p3/preprocess/Sarcoma/code/GSE233860.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE233860"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE233860"

# Output paths
out_data_file = "./output/preprocess/3/Sarcoma/GSE233860.csv"
out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE233860.csv"
out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE233860.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
# Yes - Series summary mentions "Gene expression quantification of PanCancer IO genes"
is_gene_available = True

# 2.1 Data Availability and Row Keys
trait_row = 0  # The 'outcome' data can be used for trait
age_row = None  # Age not available
gender_row = None  # Gender not available

# 2.2 Data Type Conversion Functions 
def convert_trait(value):
    """Convert outcome data to binary (SD/PD=0, PR=1)"""
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].strip()
    # PR = Partial Response is positive outcome
    # SD = Stable Disease and PD = Progressive Disease are negative outcomes
    if value == 'PR':
        return 1
    elif value in ['SD', 'PD']:
        return 0
    return None

def convert_age(value):
    return None  # Not used since age data unavailable

def convert_gender(value):
    return None  # Not used since gender data unavailable

# 3. 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. Clinical Feature Extraction
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
    )
    
    # Preview the data
    preview = preview_df(selected_clinical_df)
    print("Preview of clinical data:")
    print(preview)
    
    # Save to CSV
    selected_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 the gene identifiers
gene_ids = ['A2M', 'ABCF1', 'ACVR1C', 'ADAM12', 'ADGRE1', 'ADM', 'ADORA2A', 'AKT1', 'ALDOA', 'ALDOC', 'ANGPT1', 'ANGPT2', 'ANGPTL4', 'ANLN', 'APC', 'APH1B', 'API5', 'APLNR', 'APOE', 'APOL6']

# These look like standard human gene symbols (e.g. A2M, ABCF1, AKT1 etc.)
# No mapping needed
requires_gene_mapping = False
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols 
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)

# 3. Handle missing values systematically  
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 = "Dataset contains gene expression data from paired tumor biopsies before and after treatment. Treatment outcome (PR vs SD/PD) is used as 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 
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)