p3/preprocess/Osteoarthritis/code/GSE93720.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Osteoarthritis"
cohort = "GSE93720"

# Input paths
in_trait_dir = "../DATA/GEO/Osteoarthritis"
in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE93720"

# Output paths
out_data_file = "./output/preprocess/3/Osteoarthritis/GSE93720.csv"
out_gene_data_file = "./output/preprocess/3/Osteoarthritis/gene_data/GSE93720.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoarthritis/clinical_data/GSE93720.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)
# 1. Gene Expression Data Availability
# The background info mentions GeneChip Human Genome U133 Plus 2.0 Array, so this contains gene expression data
is_gene_available = True

# 2. Variable Data Processing

# 2.1 Data Row Identification
# Trait (OA vs RA) is in row 0
trait_row = 0  

# Age and gender are not available in the sample characteristics
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    # Convert OA/RA to 1/0 after the colon
    if not value:
        return None
    value = value.split(': ')[1].strip().upper()
    if value == 'OA':
        return 1
    elif value == 'RA': 
        return 0
    return None

def convert_age(value):
    # Not needed since age is not available
    return None

def convert_gender(value):
    # Not needed since gender is not available
    return None

# 3. Save Metadata
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. Clinical Feature Extraction
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 processed clinical data
preview_df(selected_clinical)

# Save clinical data
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])
# Observe IDs like '1007_s_at' which are Affymetrix probe IDs, not gene symbols
# They need to be mapped to human gene symbols for biological interpretation
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. Observe identifiers and determine mapping columns
# Gene expression data uses probe IDs like '1007_s_at' which match the 'ID' column
# Gene symbols are in 'Gene Symbol' column
prob_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Get gene mapping dataframe 
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Apply gene mapping to get gene-level expression
gene_data = apply_gene_mapping(genetic_data, mapping_df)

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

# Save gene expression data
gene_data.to_csv(out_gene_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, 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)