p3/preprocess/Bladder_Cancer/code/GSE203149.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE203149"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE203149"

# Output paths
out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE203149.csv"
out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE203149.csv"
out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE203149.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 Availability
# Yes - the background info indicates gene expression data from microarray platform
is_gene_available = True

# 2.1 Data Availability
# trait_row = 0 - Disease status shown in row 0, but only one value (all samples are cancer)
# age - not available in sample characteristics
# gender - not available in sample characteristics
trait_row = None  # Only one value, so not usable
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if pd.isna(x):
        return None
    value = str(x).split(': ')[-1].lower()
    if 'bladder cancer' in value:
        return 1
    elif 'control' in value or 'normal' in value:
        return 0
    return None

def convert_age(x):
    if pd.isna(x):
        return None
    value = str(x).split(': ')[-1]
    try:
        return float(value)
    except:
        return None
    
def convert_gender(x):
    if pd.isna(x):
        return None
    value = str(x).split(': ')[-1].lower()
    if 'female' in value or 'f' in value:
        return 0
    elif 'male' in value or 'm' in value:
        return 1
    return None

# 3. Save Metadata 
# trait_row is None, so trait data not available
is_trait_available = False if trait_row is None else True

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. Skip clinical feature extraction since trait_row is None
# 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)
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))
# Extract gene identifiers and gene symbols and create mapping
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SPOT_ID.1')

# Apply gene mapping to convert probe measurements to gene expression data 
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Normalize gene symbols in the gene expression data
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save preprocessed gene expression data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
gene_data.to_csv(out_gene_data_file)
# 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. Create minimal clinical data (just sample IDs) and link with gene data
sample_ids = pd.DataFrame(index=gene_data.columns)
linked_data = pd.concat([sample_ids, gene_data.T], axis=1)

# 3. Handle missing values systematically
# Since we only have gene data, we only need to handle missing gene values
linked_data = handle_missing_values(linked_data, trait)

# 4. Check for biased features
is_biased = True  # No trait data means the dataset is biased by definition

# 5. Validate data quality and save metadata
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=is_biased,
    df=linked_data,
    note="All samples are cancer cases (no controls), making trait data unavailable for associative analysis."
)

# 6. Since is_biased is True, the data is not usable, so we don't save it
# 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)

# 2-6. Skip clinical linking since no trait data available
is_biased = True  # No trait data means biased by definition
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=is_biased,
    df=gene_data.T,
    note="All samples are cancer cases (no controls), making trait data unavailable for associative analysis."
)