p3/preprocess/Prostate_Cancer/code/GSE125341.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Prostate_Cancer"
cohort = "GSE125341"

# Input paths
in_trait_dir = "../DATA/GEO/Prostate_Cancer"
in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE125341"

# Output paths
out_data_file = "./output/preprocess/3/Prostate_Cancer/GSE125341.csv"
out_gene_data_file = "./output/preprocess/3/Prostate_Cancer/gene_data/GSE125341.csv"
out_clinical_data_file = "./output/preprocess/3/Prostate_Cancer/clinical_data/GSE125341.csv"
json_path = "./output/preprocess/3/Prostate_Cancer/cohort_info.json"

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

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# 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, this is a microarray study of transcriptome profiling in prostate cancer cells
is_gene_available = True

# 2.1 Data Availability 
# For trait - we can use the cell type info in Feature 1
trait_row = 1
# Age and gender are not applicable since this is a cell line study
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(val):
    """Convert trait data to binary: 1 for cancer, 0 for normal"""
    if pd.isna(val):
        return None
    # Extract value after colon
    val = val.split(":")[-1].strip().lower()
    if "prostate cancer" in val:
        return 1
    else:
        return None

def convert_age(val):
    """Not used but defined to maintain code structure"""
    return None

def convert_gender(val):
    """Not used but defined to maintain code structure"""
    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_features = 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 = preview_df(clinical_features)
    print("Preview of clinical features:", preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 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 gene identifiers start with 'A_14_P', which indicates they are Agilent probe IDs, not human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Try searching for ID patterns in all columns
print("All column names:", gene_metadata.columns.tolist())
print("\nPreview first few rows of each column to locate numeric IDs:")
for col in gene_metadata.columns:
    sample_values = gene_metadata[col].dropna().head().tolist()
    print(f"\n{col}:")
    print(sample_values)

# Inspect raw file to see unfiltered annotation format
import gzip
print("\nRaw SOFT file preview:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    header = []
    for i, line in enumerate(f):
        header.append(line.strip())
        if i >= 10:  # Preview first 10 lines
            break
print('\n'.join(header))
# 1. Identify relevant columns
# Based on data preview, 'ID' contains probe identifiers and 'symbol' contains gene symbols
prob_col = 'ID'
gene_col = 'symbol'

# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)

# 3. Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Preview the transformed data 
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20])
# Load previously saved clinical data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols using NCBI synonym information 
gene_data = normalize_gene_symbols_in_index(gene_data)
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. Evaluate bias in features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Record cohort information
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="Cell line dataset with mapped gene expression data. Only includes cancer samples."
)

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