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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
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