p3/preprocess/Sarcoma/code/GSE162789.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE162789"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE162789"

# Output paths
out_data_file = "./output/preprocess/3/Sarcoma/GSE162789.csv"
out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE162789.csv"
out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE162789.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)
# Gene expression data availability
# Looking at Series title and sample characteristics, this appears to be gene expression data from Ewing sarcoma samples
is_gene_available = True

# Clinical feature availability and conversion functions
# Sample characteristics shows Ewing sarcoma patient data with age and gender info embedded
trait_row = 0  # The trait (sarcoma) info is in the 'soft tissue' entries

# Age can be extracted from the same entries as trait
age_row = 0  

# Gender can also be extracted from the same entries
gender_row = 0

def convert_trait(value: str) -> int:
    # Binary: 1 for Ewing sarcoma, 0 for other/control
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].lower()
    if 'ewing sarcoma' in value:
        return 1
    elif 'cell line' in value:
        return None  # Exclude cell lines
    return 0

def convert_age(value: str) -> float:
    # Continuous: Extract age in years 
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].lower()
    if 'cell line' in value:
        return None
    try:
        # Extract number before "year"
        age = float(re.search(r'(\d+)\s*year', value).group(1))
        return age
    except:
        return None

def convert_gender(value: str) -> int:
    # Binary: 0 for female, 1 for male
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].lower()
    if 'cell line' in value:
        return None
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

# Validate and save cohort info
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
)

# Extract clinical features if trait data is available
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 the extracted features
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.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])
# The gene identifiers are probe IDs from the Affymetrix microarray platform,
# not human gene symbols. They need to be mapped to gene symbols for analysis.
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure
print("Column names and first few values:")
print(preview_df(gene_annotation))
print("\nGene annotation information available in 'gene_assignment' column.")
# Get gene mapping
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

# Apply gene mapping to expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Save gene expression data 
gene_data.to_csv(out_gene_data_file)

# Preview gene data
print("Preview of gene expression data:")
print(preview_df(gene_data))
# Reload clinical data that was previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("Clinical data shape:", selected_clinical_df.shape)

# 1. Normalize gene symbols 
genetic_data = pd.read_csv(out_gene_data_file, index_col=0)
genetic_data = normalize_gene_symbols_in_index(genetic_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)
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 studies the paired tumor biopsies before and after treatment. The derived trait value 1 represents responders (PR = partial response) and 0 represents non-responders (SD = stable disease, PD = progressive disease)."
    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)
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
    )