p3/preprocess/Bladder_Cancer/code/GSE201395.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE201395"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE201395"

# Output paths
out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE201395.csv"
out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE201395.csv"
out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE201395.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
# This dataset uses Affymetrix HTA 2.0 platform for gene expression profiling
is_gene_available = True

# 2.1 Data Availability
trait_row = None  # All are cancer cell lines, no disease status comparison
age_row = None   # Age not relevant for cell lines
gender_row = None # Gender not relevant for cell lines

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    return None  # Not applicable as all samples are cancer cell lines

def convert_age(x):
    return None  # Not applicable for cell lines

def convert_gender(x): 
    return None  # Not applicable for cell lines

# 3. Save metadata
is_initial_check = 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. 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)
# The IDs end with "_st" which indicates these are Affymetrix probeset IDs
# They need to be mapped to standard human 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))
# Extract probe-gene mapping from annotation data
prob_col = 'probeset_id'
gene_col = 'gene_assignment' 

# First extract columns and rename
gene_mapping = gene_annotation[[prob_col, gene_col]]
gene_mapping = gene_mapping.rename(columns={prob_col: 'ID', gene_col: 'Gene'})
gene_mapping = gene_mapping.dropna()
gene_mapping = gene_mapping.astype({'ID': 'str'})

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

# Normalize gene symbols using NCBI synonym data 
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save processed gene data
gene_data.to_csv(out_gene_data_file)

# Print shape and preview of processed data
print("Shape of processed gene expression data:", gene_data.shape)
print("\nFirst few rows of processed 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)

# 2-6. 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."
)