p3/preprocess/Bladder_Cancer/code/GSE162253.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE162253"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE162253"

# Output paths
out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE162253.csv"
out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE162253.csv"
out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE162253.csv"
json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data
is_gene_available = True  # From Series title and samples, this appears to be gene expression data

# 2. Variable Availability and Data Type Conversion
trait_row = 1  # Feature 1 shows infection status
age_row = None  # Age not available
gender_row = None  # Gender not available

def convert_trait(value: str) -> int:
    """Convert infection status to binary (0 for control, 1 for infected)"""
    if pd.isna(value) or not isinstance(value, str):
        return None
    value = value.split(': ')[-1].lower()
    if 'none' in value or 'control' in value:
        return 0
    elif 'ecoli' in value or 'e. coli' in value:
        return 1
    return None

def convert_age(value: str) -> float:
    return None

def convert_gender(value: str) -> int:
    return None

# 3. Save Metadata
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
if trait_row is not None:
    clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait)
    preview_result = preview_df(clinical_df)
    print("Preview of clinical data:")
    print(preview_result)
    clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Based on the gene identifiers like "11715100_at", these appear to be Affymetrix probe IDs
# rather than standard human gene symbols. They will need to be mapped to gene symbols.
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# 1. Load SOFT file again and inspect data more carefully
with gzip.open(soft_file, 'rt') as f:
    first_lines = []
    for i, line in enumerate(f):
        if i < 100:  
            first_lines.append(line.strip())
        else:
            break
    
print("\nFirst few lines of SOFT file to check correct column names:")
for line in first_lines[:10]:
    print(line)

# Re-extract annotation with more careful column selection
gene_annotation = get_gene_annotation(soft_file)

# 2. Get gene mapping dataframe using correct columns
prob_col = 'ID'
gene_col = 'Gene Symbol'

# Print columns to verify
print("\nAnnotation columns:", gene_annotation.columns.tolist())

# Get probe-gene mapping
mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)

print("\nFirst few rows of mapping data:")
print(mapping_data.head())

# 3. Apply gene mapping
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Preview results
print("\nShape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
if gene_data.empty:
    print("Gene expression data is empty after mapping. Checking probe ID formats...")
    print("\nSample gene expression probe IDs:")
    print(list(gene_data.index.values)[:5])
    print("\nSample annotation probe IDs:")
    print(mapping_data['ID'].head().tolist())
    
    # Re-run gene mapping with adjusted probe IDs
    gene_data = get_genetic_data(matrix_file)
    # Convert probe IDs to match annotation format
    gene_data.index = gene_data.index.str.replace('_at', '_PM_at').str.replace('_a_at', '_PM_a_at')
    
    # Apply mapping again
    gene_data = apply_gene_mapping(gene_data, mapping_data)
    print("\nShape of remapped gene expression data:", gene_data.shape)

if gene_data.empty:
    print("Gene mapping still failed after ID adjustment")
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=True,
        is_biased=None,
        df=None,
        note="Gene mapping failed - probe ID mismatch between expression and annotation data"
    )
else:
    # 1. Normalize gene symbols and save normalized gene data
    gene_data.index = gene_data.index.str.replace('-mRNA', '')
    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)

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

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

    # 4. Check for biased features and remove them if needed
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Validate and save cohort info
    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_biased,
        df=linked_data,
        note="Mouse gene expression data for bacterial infection study"
    )

    # 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)