p3/preprocess/Osteoporosis/code/GSE20881.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Osteoporosis"
cohort = "GSE20881"

# Input paths
in_trait_dir = "../DATA/GEO/Osteoporosis"
in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE20881"

# Output paths
out_data_file = "./output/preprocess/3/Osteoporosis/GSE20881.csv"
out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE20881.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE20881.csv"
json_path = "./output/preprocess/3/Osteoporosis/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  
is_gene_available = True  # Based on background info, this is a gene expression study of intestinal biopsies

# 2.1 Data Availability
trait_row = 58  # disease field with 'healthy' vs 'crohns disease'
age_row = 2  # birth date field
gender_row = None  # No gender data

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert trait values to binary: 0 for healthy control, 1 for disease case"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].lower()
    if 'healthy' in value:
        return 0
    elif 'crohns disease' in value:
        return 1
    return None

def convert_age(value):
    """Convert birth date to age using procedure date as reference"""
    from datetime import datetime
    if not isinstance(value, str) or ': ' not in value:
        return None
    try:
        birth_date = datetime.strptime(value.split(': ')[1], '%m/%d/%y')
        # Use 2005 as reference year since procedures were in 2004-2005
        ref_date = datetime(2005, 1, 1)
        age = ref_date.year - birth_date.year
        # Adjust age if birthday hasn't occurred yet
        if ref_date.month < birth_date.month or (ref_date.month == birth_date.month and ref_date.day < birth_date.day):
            age -= 1
        return age
    except:
        return None

convert_gender = None  # No gender data

# 3. Save Initial Metadata
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)
)

# 4. Clinical Feature Extraction
clinical_features = 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 features
print(preview_df(clinical_features))
clinical_features.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 row IDs are just numbers (1, 2, 3, etc) and not recognizable gene symbols
# So they need to be mapped to proper human 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. From the preview, we can see 'ID' column in gene annotation maps to row IDs in expression data,
#    and 'GENE_SYMBOL' column contains the gene symbols

# 2. Create gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')

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

# Print info about the mapping and conversion
print("\nShape after mapping to gene symbols:", gene_data.shape)
print("\nFirst few gene symbols:", list(gene_data.index)[:10])
# 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, genetic_data)

# 3. Handle missing values systematically  
linked_data = handle_missing_values(linked_data, trait)

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

# 5. Final validation and information saving
note = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
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)