p3/preprocess/Eczema/code/GSE120899.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Eczema"
cohort = "GSE120899"

# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE120899"

# Output paths
out_data_file = "./output/preprocess/3/Eczema/GSE120899.csv"
out_gene_data_file = "./output/preprocess/3/Eczema/gene_data/GSE120899.csv"
out_clinical_data_file = "./output/preprocess/3/Eczema/clinical_data/GSE120899.csv"
json_path = "./output/preprocess/3/Eczema/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 Analysis
# Yes, this appears to be gene expression data from skin biopsies, not miRNA/methylation
is_gene_available = True

# 2.1 Data Row Identification
# Trait can be inferred from tissue type in row 1 (lesional vs non-lesional/normal skin)
trait_row = 1
# Age and gender not available in characteristics
age_row = None  
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert tissue type to binary trait status
    lesional skin = 1 (has eczema)
    non-lesional/normal = 0 (no eczema)"""
    if pd.isna(value) or ":" not in value:
        return None
    value = value.split(":")[1].strip().lower()
    if "lesional skin" in value:
        return 1
    elif "non-lesional skin" in value or "normal" in value:
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    """Placeholder - age data not available"""
    return None

def convert_gender(value: str) -> Optional[int]:
    """Placeholder - gender data not available"""
    return None

# 3. Save Initial Metadata
is_usable = 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 
if trait_row is not None:
    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 extracted features
    preview = preview_df(clinical_features)
    print("Preview of extracted clinical features:")
    print(preview)
    
    # Save to CSV
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    clinical_features.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])
# The identifiers in the data follow the format of Affymetrix probe IDs (eg. "1007_s_at") rather than gene symbols
# These need to be mapped to corresponding gene symbols for proper 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 for mapping:
# 'ID' column in annotation matches probe IDs in expression data
# 'Gene Symbol' column contains the target gene symbols
prob_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Get probe-to-gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)

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

# Print shape and preview of mapped gene data
print("\nMapped gene expression data shape:", gene_data.shape)
print("\nPreview of first few rows and columns:")
print(gene_data.head().iloc[:, :5])
# 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
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_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="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"
)

# 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)