p3/preprocess/Cystic_Fibrosis/code/GSE60690.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE60690"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE60690"

# Output paths
out_data_file = "./output/preprocess/3/Cystic_Fibrosis/GSE60690.csv"
out_gene_data_file = "./output/preprocess/3/Cystic_Fibrosis/gene_data/GSE60690.csv"
out_clinical_data_file = "./output/preprocess/3/Cystic_Fibrosis/clinical_data/GSE60690.csv"
json_path = "./output/preprocess/3/Cystic_Fibrosis/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = 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)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Analysis of LCL gene expression data to identify disease-related pathways

# 2. Variable Availability and Data Type Conversion
# Trait data - consortium lung phenotype is continuous
trait_row = 1
def convert_trait(x):
    if ':' not in str(x):
        return None
    value = str(x).split(':')[1].strip()
    try:
        return float(value)
    except:
        return None

# Age data - age of enrollment in years is continuous
age_row = 2  
def convert_age(x):
    if ':' not in str(x):
        return None
    value = str(x).split(':')[1].strip()
    try:
        return float(value)
    except:
        return None

# Gender data - binary (Female=0, Male=1)
gender_row = 0
def convert_gender(x):
    if ':' not in str(x):
        return None
    value = str(x).split(':')[1].strip().lower()
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return None

# 3. Validate and 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. Extract clinical features 
# Since trait_row is not None, we proceed with feature extraction
clinical_features_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
)

# Preview the extracted features
print("Preview of extracted clinical features:")
print(preview_df(clinical_features_df))

# Save clinical features
clinical_features_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# Based on the numerical IDs (e.g. '2315554') shown in the row indices,
# these are likely probe IDs from a microarray platform rather than human gene symbols.
# They will need to be mapped to standard gene symbols for analysis.
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Identify relevant columns from annotation data:
# ID column matches the probe IDs in gene expression data
# gene_assignment column contains gene symbols but needs parsing

# 2. Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')

print("\nPreview of initial mapping data:")
print(preview_df(mapping_df))

# Clean up the gene assignments to extract symbols
def parse_gene_symbols(gene_assignment):
    if pd.isna(gene_assignment) or gene_assignment == '---':
        return None
    # Extract portions between // delimiters and take the second item which is the gene symbol
    parts = gene_assignment.split('//')
    if len(parts) < 2:
        return None
    return parts[1].strip()

mapping_df['Gene'] = mapping_df['Gene'].apply(parse_gene_symbols)
mapping_df = mapping_df.dropna()

print("\nPreview of cleaned mapping data:")
print(preview_df(mapping_df))

# 3. Apply the mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print info about the result
print("\nOriginal data shape (probes):", genetic_df.shape)
print("Mapped data shape (genes):", gene_data.shape)
print("\nPreview of gene expression data:")
print(preview_df(gene_data))

# Save the gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features_df, gene_data)

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

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
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="Cell line study comparing deltaF508 CFTR mutant with wildtype CFTR in cystic fibrosis bronchial epithelial cells"
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)