p3/preprocess/Sarcoma/code/GSE118336.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE118336"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE118336"

# Output paths
out_data_file = "./output/preprocess/3/Sarcoma/GSE118336.csv"
out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE118336.csv"
out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE118336.csv"
json_path = "./output/preprocess/3/Sarcoma/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
# This is human transcriptome array data which contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Row Identification

# Trait (Sarcoma): Can be determined from genotype in row 1
trait_row = 1  

# Age/Gender: Not available in sample characteristics
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert genotype to binary trait (presence of mutation)
    FUSWT/WT (no mutation) -> 0 
    FUSWT/H517D or FUSH517D/H517D (has mutation) -> 1
    """
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if 'FUSWT/WT' in value:
        return 0
    elif 'H517D' in value:  # Either hetero or homo mutation
        return 1
    return None

def convert_age(value):
    """Placeholder since age data not available"""
    return None

def convert_gender(value):
    """Placeholder since gender data not available"""
    return None

# 3. Save Metadata
# Initial validation (is_final=False)
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. Clinical Feature Extraction
if trait_row is not None:
    clinical_df = 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 = preview_df(clinical_df)
    print("Preview of clinical data:", preview)
    
    # Save clinical data
    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])
# Review gene identifiers - these look like probe IDs from the Affymetrix Human Transcriptome Array 2.0 platform
# Based on format 'XXXXXXX_st' and high number of probes (70523)
# Need to map these probe IDs to human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and values from annotation dataframe
print("Gene annotation DataFrame preview:")
print(preview_df(gene_annotation))
# Extract mapping between probe IDs and gene symbols
mapping_df = gene_annotation[['probeset_id', 'gene_assignment']]
mapping_df = mapping_df.rename(columns={
    'probeset_id': 'ID',
    'gene_assignment': 'Gene'
})

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

# Normalize gene symbols to standardized format
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview processed gene data
print("Gene expression data preview:")
print(preview_df(gene_data))
print("\nShape:", gene_data.shape)

# Save gene data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
print("Gene data shape after normalization:", gene_data.shape)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
gene_data.to_csv(out_gene_data_file)

# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)

# 3. Handle missing values systematically  
if trait in linked_data.columns:
    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 = "This dataset contains gene expression data from myxoid liposarcoma samples, with metastasis status as the trait."
    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 and not biased
    if is_usable and not trait_biased:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)
else:
    # Handle case where clinical features were not properly extracted
    note = "Failed to extract clinical trait information from sample characteristics."
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path, 
        is_gene_available=True,
        is_trait_available=False,
        is_biased=None,
        df=None,
        note=note
    )