No data found for {id_type} = {protein_id}.")
+
+    # Debug: Ensure filtered data has SampleId
+    if "SampleId" not in filtered_data.columns:
+        raise KeyError("Column 'SampleId' not found in filtered proteins data.")
+    print(f"Filtered Data for {protein_id}:")
+    print(filtered_data.head())
+
+    # Match SampleId in proteins with SubjectID in metadata
+    sample_ids = filtered_data["SampleId"].unique()
+    metadata_info = metadata[metadata["SubjectID"].isin(sample_ids)]
+
+    if metadata_info.empty:
+        raise ValueError(f"No metadata found for Sample IDs: {sample_ids}.")
+
+    # Debug: Print metadata subset
+    print("Metadata Info:")
+    print(metadata_info.head())
+
+    # Merge filtered_data with metadata_info on SampleId
+    merged_data = pd.merge(
+        filtered_data,
+        metadata_info,
+        left_on="SampleId",
+        right_on="SubjectID",
+        how="inner"
+    )
+
+    # Debug: Print merged data
+    print("Merged Data:")
+    print(merged_data.head())
+
+
+    return merged_data
+