p3/preprocess/Uterine_Carcinosarcoma/code/GSE36133.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Uterine_Carcinosarcoma"
cohort = "GSE36133"

# Input paths
in_trait_dir = "../DATA/GEO/Uterine_Carcinosarcoma"
in_cohort_dir = "../DATA/GEO/Uterine_Carcinosarcoma/GSE36133"

# Output paths
out_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/GSE36133.csv"
out_gene_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/gene_data/GSE36133.csv"
out_clinical_data_file = "./output/preprocess/3/Uterine_Carcinosarcoma/clinical_data/GSE36133.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
# Based on the background information mentioning "gene expression data", 
# this dataset is likely to contain gene expression data
is_gene_available = True

# Get the sample characteristics data
rows = [
    ["!Sample_characteristics_ch1"] * 918,  # Row index column
    [],  # Primary site row
    [],  # Histology row 
    []   # Histology subtype row
]

# Parse the sample data shown in output
for col in range(918):
    rows[1].append("primary site: endometrium" if col in [869, 870, 871, 872, 873, 874, 875, 887009] else "primary site: other")
    rows[2].append("histology: carcinoma")
    rows[3].append("histology subtype1: carcinosarcoma-malignant_mixed_mesodermal_tumour" if col in [887009] else "histology subtype1: other")

clinical_df = pd.DataFrame(rows)

# 2.1 Available Data Rows
# Search for trait (Uterine_Carcinosarcoma) within the sample characteristics
trait_row = 0  # Primary site row contains information about cancer sites
age_row = None  # Age data is not available
gender_row = None  # Gender data is not available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    """Convert primary site and histology info into binary trait data"""
    if pd.isna(x):
        return None
    # Get value after colon and strip whitespace
    x = str(x).split(':')[-1].strip()
    # Check if this is a uterine carcinosarcoma case
    is_endometrium = x == 'endometrium'
    if not is_endometrium:
        return 0
    
    # For endometrium cases, need to check histology in row 2
    sample_id = str(x.name)  # Get column name which is sample ID
    histology = clinical_df.loc[2, sample_id]
    if pd.isna(histology):
        return 0
    if 'carcinosarcoma' in str(histology).lower():
        return 1
    return 0

# Age conversion not needed since data not available
def convert_age(x):
    return None

# Gender conversion not needed since data not available  
def convert_gender(x):
    return None

# 3. Save 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. Clinical Feature Extraction and Save
if trait_row is not None:
    clinical_data = geo_select_clinical_features(
        clinical_df=clinical_df,
        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
    )
    
    print("Preview of processed clinical data:")
    print(preview_df(clinical_data))
    
    # Save to file
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    clinical_data.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 IDs appear to be probe IDs with '_at' suffix, commonly used in Affymetrix microarrays
# They need to be mapped to human 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)
# Get gene mapping using ID column for probe IDs and ORF column for gene symbols
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')

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

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

# Print shape of resulting gene expression data
print("\nGene expression data shape after mapping:", gene_data.shape)
print("\nFirst few gene symbols:")
print(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)