p3/preprocess/Parkinsons_Disease/code/GSE49126.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Parkinsons_Disease"
cohort = "GSE49126"

# Input paths
in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE49126"

# Output paths
out_data_file = "./output/preprocess/3/Parkinsons_Disease/GSE49126.csv"
out_gene_data_file = "./output/preprocess/3/Parkinsons_Disease/gene_data/GSE49126.csv"
out_clinical_data_file = "./output/preprocess/3/Parkinsons_Disease/clinical_data/GSE49126.csv"
json_path = "./output/preprocess/3/Parkinsons_Disease/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# Yes - series uses Agilent expression microarrays on PBMC samples
is_gene_available = True

# 2.1 Data Availability
# Trait (PD) data is in row 0, binary control vs PD 
trait_row = 0
# Age and gender not available in characteristics
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert PD status to binary (0=control, 1=PD)"""
    if not isinstance(value, str):
        return None
    value = value.lower().split(": ")[-1].strip()
    if "control" in value:
        return 0
    elif "parkinson" in value:
        return 1
    return None

convert_age = None
convert_gender = None

# 3. Save metadata for initial filtering
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)

# 4. Extract clinical features since trait data is available
# Create DataFrame from characteristics 
characteristics_data = {0: ['disease state: control', "disease state: Parkinson's disease"]}
clinical_data = pd.DataFrame.from_dict(characteristics_data, orient='index')

selected_clinical = geo_select_clinical_features(clinical_data,
                                               trait=trait,
                                               trait_row=trait_row,
                                               convert_trait=convert_trait,
                                               age_row=age_row,
                                               convert_age=convert_age, 
                                               gender_row=gender_row,
                                               convert_gender=convert_gender)

# Preview the clinical data
preview_df(selected_clinical)

# Save clinical features
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# The IDs are numeric values starting from 12, which are definitely not human gene symbols
# They appear to be probe IDs that need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# 1. Get gene mapping from ID to GENE_SYMBOL
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

# 2. Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview gene data 
print("Gene data preview:")
print(gene_data.head())
print("\nGene data shape:", gene_data.shape)
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data) 
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df.T, genetic_data)

# Print column names to debug
print("Columns in linked data:")
print(linked_data.columns[:10])  # Show first 10 columns

# 3. Handle missing values systematically
# The trait column name needs to match what's in the data  
linked_data = handle_missing_values(linked_data, trait_col="PD")

# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, "PD")

# 5. Final validation and information saving
note = "Gene expression data from PBMC cells of PD patients and controls"
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note=note
)

# 6. Save linked data only if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df.T, genetic_data)

# 3. Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait_col='PD') 

# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'PD')

# 5. Final validation and information saving
note = "Gene expression data from peripheral blood mononuclear cells (PBMC) of Parkinson's Disease patients and controls"
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note=note
)

# 6. Save linked data only if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# Check gene expression data availability
is_gene_available = True  # Agilent expression microarrays indicate gene expression data

# Find rows for trait, age and gender
trait_row = 0  # Disease state is recorded in row 0
age_row = None  # Age not available in sample characteristics
gender_row = None  # Gender not available in sample characteristics

# Define conversion functions
def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.lower().split(': ')[-1]
    if "parkinson" in value:
        return 1
    elif "control" in value:
        return 0
    return None

def convert_age(value):
    return None  # Not used since age data not available

def convert_gender(value): 
    return None  # Not used since gender data not available

# Validate and save initial cohort info
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=(trait_row is not None)
)

# Extract clinical features since trait data is available
clinical_df = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    age_row=age_row,
    convert_age=convert_age,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# Preview and save clinical data
preview_df(clinical_df)
clinical_df.to_csv(out_clinical_data_file)