p3/preprocess/COVID-19/code/GSE185658.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "COVID-19"
cohort = "GSE185658"

# Input paths
in_trait_dir = "../DATA/GEO/COVID-19"
in_cohort_dir = "../DATA/GEO/COVID-19/GSE185658"

# Output paths
out_data_file = "./output/preprocess/3/COVID-19/GSE185658.csv"
out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE185658.csv"
out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE185658.csv"
json_path = "./output/preprocess/3/COVID-19/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 from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get unique values per row in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Yes, this contains microarray gene expression data (mentioned in background)
is_gene_available = True

# 2.1 Data Availability
# Trait (asthma) is available in group field (row 1)
# Using asthma status as relevant trait for COVID-19 research
trait_row = 1
# Age and gender are not available
age_row = None  
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert asthma status to binary (0: healthy, 1: asthma)"""
    if not value:
        return None
    # Extract value after colon
    value = value.split(': ')[-1].strip().lower()
    if 'asthma' in value:
        return 1
    elif 'healthy' in value:
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    """Convert age to float"""
    return None  # Not used since age data not available

def convert_gender(value: str) -> Optional[int]:
    """Convert gender to binary (0: female, 1: male)"""
    return None  # Not used since gender data not available

# 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. Extract Clinical Features
if trait_row is not None:
    selected_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
    )
    
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical_df))
    
    # Save to CSV
    selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(list(genetic_data.index)[:20])
# The gene identifiers appear to be probe IDs from an array platform (7892XXX format)
# These numeric identifiers are not standard human gene symbols and will need to be mapped
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path)

# Preview column names and first few values
print("Gene annotation columns preview:")
print(preview_df(gene_metadata))
# Extract probe IDs and gene assignments from gene annotation data
# The 'ID' column contains probe IDs matching gene expression data
# The 'gene_assignment' column contains gene symbols
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')

# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Save gene data to CSV
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

# 4. Determine if features are biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
note = ""
if os.path.getsize(out_gene_data_file) == 0:
    note = "Gene mapping failed - empty gene expression data"

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 if usable
if is_usable:
    linked_data.to_csv(out_data_file)