p3/preprocess/Uterine_Carcinosarcoma/code/GSE68950.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Uterine_Carcinosarcoma"
cohort = "GSE68950"

# Input paths
in_trait_dir = "../DATA/GEO/Uterine_Carcinosarcoma"
in_cohort_dir = "../DATA/GEO/Uterine_Carcinosarcoma/GSE68950"

# Output paths
out_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/GSE68950.csv"
out_gene_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/gene_data/GSE68950.csv"
out_clinical_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/clinical_data/GSE68950.csv"
json_path = "./output/preprocess/3/Uterine_Carcinosarcoma/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 shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

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, this dataset contains gene expression data as indicated by:
# - Background info mentions "Affymetrix gene expression project" and "Array Designs: HT_HG-U133A"
is_gene_available = True

# 2. Variable Availability and Type Conversion
# 2.1 Get row numbers
# Trait (carcinosarcoma) data can be found in disease state field, row 1
trait_row = 1 
# Age data is not available
age_row = None
# Gender data is not available
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait value to binary (0/1)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    # Consider carcinosarcoma-malignant mesodermal mixed tumor as target disease
    if 'carcinosarcoma' in value:
        return 1
    return 0

# Age data is not available
convert_age = None

# Gender data is not available
convert_gender = None

# 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. Extract Clinical Features
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 data
    print("Processed clinical data preview:")
    print(preview_df(clinical_df))
    
    # Save to file
    clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs 
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# These identifiers appear to be Affymetrix probe IDs (e.g. '1007_s_at', '1053_at'), not human gene symbols
# They will need to be mapped to their corresponding gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# 1. The gene annotation data shows probes under 'ID' and gene symbols under 'Gene Symbol'
prob_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Extract mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Apply the mapping to convert probe measurements to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Print gene expression data dimensions
print("\nGene expression data dimensions:", gene_data.shape)
print("\nFirst 5 gene symbols:", list(gene_data.index[:5]))
# 1. Normalize gene symbols in gene expression data
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)
print("\nGene data shape (normalized gene-level):", gene_data.shape)

# 2. Link clinical and genetic data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

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

# 4. Check for bias in features
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save dataset metadata
note = "Dataset contains gene expression data from cancer cell lines, but has severely imbalanced distribution of carcinosarcoma cases."
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=is_trait_biased,
    df=linked_data,
    note=note
)

# 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)