p3/preprocess/Osteoporosis/code/GSE84500.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Osteoporosis"
cohort = "GSE84500"

# Input paths
in_trait_dir = "../DATA/GEO/Osteoporosis"
in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE84500"

# Output paths
out_data_file = "./output/preprocess/3/Osteoporosis/GSE84500.csv"
out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE84500.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE84500.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
# Yes, this is gene expression microarray data studying differentiation and gene regulation
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Osteoporosis trait can be inferred from treatment condition in row 2
# BMP2+TGFB+IBMX treatment promotes osteogenic differentiation while others don't
def convert_trait(value: str) -> int:
    if not value or ':' not in value:
        return None
    treatment = value.split(': ')[1].strip().lower()
    # Treatment with BMP2+TGFB+IBMX promotes osteogenic differentiation
    return 1 if treatment == 'bmp2+tgfb+ibmx' else 0

trait_row = 2

# Age and gender not available - these are cell line samples
age_row = None 
gender_row = None
convert_age = None
convert_gender = None

# 3. Save 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
# Since trait_row is not None, we extract clinical features
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 the processed clinical data
print(preview_df(clinical_features))

# Save clinical data
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])
# Based on the format of gene IDs like '1007_s_at', these appear to be Affymetrix probe IDs
# rather than human gene symbols, which would look like 'BRCA1', 'TP53', etc.
# Therefore, these IDs 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. Identify mapping columns from gene annotation data
# Gene identifiers are in 'ID' column as probe IDs (e.g., '1007_s_at')
# Gene symbols are in 'Gene Symbol' column (e.g., 'DDR1')

# 2. Extract mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')

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

# Print dimensions to verify the mapping
print("\nDimensions:")
print(f"Original probe data: {genetic_data.shape}")
print(f"After mapping to genes: {gene_data.shape}")

# Preview first few rows
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
# 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)