p3/preprocess/Bladder_Cancer/code/GSE253531.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE253531"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE253531"

# Output paths
out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE253531.csv"
out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE253531.csv"
out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE253531.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
# From background information, this dataset uses gene expression microarrays
is_gene_available = True

# 2.1 Data Availability
# From Feature 1, we can infer tumor molecular subtypes which indicate bladder cancer status
trait_row = 1
# Age and gender data are not available in sample characteristics
age_row = None  
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Extract value after colon and strip whitespace
    if ':' in str(x):
        value = str(x).split(':')[1].strip().lower()
        # Convert subtypes to binary - 1 for cancer samples (all samples here are cancer)
        return 1
    return None

def convert_age(x):
    return None

def convert_gender(x):
    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:
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=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 data
    print(preview_df(selected_clinical_df))
    
    # Save to CSV
    selected_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)
# Examining the row identifiers
# The identifiers seem to be numeric probe IDs (e.g., "2315554", "2315633") rather than human gene symbols
# These appear to be from a microarray platform and 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))
# Looking at the gene annotation data, 'ID' matches the probe IDs in gene expression data
# The 'gene_assignment' column contains gene symbols embedded in longer descriptions

# Get the gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

# Convert probe-level measurements to gene-level data using gene symbols extracted from descriptions
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Print summary statistics before and after mapping to check the result
print("Number of unique gene symbols after mapping:", len(gene_data.index.unique()))
print("\nShape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
# 1. Normalize gene symbols and save normalized gene data
# Remove "-mRNA" suffix from gene symbols before normalization
gene_data.index = gene_data.index.str.replace('-mRNA', '')
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data and trait
# First get selected clinical features using the extraction function from previous step
selected_clinical = geo_select_clinical_features(
    clinical_df=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
)

# Debug data structures before linking
print("\nPre-linking data shapes:")
print("Clinical data shape:", selected_clinical.shape)
print("Gene data shape:", gene_data.shape)
print("\nClinical data preview:")
print(selected_clinical.head())

# Transpose gene data to match clinical data orientation
gene_data_t = gene_data.T
linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)

# 3. Handle missing values systematically
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 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=True, 
    is_biased=is_biased,
    df=linked_data,
    note="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)