p3/preprocess/Osteoarthritis/code/GSE56409.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Osteoarthritis"
cohort = "GSE56409"

# Input paths
in_trait_dir = "../DATA/GEO/Osteoarthritis"
in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE56409"

# Output paths
out_data_file = "./output/preprocess/3/Osteoarthritis/GSE56409.csv"
out_gene_data_file = "./output/preprocess/3/Osteoarthritis/gene_data/GSE56409.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoarthritis/clinical_data/GSE56409.csv"
json_path = "./output/preprocess/3/Osteoarthritis/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)
# Gene expression data availability
is_gene_available = True  # Background info suggests this is gene expression microarray data

# Define row indices and conversion functions for trait, age, and gender
trait_row = 1  # Disease information in row 1
age_row = None  # Age not available 
gender_row = None  # Gender not available

def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.split(": ")[-1].strip()
    # Convert to binary where OA=1 (our trait of interest), RA=0
    if value == "OA":
        return 1
    elif value == "RA": 
        return 0
    return None

# Since age/gender not available, don't need conversion functions
convert_age = None
convert_gender = None

# Save initial validation results
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)

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

# Preview the processed clinical data
print("Preview of clinical features:")
print(preview_df(clinical_df))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.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])
# Looking at the row IDs, we can see they follow Affymetrix probe ID format (e.g. "1007_s_at", "1294_at")
# These are 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))
# Get gene mapping dataframe from the annotation data
# The 'ID' column in annotation matches probe IDs in expression data
# The 'Gene Symbol' column contains the target gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Convert probe measurements to gene expression values using the mapping
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Normalize gene symbols for cases when same gene appears with different symbols  
gene_data = normalize_gene_symbols_in_index(gene_data)

print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nPreview of mapped gene expression data:")
print(preview_df(gene_data))
# 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)