p3/preprocess/Endometrioid_Cancer/code/GSE73614.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE73614"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73614"

# Output paths
out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE73614.csv"
out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE73614.csv"
out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE73614.csv"
json_path = "./output/preprocess/3/Endometrioid_Cancer/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on the series summary mentioning "transcriptional profile" and "gene expression signatures",
# this dataset appears to contain gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# We cannot reliably determine case/control status from tissue field, so trait data is not available
trait_row = None
age_row = None  
gender_row = None

def convert_trait(value: str) -> Optional[int]:
    if value is None:
        return None
    val = value.split(": ")[-1].strip().lower()
    if "endometrioid" in val:
        return 1
    elif val in ["healthy", "normal", "benign"]:
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    if value is None:
        return None
    val = value.split(": ")[-1].strip()
    try:
        return float(val)
    except:
        return None

def convert_gender(value: str) -> Optional[int]:
    if value is None:
        return None
    val = value.split(": ")[-1].strip().lower()
    if val in ["female", "f"]:
        return 0
    elif val in ["male", "m"]:
        return 1
    return None

# 3. Save Metadata
# Initial filtering - trait data not available
is_trait_available = trait_row is not None
_ = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)

# 4. Clinical Feature Extraction
# Skip since trait_row is None
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# These appear to be Agilent probe IDs (e.g. A_23_P100001) rather than gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. From the preview, we can see that 'ID' contains probe IDs like A_23_P100001
# and 'GENE_SYMBOL' contains human gene symbols

# 2. Get mapping between probe IDs and gene symbols
gene_mapping = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

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

# Preview results
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few genes and samples:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
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)

# Final validation with the gene expression data
is_usable = validate_and_save_cohort_info(
    is_final=True, 
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True,  # No trait data means biased for our purpose
    df=gene_data,
    note="Gene expression data available but no trait information could be extracted"
)