p3/preprocess/Bladder_Cancer/code/GSE145261.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE145261"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE145261"

# Output paths
out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE145261.csv"
out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE145261.csv"
out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE145261.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
is_gene_available = True  # Based on context, this dataset studies carcinoma with molecular analysis

# 2. Variable Availability and Data Type Conversion
# 2.1 Row identification
trait_row = 3  # "tissue type" contains SCC vs UC info
age_row = 0    # "subject age" contains age info
gender_row = 1 # "subject gender" contains gender info

# 2.2 Conversion functions
def convert_trait(x):
    # Binary: SCC (1) vs UC (0) 
    if not isinstance(x, str):
        return None
    x = x.lower()
    if 'scc' in x or 'small cell' in x:
        return 1
    elif 'uc' in x or 'urothelial' in x:
        return 0
    return None

def convert_age(x):
    # Continuous: extract age in years
    if not isinstance(x, str):
        return None
    try:
        age = int(''.join(filter(str.isdigit, x)))
        if 0 <= age <= 120:  # Basic age validation
            return age
        return None
    except:
        return None

def convert_gender(x):
    # Binary: female (0) vs male (1)
    if not isinstance(x, str):
        return None
    x = x.lower()
    if 'female' in x:
        return 0
    elif 'male' in x:
        return 1
    return None

# 3. Save metadata
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. Extract clinical features
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 extracted features
    preview_data = preview_df(selected_clinical_df)
    print("Preview of extracted clinical features:", preview_data)
    
    # 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)
# These are Illumina probes (starting with ILMN_), not gene symbols
# We'll need to map them to human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation from SOFT file using default prefixes
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation columns and first few values:")
print(preview_df(gene_annotation))

# Also inspect the raw SOFT file annotation section to verify parsing
import gzip
with gzip.open(soft_file, 'rt') as f:
    found_table = False
    lines = []
    for line in f:
        if '!platform_table_begin' in line.lower():
            found_table = True
            lines.append(next(f))  # Get header line
            for _ in range(3):  # Get first 3 data lines
                lines.append(next(f))
            break
            
if found_table:
    print("\nRaw annotation format in SOFT file:")
    for line in lines:
        print(line.strip())
# Extract gene mapping from annotation
# 'ID' column contains probe IDs (ILMN_*) matching the gene expression data
# 'Symbol' column contains gene symbols we want to map to
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

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

# Print info about the conversion
print("Shape of mapped gene expression data:", gene_data.shape) 
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())
# 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
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 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="NanoString nCounter RNA profiling data for bladder cancer recurrence 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)